Dimitrios Galiatsatos
email: dgaliatsatos2010@hotmail.com

George C Anastassopoulos

Georgios Drosos
email: drosos@otenet.gr

Athanasios Ververidis
email: athanasios@ververidis.net

Konstantinos Tilkeridis
email: tilkerorth@yahoo.com

Konstantinos Kazakos
email: kazakosk@yahoo.gr

Prediction of 30-day mortality after a hip fracture surgery using Neural and Bayesian Networks

Keywords: hip fracture, artificial neural networks, Bayesian networks, 30-day mortality

Osteoporotic hip fractures have a significant morbidity and excess mortality among the elderly and have imposed huge health and economic burdens on societies worldwide. A medical database of 349 patients that have been operated for hip fracture has been analyzed. Two models of data were used in Multi-Layer Perceptrons, Radial Basis Function and Naïve Bayes networks, in order to predict the 30-day mortality after a hip fracture surgery and also to investigate which is the most appropriate risk factor between the New Mobility Score and Institution factor for the Greek population. The proposed method may be used as a screening tool that will assist orthopedics in the surgery of the hip fracture according to each different patient.

Introduction

Hip fractures are common in elderly people although their incidence varies among different countries and populations [START_REF] Chang | Incidence of hip and other osteoporotic fractures in elderly men and women: Dubbo Osteoporosis Epidemiology Study[END_REF][START_REF] Court-Brown | Epidemiology of adult fractures: a review[END_REF][START_REF] Dhanwal | Epidemiology of hip fracture: Worldwide geographic variation[END_REF]. It has been estimated that according to the epidemiologic projections, the worldwide annual number of hip fractures will rise from 1.66 million in 1990 to 6.26 million by the year 2050 [START_REF] Kannus | Epidemiology of hip fractures[END_REF]. The mortality, morbidity social-economic costs after a hip fracture are significant [START_REF] Keene | Mortality and morbidity after hip fractures[END_REF][START_REF] Braithwaite | Estimating hip fracture morbidity, mortality and costs[END_REF][START_REF] Abrahamsen | Excess mortality following hip fracture: a systematic epidemiological review[END_REF]. Mortality in particular remains significant for decades although there is a geographical and Race/Ethnicity Differences [START_REF] Giversen | Time trends of mortality after first hip fractures[END_REF][START_REF] Haleem | Mortality following hip fracture: trends and geographical variations over the last 40 years[END_REF][START_REF] Sterling | Gender and race/ethnicity differences in hip fracture incidence, morbidity, mortality, and function[END_REF]. Mortality rates are greatest within the first month [START_REF] Bass | Risk-adjusted mortality rates of elderly veterans with hip fractures[END_REF][START_REF] Giversen | Time trends of mortality after first hip fractures[END_REF][START_REF] Maxwell | Development and validation of a preoperative scoring system to predict 30-day mortality in patients undergoing hip fracture surgery[END_REF][START_REF] Hu | Preoperative predictors for mortality following hip fracture surgery: a systematic review and meta-analysis[END_REF]. The 30day mortality has been used in several studies because the mortality during this time is directly related to the fracture, its therapy and complications, while the mortality rate beyond this time it may be related to other unrelated causes.

The ability to recognize patients at high risk of poor outcomes before operation would be an important clinical advance. Several preoperative risk factors for the high mortality have been identified but the most predominant did not, and whether other factors could be considered as predictors are still unknown [START_REF] Hannan | Mortality and locomotion 6 months after hospitalization for hip fracture: risk factors and risk-adjusted hospital outcomes[END_REF][START_REF] Johansen | Outcome following hip fracture: post-discharge residence and long-term mortality[END_REF][START_REF] Ozturk | The risk factors for mortality in elderly patients with hip fractures: postoperative one-year results[END_REF].

There are several reports concerning the pre-operative risk factors or scores like the Abbreviated Mental Test Score (AMTS) or the New Mobility Score that have been used as predictors for the 30-day mortality in patients with hip fractures. One of these scores is the Nottingham Hip Fracture Score (NHFS) [START_REF] Maxwell | Development and validation of a preoperative scoring system to predict 30-day mortality in patients undergoing hip fracture surgery[END_REF][START_REF] Moppett | Nottingham Hip Fracture Score: longitudinal and multi-assessment[END_REF][START_REF] Gunasekera | Hip fracture audit: the Nottingham experience[END_REF]. This score is using the following factors: age, sex, hemoglobin in admission, Abbreviated Mental Score (MMTS), and living or not in an institution, number of co-morbidities (other illness) and suffering from malignancy. Patients that live in an institute are those that cannot live alone and the factor "living in an institute" is related to the "mobility status" of the patients. In some countries -like our country-elderly people usually live with relatives (in their own homes or in their relatives' homes). Therefore, the factor "living in an institute" is not a reliable factor. The New Mobility Score is a score related with the "mobility status" of these patients and has been found dependent with morbidity [START_REF] Parker | A new mobility score for predicting mortality after hip fracture[END_REF]. Patients with New Mobility Score 6 and above can walk out of the house and go for shopping even if the use of the stick, while patients with score below 6 need help from another person. This score was used in this study in order to identify whether replacing the factor "living in an institute" with this score the results are more reliable or not.

The Artificial Neural Network (ANN) simulating high-level human brain functions, is a computational modeling tool that has become widely accepted for modeling complex real-world problems. Although it has been explored in many areas of medicine, such as nephrology, microbiology, radiology, neurology, cardiology, etc, its use in the orthopedic field is still rare [START_REF] Tseng | Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age-and sexmatched case control study[END_REF].

The aim of this work was to apply three different ANN -based methods (Multi-Layer Perceptrons (MLPs), Radial Basis Function (RBF) Networks and Bayesian Networks) to evaluate their prediction of mortality using the factors of NHFS. In more details, the 3 methods were tested in a given number of patients with known characteristics (factors) and a known 30-day mortality. More specifically a decision support tool has been developed to help clinicians identify which people are at increased risk after the hip fracture surgery. This application area is considered as extremely important since it is associated with increased morbidity and mortality and high socio-economic costs. This paper was tried to focus on the three proposed methods and their performance in clinical data.

The paper is structured as follows: In the next section the hip fracture, as well as the risk factors that affect the 30-day mortality after the operation of the hip fracture are presented. Also, two models of data are described and three methods are illustrated. Section 3 analyzes the results, while in section 4 the conclusions are discussed.

Materials and Methods

Hip fracture

This study assessed the predictive capability of Neural and Bayesian networks for 30-day mortality after surgery for hip fracture in the Greek population. Data were collected from 349 patients that have been operated for hip fracture in the Orthopedic department of the University Hospital of Alexandroupolis. All these patients were followed-up and the 30-day mortality was recorded. The number of patients who died after 30-day of monitoring was 33. Apart from patients demographics, other factors that may be related to patients' mortality were also recorded and categorized as follows; Age (between 65 and 85 and above 85 years), Sex (male or female), Hemoglobin (Hb) in admission (below 10 g/dl and above 10 g/dl), Mini Metal Score (MMTS) (below 6 or above 6), Living or not in an Institute, Number of co-morbidities (other illness) (less than 2 or more than 2), suffering from Malignancy or not, and the New Mobility Score (score 6 and below or above 6).

Two models of data set have been used as follows: The first model (Model 1) depends on the factors: Age, Sex, Hb, MMTS, New Mobility Score, Number of co-morbidities and Malignancy. In the second model the New Mobility Score had been replaced by the Institute factor (Model 2). This model is using the factors that are being used in the Nottingham Hip Fracture Score. Both models consisted of seven almost identical attributes (independent variables), while the death was the dependent variable.

Neural and Bayesian Networks

Trying to find the best model for 30-day mortality after surgery for hip fracture, two most common algorithms of Artificial Neural Networks have been used, the Multilayer Perceptron and the Radial Basis Function. In addition to these ANN algorithms, the simplest Bayesian algorithm (Naïve Bayes) has been utilized. As a validation measurement of the data the k-fold cross validation has been utilized in all of the above methods.

All data were analyzed and processed with the assistance of Weka, which is a machine learning software written in Java and is an open source application that is freely available. Weka contains tools for data processing, classification, clustering, feature selection and visualization. The greatest capability of this machine learning is to perform useful information after learning from training data. [22].

Multilayer Perceptron is a feed-forward algorithm with one or more hidden layers between the input and the output layer. It utilizes a supervised learning technique called back propagation for training the network. In the medical field, MLP algorithm is widely used and gives great classification accuracy for some diseases such as breast cancer [START_REF] Fauzi Bin Othman | Application of a radial basis function neural network for diagnosis of diabetes mellitus[END_REF][24].

Radial Basis Function networks are feed-forward networks trained using a supervised training algorithm. They are similar to back propagation networks in many aspects although RBF have a few advantages. The main advantage in the biomedicine applications is that they train much faster and more accurate than back propagation networks [25][START_REF] Yavuz | Evolutionary Algorithms Based RBF Neural Networks For Parkinson's Disease Diagnosis[END_REF]. In contrast with the time, the models are less robust than other methods (for example logistic regression) [START_REF] Tseng | Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age-and sexmatched case control study[END_REF][START_REF] Mariano | Logistic Regression Versus Neural Networks For Medical Data[END_REF].

Bayesian Networks are very efficient classifiers in many health related datasets (Natural Language Processing, Computer Vision, Medical Diagnosis, Bioinformatics, etc) [START_REF]Comparison of Different Classification Methods Reihaneh Rabbany Khorasgani[END_REF]. Naïve Bayes (NB) Classifier is the simplest Bayesian approach algorithm assuming conditional independence between the variables in the models [START_REF] Tomar | A survey on data mining approaches for healthcare[END_REF]. This simplicity makes naïve bayes techniques attractive and suitable in many domains. The connection in this classifier appears only between the variables and the main node in each model (output/dependent variable). The main advantages are its easy construction (known a priori) and its effective classification process. However there is a strong disadvantage that makes it unsuitable for every set of data. The conditional independence assumptions between nodes rarely are true in the most real world applications. Despite although strong dependencies between nodes, researchers had shown that naïve bayes performs good classification [START_REF] Zhang | The Optimality of Naïve Bayes[END_REF]. An updated form of the naïve bayes algorithm such as the augmented naïve bayes algorithm overcomes the conditional independence and it is an innovative algorithm based on the original one. In recent years many researchers had tried to evolutes naive bayes classifiers as they select feature subset and relax independence assumptions [START_REF] Langseth | Classification using Hierarchical Naïve Bayes models[END_REF][START_REF] Zhang | Hierarchical latent class models for cluster analysis[END_REF].

In k-fold cross validation the data set separated in k equal size subsets. From the k subsets, a single subset preserved as a validation data and the remaining k-1 samples are utilized as training data. The cross validation process repeated k times with each of the k subsets used exactly once. Then the k results are averaged and came of the estimation. The advantage of this method is that each observation is used in training and validation process. In this work, 3, 10 and 20 fold cross validation has been performed in each technique. In experiments, such as in Hyperthyroid disease, a 6-fold cross validation performed maximum accuracy [START_REF]Diagnosis And Classification Of Hypothyroid Disease Using Data Mining, Techniques[END_REF].

Results

For each one of the two models, the entire set of 349 data records were used, in order to evaluate the best classification method between multilayer perceptron, radial basis function and naïve bayes networks. For the validation of the data, 3, 10 and 20-fold cross validation for each classified algorithms has been used. For each neural network (MLP and RBF) many experiments have been made using a lot of different topologies in terms of hidden layer etc. The results presented below correspond to the best topology of each method which had the best performance.

In figure 1a, MLP topology of the second model is presented. Seven factors are presented in the input layer, whereas four nodes consist the hidden layer. The same topology had been used in the RBF network.

For the evaluation of the models except from Artificial Neural Networks (MLP and RBF), Bayesian neural networks (naïve Bayes) had utilized in order to find the best classified model.

In figure 1b the direct dependencies between the dependent variable (Death) and the independent variables of the first model in the naïve bayes networks are presented. As it can be observed this specified algorithm has given no direct dependencies between the independent variables. For the better explanation of the Bayesian network, probabilities were concluded in each confusion matrix between variables given the output. The same topology with figure 1a has the naïve bayes form of the second model. This is due to the assumption of non-existence of direct dependencies between the independent variables. Even though the similarity of the graphs, there are strong differences between probabilities in the factors. The most important in the first model which is illustrated below are the probabilities of New Mobility Score which are above or equal to six given that the patient is dead (Pr=0.809) and the number of diseases which are over or equal to three given death (Pr=0.962). In the second model the highest scoring probability corresponds to Number of co-morbidities factor given death (Pr=0.926). The lowest scoring in both models is the probability between institute given death (Pr=0.126). The probability of having a New Mobility Score below 6 given Death has a huge difference than the probability living in an Institution given death. The real differentiation between the two pre-mentioned results reflected into the patients with hip fracture in the Greek society. A probability result of 0.809 in New Mobility score translated that a patient with score below 6 has over 80% possibility to die in contrast with the patient who was living in an Institute who has approximately 12%. Due to this reason, the institution factor in the Greek population is unsuitable for study although the classification of the Bayesian network are higher in the model which included. 1 presents the comparisons between multilayer perceptron, radial basis function and naïve bayes networks between model 1 and model 2 for different cross validation folds. Results had shown that in hip fracture models, the 10-fold cross validation was the most appropriate for the most of the cases. In the first and second columns of the table 1, the experiments of the neural and Bayesian networks are presented for each model, respectively. For the accuracy of the networks a correctly classified and an incorrectly classified index was used. The first index sums the true positive and the false negatives values of the confusion matrix and shows the proportion of the total number of predictors that were correctly identified, whereas the second index sums false positives and true negatives values and indicates the predictors that were incorrectly recognized. The time is the processing time value that is required to build the model, measured in seconds.

Τhe processing time of the MLP algorithm is much higher than that of RBF and naïve bayes network algorithms. The same results are illustrated graphically in figure 2 for the 10-fold cross validation. Although both the neural networks classifiers had resulted that the first model are the most appropriate to predict the 30-day mortality after surgery for hip fracture, the results from naïve Bayes classifier had given the impression that the second model is the most suitable. 2 it is denoted that the instances in the first model were classified in a better way than in the second model for MLP (92.26% in the first, whereas 91.97% in the second model) and RBF (92.83% in the first, whereas 92.26% in the second model) algorithms. The naïve Bayes classifier evaluated significantly good classification for the second model (93.12%) in hip fracture data (all the classification results are above 90%). Also, the processing time for the classification is quite smaller in NB networks. The first model which included the New Mobility Score matched better in the citizens of Greece than the model included institution factor.

89,00%

Conclusion

The Artificial Neural Networks and the Bayesian Networks are subfields of the computational intelligence. Although they are widely used in most research areas, their use in the field of medicine is still limited, especially the use of Bayesian Networks is almost nonexistent in the orthopedic field.

In this paper, the application of MLP, RBF and NB networks that have been applied to medical data, in order to predict the 30-day mortality after surgery for hip fracture in the Greek population is presented. In addition, two models of the medical data have been used, in order to investigate who is the most appropriate risk factor for the Greek population, between the New Mobility Score and the Institution factor, as it is described in the NHFS.

All the methods performed prediction of 30-day mortality over 90%. That means that all these methods are suitable for designing and developing a decision support tool to help clinicians identify which people are at increased risk after the hip fracture surgery. Moreover, the neural networks had resulted that the first model are the most appropriate to predict the 30-day mortality after surgery for hip fracture, but the results from naïve Bayes network had given the impression that the second model is the most suitable.

In the future, more computational intelligence methods have to be investigated (especially Bayesian Networks) in more clinical records from the Greek population, in order to find the best method that could predict the 30-day mortality after hip fracture surgery with maximum reliability. In addition, the institution factor in accordance with the New Mobility Score must be further investigated in order to decide which one of these factors is the most suitable for the Greek population.

Figure 1 a

 1 Figure 1 a, b: Neural and Naïve Bayes Networks

Table 1 :

 1 Evaluation results of MLP, RBF and NB for the two models

	3-fold cross validation	Model	Correctly Classified Instances	Incorrectly Instances Classified	Time (sec)
	MLP	1	320 (91.6905%)	29 (8.3095%)	1.28
		2	322 (92.2636%)	27 (7.7364%)	0.95
	RBF	1	320 (91.6905%)	29 (8.3095%)	0.59
		2	324 (92.8367%)	25(7.1633%)	0.05
	NB	1	317(90.8309%)	32(9.1691%)	< 0.01
		2	322(92.2636%)	27(7.7364%)	< 0.01
	10-fold cross validation	Model	Correctly Classified Instances	Incorrectly Instances Classified	Time (sec)
	MLP	1 2	322 (92.26%) 321 (91.97%)	27 (7.74%) 28 (8.03%)	1.09 0.92
	RBF	1 2	324 (92.83%) 322 (92.26%)	25 (7.17%) 27 (7.74%)	0.48 0.03
	NB	1 2	316 (90.54%) 325 (93.12%)	33 (9.46%) 24 (6.88%)	0.01 < 0.01
	20-fold cross validation	Model	Correctly Classified Instances	Incorrectly Instances Classified	Time (sec)
	MLP	1 2	320 (91.6905%) 327 (93.6963%)	29 (8.3095%) 22 (6.3037%)	0.87 1.13
	RBF	1 2	319 (91.404%) 323 (92.5501%)	30 (8.596%) 26 (7.4499%)	0.03 0.03
	NB	1 2	314 (89.9713%) 325 (93.1232%)	35 (10.0287%) 24 (6.8768%)	< 0.01 < 0.01


Olga E Baklanova
email: o_e_baklanova@mail.ru

Olga Ya Shvets
email: oshvets@ektu.kz

Zhanbai Uzdenbaev
email: zhuzdenbaev@ektu.kz

Automation System Development For Micrograph Recognition For Mineral Ore Composition Evaluation In Mining Industry

Keywords: Petrographic analysis, Digital microscopy system, Image recognition, Computer vision, SCADA system. 1

The article deals with the problem of micrographs automated obtaining and micrographs automated processing ore samples of ore dressing processes in ferrous metallurgy. It is described the task of interpreting the results and interaction subsystem automated obtaining and automated processing of samples of ore mining micrographs and beneficiation processes ferrous metals, used to analyze the quality of mineral rocks with other automation systems. It is defined specifies requirements for the subsystem micrographs analysis.

Introduction

Petrography is the science that studies the material composition of the rocks. Unlike minerals, rocks are aggregates composed of different minerals [START_REF] Harvey | Petrology: Igneous, Sedimentary, and Metamorphic[END_REF]. Minerals are homogeneous in composition and structure of the rocks and ores. They are natural chemical compounds resulting from various geological processes. Historically minerals initially determined by color and shape. Some mining and processing enterprise mixed ore from several fields. In this case it is important to determine the mixing ratio. It is necessary to find a mineral composition of the ore, which can be determined by optical microscopic analysis. In some enterprises, it is implemented manually by technologist-mineralogist.

Nowadays, some complexes micrographs analyzed manually locally in the enterprise by technologist -mineralogist, or in the center of the head group of companies with the transition to the Internet or other communication channels. Manual analysis has drawbacks such as dependency from psychophysiological properties of laboratory specialist (human factor) and the long duration of treatment of one of the micrograph. But the process of analyzing ore micrographs can be automated and implement it locally in the enterprise, due to the fact that subsystem micrographs analysis oriented to samples of a particular ore easier to create than a universal program for quantitative analysis of samples of any material [START_REF] Clarke | Microscopy Techniques for Materials[END_REF].

There are the following automation tasks from all at mining complexes:

─ Automated obtaining of micrographs; ─ Quantitative analysis of mineral ore samples by micrographs.

Materials and Methods

Methods of Automated Acquisition of Mineral Rocks Images

It is required for automated obtaining of micrographs automated microscope containing a motorized specimen stage, a mechanism to change the filters, focusing mechanism, a revolver change lenses [START_REF] Panteleev | Computer microscopy[END_REF]. Typically, in this case it is supposed to use the manufacturer's software, which is responsible for the control for motorized units of microscope. Simplified classification of microscopy cameras is shown in Figure 1. Preferably use trinocular microscope with a camera that does not require an optical adapter with a digital interface USB, controlled by a computer and with the TWAIN support.

Fig. 1. Classification of microscopy cameras

Thus, the process of obtaining images can pass without operator as in the case of a fully automated microscope and with the participation of the operator. In the case of automated microscope software fully controls the microscope, after getting photo in the memory of a computer program for image analysis starts. It is required from analysis program that the results were collected to a database accessible over the LAN. From database, these results will be perceived by SCADA-system, and the system makes decisions.

In the case of partially automated or non-automated microscope operator is responsible for changing the samples, the selection of the microscope objective, take a photo on your computer, run the program and image analysis. As in the previous case, the analysis program is required to enter the results into a database, where SCADAsystem can use it, and the system makes decisions.

This paper describes the problem of optical microscopy analysis on the mining complex. It is determined the requirements for obtaining and analyzing subsystem micrographs. The most important of them is the requirement to the camera, the serial processing micrographs of various samples of ore analysis program should each segment micrograph segmentation method has been proposed, then point of interest must be identified as certain minerals, the quality of the segmentation is more significant than the processing of a single image, the program should transmit the results of its work to the database [START_REF] Baklanova | Development of algorithms for image recognition needed to assess the quality of the mineral species in the mining industry[END_REF].

Theoretically possibility to determine the mineral ore targets on the microscopic image substantiated by author M.P. Isayenko [START_REF] Isayenko | Keys to the main ore minerals in the reflected light[END_REF].

Methods of Automated Processing of Mineral Rocks Images

Very often different minerals on the micrographs correspond to objects of different types of shapes and colors. This allows the identification of various minerals in the form and color of objects. In some cases, also take into account the polarization of certain minerals sample [START_REF] Farndon | The practical encyclopedia of rocks and minerals. How to Find, Identify, Collect and Maintain the World's best Specimens, with over 1000 Photographs and Artworks[END_REF][START_REF] Chris | Rocks and Minerals[END_REF].

In this case, it is necessary to make several pictures, accompanying it by turning the sample. Consider a sample of slag copper anode as an example (Figure 2). Micrographs of this sample were kindly provided Eastern Research Institute of Mining and Metallurgy of Non-ferrous Metals (Kazakhstan, Ust-Kamenogorsk).

Fig. 2. Micrograph of a sample of slag copper anode, increasing in 500 times

According to experts on microscopy of minerals from Eastern Research Institute of Mining and Metallurgy of Non-ferrous Metals at this picture there is no minerals having dependent on the direction of the plane of polarization of light. In this picture you can detect metallic copper and the following minerals: cuprite Cu2O, magnetite Fe3O4, Delafosse CuFeO2, silicate glass.

Cuprite Cu2O can be identified as follows: it is characterized by the shape of a round shape, color -it is light gray (sometimes with a slight bluish tint). Figure 3 shows the graphical representation of cuprite.

Fig. 3. Cuprite on micrographs

Fe3O4 magnetite on micrographs may also be detected by color and shape. Color of magnetite on micrographs is dark gray. Shape is angular, as expressed by technologists, "octahedral". Figure 4 shows magnetite apart from other minerals picture.

Fig. 4. Magnetite on micrographs

Delafossite CuFeO2 micrographs can allocate to the needle shape and gray (with a brownish tint) color. On Figure 5 it can be seen delafossite on the micrographs

Fig. 5. Delafossite on micrographs

Metallic copper on the micrographs can be found on the following criteria: coloryellow, shape -round, without flat faces. Figure 6 represents a micrograph metallic copper Fig. 6. Metallic copper on micrographs Silicate glass -is a dark gray mass fills the rest of the space that is left of the other minerals.

These data indicate that for real micrographs slag samples (and some other minerals) it is possible to use automated qualitative assessment of the mineral composition.

After receiving the full image it is often needed to treat it, mainly to simplify further analysis. Available processing capabilities are as follows:

• color correction / color control;

• sharper image display of objects;

• contrast enhancement;

• contrast or inversion;

• alignment of images;

• mosaics;

• correction of spherical aberration;

• morphological filters. Figure 2 processing by morphological dilation filter and effects of some morphological filters can be viewed on Figure 7 [9].

Basically, image processing is required because of the need to simplify the process of analysis and / or to improve visual perception of the operator-mineralogist [START_REF] Baklanova | Development of methodology for analysis of mineral rocks in the mining industry[END_REF]. Micrographs analysis suggests image segmentation, classification of found objects and calculating the percentage ratio of each type of object. It can provide such a method of segmentation as color segmentation. The user determines what colors correspond to certain types of objects, which are created on the basis of the color clusters. Micrograph segment to be allocated may not contain all the colors of the cluster, but includes most of these colors. Micrograph is divided on contours according to the information of the color clusters.

As a criterion for shape analysis it can be suggested the following. Selected contour is transferred to the Cartesian coordinate system. Then, from the bottom left point after some distance is drawn straight to the point of standing up and to this line is constructed perpendicular and calculated angle perpendicular. It would be considered the first perpendicular. Next contour is gone by the same way clockwise. Next perpendicular constructed similarly. The absolute value of the difference between the angle of each of the current and preceding perpendicular will be remembered by the program.

When all of the perpendiculars will be built program adds all absolute difference values of angles and divides it to the length of their boundary contour formed by segments, which was perpendicular. Cuprite calculated value for the relative rotation angle correlated to the length of the contour composed segments, which was perpendicular, is more than for magnetite. Method can be very sensitive to random points of the real contour, so you should carefully choose the length of the segments, which are constructed perpendiculars. For each type of object it is required to make the range sum of relative angles values correlated to the length of the contour.

Segmentation is also possible by determining the differences of brightness, color gradients, calculating the area of contours [START_REF] Isayenko | Keys to the main ore minerals in the reflected light[END_REF].

For analysis micrograph program more qualitative segmentation is override the minimum analysis time of one micrograph. It also requires a serial processing of multiple images ore samples.

The analysis result of a micrograph is a table in the same column indicates the name of a mineral in the other relative area occupied by them in the micrograph. Also there are specifies the identity of the image, such as the name of the field, the number of test samples, the number of files in a batch of samples.

Results and Discussion

Feature optical microscopic analysis is the percentage of minerals detected on the surface of the sample may be different from the ratio, identified by chemical analysis. This is due to the fact that a picture of the sample surface cannot be analyzed, but it can be different from that on the surface. Therefore it is necessary to analyze more than one ore sample and calculate the percentage of minerals in several pictures. Next SCADA-system calculates the corrected value ratios of minerals in the ore [START_REF]Python vs C++ comparison[END_REF].

Interpretation of the results is required due to the fact that the quality measures adopted by the mining industry cannot be directly obtained as a result of analysis techniques. I.e. there are input data as a percentage of a certain type of objects. It is required to interpret these data to obtain quality indicators adopted in the enterprise. Since the quality criteria against mining products in the enterprise depend on the ore and technology enterprise, the system should provide the ability to customize rules interpretation.

Interpretation rules include some mathematical and logical operations. Speed is important. It is required to accept the interpretation of the rules on the basis of the SCADA-system applicability, which has become an integral part of APCS. Image analysis results also include identification data, carrying information about the name of the field and the party ore. The scheme of the flow of information in the automated system of processing of the image to evaluate the qualitative composition of mineral rocks is shown on Figure 8.

Let us consider implementing storage of analysis results. Imaging results of microscopic images are stored in databases. Each field has its own table in the database. This is due to the fact that a single table for the different fields can be unjustified, as the number of allocated objects on microscopic images for a variety of fields may be different. Results interpretation system would be a database, and a shell, leading these databases. It can be used in multiple tables within a single database. Each source image corresponds with own table. Each table contains information such as the maintenance of a certain color and morphology, identification of mineral rock party. Example of a table for one field is shown on Figure 9. The most obvious solution to the problem of interpretation of the rules will be use to build support scripting subsystem in interpreting the results. For this purpose scripting languages were considered for claims processing speed, ease of syntax, prevalence, the ability to integrate into the overall system.

Identification data of party

Input interpretation subsystem in this case composed of the main attributes such as:

─ type of treatment -planimetric method or Delesse-Rozival; ─ internal identifying name of the object and percentage; ─ information on the name field , she entered during the setup phase of the source image; ─ identification number of the party ore, it is removed from the file names. Also it is possible to change the interpretation or clarification of the rules by transferring them from the SCADA-system.

It was analyzed the most common languages Python, Ruby, Perl. However, these scripting languages have not satisfied the required specification [START_REF]Python vs C++ comparison[END_REF][START_REF]Ruby vs C++ comparison[END_REF][START_REF]PERL vs C++ comparison[END_REF]. Next, it was considered possible solutions to the problem, based on scripting languages by going to rule language of embedded scripting languages SCADA-systems.

Using a scripting language built into SCADA-system, it is convenient that the technical staff of enterprises has gained considerable experience in dealing with them, which means less time debugging rules interpretation.

Table 1 shows a comparison of SCADA-systems according the presence of the scripting language. For comparison, we have used some common SCADA-system. Most SCADA-systems support the use of Visual Basic for Applications 6.x. Regarding the solution of the problem it is appropriate to use Visual Basic for Applications, as it is available in the script editor of leading SCADA-systems, which is the reason that the technical staff are well acquainted with the language.

An important requirement is to ensure the transfer of the results of image processing micrographs mineral rocks in SCADA-system with the speed with which the act interpreted results of treatment.

It was considered existing technologies and exchange of data between different applications used in process automation OLE for Process Control (OPC) [START_REF] Baklanova | Development of methodology for analysis of mineral rocks in the mining industry[END_REF], [START_REF] Grigoriev | Standard OPC path to integrate disparate systems, part 1[END_REF], [START_REF] Grigoriev | Standard OPC path to integrate disparate systems, part 2[END_REF]. It was analyzed OPC DA, OPC AE, OPC Batch, OPC DX, OPC HDA, OPC Security, OPC XML-DA, OPC UA. In all of these (Table 1) SCADA-systems standards implemented OPC DA and the vast majority of systems OPC HDA.

Exchange of information with superior enterprise systems can be implemented by standard OPC DA -it should be considered an automated image processing system as several pseudo sensors, the number of which is determined by the number of image sources. Scheme of interaction between source and receiver data is shown on Figure 10.

Fig. 10. Scheme of interaction between source and receiver data

Standard OPC DA implements the transfer of three types of attributes; it is a variable, the value and time of receipt of the data.

Based on the aforementioned arguments, as the basis of information exchange it was recommended OPC DA technology.

Conclusion

It is described the problem of optical microscopy analysis on the mining complex. It is determined the requirements for obtaining and analyzing subsystem micrographs.

The most important of them is the requirement to the camera, the serial processing micrographs of various samples of ore analysis program should each segment micrograph segmentation method has been proposed, then point of interest must be identified as certain minerals, the quality of the segmentation is more significant than the processing of a single image, the program should transmit the results of its work to the database. It is considered tasks of the interpretation results of the analysis to convert the results of the image processing in accordance with some specified quality requirements. It is analyzed for the interpretation of languages, such as, Python, PERL and Ruby. It is considered a variant of the implementation language interpretation rules based on the syntax of Visual Basic for Applications as a scripting language with a rather simple syntax. Among the possible ways to implement the technology options were considered OLE for Process Control (OPC) and CORBA.

Further research should be focused on the development of methods and algorithms by which can be automated solving phase analysis of samples of mineral rocks. In order to define a class of algorithms and techniques required to make a list of functions "universal" system, which allows automates key tasks of microscopy. Also, the system should be implemented data transmission module to higher systems.

Fig. 7 .

 7 Fig. 7. Micrograph of slag copper anode after 6 times of the use of morphological dilation

Fig. 8 .

 8 Fig. 8. The scheme of the flow of information in the automated system of processing of the image to evaluate the qualitative composition of mineral rocks

Fig. 9 .

 9 Fig. 9. Identification of mineral rock party

Table 1 .

 1 Comparison of SCADA-systems according the presence of a scripting language

	Name of SCADA-	Presence of built script-	Name of built scripting language
	system	ing language
	Genesis 32	yes	Visual Basic for Applications, JScript, VBScript
	InTouch	yes	QuickScripts
	Citect	yes	CiCode, CitectVBA.
	TraceMode	yes	Control algorithms based on language of IEC 61131-3 standard
	MasterSCADA	yes	Propriety on 150 functions
	iFix	yes	Visual Basic for Applications
	Advantech Studio	yes	Propriety on 100 functions
	GeniDAQ	yes	BasicScript, identical VBA
	WinCC	yes	Visual Basic for Applications, ANSI-C


Stefan Jenisch

Andreas Uhl
email: andreas.uhl@sbg.ac.at

Visual Security Evaluation Based on SIFT Object Recognition

The paper presents a metric for visual security evaluation of encrypted images based on object recognition using the Scale Invariant Feature Transform (SIFT). The metrics' behavior is demonstrated using three different encryption methods and its performance is compared to that of the PSNR, SSIM and Local Feature Based Visual Security Metric (LFBVSM). Superior correspondance to human perception and better responsiveness to subtle changes in visual security are observed for the new metric.

Introduction

Today a number of (format compliant) image encryption techniques exist which allow the encrypted content to be decoded and viewed. To determine the level of security offered by these techniques it is not enough to simply evaluate the cryptographic strength of the encryption cipher used.

For some methods the decoded encrypted image is a low quality version of the original image and certain image features can still be recognised. So beside evaluating the encryption cipher also the visual security of the result has to be assessed. In this context we need to deal with the remaining image quality left behind by the encryption process and the recognizability and intelligibility of the encrypted image content.

In order to be able to discuss the exact notion of visual security, we need to distinguish distinct application scenarios of media encryption schemes [START_REF] Jenisch | A detailed evaluation of format-compliant encryption methods for JPEG XR-compressed images[END_REF]:

Confidentiality Encryption: Means MP security (mes-sage privacy). The formal notion is that if a system is MP-secure an attacker cannot efficiently compute any property of the plain text from the cipher text. This can only be achieved by the conventional encryption approach, i.e. applying a cryptographically strong cipher to compressed (redundancy-free) image data.

Content Confidentiality: Is a relaxation of confidential encryption. Side channel information may be reconstructed or left in plaintext, e.g. header information, packet length, but the actual visual content must be secure in the sense that the image content must not be intelligible / discernible.

Sufficient Encryption: Means we do not require full security, just enough security to prevent abuse of the data. The content must not be consumable due to high distortion (e.g. for DRM systems) by destroying visual quality to a degree which prevents a pleasant viewing experience or destroys the commercial value. This implicitly refers to message quality security (MQ), which requires that an adversary cannot reconstruct a higher quality version of the encrypted material than specified for the application scenario.

Given these different application scenarios it is clear that depending on the goal, a security metric has to fulfill dif-ferent roles. For example, under the assumption of sufficient encryption a given security metric would have to evaluate which quality is low enough to prevent a pleasant viewing experience.

When it comes to content confidentiality the question of quality is no longer applicable. Content confidentiality requires that image content must not be identified by human or automated recongnition. This requirement also has to be maintained for any part of the image. Image metrics, in general, do not deal with such questions but rate the overall image quality, the question of intelligibility is usually not covered at all. Thus, it seems to be clear that a general purpose metric covering all application scenarios is probably very hard or impossible to design.

Additionally we have to face the fact that different encryption methods introduce different kind of distortions. While some methods shift and morph the images (i.e. chaotic encryption which is mainly based on permutations) others introduce noise and noise like patterns. An ideal metric for assessment of visual security has to be able to deal with those different kind of distortions.

To evaluate the visual security of an encrypted image in an objective manner, often the Peak Signal to Noise Ratio (PSNR) or the Structural Similarity (SSIM) Index are used. Despite the fact that both originally have been developed for image quality assessment, they have also been used for the assessment of encrypted images [START_REF] Au Yeung | Quality assessment for a perceptual video encryption system[END_REF][START_REF] Khan | On perceptual encryption: Variants of DCT block scrambling scheme for JPEG compressed images[END_REF][START_REF] Droogenbroeck | Techniques for a selective encryption of uncompressed and compressed images[END_REF][START_REF] Yeung | Partial video encryption based on alternating transforms[END_REF].

Also several attempts have been made to develop a metric specifically for the task of visual security assessment. One popular example is the Local Feature Based Visual Security Metric (LFBVSM [START_REF] Tong | Visual security evaluation for video encryption[END_REF]), which compares corresponding image regions of the cipher and plain images by their luminance and contour information to evaluate the visual security of an encrypted image. Also further dedicated metrics for visual security evaluation have been proposed (e.g. [START_REF] Mao | Security evaluation for communication-friendly encryption of multimedia[END_REF][START_REF] Sun | An objective visual security assessment for cipherimages based on local entropy[END_REF][START_REF] Yao | Visual security assessment for cipher-images based on neighborhood similarity[END_REF]).

While there exist particular image encryption techniques for which PSNR, SSIM, and LFBVSM do a reasonable job to rate the visual security of a ciphered image, for many encryption methods these metrics tend to have troubles in the correct assessment of visual security in correspondence to visual perception (as we shall see in the experiments).

Since most of these metrics compare the plain and the cipher images pixel by pixel or region by region (fundamental principles of the Human Vision System (HVS) in terms of luminance and edge perception are considered) a warped image may still be recognisable while the metric rates the image as secure due to large dissimilarities in terms of pixel or local region differences. Also, noise patterns tend to decrease the score rater quickly but leave the content of the image still intelligible. Thus, answering the question if an encryption of this type results in a content confidential image, i.e. an image without any intelligible content, can become quite challenging with those metrics.

In this paper, we aim to apply object recognition methods to design a metric for visual security assessment in order to tackle the issue of content recognition and intelligibility in a more appropriate manner. The basic idea of the metric presented is to compare the recognizability of objects found in a reference and a cipher image instead of measuring image quality as such.

In particular, we propose to employ the Scale Invariant Feature Transform (SIFT) [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF] for object recognition in ciphered images. Therefore, the metric is termed "Scale Invariant Feature Transform Similarity Score" (SIFTSS).

The paper is divided into four parts. First a description of the SIFTSS is given, followed by a description of the encryption methods used to test the metrics performance. Then the results of the experiments are presented and finally they are discussed in the last section of the paper.

SIFT Similarity Score

The SIFT algorithm derives a set of key-points for each image. Each key-point is associated with a descriptor vector (edge histograms). The images are compared using these key-points, i.e. all key-points of the target (ciphered) image are compared to the key-points found in the reference (original) image. The matching process compares the Euclidian distances between descriptor vectors of the reference key-points and the target key-points. A search for the minimum Euclidian distance between their descriptor vectors is carried out.

To improve matching performance, a validity check is performed. The minimum distance found is multiplied by the value of 1.5 and again compared to the set of distances. If the multiplied distance is still smaller than all other distances, the key-point pair involved is considered a match. The check ensures that the match of the key-points is distinctive or dominant in comparison to all other possible matches.

For the implementation of the metric the VLFeat.org1 implementation of the SIFT algorithm was used. The matching process returns an array of matching image key-points along with their corresponding Euclidian distances, measured between their descriptor vectors. The number of matched key-points as well as the average Euclidian distance of the edge histograms is used to derive a matching score.

In the formula

SIFTSS(A, B) = dim(m) min(n A , n B ) µ m |m| 2 (1)
the calculation of the SIFTSS between the images A and B is shown, where m is a vector containing a list of the Euclidian distances of the matched keypoints between A and B, and n A ,n B is the total number of key-points found in each of the two images. The number of matched key-points dim(m) is divided by the maximum number of possible matches, and thus mapped into the interval [0 : 1]. The term is then taken to the power of the average Euclidian distance µ m divided by the L 2 -Norm of m. This also maps the exponent into the interval [0 : 1]. Consequently, while an increasing number of matched key-points increases the score, large Euclidian distances decrease it. Since the SIFT matching process is not commutative and returns different values when matching imageA to imageB than when matching imageB to imageA the algorithm calculates both directions and averages the result to restore symmetry.

In Listing 1.1 a pseudo code for calculating the SIFTSS is shown. The range of SIFTSS values is between 0 and 1 where scores close to 0 signify a better visual security and 1 indicates identical images.

Experimental Settings

To give an overview of the metrics performance three case studies using different encryption methods have been selected and will be briefly described below. To establish a standard of comparison these cases are also evaluated using the PSNR, SSIM and LFBVSM. For the experiments the images of the Kodak Lossless True Color Image Suite2 where cropped into a square format (which is required for Arnolds's Cat Map encryption), and scaled down to 150 × 150 pixels. Metrics results are averaged for this data set, all images have been encrypted using individual random encryption keys.

The first encryption approach uses Arnold's Cat Map, a chaotic map, for image encryption [START_REF] Chen | Authentication of lossy compressed video data by semi-fragile watermarking[END_REF][START_REF] Huang | Data quality inspection of watermarked GIS vector map[END_REF]. This type of encryption uses warp and shift operations for rendering the image unintelligible. The map works on an image of size N xN and is defined by the formula

x k+1 y k+1 = C(x k , y k ) = 1 b a ab + 1 * x k y k mod N (2)
where (x k , y k ) satisfying 0 ≤ x k , y k < N are the positions of the pixels in the original (square) image area while (x k+1 , y k+1 ) is the position of the pixel in the target image, k is the number of the current iteration and 0 ≤ a, b < N , a, b ∈ N are the control parameters of the function used as key.

In each iteration the image is warped, cut and transformed back into its squared shape rendering the image more and more random. The operation has the property of a torus restoring the image after a discrete number of iterations. A visual explanation of a single iteration step is shown in Figure 1. The iteration stages evaluated in the experiment were 0, 1, 3, 132, 155, 157, 200, 211, 250, 275, 299 and 300. In Figure 2 one of the images can be seen in all investigated iteration stages. The second encryption method is integrated into the JPEG XR compression standard [START_REF] Sohn | Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEG XR[END_REF]. It is suggested to encrypt the DC coefficients using Random Level Shifts (RLS), i.e. to alter the value of the DC coefficients by adding or subtracting random numbers which are derived from a key. To increase the impact of the encryption method on visual security we have recently suggested to apply RLS to all coefficients of a transform block, not only to its DC coefficient [START_REF] Jenisch | A detailed evaluation of format-compliant encryption methods for JPEG XR-compressed images[END_REF].

This encryption method introduces noise into the image and the impact on image perception can be seamlessly adjusted from low to high by setting the maximum allowed shift value accordingly. In Figure 3 sample images for increasing maximum shift values can be seen. The third encryption method used for SIFTSS evaluation is the permutation of the coefficients' scan order in a JPEG XR code stream. This method has first been discussed for the JPEG standard [START_REF] Tang | Methods for encrypting and decrypting MPEG video data efficiently[END_REF] and was later proposed to be used for encrypting the LP frequency band of JPEG XR encoded images [START_REF] Sohn | Region-of-interest scrambling for scalable surveillance video using JPEG XR[END_REF].

In JPEG XR the coefficients are grouped into three frequency bands, the DC-, the Lowpass-(LP) and Highpass (HP) band. In the experiment only the coefficients of the LP and HP band are subject to the permutation process [START_REF] Jenisch | A detailed evaluation of format-compliant encryption methods for JPEG XR-compressed images[END_REF]. Swapping of coefficients across frequency bands is not carried out.

As a result there are six possible encryption settings. For each of the two storage modes (spatial and frequency storage mode), the encryption of the LP, of the HP and of both frequency bands can be selected. Sample images for each encryption mode can be found in Figure 4.

To establish a subjective order in terms of visual security the images in Figure 4 are ordered from left (low security) to right (high security). Corresponding settings are: Spatial store mode and HP band encryption, Frequency store mode and HP band encryption, spatial store mode and LP band encryption, spatial store mode LP+HP band encryption, frequency store mode LP band encryption and frequency store mode LP+HP band encryption. The objective image metrics return values for the first experiment using Arnold's Cat Map can be seen in Figure 5. The desired behavior of a metric evaluating this set of images would be an indication of content intelligibility for iteration stages 1, 200 and 299 and a contraindication for all others (stages 0 and 300 set aside). Subjectively, these iteration stages do show some resemblance of the original image (see Figure 2).

The SIFTSS indeed shows the desired behaviour for the suspected iteration stages 1, 200 and 299. Interestingly, also the SSIM indicates intelligibility for these stages. The PSNR and the LFBVSM do not show exactly the desired behavior. Notice that no values for iteration stage 0 and 300 are plotted in Figure 5 since the metric returns ∞ for identical images. Both metrics indicate some intelligibility for iteration stage 200 when comparing the return values for this stage with iteration stages 3 to 275. However, they fail to indicate it for iteration stage 1 and 299 which show similar values as for all other iteration stages.

The reason why iteration stage 1 and 299 is handled well by the SSIM may be explained with the fact that most pixels are still neighbouring each other in these stages. Certain image regions are not moved far from their original position (bottom left and top right corner of the image) and additionally certain regions show the same structure after the operation (e.g. an area showing clear blue sky is replaced with clear blue sky during warping). The SSIM metric uses a sliding window approach assigning each pixel of the image (the pixel aligned to the center pixel of the window) a similarity score derived from the area covered from the window. Also the metric uses mainly mean luminance and variance to describe the local structure information but ignores edge orientation and magnitude information which becomes disturbed during the warp operation. The LFBVSM metric however, which also compares image regions, uses edge orientation and magnitiude information contrasting to SSIM. This is probably the reason why the LFBVSM does not indicate intelligibility for those two iteration stages and SSIM does. The objective image metrics averaged return values for the RLS encrypted Kodak Image Database images can be seen in Figure 6. The PSNR, SSIM and LFBVSM values exhibit a steep drop when increasing the maximum shift level from 0 to 300. After reaching a maximum shift value of 300 their slopes flatten out. As can be seen in Figure 3, this behaviour does not correspond well with visual perception where a gradual worsening of image quality and content intelligibility is observed across the entire range of considered shift values.

The SIFTSS on the other hand shows a much less steep drop in its return values when increasing the maximum shift level and flattens out at a much later stage as compared to the other metrics. In the area of a maximum shift value from 500 to 2000 the SIFTSS is still tributing the changes in visual security with much more distinct return values than the other metrics do.

Finally, the objective image metrics' averaged return values for the Coefficient Scan Order Permutation encrypted Kodak Image Database images are shown in Figure 7. The SIFTSS values suggest an ordering with respect to visual security which corresponds to the subjective one shown in Figure 4. Contrasting to that, the return values of the PSRN, SSIM, and LFBVSM do not at all correspond to this ordering of the images. These metrics attest HP encrypted images in spatial store mode a better visual security than images which have an encrypted LP band (also in spatial store mode) which is found in the middle of the plot. Notice that the LFBVSM shows a valley or dent instead of a peak because the metric uses an inverted scale.

Conclusion

We have found that overall, SIFTSS is well suited to assess visual security and its ratings correspond well to subjective perception. In particular, for the three encryption techniques considered, SIFTSS clearly outperforms PSNR, SSIM, and LFBVSM in terms of correspondence to human perception and responsiveness to subtle changes in visual security. On major drawback of SIFTSS is the computational effort required by the metric. Due to the complexity of the SIFT algorithm itself the metric is significantly more demanding as compared to PSNR and SSIM.

The behaviour of SIFTSS might be further improved by extracting additional characteristics from the area around each matched key-point, for example by including the mean luminance and deviation of the subregions around the keypoint. These options have not been investigated so far and remain as a suggestion for future work.

Listing 1 . 1 .

 11 The calculation of the SIFTSS 1 [ m a t c h S c o r e s A ] = SIFTmatch ( imageA , imageB ) ; 2 [ m a t c h S c o r e s B ] = SIFTmatch ( imageB , imageA ) ; 3 4 matchCountA = l e n g t h ( m a t c h S c o r e s A ) ; 5 matchCountB = l e n g t h ( m a t c h S c o r e s B ) ; 6 7 i f ( matchCountA == 0 | | matchCountB == 0 ) 8 return 0 9 e l s e 10 normA = Norm ( m a t c h S c o r e s A ) ; 11 normB = Norm ( m a t c h S c o r e s B ) ; 12 i f ( normA == 0 | | normB == 0 ) t h e n 13 return 0 14 e l s e 15 normA = Norm ( m a t c h S c o r e s A ) ; 16 normB = Norm ( m a t c h S c o r e s B ) ; 17 i f [ normA == 0 | | normB == 0 ] t h e n ; 18 return 1 ; 19 e l s e 20 m e a n E u c l i d i a n D i s t a n c e A = mean( m a t c h S c o r e s A /normA ) ; 21 m e a n E u c l i d i a n D i s t a n c e B = mean( m a t c h S c o r e s B /normB ) ; 22 23 k e y P o i n t s O f A = g e t N u m b e r O f K e y p o i n t s ( imageA ) ; 24 k e y P o i n t s O f B = g e t N u m b e r O f K e y p o i n t s ( imageB ) ; 25 26 matchScoreA = matchCountA / min ( k e y P o i n t s O f A , k e y P o i n t s O f B ) ; 27 matchScoreA = power ( matchScoreA , m e a n E u c l i d i a n D i s t a n c e A ) ; 28 29 matchScoreB = matchCountB / min ( k e y P o i n t s O f A , k e y P o i n t s O f B ) ; 30 matchScoreB = power ( matchScoreB , m e a n E u c l i d i a n D i s t a n c e B ) ; 31 32 m a t c h S c o r e = ( matchScoreA + matchScoreB )

Fig. 1 .

 1 Fig. 1. Arnold's cat map in pictures

Fig. 2 .

 2 Fig. 2. Image transformed with Arnold's cat map (iterations ordered from left to right and top to bottom)

Fig. 3 .

 3 Fig. 3. RLS encryption using increasing maximum shift values (values from left to right: 80, 160, 280, 480, 800, 2000)

Fig. 4 .

 4 Fig. 4. Lena image scrambling using coefficient scan order permutation.

Fig. 5 .

 5 Fig. 5. Averaged return values for Arnold's Cat Map encrypted Kodak Image Database images.

Fig. 6 .

 6 Fig. 6. Averaged return values for RLS encrypted Kodak Image Database images.

Fig. 7 .

 7 Fig. 7. Averaged return values for Coefficient Scan Order Permutation encrypted Kodak Image Database images.

http://www.vlfeat.org

http://r0k.us/graphics/kodak/


Claudia Schütze
email: claudia.schuetze@ufz.de

Thomas Vienken
email: thomas.vienken@ufz.de

Ulrike Werban
email: ulrike.werban@ufz.de

Peter Dietrich
email: peter.dietrich@ufz.de

Anthony Finizola
email: anthony.finizola@univ-reunion.fr

Carsten Leven
email: carsten.leven-pfister@uni-tuebingen.de

Joint application of geophysical methods and Direct Push-soil gas surveys for the improved delineation of buried fault zones

This study provides an example of fault structure delineation using both geophysical measurements and soil-gas surveys. Seismic refraction and electrical resistivity tomography investigations were performed in com-bination with Direct Push (DP) soil gas concentration measurements, with the main objective being the char-acterization of an assumed permeable fault structure which is covered by sediments that are over 20 m thick. Geophysical methods were used to locate a potential fault zone and to provide an insight into the structural features of the covering sediments. Methods for quantifying the soil-gas concentration were applied to eval-uate the permeability of the fault zone. The positioning of gas sampling points was based on results of a geo-physical survey undertaken beforehand. Gas sampling was performed using DP-technology to obtain concentration profiles for the inert gas Radon-222 and its carrier gas CO 2 along the profile at different depths. Joint interpretation of the spatial distribution of geogenic gases and results from the geophysical survey allowed us to produce a representative model image of the fault structure consisting of two fault branches. Based on this image, it was possible to interpret the observed gas concentration patterns.

Introduction

Zones of higher permeability (e.g. tectonic faults) can be responsible for increased fluid flow within subsurface. Identification of zones with fluid flow has been used for highlighting fault zones and their permeability (e.g. [START_REF] Byrdina | Dipolar self-potential anomaly associated with carbon dioxide and radon flux at Syabru-Bensi hot springs in central Nepal[END_REF][START_REF] Ciotoli | The detection of concealed faults in the Ofanto Basin using the correlation between soil-gas fracture surveys[END_REF][START_REF] Etiope | Carbon dioxide and radon geohazards over a gas-bearing fault in the Siena Graben (Central Italy)[END_REF]. These fractured regions often act as a primary control for fluid flow into fault-hosted geothermal reservoirs. As such, fault zones are typically not easily detectable; mostly due to thick sedimentary covers. Increased technical and financial exploration risks exist, in relation to investigating their geothermal productivity. Therefore methods are required that can reliably detect and identify permeable fault zones prior to undertaking costly drilling programs, thereby reducing exploration risks for geothermal reservoirs. For this purpose, geochemical or geophysical methods are typically applied when undertaking a geological exploration, to be able to provide information about the structural setting of the host rock.

A geochemical approach that can be utilized for the delineation of fault zones is a soil-gas emanation survey. Here, soil-gas is typically sampled at the surface or in shallow depths down to 2 m. The concentration of radon gas ( 222 Rn), as a product of the radioactive decay of uranium ( 238 U) with a half-life of 3.8 days, is determined by the abundance of its mother nuclide 226 Ra, by migration and decay rate. The presence of carrier gases (CO 2 , CH 4 ) exhaled by deeper reservoirs may play a dominant role in controlling transport and redistribution of radon towards the earth's surface [START_REF] Ciotoli | The detection of concealed faults in the Ofanto Basin using the correlation between soil-gas fracture surveys[END_REF][START_REF] Etiope | Evidence for radon transport by carrier gas through faulted clays in Italy[END_REF][START_REF] Etiope | Carbon dioxide and radon geohazards over a gas-bearing fault in the Siena Graben (Central Italy)[END_REF][START_REF] Walia | Soil-gas monitoring: a tool for fault delineation studies along Hsinhua Fault (Tainan), Southern Taiwan[END_REF][START_REF] Yang | Exhalation of radon and its carrier gases in SW Taiwan[END_REF][START_REF] Yang | Variations of soil radon and thoron concentrations in a fault zone and prospective earthquakes in SW Taiwan[END_REF]. However, the degassing process can be influenced by several factors, e.g. geological structure within the fault, the sedimentary cover itself, groundwater level variation, and the meteorological situation [START_REF] Heinicke | CO 2 and Radon measurements in the Vogtland area (Germany)a contribution to earthquake prediction research[END_REF][START_REF] Kemski | Classification and mapping of radon-affected areas in Germany[END_REF][START_REF] Papp | A new method for the determination of geophysical parameters by radon concentration measurements in bore-hole[END_REF]. In particular, several meter thick heterogeneous sediments or lowpermeable layers above the fractured bedrock could reduce the ability for performing a successful validation during a soil-gas emanation survey and reduce our ability to successfully delineate permeable fault zones using soil-gas sampling methods applied at ground surface level [START_REF] Ciotoli | The detection of concealed faults in the Ofanto Basin using the correlation between soil-gas fracture surveys[END_REF].

Geophysical methods may be able to provide an image of subsurface structural settings within the fault zone areas from a position on the near surface [START_REF] Flechsig | The Hartoušov mofette field in the Cheb Basin, Western Eger Rift (Czech Republic): a comparative geoelectric, sedimentologic and soil gas study of a magmatic diffuse CO 2 -degassing structure[END_REF][START_REF] Mcclymont | Visualization of active faults using geometric attributes of 3D GPR data: an example from the Alpine Fault Zone[END_REF][START_REF] Tronicke | Detailed shallow geometry and displacement estimates of the Maleme Fault Zone, New Zealand, using 2-D and 3-D georadar[END_REF] down to a few kilometers depth [START_REF] Becken | A deep crustal fluid channel into the San Andreas Fault system near Parkfield, California[END_REF][START_REF] Haimberger | High-resolution seismic survey on the Rhine River in the northern Upper Rhine Graben[END_REF][START_REF] Schütze | Large-scale geoelectrical measurements at the Central Bohemian shear zone nearby the research well Rittsteig (Bavaria, Germany)[END_REF]. In particular, information on the distribution of seismic velocity and electrical resistivity can be applied to map shallow structures down to 50 m depth with adequate spatial resolution according to electrode/geophone spacing and alignment. In this context, the determination of resistivity anomalies is considered to be useful when investigating disturbances caused by variations in lithological parameters and fluid content [START_REF] Flechsig | Geoelectrical investigations in the Cheb Basin/W-Bohemia: an approach to evaluate the near-surface conductivity structure[END_REF][START_REF] Nguyen | Subsurface electrical imaging of anisotropic formations affected by a slow active reverse fault, Provence, France[END_REF][START_REF] Wannamaker | Fault zone fluids and seismicity in compressional and extensional environments inferred from electrical conductivity: the New Zealand Southern Alps[END_REF]. Seismic sections have been successfully used to identify structural geological boundaries of the shallow subsurface (e.g. [START_REF] Giustiniani | P and S reflection and P refraction: an integration for characterising shallow subsurface[END_REF][START_REF] Wise | Geophysical imaging of the Quaternary Wairoa North Fault, New Zealand: a case study[END_REF].

However, using geophysical methods alone is often insufficient for a reliable imaging of fault zones. A promising approach is the combination of geophysical methods and soil-gas analysis. Such a combination of methods has already been used for investigating volcanically or tectonically active regions [START_REF] Finizola | Importance of structural history in the summit area of Stromboli during the 2002-2003 eruptive crisis inferred from temperature, soil CO 2 , self-potential, and electrical resistivity tomography[END_REF][START_REF] Revil | Geophysical investigations at Stromboli volcano, Italy. Implications for ground water flow[END_REF]). An insight into the structural and gas-dynamic features of a hydrothermally active fault was presented by [START_REF] Byrdina | Dipolar self-potential anomaly associated with carbon dioxide and radon flux at Syabru-Bensi hot springs in central Nepal[END_REF] using a combination of radon and CO 2 flux measurements at the surface level with geoelectrical methods. However, the application of such a combination in non-volcanic regions with characteristically low gas signals was, to our knowledge, not reported yet.

In this work, we show how a joint application of geophysical methods in combination with Direct Push (DP)-based soil-gas sampling techniques can be used for the delineation of fault zones covered by several meter thick sediments. During our investigations seismic refraction measurements were performed then followed by DC-geoelectrical measurements. DP methods were subsequently applied, to yield a further refined geophysical characterization of structures in the sediments and to obtain depth oriented soil-gas samples. The resulting geophysical images were used to select a suitable grid point for vertical DP profiling and samplings. Joint interpretation of geophysical results and determined distributions of radon and carbon dioxide concentration allowed the identification of permeable fractured zones hidden underneath sedimentary covers.

Geological setting

The work described was completed within the frame of a multidisciplinary project, which aimed at developing an integrated prospection methodology concerning the investigation and evaluation of potentially enhanced geothermal systems (EGS) reservoirs within the Variscian basement in general.

The general setting of the northwestern Alpine foreland can be described as largely resulting from the interaction that took place between extensional and collosional tectonics in early Cenocoic times (Fig. 1). Currently, the southern part of the Upper Rhine Graben and adjacent regions are characterized by increased seismicity [START_REF] Ustaszewski | Latest Pliocene to recent thick-skinned tectonics at the Upper Rhine Graben-Jura Mountains junction[END_REF]. Seismic data shows evidence of active faulting in the basement underneath the detachment of the Jura Mountains. These faults, which are interpreted as reactivated Permo-Carboniferous troughs, coincide with the frontal thrusts of the Jura Mountains [START_REF] Lopes Cardozo | New insight in the tectonics of the southern Rhine Graben-Jura region using local earthquake seismology[END_REF].

The major study area is the southern part of the Black Forest (Germany). This area is a typical low mountain range of the Variscian basement and forms the eastern shoulder of the Upper Rhine graben system. The geothermal potential of the southern Black Forest is well documented due to the surface heat flow, which reveals an anomaly of up to 140 mW/m 2 in the Waldshut-Tiengen area [START_REF] Medici | Geothermal Map of Switzerland[END_REF], next to the Swiss border. It is assumed that this area is controlled by fault zones, which provide pathways for deep circulation. A set of regional parallel WNW-ESE striking fault zones (Eggberg, Vorwald and Zurzach) can be traced from the surface in the Black Forest to the northern part of Switzerland.

The prevailing local geological situation in the Tiengen area is sketched in Fig. 1, and can be characterized as being made up of Triassic Middle Muschelkalk (dolomitic marls, gypsum, and anhydrite) and Upper Muschelkalk layers (limestone), and local shallow Quaternary sediments that cover the Upper Bundsandstein and the basement. The Muschelkalk is known to act as a karstified aquifer. All DP drillings during the campaign carried out along the profile yielded quarried clayey and marl rich sediments. Groundwater was not found to be present. A distinct tectonic fault zone, perpendicularly aligned to the main fault system, is documented (LGRB, 2006) and is traceable at some points in the field, due to subtle topographic scarps.

The profile location for the study was aligned perpendicularly with respect to the supposed fault zone location and under the constraints of relief and infrastructural facilities (streets, power lines, agricultural use).

Geophysical data

We applied geophysical methods that can provide information about the location of the supposed fault zone and about structures in overlying sediments. Refraction seismic measurements were carried out to delineate the possible fault zone by identifying deformed refractor horizons which would indicate the presence of a fault structure [START_REF] Wise | Geophysical imaging of the Quaternary Wairoa North Fault, New Zealand: a case study[END_REF]. Tomographic interpretation of seismic refraction data offers the possibility of obtaining an insight into functions of the overlying layers. To achieve further improved images of the setting of the overlying sediments, surface geoelectrical measurements were additionally carried out. Resistivity anomalies are often related to changes in clay and/or pore fluid content. Thus, an image of the resistivity distribution can be used to support the selection of interesting sampling points for soil gas analysis and for interpretation of the results obtained from these measurements. In this regard, geophysical images were used in order to create a grid for the soil-gas measurements with sufficient spacing and sampling point locations in significant depths aligned along the geophysical profiles.

Refraction seismics

A conventional refraction seismic survey was carried out to identify the main structures. The seismic line was 158 m long with 80 Pwave geophones (14 Hz) and 2 m spacing. The survey was conducted with 3 m shot point spacing, using an accelerated heavy weight drop of 40 kg with a fourfold energy stacking. Data was acquired and recorded from the 54 shots using Geometrics Geode seismic recorders. Signal processing was performed using the REFLEXW v 5.5 software [START_REF] Sandmeier | Reflexw manual ver[END_REF] to improve the signal-to-noise ratioincluding muting, bandpass filtering and picking the first breaks (Fig. 2a,b). A wavefront inversion was carried out using 3767 first P-wave travel time values in order to deduce a 2-D refractor model down to a depth of 40 m below surface level. The estimated model shows a typical three layer situation (shown in Fig. 2c) with a mean travel time approximation error of 3 ms. The overburden is characterized by a mean slow P-wave velocity of less than 500 m/s, while the velocity of the second layer is much higher, with mean values of 1500 m/s. A slight eastward dip is apparent. The velocity of the third layer was determined using values greater than 3000 m/s, and is assigned to the bedrock. There is also an eastward dipping with two prominent steps at distances of 80 m and 120 m respectively. This eastward dipping is consistent with the geological map, which shows a downward displacement of the Tiengen side of the fault in order to explain the juxtaposition of Middle and Upper Muschelkalk from both parts of this fault.

Geoelectric investigations

An electrical resistivity tomography (ERT) survey was carried out in close proximity to the seismic profile. The ERT measurements were used to provide a refined image of internal near surface structures down to a depth of 20 m. The DC data acquisition was performed on the 158 m profile which consisted of 80 electrodes with 2-m electrode spacing, using the RESECS multi-electrode device (GeoServe, Germany). We measured both Wenner-alpha and Wenner-beta configurations along the profile, due to their advantageous signal-to-noise ratio for larger offsets and the requirement for vertical resolution. Apparent resistivity data obtained from the measurements ranged from 37 to 170 Ωm with a mean standard deviation error of 2.3%. 1846 DC-data values were jointly inverted using the BERT-code (Boundless Electrical Resistivity Tomography [START_REF] Günther | Three-dimensional modelling and inversion of dc resistivity data incorporating topography -II. Inversion[END_REF][START_REF] Rücker | Three-dimensional modelling and inversion of dc resistivity data incorporating topography -I[END_REF]). The final resistivity model of the subsurface down to a depth of 20 m, shown in Fig. 3, can be characterized by resistivity variations from 40 to 130 Ωm. The top layer features higher resistivity values up to 130 Ωm. Conductive anomalies appear in shallow depths, at 5 m below ground level. Distinct conductive areas with resistivity values less than 50 Ωm were found, at distances between 20 and 45 m and at the distance of 110 mend of the profile. The central part of the profile is characterized by a heterogeneous resistivity distribution with small-scale anomalies near the ground surface. In particular, higher resistivity values (70 Ωm) were observed in this region from below a depth of approx. 4 m.

The inversion of the ERT data results in a smoothed image of the subsurface, due to the regularization of the inversion algorithm. The regularization parameter has to be chosen to fit the data within their variances using the least squares method. In the case of 2D inversion, a model misfit error of rms = 9.4% was achieved. Furthermore, inherent ambiguities of the geoelectrical method lead to the problem of equivalent solutions, which exist for all geoelectrical inversion schemes [START_REF] Hoffmann | An approach to determine equivalent solutions to the geoelectrical 2D inversion problem[END_REF]. Therefore a vertical electrical conductivity profile with a high spatial resolution in the central part of the geoelectric profile at x = 60 m was additionally acquired, to provide an enhanced verification of ERT data.

In our study, we measured an Electrical Conductivity log (EC log Geoprobe® Systems) using Direct Push technology to obtain the electrical conductivity as a function of depth (e.g. [START_REF] Leven | Direct Push technologies-an efficient investigation method for subsurface characterization[END_REF][START_REF] Schulmeister | Direct-push electrical conductivity logging for high-resolution hydrostratigraphic characterization[END_REF] (Fig. 4). DP also allows for an efficient sampling of soil gas, which was subsequently utilized as discussed in the following paragraph. The EC Log system is based on a classical Wenner array, with four poles arranged at distances of 2.5 cm from each other at the site of the probe. Using the EC logging tool, we were able to rapidly obtain information about the vertical variation of the rock parameter conductivity with a depth resolution of 1.5 cm. Thus, it is possible to define zones characterized by lower conductivity values, equivalent to coarser grained and potentially more gaspermeable sediments (Fig. 4). The uppermost soil horizon and a zone between 3 and 6.5 m below ground exhibited low electrical conductivity. The EC log reveals sharp resistivity contrasts which cannot be resolved with the DC surface measurements, due to decreasing sensitivity with increasing investigation depth. Due to the same measurement principle, the lateral investigation depth of the EC log is also limited to decreasing sensitivities, i.e. its scope is limited to only a few centimeters from the probe into the soil. Therefore, the small electrode spacing allows the sensor to resolve only a small lateral radius around the probe (5 to 10 cm; [START_REF] Beck | Location and characterization of subsurface anomalies using a soil conductivity probe[END_REF]. In contrast, a greater Fig. 5. Sketch of the Direct Push technology applied for depth-oriented soil-gas sampling. The data acquisition is performed using an AlphaGuard radon gas monitor and an ANSYCO GA94 mobile soil-gas analyzer. The devices are connected in series with the sampling point via air-tight tube system including filters.

averaging volume (greater than 1 to 10 m 3 ) causes the surface geoelectrical technique to exhibit a smoothed image of the direct-push EC log [START_REF] Schulmeister | Direct-push electrical conductivity logging for high-resolution hydrostratigraphic characterization[END_REF]. The lithological boundaries which are seen as sharp changes in the direct-push log, appear as gradual change in ERT imaging (Figs. 3,4). Hence, both methods have characteristic but different fields of investigation and their results are qualitatively comparable.

Soil-gas analysis

Several meter thick sediments and the occurrence of lowly permeable layers (e.g. clayey sediments) can impede soil-gas exhalation from greater depths to the surface and can often make the detection of permeable fault zones difficult, when using shallow soil radon measurements in the standard sampling depth of approx. 1 m. Previous work [START_REF] Kemski | Classification and mapping of radon-affected areas in Germany[END_REF] and our own first onsite test results confirmed this assumption. For this reason, we decided to use DPtechnology to circumvent the low radon signals at the surface due to the given geological situation and to additionally have the chance to characterize the spatial radon distribution as a function of depth.

For this study, depth-oriented soil-gas sampling was carried out along the profile (see Fig. 5 for a sketch of the system). Sounding locations were selected based on the results of our geophysical measurements. The soil-gas was sampled following the "whole air-active approach" (ASTM, 2006), using a Geoprobe® Post Running Tube System (PRT). The PRT uses a probe with a retractable tip that is advanced into the ground using the DP technology. Upon reaching the desired sampling depth, the probe is raised a few centimeters whilst the retractable tip was pushed out, exposing an air inlet as sampling point to the formation. The soil gas is pumped from the sampling point via a tubing system that is attached with an air-tight coupling to the sampling point. The tubing runs to a mobile AlphaGUARD Radon Monitor with an integrated gas pump. The radon monitor is based on a pulse-counting ionization chamber and is used for monitoring the radon concentration of soil gas, due to the alpha particle occurrence during the radon decay. Besides radon concentration, air temperature, pressure, and humidity were also measured.

Every measurement started and ended with fresh-air flushing of the chamber and tubes, to avoid artefacts gathering (caused by radon contaminated gas residuals). The duration of the cleaning procedure is adapted to the measured radon concentrations, which should attain values below 100 Bq/m 3 , and typically take between 5 and 15 min to perform. After flushing, soil gas is pumped at a rate of 1 l/min into the AlphaGUARD chamber. An increase in radon concentration is observed in this measurement period. Once the radon concentration in the chamber is in equilibrium with the sampled soil gas, a plateau with constant concentration will be reached (Fig. 6a). At this time, the inlets and outlets of the AlphaGUARD are closed so as they are gas-tight and the actual radon concentration estimating starts. During this process, flushing procedure of the chamber with soil gas took approximately 15 min and the determination of radon concentration took between 20 and 60 min.

In the sampling concept, a distance of 10 m for boreholes was decided upon and 5 different sampling depths (at 3 m, 5 m, 7 m, 10 m and 15 m) were planned along the geophysical profile, situated in the vadose zone. However, at some locations sampling had to be abandoned due to insufficient soil gas yield at the envisioned sampling depths or due to the occurrence of mixing with ambient air. In such cases, an instable radon concentration curve in terms of the occurrence of very low concentration values was typical.

Migration and transportation of trace gases such as radon towards the surface is also controlled by geogenic carbon dioxide which acts as a carrier medium [START_REF] Ciotoli | The detection of concealed faults in the Ofanto Basin using the correlation between soil-gas fracture surveys[END_REF][START_REF] Kemski | Classification and mapping of radon-affected areas in Germany[END_REF][START_REF] Yang | Variations of soil radon and thoron concentrations in a fault zone and prospective earthquakes in SW Taiwan[END_REF]. It is important to note that the occurrence of geogenic carbon dioxide is a reliable tracer for deep fluid circulation within the fault [START_REF] Walia | Soil-gas monitoring: a tool for fault delineation studies along Hsinhua Fault (Tainan), Southern Taiwan[END_REF]. Therefore, the soil gas CO 2 concentration was measured simultaneously at the radon sampling points (Fig. 5). For this purpose, an ANSYCO GA94 mobile gas analyzer was used to determine CO 2 , O 2 , and CH 4 concentrations. This device is able to detect these targets very quickly and reliably. This analysis is based on the infrared absorption behaviour of the soil-gas compounds. Typically, a single CO 2 concentration measurement takes only 30 s to carry out. All measured data showed distinct concentration patterns of increased CO 2 values in coincidence with decreased O 2 concentrations.

Finally, 26 radon sampling points with a concentration value range from 0.9 kBq/m 3 to 143 kBq/m 3 and a mean standard deviation of 3.3 kBq/m 3 was obtained in 9 wells (Fig. 3c,d). The CO 2 concentration varies from 0.1% to 3.2% with a mean standard deviation of 0.2%. In our study, a positive correlation between radon and CO 2 concentration was observed (Fig. 6b), similar to the findings of [START_REF] Guerra | Soil-gas method for tracing neotectonic faults in clay basins: the Pisticci field (Southern Italy)[END_REF]. The measurement of gas profiles at different locations allows identification of a gas concentration pattern (Fig. 7a,b).

Joint interpretation of geophysical and gas geochemical data

The assumption of the occurrence of a gas-bearing fault zone in the investigation area seems to be confirmed by both geophysical and soil-gas analysis. From seismic refraction imaging the transition from sedimentary overburden to the bedrock can be clearly observed, which shows a slight vertical displacement and potentially results from the suspected fault zone. The seismic refraction image suggests the presence of 2 normal faults dipping in a South Easterly direction at the basement (Fig. 8). These two normal faults (F1 and F2) are spaced approximately 20 m apart at the surface level, and exhibit two different dip angles, 50°and 35°respectively. Such a downward displacement of the SE blocks is consistent with the geology of this area (Fig. 1), and two sectors can be observed which show the contact between Upper Muschelkalk formations on the SE side of the fault and Middle Muschelkalk formations on the NW side of the fault.

As a part of this incidence of sedimentary overburden, an occurrence of resistivity anomalies is supported by DC-geoelectrical measurements, which are interpreted as sediments with high clay content. A disturbed layering sequence is evident due to the spatial pattern of the resistivity of the above anomalies. However, ERT typically presents a smooth image of the subsurface, despite its typical imaging uncertainties. In addition, the EC log provides a high resolution vertical profile of electrical resistivity and indicates that the transition from high and low resistive areas is relatively sharp.

High resistivity values in both ERT and EC log measurements can be interpreted along with data from soil-gas analysis as being subsurface areas with partly higher permeability for gas transport. Fig. 4 shows the high resolution EC log in comparison to radon concentration data from 5 different sampling depths at the same location. High radon concentrations, greater than 60 kBq/m 3 , were found in horizons with significantly lower conductivities than 10 mS/m. Evaluation of the additional carrier gas indicator CO 2 supports that assumption.

The measurement of gas profiles at different locations results in an image of gas concentration patterns. Reconstructed resistivity distributions can help our understanding of this pattern. Higher gas concentration values were measured, due to the abundance of more permeable coarser sediments with lower clay content coinciding with more resistive areas (Fig. 8). Considering the lack of sampling points due to probing difficulties, it seems that the highest radon concentration values are evident below zones characterized by low resistivities. This effect can be explained by accumulation processes, typical for areas below gas impermeable clayey layers. Thus, highest radon concentrations can be caused by the gas retention. Additional capture of CO 2 supports the results of inert trace gas investigations. Geogenic carbon dioxide is a supporting tracer for fluid circulation within the fault and, in this case, the determined radon gas movement is linked to advection transport, using CO 2 as a carrier medium. Hence, the decreased radon concentration with increasing distance from the fault can be caused by the short decay time of 3.8 days. Joint interpretation using both approaches allows a conceptual fault model to be derived. Both CO 2 and 222 Rn vertical concentration maps display a higher concentration area with a clear SE dip in coincidence with the fault structure supposed by seismic refraction data. Consequently, a relevant structure for gas movement represented by significant geophysical and gas concentration anomalies was identified.

Summary and conclusions

This study provides an example of a successful delineation of a gasbearing fault structure, covered by several meter-thick partly clayey sediments for which a low permeability can be expected (e.g. based on Schön (2011) less than 10e -5 m²). Delineation and characterization were only made possible due to the combination of geophysical methods and soilgas sampling. Geophysical methods gave structural insights into the subsurface concerning the possible location of the fault zone, as well as information on structures in the covering sedimentary layer. Furthermore, geophysical information was used for selecting and positioning of gas sampling points. Application of DP-technology offered the possibility of obtaining gas concentration data at different depths, including for regions beneath lowly permeable sediments. The data was used for producing a concentration-depth cross-section, as well as for obtaining additional information that allowed improved interpretation of geophysical data to take place. Soil-gas emanation surveys alone, with typical sampling depths of only up to 1 m (maximum), would be highly susceptible to atmospheric influences. It can be expected that this would result in biased, vague or misleading concentration data for both radon and CO 2 values, and therefore yield incorrect information concerning the location and extent of a possible fault zone.

Gas concentration mapping in greater depths and for several locations was essential, allowing for the analysis of the fault system, as well as inward migration of gases into the sediments. The geophysical results were helpful for this interpretation of observed gas concentration patterns in the sediments. The occurrence of both high radon and carbon dioxide concentrations leads to the conclusion that radon transport is linked to the migration of CO 2 into the fault zone, which can be an additional indicator of fluid circulation and permeability increase. The combination of complementary methods such as the high resolution EC log, gas concentration maps as function of depth, electrical resistivity tomography and seismic refraction profile, were successfully adopted for this investigation within the frame of geothermal systems prospection, also offering potential opportunities for other applications. Consequently, joint data interpretation leads to more reliable models being derived of the investigated structures.

Fig. 1 .

 1 Fig. 1. General tectonic map based on Lopes Cardozo and Granet (2003) and a view of the geological situation in the vicinity of the investigation profile near the town of Tiengen in the southern Black Forest (based on general geological map; LGRB, 2006).

Fig. 2 .

 2 Fig. 2. a) Example of raw data obtained at shot location x = 45 m. b) Travel times for all picked P-wave first arrivals. c) Result of seismic refraction tomography shows a three-layer P-wave velocity model. The third layer is characterized by slight vertical displacement.

Fig. 3 .

 3 Fig. 3. Resistivity distribution model calculated from combined Wenner-alpha and Wenner-beta measurements. EC log was measured at the location x = 60 m. Soil-gas sampling points along the profile were selected based on the results of geophysical measurements ("+").

Fig. 4 .

 4 Fig. 4. EC log result at x = 60 m with low conductive horizons embedded in clayey conductive layers, in comparison with measured radon and CO 2 soil-gas concentrations in 5 depth horizons.

Fig. 6 .

 6 Fig. 6. a) Determination of radon gas is based on alpha particle counting as a function of time. The counting process starts after flushing of the ionization chamber with soil gas. The radon concentration is determined as a mean value at stable counting rates. This figure shows two different measurements at two sampling depths.b) Scatter plot of radon concentration versus CO 2 concentration. A positive correlation with a correlation coefficient R 2 = 0.72 was determined for n = 26 sampling points.

Fig. 7 .

 7 Fig. 7. Results of soil-gas analysis. a) Radon concentration pattern. b) Carbon dioxide concentration pattern. ("+" gas sampling points, "o" gas sampling not possible).

Fig. 8 .

 8 Fig. 8. Synoptical interpretation combining the geophysical and soil-gas data. Seismic refraction image suggests the presence of 2 normal faults dipping in a South Easterly direction at the basement. These two normal faults (F1 and F2) are spaced approximately 20 m apart at the surface level, and exhibit two different dip angles, 50°and 35°respectively. Both CO 2 and 222 Rn vertical concentration maps display a higher concentration area with a clear SE dip in coincidence with the supposed fault structure.

depth horizons.

Acknowledgement

This research was carried out within the frame of the joint research project "EGS Technology Development"support code 0325040B. Financial support provided by the German Federal Ministry for the Environment (BMU) is gratefully acknowledged. We would like to offer special thanks to all who gave technical assistance during the field work and to Christopher Higgins for proof reading. Furthermore, we would like to thank Dr. Heiko Woith and one anonymous reviewer for their helpful and invaluable comments.