François De Vleeschouwer
email: fdevleeschouwer@gmail.com

Jarosław Sikorski

Nathalie Fagel

DEVELOPMENT OF LEAD-210 MEASUREMENT IN PEAT USING POLONIUM EXTRACTION. A PROCEDURAL COMPARISON

Keywords: 210 Pb, polonium, ashing, peat bog, recent sediment deposits

Two chemical treatments for lead-210 measurement were compared on the sub-surface samples of a core from an ombrotrophic bog from East Belgium. The classical procedure involves a concentrated acid extraction of polonium. However, this treatment represents substantial health risks together with unknowns regarding both the degree of cleanliness and the Po extraction rate, and most importantly, is rather time consuming. We developed here an improved procedure involving an ashing step prior to acid extraction. This allows substantial improvements such as: 1/ the use of a relatively small amount of acid compared to the classical procedure and 2/ the substantial reduction of a total sample digestion time. Measurements of 210 Pb concentrations were conducted by alpha spectrometry. Results show a good agreement of unsupported 210 Pb activity obtained for both procedures, although some unknowns remain concerning the adsorption of 210 Po on the plastic test tube, the volatilization of a small amount of ash, or the absorption of alpha particle at the alpha source surface. This however should not affect the 210 Pb measurement as all the samples are spiked prior to ashing (i.e. the recoveries are fully monitored). Through this study, we are suggesting researchers to follow this new procedure in order to increase safety, cleanliness, better recovery and substantial time gain.

INTRODUCTION

The phenomenon of radioactive disequilibrium is frequently used in geochronological studies. In particular, 210 Pb is often used in order to provide a chronological framework in young sediments (i.e. not more than 150-200 years). Lead-210 originates mainly from the decay of 222 Rn present in the atmosphere [START_REF] Turekian | Geochemistry of Atmospheric Radon and Radon Products[END_REF][START_REF] Robbins | Geochemical and geophysical applications of radioactive lead[END_REF]. Lead-210 binds to the surface layer of aerosols. These aerosols are deposited at the Earth's surface and therefore increase the initial (e.g. supported) 210 Pb content of the sediment, leading to a radioactive disequilibrium. As time goes on, aerosols are progressively buried into the sediment, and therefore cut off from the atmospheric (i.e. unsupported) 210 Pb supply. This burial leads to the decay of the unsupported 210 Pb, rendering possible the reconstruction of a decay curve using the radioactive equation of 210 Pb. The half-life of 210 Pb is ca. 22.3 years (e.g. [START_REF] Appleby | Tracking environmental change using lake sediments, volume 1: Basin analysis, coring and chronological techniques[END_REF][START_REF] Carroll | Sedimentary Processes: Quantification Using Radionuclides[END_REF]. Therefore, the lowest theoretical measurement of unsupported 210 Pb being around 9 halflives, the age of the oldest sediment that can be dated will average around 200 years. However, the measurement of 210 Pb is often limited to younger samples, mainly because of the quality of analyses, and the background levels obtained in each specific laboratory. For example, Appleby (2001) reported a practical limit averaging around 150 years, but also reported that this age can be reduced towards ca. 130 years if the initial excess 210 Pb surface activity is low (< 200 Bq kg -1 ), which may be the case in peat records (see also tab. 1). Lead-210 was initially used in the determination of glacier accumulation, the assessment of environment changes and the age-dating of various sediments (e.g. [START_REF] Appleby | The calculation of 210 Pb dates assuming a constant rate of supply of unsupported 210 Pb to the sediment[END_REF][START_REF] Krishnaswami | Geochronology of lake sediments[END_REF][START_REF] Tobin | Recent developments sedimentation modeling and the statistical reliability of 210 Pb dating method[END_REF][START_REF] Sikorski | Rekonstrukcja historii depozycji osadów w zbiorniku wodnym Kozłowa Góra na podstawie pomiarów izotopu ołowiu 210 Pb (The reconstruction of the deposition history of the water reservoir Kozłowa Góra on the basis of 210 Pb activity measurements)[END_REF][START_REF] Ebaid | Determination of 210 Pb in environmental samples[END_REF]. At present, 210 Pb is still abundantly used to date lake sediments (e.g. [START_REF] Luque | Lake sediment response to land-use and climate change during the last 1000 years in the oligotrophic Lake Sanabria (northwest of Iberian Peninsula)[END_REF][START_REF] Xiang | Recent lake sedimentation in the middle and lower Yangtze basin inferred from 137 Cs and 210 Pb measurements[END_REF][START_REF] Gąsiorowski | Recent changes of sedimentation rate in three vistula oxbow lakes determined by 210 Pb dating[END_REF][START_REF] Tylman | Estimating recent sedimentation rates using 210 Pb on the example of morphologically complex lake (upper lake Raduńskie, N Poland)[END_REF][START_REF] Erlinger | Determining the Chernobyl impact on sediments of a pre-Alpine lake with a very comprehensive set of data[END_REF] and more recently in organic soils [START_REF] Klaminder | Soil carbon accumulation in the dry tundra: Important role played by precipitation[END_REF]. In peat bogs, the technique proved to be very efficient to provide an accurate control of the sub-surface peat layers, provided that the peat is ombrotrophic, i.e. exclusively fed by atmospheric inputs (e.g. [START_REF] Appleby | 210 Pb age dating of three peat cores in the Jura Mountains, Switzerland[END_REF][START_REF] Shotyk | History of atmospheric lead deposition since 12,370 14C yr BP from a peat bog, Jura mountains, Switzerland[END_REF][START_REF] Roux | Recent atmospheric Pb deposition at a rural site in southern Germany assessed using a peat core and snowpack, and comparison with other archives[END_REF][START_REF] Vleeschouwer | Atmospheric lead and heavy metal pollution records from a Belgian peat bog spaning the last two millennia: Human impact on a regional to global scale[END_REF][START_REF] Novak | A comparison of lead pollution record in Sphagnum peat with known historical Pb emission rates in the British Isles and the Czech Republic[END_REF][START_REF] Olid | Role of surface vegetation in 210 Pb-dating of peat cores[END_REF]. Ombrotrophic peat bogs are indeed able to trap any particle coming from the atmosphere. Their high acidity (pH around 3.5) and anoxic condition provide the ideal environment to preserve mineral micro-particles such aerosols, leading to the accumulation of the externally supplied unsupported 210 Pb. Since the middle eighties, ombrotrophic peat bogs have been dated using 210 Pb in various locations in the world (e.g. [START_REF] Holynska | Characterisation of 210 Pb dated peat core by various X-ray fluorescence techniques[END_REF][START_REF] Shotyk | History of atmospheric lead deposition since 12,370 14C yr BP from a peat bog, Jura mountains, Switzerland[END_REF][START_REF] Turetsky | Dating recent peat deposits[END_REF][START_REF] Roux | Recent atmospheric Pb deposition at a rural site in southern Germany assessed using a peat core and snowpack, and comparison with other archives[END_REF][START_REF] Mizugaki | Using dendrogeomorphology and 137 Cs and 210 Pb radiochronology to estimate recent changes in sedimentation rates in Kushiro Mire, Northern Japan, resulting from land use change and river channelization[END_REF][START_REF] Vleeschouwer | Atmospheric lead and heavy metal pollution records from a Belgian peat bog spaning the last two millennia: Human impact on a regional to global scale[END_REF][START_REF] Ali | Recent peat accumulation rates in minerotrophic peatlands of the Bay James region, Eastern Canada, inferred by 210 Pb and 137 Cs radiometric techniques[END_REF][START_REF] Lamentowicz | Last millennium palaeoenvironmental changes from a Baltic bog (Poland) inferred from stable isotopes, pollen, plant macrofossils and testate amoebae. Palaeogeography[END_REF][START_REF] Novak | A comparison of lead pollution record in Sphagnum peat with known historical Pb emission rates in the British Isles and the Czech Republic[END_REF][START_REF] Olid | Role of surface vegetation in 210 Pb-dating of peat cores[END_REF][START_REF] Vleeschouwer | Multiproxy evidence of `Little Ice Age' palaeoenvironmental changes in a peat bog from northern Poland[END_REF], mainly using a Constant Rate of Supply (CRS) model first developed by [START_REF] Appleby | The calculation of 210 Pb dates assuming a constant rate of supply of unsupported 210 Pb to the sediment[END_REF]. In terms of environmental reconstruction in peat bogs, several studies have, for example, showed that combined with elemental geochemistry and non radioactive lead isotopes, 210 Pb was a powerful technique to evidence the industrial revolution through Europe, as well as the introduction of unleaded gasoline (e.g. [START_REF] Shotyk | History of atmospheric lead deposition since 12,370 14C yr BP from a peat bog, Jura mountains, Switzerland[END_REF][START_REF] Vleeschouwer | Atmospheric lead and heavy metal pollution records from a Belgian peat bog spaning the last two millennia: Human impact on a regional to global scale[END_REF]. Others have shown recent vegetation change (e.g. [START_REF] Vleeschouwer | Multiproxy evidence of `Little Ice Age' palaeoenvironmental changes in a peat bog from northern Poland[END_REF][START_REF] Lamentowicz | Climate-and human-induced hydrological change since AD 800 in an ombrotrophic mire in Pomerania (N Poland) tracked by testate amoebae, macro-fossils, pollen, and tree-rings of pine[END_REF].

Lead-210 measurements can be achieved by either directly using low background gamma spectrometry, or by measuring its decay product ( 210 Po) by alpha spectrometry. Despite having been proven efficient for low-density samples (e.g. peat, lake sediment), gamma spectrometry is limited in small samples (e.g. [START_REF] Ebaid | Determination of 210 Pb in environmental samples[END_REF] where the measurement of low-energy 210 Pb gamma photons (E 210Pb = 46.5 keV) is virtually impossible. Moreover, low background and high resolution gamma spectrometers are rather expensive compared to alpha spectrometers. Therefore, for low-activity samples, alpha spectrometry is preferred as the activity of 210 Pb can be determined indirectly through the measurement of its decay product 210 Po. However, 210 Po needs to be chemically extracted from the material. In order to determine the extraction recovery, each sample also needs to be spiked with a known amount of 208 Po, which has the same chemical properties as 210 Po but emitted alpha particles of a different energy.

At the Gliwice Absolute Dating Method Centre (Gliwice, Poland), the common procedure used is alpha spectrometry after polonium extraction using and incomplete HN0 3 digestion (e.g. [START_REF] Sikorski | Rekonstrukcja historii depozycji osadów w zbiorniku wodnym Kozłowa Góra na podstawie pomiarów izotopu ołowiu 210 Pb (The reconstruction of the deposition history of the water reservoir Kozłowa Góra on the basis of 210 Pb activity measurements)[END_REF][START_REF] Sikorski | Application of α and γ spectrometry in the 210 Pb method to model sedimentation in artificial retention reservoir[END_REF]. However, Po activity in peat is relatively low, due to high peat accumulation rate (≥1mm/year or more) and the low content of aerosols per volume of peat, a material being almost exclusively composed of organic matter. A second difficulty resides in the fact that Po needs to be extracted from an organic matrix, which is still challenging due to its low dissolution rate, even in strong acids. However, most of studies have been using the extraction of Po from wet or dry sediment (e.g. [START_REF] Holynska | Characterisation of 210 Pb dated peat core by various X-ray fluorescence techniques[END_REF][START_REF] Olid | Role of surface vegetation in 210 Pb-dating of peat cores[END_REF]. Several alternative procedures were summarized by El-Daoushy et al. (1991, and reference therein). Their concluding remarks were that the radiochemical procedures needed to be optimized to achieve accurate analyses, especially at low 210 Pb concentrations and small sample size. The type, size and composition of deposits analyzed are also crucial parameters needing to be checked and optimized. However, most of these procedures still involve either high amount of strong acids (leading to potential health hazards) or relatively time-consuming and complex procedures (i.e. involving a substantial amount of laboratory steps) leading to possible reproducibility problems. A step that has, to our knowledge, never been systematically investigated or applied is the calcination of the 208 Po-spiked sample prior to Po extraction (e.g. protocols reported in [START_REF] Figgins | The radiochemistry ot Polonium[END_REF][START_REF] Edgington | Determination of the activity of lead-210 in sediments and soils[END_REF][START_REF] Ebaid | Determination of 210 Pb in environmental samples[END_REF], despite its recent application for Po measurement [START_REF] Fiałkiewicz-Kozieł | Dynamika zmian zanieczyszczenia metalami ciężkimi na przykładzie wybranych torfowisk wysokich Kotliny Orawsko -Nowotarskiej (Dynamic of heavy metals concentrations in ombrotrophic peat bogs of the Orawa-Nowy Targ Basin[END_REF]. This lack of investigation is mainly due to the fact that Po may volatilize at relatively low temperature. However, no fractionation is expected during volatilization allowing further measurement of 210 Po. Moreover, this ashing step greatly facilitates the whole chemical treatment in the case of peat by removing the organic matter and therefore allowing for low acid quantities to be used to dissolve the small remaining ash residue (1-5% of the total dry mass).

In this paper, we present a comparison of Po extraction with and without ashing. The recent peat layers of a peat bog from Eastern Belgium were measured using both protocols. The extraction rate is compared together with the activity spectra and the absolute activities after one and two days of measurement per sample. It is obvious that the classical procedure involving chemical extraction has been used in numerous laboratories performing 210 Pb measurements. Therefore, this article aims not to report a well-known procedure, but to emphasize some simple improvements in terms of safety, cleanliness, time consumption and possible extraction improvements.

SITE DESCRIPTION

The Misten bog is located in the Hautes-Fagnes Plateau (East Belgium, Fig. 1). This plateau displays several peat infillings, part of which are ombrotrophic peat bogs. The Misten bog is among the thickest peat deposit of this area, with more than 8 m of peat in its centre (Fig. 1). Previous studies have already demonstrated its great potential for past environmental reconstruction [START_REF] Vleeschouwer | Atmospheric lead and heavy metal pollution records from a Belgian peat bog spaning the last two millennia: Human impact on a regional to global scale[END_REF][START_REF] Persh | Zur postglazialen Wald-und Moorentwicklung im Hohen Venn (About the post-glacial evolution of forests and peatlands in the Hohes Venn)[END_REF].

CORING AND SUBSAMPLING

As this work was carried out within the framework of other ongoing projects needing long sequences, it was decided to core the bog where the thickest peat sequence would be found. The upper meter was retrieved using the titanium Wardenaar corer [START_REF] Wardenaar | A new handtool for cutting soil monoliths[END_REF] of 1m length and 15 by 15 cm section from the Institute for Environmental Geochemistry (University of Heidelberg, Germany). This type of corer enables to retrieve the softer upper parts of the peat, mainly composed of poorly decomposed plant remains, that could be compressed using a Bielo-Russian corer (Belokopytov and Beresnevitch, 1955). The Wardenaar core was frozen and cut using a band saw at the Institute for Environmental Geochemistry (University of Heidelberg, Germany). Each sample was controlled for its thickness in order to assess the loss of material during the cutting. In this paper, we will only present the results concerning the 210 Pb measurements on the topmost samples (0-30cm) and on two bottom samples (97-99cm, see Table 1 and2).

PRINCIPLES

Lead-210 formation and deposition

In a peat bog, 210 Pb has two sources [START_REF] Oldfield | Empirical testing of 210 Pb dating models for lake sediments[END_REF]. The supported 210 Pb is produced by the in situ decay of U contained in mineral grains trapped in the peat bog (Fig. 2). The decay of supported 210 Pb will be continuously compensated by the continuous uranium decay, leading to a constant concentration of supported 210 Pb along the peat profile. The unsupported 210 Pb is created by the decay of 222 Rn in the atmosphere. It is adsorbed on aerosols that are further deposited and preserved in the peat bog. Wet or dry deposition is possible. Wet deposition occurs during precipitation, when aerosols serve as nuclei for the condensation of water vapour. Aerosols are also frequently washed out mechanically from the air. Dry deposition results of the gravitational fall of aerosols, or is due to air masses movement towards the surface of the peat bog [START_REF] Vile | Mobility of Pb in Sphagnum-derived peat[END_REF][START_REF] Martinez-Cortizas | Atmospheric Pb depo-sition in Spain during the last 4600 years recorded by two ombrotrophic peat bogs and implications for the use of peat as archive[END_REF]. The aerosols will eventually be buried in the peat, leading to the slow decay of unsupported lead according to its radioactive decay law.

Sample preparation

The classical procedure

The classical procedure used in most of the laboratories to extract the polonium from the sample is the digestion of the sample powder into concentrated HNO 3 (e.g. [START_REF] Flynn | The determination of low levels of 210 Po in environmental materials[END_REF]). In our case, 0.5g of dry peat powder spiked with 1ml of 208 Po (same chemical properties than 210 Po but different alpha particle energy) was digested with 20 ml of concentrated HNO 3 (Fig. 2). The spike consists in diluted HCl 0,05M containing a known amount of certified 208 Po (manufactured by AERE Harwell, United Kingdom) which specific activity is 3 Bq g -1 .

However, in the case of peat, the high content of organic matter renders this extraction challenging. Indeed, the reaction of organic matter with HNO 3 is strongly exothermic and causes a lot of degassing, leading to potential health hazards. Usually, to avoid this problem, the reaction is achieved in a Teflon beaker sealed in a metallic bomb and heated at 105°C for 12 hours. However, the reaction is still incomplete as a lot of organic matter is not digested and needs to be centrifuged (3500 rpm, 20 min.) afterwards. After this step, the liquid residue is transferred in a Teflon beaker where 30 ml of H 2 O 2 is added to oxygenate the organic matter during 12 hours. Again, the reaction of H 2 O 2 with dissolved organic matter is strongly exothermic, leading to potential over-leaking of the sample out of the beaker. Then the liquid is evaporated and 20 ml of concentrated HCl is added and evaporated. This operation is repeated twice to guarantee the total removal of HNO 3 . The sample is then dissolved in diluted 50 ml HCl together with 40 mg of hydroxylamine hydrochloride and 40 mg of tri-sodium citrate dihydrate. The solution is heated and stirred on a magnetic stirrer and the deposition is achieved on a silver disc dipped into the liquid for ca. 3 hours. This last step is again crucial as other oxidants such as organic matter may bind to the silver disc despite the use of hydroxylamine hydrochloride and tri-sodium citrate dihydrate (El-Daoushy et al., 1991 and reference therein). It is therefore important to entirely remove the organic fraction of the sample. This is not achieved using the classical procedure but can be achieved by ashing the sample.

The overall extraction procedure takes 3 days per sample (as generally, one sample is processed at one time) and is relatively efficient. However, there are nonnegligible health hazards due to the high amount of concentrated acids used during the extraction (20ml HNO 3 and 40ml HCl) but also to the degassing and potential over-leaking of the sample. Moreover, the latter problem can cause the loss of a part of the sample, leading to problems in the measurement and repeated extraction for the same sample. Finally, the overall extraction is not of the Fig. 1. The Misten bog and the location of the coring. Peat thickness after [START_REF] Wastiaux | Topographie de surface et de subsurface des zones tourbeuses des réserves naturelles domaniales des Hautes-Fagnes (Surface and subsurface topography of peatlands from the Hautes-Fagnes Natural Reserve)[END_REF]. Dashed line represents pedestrian trails. [START_REF] Robbins | Geochemical and geophysical applications of radioactive lead[END_REF][START_REF] Goslar | First measurements of natural radioactivities of 210 Pb in the Insitute of Physics, Silesian University of Technology[END_REF]. cleanest procedure, leading to possible relatively important background and/or peak scatter. For these reasons, an alternative extraction (see § "The ashing procedure") was developed to circumvent the high organic matter content of the sample and the high amount of concentrated acids.

Fig. 2. The sources of lead-210 in a peat bog (after

The ashing procedure

Half a gram of each dry sample powder was transferred in a 10 ml plastic test tube, where 1 ml of Po was added (Fig. 3). The tubes were closed for 24 hours in order to let the Po impregnate the peat powder. Afterwards, the tubes were opened and slowly evaporated (ca. 60°C). The resulting dry powder was transferred in ceramic crucibles with caps and was combusted at 550°C for 4 hours in a muffle furnace. The resulting ash was transferred in 15ml Teflon beaker (Savilex). This transfer required high caution as: 1/ the amount of ash was very low (ca. 1% of the initial dry powder) and 2/ the ash was very volatile. From this point, all the analytical procedure was performed in the class-100 workstations of the AGEs (ULg, Belgium). All the acids were Merck quality or equivalent, were sub-boiled twice and were of the highest concentration. Four millilitres of HNO 3 was added to each beaker. The beakers were tightly closed and heated at 120°C for 24 hours. The liquid was evaporated and 2 ml of HCl was added. The beakers were tightly closed and heated at 120°C for 24 hours. Sometimes, a very few particles were remaining undigested. The samples were therefore put in ultrasonic bath for 20 minutes. If any particle remained after this step, the sample was transferred in an Ependorf 2.5 ml microtube and centrifuged Table 1. Oneand several days ("more": 1-2 days more) total 210 Pb activity measurements using the classical and ashing procedures. Some samples were not measured for both treatments due to the lack of material. Note that, using the ashing procedure, the overall recoveries are higher in 19 of the 27 samples measured after the extraction using both procedures. "Higher" and "lower" refer to the lower or higher recovery for the ashing procedure compared to the classical procedure. (7500 rpm, 20 min.). The remaining liquid was transferred in a 7 ml Teflon snap-cap beaker and evaporated.

Classical procedure

The dry residue was in the form of a very small orange bubble at the bottom of the beaker. The deposition of silver discs was achieved using the same procedure as for the classical procedure.

Lead-210 measurement

The total (i.e. supported and unsupported) activity of 210 Pb was determined indirectly through the measurement of its decay product 210 Po using alpha spectrometry. The spectrometer was tuned to cover the energies emitted by alpha particles from 210 Po. Alpha activity was measured with a spectrometer Canberra model 7401, with a surfacebarrier Si semiconductor detector. Sensitive area of the detector is 300 mm 2 and its energy resolution is ca. 20 keV. This enabled a very good separation of peaks from 210 Po -E = 5.308 MeV and 208 Po -E = 5.105 MeV [START_REF] Sikorski | Inventory of sediments of the dammed lake in Kozłowa Góra and first measurements of 210 Pb activities in the lake deposits[END_REF]. In order to assess the activity of supported 210 Pb, two deep samples (see Table 1 and2) where unsupported 210 Pb had decayed were analysed.

The amount of supported 210 Pb in a peat bog depends mainly on the presence of mineral grains bearing elements from the uranium decay series. The Misten bog is located in an area which is surrounded by forests with dense bushes, which efficiently prevents the amount of local mineral grains to be transported to the bog. As a result there was a small concentration of supported 210 Pb in the samples. Moreover, non-atmospheric inputs, as e.g. river inputs in lakes, are not likely as the bog is exclusively fed by atmospheric inputs.

The measurements were carried out on both classicaland ashing-processed samples. Moreover, in order to control the gain of recovery along time, all the samples there is a solid peat powder residue that need to be isolated), while in the ashing procedure, the digestion is full.

were measured one day, then 1-2 days more. The specific activity of 210 Pb was determined using the Eq. 4.1. This formula makes allowance for short-lived isotopes decay and efficiency of chemical procedure and spectrometry measurement. The uncertainties on the specific and surface activities were estimated using the method of propagation of errors. The results are given in Table 1 and2 The recovery is determined by comparing the activity measured in the standard with the one of the sample (spiked with a known amount of standard). The recovery ranges between 17% and 91% (mean 44%) and between 24% and 97% (mean: 54%) for the classical and the ashing procedures, respectively.

DISCUSSION

The raw (i.e. not modelled) data shows decreasing 210 Pb specific activities with depth (Fig. 4). The data resulting from the one or several days of measurement are relatively similar for each pre-treatment (Table 1). The total 210 Pb activity is however generally lower with the ashing procedure than with the classical one (Fig. 4). Possible reasons are the adsorption of 210 Po on the plastic test tube, the volatilization of a small amount of ash, or the absorption of alpha particle at the alpha source surface. The volatilisation of elemental lead or polonium may also be possible. In peat, Pb and Po are bound to trapped aerosols than being present as elements. The volatilization of such Pb-or Po-bearing particle is therefore questionable. A recent investigation dealing with lead isotopes extraction in a peat bog showed that Pb volatilisation is unlikely during the ashing [START_REF] Kylander | Sample preparation procedures for accurate and precise isotope analysis of Pb in peat by multiple collector (MC)-ICP-MS[END_REF]. [START_REF] Jia | Determination of 210 Pb and 210 Po in mineral and biological environmental samples[END_REF] also reported minimum effect of fusion on the 210 Pb and 210 Po of rock and soil samples, which supports that the temperature should not be the factor provoking the loss in activity reported in the ashing of our samples. More recently, [START_REF] Jia | Determination of 210 Pb and 210 Po in soil or rock samples containig refractory matrices[END_REF] reported that around 14% 210 Po was volatilized when dust samples at 600°C but that the ashing time did not affect the rate of volatilization. In our case, the ashing temperature was rather similar (550°C). Therefore, in our experiment, 210 Pb volatilization is negligible and 210 Po volatilization is low. Further investigations should be carried out to better understand these discrepancies, which are however, not observed for unsupported lead. We therefore expect that the further age-modeling of these data will give similar pattern using the data from either the classical or the ashing procedure.

The profile obtained using the classical procedure displays two peaks around 10cm and 15cm (Fig. 4). So far these two peaks remained unexplained as periods of enhanced 210 Pb activity and/or deposition should be recorded using both procedures. One possible cause of such high activities may be contamination during the extraction process as the classical procedure was performed in a laboratory that was not as Po-and Pb-free as the clean air workstations used for the ashing procedure. We do however have no evidence so far to argument this hypothesis. Another possibility is external alpha particles disturbing signal during measurement. However, this does not question the reliability of the data, because the resulting CRSmodelled ages are very similar (Table 2).

The overall recoveries are greatly fluctuating from 17% to 91% and 24-97% using the classical and ashing procedures (Table 1), respectively. However, a pairedsamples T-test (SPSS 18.0.1 software) showed that recoveries after ashing are 9% to 61% significantly higher (p (two-tailed) < 0.1) than using the classical procedure in more than 2/3 of the samples. Therefore, together with its low need in acids the ashing procedure enhances the better safety and reliability of the procedure. Finally, besides giving a cleaner safer procedure, the ashing procedure provides a major improvement in the time needed to prepare the samples prior to measurement. Twenty samples can be digested in 2 days, instead of 1 sample per day using the classical procedure.

SUMMARY

Two chemical pre-treatments for gamma measurement of 210 Pb were compared for low-density peat samples. Advantages and disadvantages of the classical and the ashing procedures of Po extraction are listed in Table 3. The classical procedure used involves the chemical extraction of 210 Po using a large quantity of nitric acid. It leads to an incomplete digestion and therefore uncertainties concerning the extraction rate, but also to substantial chemical hazards. It is also time consuming. The main advantage of the ashing procedure is a 10time decrease in both the volume of concentrated acids needed and the pre-treatment time. This will lead to an overall safer procedure. Moreover, as no H 2 0 2 is required, the risk of leaking is negligible. However, a possible source of loss of sample is the transfer of the very light and highly volatile ash in the Teflon beakers. The second important advantage is that the overall extraction uses a cleaner procedure, leading in a better quality in the Po measurements. Finally, the most direct advantage is that up to 20 samples can be extracted in one week, which increased by 10 times the laboratory turnover. The most direct disadvantage is of course the increased costs of such clean workstation. However, we are convinced that the sole ashing, the use of clean Teflon beakers and pro analisi acids will lead to a much better extraction compared to the use of the Teflon bomb and the dry peat without any ashing. Unsupported 210 Pb activities are relatively similar for both pre-treatments promising to give similar age-models after calculation.

Fig. 3 .

 3 Fig. 3. Flowsheet summarizing the different steps of each extraction procedure. In the classical procedure, the extraction is always incomplete (i.e.there is a solid peat powder residue that need to be isolated), while in the ashing procedure, the digestion is full.

  total -(Bq kg -1 ) specific activity of whole 210 Pb (supported and unsupported) in measured sample (layer) I 210Po -(cps) net counting rate of impulses in 210 Po peak I 208Po -(cps) net counting rate of impulses in 208 Po peak A ps -(Bq kg -1 ) specific activity of 208 Po standard sample m s -(kg) mass of standard sample λ 208Po , λ 210Po -(s -1 ) decay constant for 208 Po i 210 Po t 1 -(s) time from preparation of standard to half of measurement t 2 -(s) time from deposition to half of measurement M -(kg) dry mass of sample

Fig. 4 .

 4 Fig. 4. Comparison between the total activity obtained on the Misten profile after several days of measurements using the classical procedure (open squares) and the ashing procedure (black circles).

Ashing procedure Measured days Total 210 Pb activity

				Uncertainty on total activity	Recovery	Total 210 Pb activity	Uncertainty on total activity	Recovery	Comparison of ash classical proce-dures
	(cm)		(Bq/kg)	(Bq/kg)	(%)	(Bq/kg)	(Bq/kg)	(%)	(%)
	0.97	1	-	-	-	822	44	46	-	-
	2.64	1	-	-	-	808	38	62	-	-
	3.8	1	-	-	-	743	26	38	-	-
	4.86	1	752	23	31	719	30	24	23	lower
	4.86	More	730	19	32	-	-	-
	5.98	1	697	27	54	656	32	97	44	higher
	5.98	More	724	30	53	-	-	-	-	-
	7.19	1	659	18	33	610	33	45	27	higher
	8.45	1	632	25	27	612	23	69	61	higher
	8.45	More	661	27	28	625	19	71	61	higher
	9.72	1	897	44	91	542	21	51	44	lower
	9.72	More	871	41	88	539	17	51	42	lower
	11	1	558	22	51	485	13	69	26	higher
	11	More	547	18	54	-	-	-	-	-
	12.3	1	300	13	38	340	15	49	22	higher
	12.3	More	433	15	36	329	11	50	28	higher
	13.7	1	426	14	66	230.3	7.8	52	21	lower
	13.7	More	444	12	65	-	-	-	-	-
	15	1	432	20	19	125.5	5.6	42	55	higher
	16.3	1	175	6.3	17	187.3	6.9	25	32	higher
	17.5	1	44.7	2	33	27	5.1	78	58	higher
	17.5	More	-	-	-	31.2	3.2	75	-	-
	18.8	1	126.9	5.9	41	24.8	2.6	45	9	higher
	18.8	More	-	-	-	22.5	1.7	47
	20.2	1	75.8	5.7	43	59.4	2.9	74	42	higher
	20.2	More	77	3.8	45	-	-	-	-	-
	21.5	1	36.7	4.3	44	18.1	1.7	76	42	higher
	22.9	1	37.5	3.3	20	10.1	1.3	41	51	higher
	24.2	1	35.9	3.9	56	8.1	0.9	42	25	lower
	25.5	1	33.6	2.4	55	11.4	2.2	24	56	lower
	26.7	1	17.2	2	34	6.6	0.9	41	17	higher
	28.1	1	47.2	4	20	11	2.1	35	43	higher
	29.4	1	35.1	3.8	69	6.6	1.4	37	46	lower
	30.7	1	15	2.2	51	6	0.7	34	33	lower
	32	1	19	2.3	59	9.2	2.2	68	13	higher
	32	More	-	-	-	7.2	1.4	65	-	-
	96.4	1	18.3	1.9	35	30.8	4.7	44	20	higher
	97.9	1	14.1	1.3	32	29.3	4	69	54	higher

Table 2 .

 2 Constant Rate of Supply age model[START_REF] Appleby | Tracking environmental change using lake sediments, volume 1: Basin analysis, coring and chronological techniques[END_REF] applied on results obtained both by the classical and ashing procedures.

		Classical		Ashing
	Depth Top Age	Uncertainty	Top Age	Uncertainty
	(cm)	yr AD	yr	yr AD	yr
	0.97	2006	2	2006	2
	2.64	2005	2	2005	2
	3.8	2004	2	2003	2
	4.86	2003	2	2002	2
	5.98	2003	2	2001	2
	7.19	2001	2	1999	2
	8.45	1998	2	1994	2
	9.72	1995	2	1990	2
	11	1985	2	1980	2
	12.3	1978	2	1969	3
	13.7	1967	3	1952	4
	15	1947	5	1930	5
	16.3	1901	6	1912	7
	17.5	1765	9	1745	9

Table 3 .

 3 Comparison of the two extraction procedures.

		ADVANTAGES	DISADVANTAGES
		Low cost	High amount of concentrated acids (overall 60 ml)
	CLASSICAL PROCEDURE	No specific lab equipment needed	Requires H202 Highly exothermic High acid degassing High risk of leaking (principally during H2O2 reaction) Non-negligible health hazard Very slow (1 sample per days)
		Low amount of concentrated acids (overall 6 ml)	Possible risk of loss during ash transfer
	ASHING PROCEDURE	No H202 required No risk of leaking Not very exothermic Minimal health hazard Very rapid (up to 20 samples per week) Very tight peaks leading to increased measurement quality	Increased lab costs (high quality acid, clean lab)

ACKNOWLEDGEMENTS

W. Shotyk (University of Heidelberg) is warmly thanked for having borrowed us his Titanium Wardenaar corer and for having let us using the peat-cutting facilities at the IES (University of Heidelberg, Germany). This research was funded through the ATIS ('Absolute Time Scales and Isotope Studies for Investigating Events in Earth and Human History') Marie Curie Transfer of Knowledge project MTKD-CT-2005-029642. We thank William Marshall and one anonymous reviewer for the useful comments made on an earlier version of this manuscript.


Xin Zhuang

Marc Lam

Chok Sing

Christophe Cordier

Sébastien Saez

Christophe Dolabdjian

Jaydip Das

Junqi Gao

Jiefang Li

Dwight Viehland

Analysis of noise in Magneto-Electric thin layer composites used as magnetic sensor

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.


C Hurter

A R Taylor

S Carpendale

A Telea

Color Tunneling : Interactive Exploration and Selection in Volumetric Datasets

Keywords: I, 3, 6 [Methodology and Techniques]: Interaction techniques-

Interactive data exploration and manipulation are often hindered by dataset sizes. For 3D data, this is aggravated by occlusion, important adjacencies, and entangled patterns. Such challenges make visual interaction via common filtering techniques hard. We describe a set of realtime multi-dimensional data deformation techniques that aim to help users to easily select, analyze, and eliminate spatial-and-data patterns. Our techniques allow animation between view configurations, semantic filtering and view deformation. Any data subset can be selected at any step along the animation. Data can be filtered and deformed to reduce occlusion and ease complex data selections. Our techniques are simple to learn and implement, flexible, and real-time interactive with datasets of tens of millions of data points. We demonstrate our techniques on three domain areas: 2D image segmentation and manipulation, 3D medical volume exploration, and astrophysical exploration.

INTRODUCTION

Volumetric datasets are found in many fields of science, such as engineering, material sciences, medical imaging, and astrophysics. One of the most used visualization methods for such datasets is direct volume rendering (DVR), which can show all values in the dataset. In contrast, techniques such as isosurfaces or slicing focus on data subsets, which requires users to select a priori the structures of interest.

Although DVR does not require an a priori selection step, it also comes with one major challenge: occlusion. On two-dimensional display devices, one cannot see more than a single data value per pixel. Yet, there are typically tens, or even hundreds, of such data values along each view ray centered at such a pixel. Hence, discovering patterns of interest hidden inside the data volume can be challenging.

Apart from a priori selection of structures of interest, several techniques have been proposed for exploring 3D data volumes. Transfer functions map values along a view ray to RGBA components which are next combined to convey an aggregated insight at each screen pixel. However, to make deep-hidden patterns visible in the final 2D image, transfer functions require careful, and often non-trivial settings. Interactive focus-plus-context (F+C) techniques offer an alternative by allowing users to locally manipulate the geometry and/or appearance of the data volume in order to 'peek' inside it, while keeping the overall spatial context of the entire volume.

In this paper, we extend F+C interactive exploration of 2D and 3D datasets in several directions. We propose a set of linked views that display subsets of data attributes. Example views are 3D DVR plots, 2D scatterplots, and 2D and 3D histograms. Views are linked by brushing and free-form selection. Using the optimal view(s), one can find structures of interest, e.g. spatially compact zones in DVR renderings or peaks in histograms, and highlight or erase such structures in all views at once. We enhance classical histogram views with shading, sorting, and depth, to allow spotting complex patterns with greater ease. We propose a smooth animation between multiple views, to locate (and select) data patterns which are hard to isolate in static plots. We integrate a F+C deformation that adds the ability to uncover locally occluded spatial patterns in our views. We present a GPU implementation of our techniques, which we call color tunneling, that creates real-time interactive, animated, explorations of datasets of tens of millions of points on a modern PC. We illustrate color tunneling with examples on 2D image editing, 3D medical visualization, and astrophysics visualization.

This paper is structured as follows. Section 2 presents related work in volume F+C exploration. Section 3 outlines our design. Section 4 details the proposed interactive exploration-and-selection techniques: view linking, warp animation, lock, brush, and dig. Section 5 presents three applications. Section 6 details the implementation. Section 7 discusses our F+C technique. Finally, Sec. 7.1 concludes the paper.

RELATED WORK

Occlusion is an inherent problem in 3D volume visualization. Several types of techniques alleviate this problem and help users to locate and/or select structures of interest from 3D data volumes, as follows.

Magic lenses: Magic Lenses locally modify a screen area by userselected operators to change the appearance of shapes [START_REF] Bier | Toolglass and magic lenses: The see-through interface[END_REF]. The idea was extended for complex effect compositing and interactive lens parameter editing [START_REF] Bier | Enhanced illustration using MagicLens filters[END_REF]. Tangible magic lenses extend the base concept to slice through, or zoom in, layered 2D or 3D datasets by interactively moving a 3D tracked physical planar object (the lens) which is either rigid [START_REF] Spindler | Exploring information spaces by using tangible magic lenses in a tabletop environment[END_REF] or flexible [START_REF] Looser | A 3D flexible and tangible magic lens in augmented reality[END_REF]. Nonlinear projection deforms 3D scenes in image space, as if seen through a cylindrical or spherical lens [START_REF] Yang | Nonlinear perspective projections and magic lenses: 3D view deformation[END_REF].

Semantic lenses, focus + context, and deformation: The 'dust & magnet' tool declutters scatterplots by several data-attribute-driven magnets in screen space [START_REF] Yi | Dust & magnet: Multivariate information visualization using a magnet metaphor[END_REF]. Niels et al. visualize ship motions on a map by blending trajectories into smoothly shaded shapes [START_REF] Willems | Visualization of vessel movements[END_REF]. They highlight specific trajectories by a semantic lens that works on the shading values, but does no deformation, as positions are found too important to be altered. For large datasets, deformation techniques locally change the spatial data layout to give more space to important data elements. Many variations of the original fisheye view [START_REF] Furnas | Generalized fisheye views[END_REF] exist, e.g. Elastic Presentation [START_REF] Carpendale | A framework for unifying presentation space[END_REF], Sigma Lenses [START_REF] Pietriga | Sigma lenses: focus-context transitions combining space, time and translucence[END_REF], and Jelly Lenses [START_REF] Pindat | JellyLens: Content-aware adaptive lenses[END_REF]. The table lens locally distorts the Cartesian layout of cells in a data table to give more space to specific table rows or columns [START_REF] Rao | The table lens: merging graphical and symbolic representations in an interactive focus+context visualization for tabular information[END_REF]. For node-link layouts, techniques include edge deformations, e.g. EdgeLens [START_REF] Wong | EdgeLens: An interactive method for managing edge congestion in graphs[END_REF], bring-neighbors lens [START_REF] Tominski | Fisheye tree views and lenses for graph visualization[END_REF], edge plucking [START_REF] Wong | Supporting interactive graph exploration using edge plucking[END_REF][START_REF] Wong | Supporting interactive graph exploration with edge plucking[END_REF], and link sliding and 'bring & go' techniques [START_REF] Tominski | Fisheye tree views and lenses for graph visualization[END_REF][START_REF] Gansner | Topological fisheye views for visualizing large graphs[END_REF] and their generalizations [START_REF] Riche | Exploring the design space of interactive link curvature in network diagrams[END_REF]. The MoleView technique deforms data based on both spatial position and data values, allowing to 'dig' in hidden data layers [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF]. Histomages links two element-based plots (images and their histograms) to allow an easier selection and editing of features in the 2D or color space [START_REF] Chevalier | Histomages: fully synchronized views for image editing[END_REF].

Occlusion challenge: In volume datasets, occlusion is typically larger than in the above examples. If position has specific semantics, it should be carefully preserved. Several F+C techniques refine the above interaction principles for this context. A viewer-aligned radial warping of elements close to the focus is used to push data points away in 2.5D [START_REF] Carpendale | Case study: Visual access for landscape event based temporal data[END_REF].

BalloonProbe uses the same radial warp idea for elements close to a focus in 3D synthetic scenes [START_REF] Elmqvist | BalloonProbe: Reducing occlusion in 3D using interactive space distortion[END_REF]. For 3D medical scans, complex manipulations (cut, peel, ply, dilate, retract) are used to expose inner structures [START_REF] Correa | Feature aligned volume manipulation for illustration and visualization[END_REF], with optional animation [START_REF] Mcguffin | Using deformations for browsing volumetric data[END_REF]. Similar techniques are provided by Gimlenses for segmented mesh datasets [START_REF] Pindat | Drilling into complex 3D models with gimlenses[END_REF]. Illustrative F+C techniques are further generalized in [START_REF] Correa | Illustrative deformation for data exploration[END_REF]. Multiple foci help to selectively enhance specific structures [START_REF] Hsu | A rendering framework for multiscale views of 3D models[END_REF]. A detailed taxonomy of F+C techniques for volume data is given in [START_REF] Cohen | Focus and Context for Volume Visualization[END_REF].

Transfer functions: Identification and selection of features of interest in volumetric data is also supported by transfer functions. Essentially, these are mappings from subsets of the attribute domain to specific color and transparency values. Kniss et al. extend classical 1D and 2D transfer functions to higher dimensions in order to easier find and isolate structures of interest in volumetric data [START_REF] Kniss | Multidimensional transfer functions for interactive volume rendering[END_REF]. Conceptually, transfer functions propose a different way for feature isolation than F+C techniques: While the latter rely chiefly on brushing to select such features, transfer functions require the (careful) design of a data-to-appearance mapping for the same task. Although the design of this mapping can be significantly assisted by automatic data analysis [START_REF] Correa | Size-based transfer functions: A new volume exploration technique[END_REF][START_REF] Sereda | Visualization of boundaries in volumetric data sets using lh histograms[END_REF][START_REF] Patel | Moment curves[END_REF], or direct WSYWIG interaction [START_REF] Guo | WYSIWYG (what you see is what you get) volume visualization[END_REF], their specification still involves considerable user effort.

Overall, basic F+C deformation techniques are simple to use and learn, but are hard to control in terms of what gets deformed and what does not. More complex techniques achieve better control, but also require more complex interaction tools. We next present a way to reconcile the simplicity of the former techniques with the flexibility of the latter, by combining linked views, interaction, and animation.

PRINCIPLE

Our principle, called color tunneling, is a set of interactive techniques that expand the possibilities for selection and exploration of images and volume datasets, while avoiding the use of complex menus and interface components. We provide support for a rich spectrum of exploratory activities via the integration of animation and brushing.

Our input data is a uniformly sampled multivariate field F : D → V, D ⊂ R n ,V ⊂ R m . For simplicity, our examples consider n = 2 (images) and n = 3 (volumes). As data attributes V we consider RGB color (for images) and scalar data values, data gradients, and volumetric shading for volumes (i.e. the color of voxels as assigned by whichever DVR method was chosen). We use next the term data points to refer to voxels (n = 3) or pixels (n = 2). However, color tunneling works directly for higher dimensions and/or more attributes.

Given such a field F, we consider two types of views to show all data points in F: Scatterplot views map 2 or 3 dimensions or attributes in D ∪V to R 2 or R 3 respectively, and additional attributes in V to color. For instance, a DVR of F maps D to R 3 , and scalars v ∈ V to color. 2D histograms map one attribute v ∈ V to the x axis and the number of points in D having values v = x to the y axis. Fig. 1 shows such a DVR and a density histogram view for a 3D CT scan. Key to exploring F is the linking of such views by interaction and animation. A typical scenario goes as follows: The user creates a view which shows three dimensions of D, e.g. a DVR of F. Usually, such a view occludes interesting structures found deep inside F. Next, the user creates one or more scatterplot or histogram views by selecting combinations of dimensions and attributes of interest. Finally, the user employs interactive techniques to explore the data and isolate structures of interest: view linking, brush, warp, dig. These techniques are detailed next.

INTERACTION TECHNIQUES

Since we are exploring dense multivariate datasets which exhibit significant overlap between groups of voxels or pixels, we need tools to interactively unveil the occluded structures of interest. For this, we propose five interactive techniques as follows:

• View linking: Three configurable views (2 exploration views and one lock view) for data exploration (Sec. 4.1),

• Warp: Animates a view between two configurations (Sec. 4.2),

• Lock: Locks items not to be affected by brush or dig (Sec. 4.3),

• Brush: With the lock view, brushing allows adding or removing data in a view (Sec. 4.4),

• Dig: With the lock view, digging pushes data points away from the lens center to unveil occluded structures (Sec. 4.5).

View configuration and linking

We use two exploration views with possibly different configurations.

Both views offer standard pan, zoom, and camera rotation. Users can interactively choose the mapping of the input data (D ∪ V ) to the view axes (Fig. 1): Double-clicking with the left mouse button on a view axis (x, y, or z) shows all data dimensions in D ∪ V . After we select a dimension d ∈ D ∪ V , we start a smooth animation between the current view configuration and the new one given by the menu choice.

The animation, detailed in Sec. 4.2, helps users to preserve the 'mental map' [START_REF] Archambault | Animation, small multiples, and the effect of mental map preservation in dynamic graphs[END_REF] and, equally importantly, helps visually tracking patterns of interest. Both views are linked by showing the same set of data points. This enables complex data-selection operations by incremental selection or filtering, and also avoids multiple visual configuration changes. This dual-view design, originally used for trajectory analysis [START_REF] Hurter | FromDaDy: Spreading data across views to support iterative exploration of aircraft trajectories[END_REF], is extended here for the more general case of DVR data exploration.

Animation between view configurations

Given any two views V 1 and V 2 , we link the views by executing a linear interpolation p(t) = (1t)p 1 + tp 2 , or warping, of each point

p 1 ∈ V 1 to its corresponding point p 2 ∈ V 2 .
The shading s(p) of the points is fixed: When examining a 2D image, s is the color of the image pixels; for a 3D data volume, s is the color of the voxels given by DVR. Fixed shading allows following trajectories of specific groups of data points as they move from V 1 to V 2 . The key value of animation is to create a dense sequence of intermediate frames between V 1 and V 2 , in which data patterns of interest become more visible than in both V 1 and V 2 .

While the left mouse button configures views, the right button controls animation. Clicking this button starts the animation (from V 1 to V 2 ). Dragging the mouse horizontally with the button pressed controls the time t, i.e. the animation speed and direction (V 1 to V 2 or back). Releasing the button stops the animation at any moment. Next, we can use the brush tool to select patterns in the interpolated view V (t) or the dig tool to reduce occlusion at desired points.

No constraints are put on the configurations V 1 and V 2 that we animate between. Both V 1 and V 2 can be 2D or 3D views, and they can map different data attributes to axes differently. When animating towards a 2D view, e.g going from a DVR view (V 1 ) towards its 2D histogram (V 2 ), we use V 1 's z (depth) values to order pixels in the 2D view V 2 (pixels with low z values get under pixels with high z values).

Cornerstone to this work, the warp animation has proven effective in many use-cases (see also the associated video): Animating between a DVR and a scatterplot unveils brain structures (Sec. 5.1). Animating between a data cube and a 2D histogram is used to detect and select outliers in astronomical data (Sec. 5.2). Animating between two scatterplots is used to eliminate complex regions in a 2D image (Sec. 5.3).

Lock

This view allows specifying data points not to be affected by the brush or the dig tool. Lock can be done at the start or during an animation (Sec. 4.2). We use a 'lock brush' (Fig. 1) with add and remove modes to add, respectively remove, points in a given view from the locked point-set. These modes are invoked by using the Shift, respectively Control, keys with the left mouse button, as shown by the red, respectively green brush circles in Fig 2 . The lock-brush size is adjusted with the mouse wheel. Locked data points are drawn (visible), while unlocked ones are not drawn. Double-clicking in a view inverts the lock set, i.e. makes all unlocked points locked and conversely (see Fig. 2 for different lock view instances). Finally, the lock view axis can be configured as discussed in Sec. 4.1, and the user can perform the warp animation to choose a suitable visual configuration (Sec. 4.2).

Filtering brush

Data points can be removed if they are uninteresting and/or to reduce clutter. For this, we use a filtering brush combined with locking (Sec. 4.3). As for the lock tool, we can remove points in the filtering brush (with the Ctrl key), add back points which fall in the brush but were removed earlier (using the Shift key), and control the filtering brush size by the mouse wheel. In remove mode, only locked points are affected by the filtering brush. Fig. 2 shows this. Here, we first lock all points having a high density value (removal of low density values in the lock view). Using next the filtering brush in removal mode eliminates only low-density data points. This unveils the skull structure. Conversely, using the filtering brush in add mode, adds only unlocked data points. If we first lock both low and high-density values (using the removal brush on the average-density values in the lock view), only average-density brushed items are added. In our example, this restores the skin (Fig. 2, brush add).

Dig

To further alleviate occlusion, we propose a dig tool. Digging smoothly pushes data points from a focus point (mouse pointer) away in radial direction. The dig radius is controlled by the mouse wheel. Conceptually, our dig tool is a 3D version of the MoleView principle [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF]: Given a focus point x ∈ R 2 , and a radius r ∈ R + , when the user presses the mouse button, we compute, for each point p whose screen projection falls in the disk C of radius r centered at x, a displacement The dig tool helps exploring the dataset by unveiling structures hidden by the pushed items. Also, the dig tool can be used to forecast the effect of further brushing actions: The pushed items are the unlocked ones, and thus will be affected by subsequent brushing.

p disp = rv/ v . Here, v = p -x -((p -x) • n)n

APPLICATIONS

Below we illustrate the application of color tunneling on several 2D and 3D datasets from various application domains. Consider the 3D scans in Fig. 2 (128 × 128 × 112 voxels) and in the video and Fig. 3 (128 × 256 × 256 voxels). We want to "peek" inside the head to see the skull structure. For this, we first create a tissue-density histogram view and color its points by the DVR values given by gradient shading. The tall histogram peak indicates the largest voxel count in the volume, which are soft-tissue voxels. To the left of this peak, we see pink histogram points. These correspond to the skin tissue, which has the same color in the DVR view. In the middle of the peak, we see a thin dark vertical band. These are low-gradient voxels, which matches the fact that there are no density interfaces in soft tissue. In contrast, the pink histogram points are bright, which matches the DVR highlights at the skin-air and skin-soft tissue interfaces. Now that we understand the meaning of the histogram points, we select and lock all histogram points, and next select and unlock all (pink) points to the left of the peak. Finally, we apply the dig effect to the DVR view (Fig. 2 dig). This pushes unlocked voxels away from the focus, and reveals the hidden skull structure inside (gray).

Medical imaging

Fig. 4 shows a second scenario. Here, we want to expose the top part of the brain structure in our head scan. Simple filtering cannot easily achieve this. The human head in this scan consists of a succession of layers with non-monotonic density values (low for skin, high for bone, and low again for the deeply-nested brain structure). Simply filtering out the bones will not help to display the brain since the skin will not be filtered and will occlude it. Conversely, filtering on the skin density will also remove the brain which has a similar density value.

To solve our task, we use color tunneling. First, we use a density histogram view and erase the noise (low density) and bone (highest density) values (Fig. 4 a). Next, we create a 2D scatterplot of the z value of the density gradient vs the x gradient value, and use the warp tool to animate between the 3D DVR and this scatterplot (Fig. 4 b). Warping a few times back and forth, we see that the top-part of the brain is warped to the top-half of the 2D scatterplot. This matches the fact that, in this area, z density gradients are large. We now remove the brain lower part by erasing the scatterplot's lower half (Fig. 4 b, right image). However, this also erases some skin parts. To get these back, we use the density histogram view to unlock points in the skin density range (Fig. 4 c,left). Finally, we use the add brush in the DVR view to paint back the skin voxels in the damaged areas (Fig. 4 c,middle). Since only soft-density voxels are unlocked for editing, and we brush only over skin areas, only skin voxels get affected; bone or noise voxels are not painted back. Fig. 4 c (right) shows the final result.

Astrophysical data

We next consider a 3D cube of astrophysical measurements of the largescale structure of hydrogen gas intensities in our Milky Way Galaxy (1024 × 1024 × 160 16-bit integer voxels, 320 MB total) [START_REF] Taylor | The Canadian galactic plane survey[END_REF]. The x and y axes map polar sky coordinates, and z maps radiation wavelength, which translates to distance through the Galaxy along ray paths. Color maps gas intensity. Fig. 5 a shows our data cube, rendered with DVR.

In this view, astrophysicists using our tool, and who provided the feedback outlined in this section, could only see color layers that indicate regions of denser hydrogen gas from the spiral arms of the Milky Way, such as the prominent yellow slab spread over a large part of the xy subspace. These are regions of the Galaxy where the cycles of star birth and death play out. We now choose a scatterplot of intensity vs wavelength (Fig. 5 d). This shows two interesting phenomena. First, we see a thin compact horizontal black bar, not visible in the initial data cube. This tells that the respective intensity is present in all wavelengths. Secondly, we notice a white gap in the intensity-wavelength space, above the black bar at the distance of the bright spiral arm (Fig. 5 d, red marker). This tells that, for the respective wavelengths, there exist only high (purple..yelow) intensities, but no intermediate (blue..green) intensities. This situation does not occur for any other wavelengths, as there is a single such gap in the scatterplot. We now warp the scatterplot towards the original data cube: The intermediate frames (Fig. 5 b,c) show that the gap corresponds to the spatial region marked in red in Fig. 5 a, right inside the yellow wavelength band. This lack of low intensities at the location of the spiral arm shows an absence of low density hydrogen in this region. Some phenomena may have swept up the gas into high density structure, a step on the way to forming new stars.

Fig. 6 shows a second scenario. In the DVR image, we notice a few constant-intensity lines parallel with the wavelength (z) axis (Fig. 6 a). Such lines are created by radiation from objects in the far universe being absorbed by gas in our Milky Way. The properties of these lines can be used to measure the Galaxy temperature. We would like to select such lines for closer analysis. Doing this via spatial or value-range filtering is hard, since the lines are embedded in surrounding data, and also do not have a perfectly constant intensity. Also, we would like to find if similar lines exist deeper in the data cube.

We can select these lines as follows. First, we build a histogram of the intensity gradients of our data points. Gradient is a good detector for the boundaries of these lines, as intensity rapidly changes between the relatively constant value inside lines and varying values outside. We next sort histogram points vertically based on their intensity value, and order them in depth with high-intensity voxels first. Fig. 6 e shows the result. We see that relatively few voxels have high gradients, while the vast majority of the data is represented by a well defined distribution of gradients. Our lines of interest are located in the former voxels (histogram tail). Also, we see several color bands in the smooth part of the histogram, with a thin purple (high-intensity value) band at the top, and most points having low values (green). Such bands emerge because of our y sorting on intensity. This distribution reveals the kinematics of the Galaxy and the velocity structure of the gas, represented by gradients in intensity with wavelength.

Over the high-gradient tail, we mainly see the same green shade as on the lines in Fig. 6 a. This indicates that for these high gradients, points do not have high intensity values (purple). Our lines of interest thus occupy regions of low intensity values and high gradient.

To find our desired lines, we now warp between the histogram and DVR views. In the intermediate frames (Figs. 6 b-d), we see several horizontal lines appearing, which smoothly move from the histogram tail towards their spatial locations in the DVR view. To select all such lines, we thus simply select the histogram tail. For more control, we can use the transition views to select any desired line located at specific spatial positions. The animation unearths several additional such lines inside the data cube, which the DVR view (Fig. 6 a) did not show.

Image segmentation and manipulation

We next illustrate color tunneling for three use-cases for 2D images, presented in increasing order of complexity.

Dead pixel isolation: Consider a 2D color photograph, shown as a Cartesian plot (Fig. 7 left). Photos often contain isolated pixel groups whose color slightly differs from their surroundings, such as 'dead pixels' due to imperfections of digital cameras. Isolating such pixels (e.g. for retouching) is hard: They are visible neither in Cartesian nor in hue-saturation plots (Fig. 7 warp between the two plots, such pixels clearly show up as outliers (Fig. 7 middle frame). Why does our animation highlight such outliers?

right). However, if we

The explanation is as follows. Similar-color compact spatial regions in the Cartesian plot, e.g. the uniform image background or the orange fish, move as compact blocks to their corresponding regions in the hue-saturation plot. Outlier pixels in such regions have different hue and/or saturation values, so are warped on different trajectories. In our case, these are pixels on the dark image background, whose color slightly differs from their uniform vicinity. We can stop the animation at any frame showing such pixels to select them for e.g. retouching.

Complex selections: Consider an input image (Fig. 8 a), where we want to select and remove the indicated wavy colorband. Selecting this area in image-space is hard, as both its shape and color distribution are quite complex. We use instead a mix of erase and warp, as follows.

First, we use a hue-saturation scatterplot to select the band. Figs. 8 g-j show the brushing of the desired pixels in the histogram view. A dotted curve shows the brush trajectory. Directly erasing this selection also eliminates several pixels outside the desired band (Figs. 8 b-e). To correct this too large selection, we use the warp tool. Figs. 8 k-n show several frames from warping the result of our previous erase (Figs. 8 e) towards a hue-saturation plot. During the warp, our band is shifted away from its spatial position. This provides precisely the extra empty space around the undesired selections, which we can now cancel by direct brushing in the warped frame (Fig. 8 m, mouse positions). Fig. 8 n shows the warped frame after removal of the undesired selections. Fig. 8 p shows the final image, with the initial colorband precisely removed. The editing took around one minute. We also tried to isolate this color band using classical fuzzy brushing in the Graphic Converter editor [21]. To achieve the selection quality in Fig. 8 p, an expert user of this tool needed several trial-and-error passes (6 minutes). Note also that selecting these pixels using only brushing in the hue-saturation plot is equally hard: Fig. 8 o shows this plot after the unwanted selection correction was done using warping (Figs. 8 m,n). Comparing this image with the hue-saturation plot before warp editing (Fig. 8 j), we see no visible difference. Thus, the warp helped indeed to correct the selection in a way that is not possible using only the hue-saturation plot.

Image segmentation: Fig. 9 (a) shows a skin scan of a naevus, or mole, acquired with a Handyscope optical dermatoscope (2448 × 3264 pixels). Dermatology specialists need to segment such scans into normal skin, the mole, and the internal mole structure [START_REF] Claridge | Shape analysis for classification of malignant melanoma[END_REF][START_REF] Parolin | Semi-automated diagnosis of melanoma through the analysis of dermatological images[END_REF], prior to applying various metrics [START_REF] Friedman | Early detection of malignant melanoma: The role of physician examination and self-examination of the skin[END_REF] to predict the potential malignity of the mole, i.e., its chance to be(come) a melanoma. A manual segmentation produced by a dermatologist, shown in image (a), takes a few minutes to complete, depending on training level and image complexity. Processing many such images, e.g. during routine clinical screening, is time-consuming and tedious. Automatic segmentation yields in general suboptimal results, given the high variability of tumor morphologies and color ranges. Moreover, medical users typically desire to closely control of the segmentation process. Color tunneling can help this process. Image (b) shows a hue-saturation polar plot of our scan. Here, a simple selection in the mid-saturation range captures well both the inner-structure and mole-skin boundaries. Indeed, for mole images of white-skin subjects, boundaries have a saturation located between the pale (desaturated) skin and mole (saturated) colors. The brush thickness controls the allowed boundary fuzziness -thicker brushes allow selecting fuzzier boundaries. Warping this image towards a gradient magnitude vs brightness plot shows how our selection splits into two clusters (images (d-f)). To map these clusters to spatial locations, the user next warps the gradient-brightness plot towards the initial Cartesian plot (images (g-i)). Playing this animation a few times, the user sees that the upper and lower clusters encode the mole-skin boundary and inner-structure boundaries respectively, which have (on average) different brightnesses. This justifies the use of a brightness plot axis.

To separately select these two boundaries, the user erases the undesired cluster from the gradient-brightness plot (images (j,k) and (l,m)). Additionally, for noisy images, the user can erase high-gradient points (the tops of the cluster bumps) to make the selection more robust. This justifies the use of a gradient plot axis. Finally, to select the entire inner structure, the user animates a few times between the gradient-brightness and Cartesian plots, and notices that the desired structure corresponds to the 'tail' of the scatterplot in an intermediate frame (images (n,o)).

To select this structure, the user first erases a few outlier pixels (image (n)), and then adds the tail shape to the selection (image (o)). Image (p) shows the successful selection of the inner structure. Note, for both the skin-mole and internal-structure boundaries, the similarity with the manual segmentations.

The above scenario was executed by an experienced dermatologist (11 years of clinical practice) after 15 minutes of pre-training using our tool, and successfully tried out on several dermatoscopic images of different types of naevi morphologies, with resolutions from 600 2 to 2448 × 3264 pixels. The user noted that, while color tunneling is not significantly faster than manual segmentation for clearly delimited naevi, it is faster and easier to use than manual segmentation on images exhibiting fuzzy complex boundaries or acquisition noise (around 2 minutes/image). In particular, the user found selecting entire regions with just a few brush strokes and animation moves to be much easier (and requiring less concentration) with color tunneling than using classical encircle-and-flood-fill operations. The user also commented that adding new scatterplot types, e.g. image local smoothness/contrast vs hue, could make color tunneling a valuable tool for selecting and analyzing specific diagnostic factors for skin tumors. Given this positive feedback, we aim to explore this direction in future work.

IMPLEMENTATION

Interactivity is key to our proposal. For datasets of 100K elements, brushing and warping can be implemented at interactive framerates in the standard OpenGL pipeline [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF][START_REF] Chevalier | Histomages: fully synchronized views for image editing[END_REF]. Our datasets are up to two orders of magnitude larger, e.g. 3D volumetric scans of 512 3 voxels.

Even if modern GPUs can render 10 Mpixels/second, interacting with such data sizes is not trivial. We need to update in real time both selection state (for brushing) and point positions (for digging). We next detail how these operations can be efficiently done using pixel and vertex shaders. In a vertex shader, we can change the position of the drawn element, and thus achieve the digging effect. One challenging aspect for warping is writing the updated element positions. Shaders were designed to write to textures, not to arbitrary buffers. Render-to-texture is accurate but requires a complex implementation and extra writing and reading passes. Recently, OpenGL added transform feedback, a feature that allows vertex shaders to write to arbitrary buffers. We heavily rely on this feature, using a two-step process: First, position data is processed to account for both brushing (element selection) and warping (element displacement). Secondly, processed data is copied back into the rendering pipeline for the next brushing or warp step. This is more efficient than render-to-texture since it does not require texture I/O. Also, all data stays on the GPU, which gives an additional speed boost. Apart from the above, the implementation of color tunneling is straightforward. Only point primitives are used for rendering. Shading is freely specifiable on a per-point basis.

We implemented color tunneling using the transform feedback technique with OpenGL 4.1 and C#. For completeness, we also added classical range-based attribute selection by GUI sliders to our tool. The obtained rendering performance is one to two orders of magnitude larger than using render-to-texture [START_REF] Hurter | FromDaDy: Spreading data across views to support iterative exploration of aircraft trajectories[END_REF] or direct mode rendering [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF]. On a Core-i7 3.4 GHz with a GeForce GTX 580, this allows us to manipulate datasets of over 10M elements at 20 frames/second.

DISCUSSION

We next discuss several relevant aspects of color tunneling.

Data in focus: While we borrow the data-driven F+C deformation idea from MoleView [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF], a key difference exists. MoleView specifies focus points as attribute value-ranges via a range slider. Instead, we use the lock view to select our points of interest. This is more flexible, as it allows fine-grained, discontinuous, selections. In contrast to F+C deformation techniques which push away all points close to focus [START_REF] Elmqvist | BalloonProbe: Reducing occlusion in 3D using interactive space distortion[END_REF][START_REF] Hsu | A rendering framework for multiscale views of 3D models[END_REF][START_REF] Wong | EdgeLens: An interactive method for managing edge congestion in graphs[END_REF][START_REF] Wong | Supporting interactive graph exploration with edge plucking[END_REF], we explicitly specify points of interest on a fine-grained basis, using any of the lock views (Sec. 4.3). Also, in contrast to MoleView, which works purely in 2D, and to other isotropic 3D F+C techniques [START_REF] Elmqvist | BalloonProbe: Reducing occlusion in 3D using interactive space distortion[END_REF], we move points in the brush radially away from the view-ray. This creates an empty cylinder around the 2D focus point (Fig. 3), and makes our dig tool work without having to specify a focus point in 3D.

Selection space: Histomages [6] also allows selecting data in linked views. However, only pairs of static views (2D Cartesian plot and its histogram) are offered. We allow selection in an automatically generated continuum of views, created by warping between pairs of user-configured views. Animations can be stopped at any stage. Each such stage is a static intermediate view where one can explore, brush, and select data. Thus, our animation serves also the task of data exploration and selection, besides the goal of mental map preservation covered by earlier work. In contrast to 'semantic layers' [START_REF] Mcguffin | Using deformations for browsing volumetric data[END_REF], our data selection is much simpler, but equally powerful, as we use only 2D brushing instead of a complex family of 3D widgets and GUI sliders.

Scope: Several of the selection and exploration use-cases presented here are also targeted by existing techniques such as F+C deformations, range sliders, and transfer functions. However, we argue that color tunneling makes these use-cases simpler to address. As such, we see color tunneling as a complement, and not a replacement, of the rich set of existing multivariate data exploration techniques.

Scalability: All our views are essentially point-based plots. Our plot implementation using OpenGL's transform feedback allows achieving interactive frame rates for over 10M points. In contrast, earlier related techniques [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF][START_REF] Chevalier | Histomages: fully synchronized views for image editing[END_REF][START_REF] Hsu | A rendering framework for multiscale views of 3D models[END_REF][START_REF] Correa | Illustrative deformation for data exploration[END_REF][START_REF] Cohen | Focus and Context for Volume Visualization[END_REF] propose more complex deformation and rendering implementations, which cannot achieve this scalability.

Extensions: Color tunneling can be extended in several directions. For instance, fuzzy selections can be easily added, e.g. by rendering points with an alpha value based on their distance to the 2D selection brush center. This would expose the selection confidence in all views. Additionally, multiple selections can be easily added.

Limitations: Point-based rendering creates sampling artifacts, see e.g. the small-scale skin ripples in Fig. 4. Such artifacts are small, and exist only for a few frames of the dig animation (see video). They can be removed by computing a dense sampling of the space D by backtracing the dig deformation field [START_REF] Hsu | A rendering framework for multiscale views of 3D models[END_REF]. However, this is expensive (minutes or more), and would decrease our frame rate prohibitively. Separately, we note that better mechanisms to select the relevant exploration views are needed, especially for high-dimensional datasets, apart from the trialand-error procedure described here. Finally, we note that formal broad user studies are needed to confirm the early usability results of color tunneling outlined by our studies presented in Secs. 5.2 and 5.3.

Conclusions

We have presented color tunneling, a set of interactive techniques for exploration and selection of structures from multidimensional datasets.

Color tunneling combines simple operations: linked views, lock, dig, brush, and warp animation. Together, these operations, which are invoked by simple mouse-based brushing and clicking, without using any complex menus or other user-interface elements, support a rich spectrum of exploratory activities in volume datasets.

In contrast to previous animation techniques [START_REF] Hurter | MoleView: An attribute and structure-based semantic lens for large element-based plots[END_REF][START_REF] Chevalier | Histomages: fully synchronized views for image editing[END_REF], users can control and stop the animation at any stage. This yields an infinite set of in-between views where one can brush, dig, select, and explore the data. We thus use animation as an exploration tool rather than only for preserving the mental-map between two views. We illustrate this by animations of 3D cube to scatterplot (Sec. 5.1), scatterplot to scatterplot (Sec. 5.3), 3D cube to histogram (Secs. 5.1), 5.2) and 3D cube to 3D cube (Sec. 5.2). We also present a new interaction tool: the lock view. Locked items are not affected by our dig, warp and brush tools. Locking leverages brushing by allowing complex selections of brushable items, in contrast to brushing compact ranges [START_REF] Hurter | FromDaDy: Spreading data across views to support iterative exploration of aircraft trajectories[END_REF][START_REF] Ward | Xmdvtool: integrating multiple methods for visualizing multivariate data[END_REF]. Concluding, our contributions are as follows:

• using animation as a controlled data exploration-and-selection technique,

• improved brushing with a flexible selection of brushable items,

• improved dig tool (lens deformation) with a flexible selection of pushable items,

• a simple implementation able to handle over 10M displayed data points at a frame rate of 20 images per second on a modern GPU.

Many applications and extensions of color tunneling are possible. Color tunneling is directly applicable to other high-variate datasets, e.g. CFD and geophysical data. The technique can be valuable for exploring dense scatterplots created by multidimensional scaling (MDS), in particular for explaining the meaning of point clusters in such projections. Finally, computing interpolation paths between arbitrary pairs of element-based plots so that structures and patterns are optimally highlighted is a promising future work direction.

Figure 1 :

 1 Figure 1: Two exploration views to perform data exploration and a lock view to configure the brushing and dig techniques.

Figure 2 :

 2 Figure2: Brush, dig, and lock tools. Locked items are not affected by brush and dig. We used transfer functions to make air voxels transparent and standard gradient shading. We see that the head is surrounded by a large amount of uninteresting noise (yellow).

  is the shortest vector from the view ray passing through x towards p, and n is the view plane normal. While the mouse button stays pressed, we interpolate p towards p disp for all p ∈ C , i.e. compute p(t) = (1t)p + tp disp for t ∈ [0, 1], using 50..100 time steps. Upon button release, we do the inverse interpolation from the displaced position p(t) towards the original location p, i.e. let points snap back to their locations. Points close to the focus x will quickly drift towards the focus boundary ∂ C ; points close to ∂ C move slower towards it. This creates a clear gap around x and a progressively weaker deformation towards C .

Figure 3 :

 3 Figure 3: Opening a human head DVR to expose the skull structure.

Figure 4 :

 4 Figure 4: Exposing the top part of the brain structure in a 3D scan.

Figure 5 :Figure 6 :

 56 Figure 5: Finding intensity outliers with isolated ranges in an astronomical data cube.

Figure 7 :

 7 Figure 7: Dead pixel isolation. Warping between an image (left) and its hue-saturation plot (right) allows finding a few outlier pixels (marked red).

Figure 9 :

 9 Figure 9: Skin tumor segmentation scenario.

Figure 8 :

 8 Figure 8: Removing a complex area from a color image using a combination of brushing, animation, and linked scatterplots.

ACKNOWLEDGEMENTS

We thank A. Diaconeasa MD PhD and D. Boda MD PhD, dermatooncologists at the Univ. of Medicine and Pharmacy 'Carol Davila', Bucharest, Romania, for their joint efforts in the acquisition and analysis of dermatologic images with color tunneling. This project has been supported by the grant PN-II-RU-TE-2011-3-2049 "Image-assisted diagnosis and prognosis of cutaneous melanocitary tumors" offered by ANCS, Romania.


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