Airborne Gamma-ray Data Research Articles

Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, “calcl”) from the “late winter–spring” time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view.

Crustal architecture of Neoproterozoic terrains of western Gondwana revealed by integrated geophysical and structural data on the eastern Borborema Province, NE Brazil

The deformation processes (e.g. accretionary or intraplate) occurring within the Borborema Province have been widely discussed, however the eastern part of the central subprovince lacks further investigation in such aspect. In this contribution, we present integrated airborne gamma ray and magnetometric geophysical data, as well as meso- and microstructural markers of the Archean-Paleoproterozoic Alto Moxotó and Neoproterozoic Rio Capibaribe terrains (central Borborema Province), providing evidence for hidden accretionary episodes in the region. Fourteen radiometric and ten magnetic domains were described and fitted with most of the geological units and mapped structures, suggesting geological contrasts between the studied terrains. For instance, the Alto Moxotó terrain presents high-to-medium eTh and eU radiometric signatures, spatially distributed along folded geometry, whereas the lithospheric record of the Rio Capibaribe terrain is characterized by K (%) enrichment and a dominant NE-SW sheared crust. In addition, the obtained Euler solutions show good correlations with the main structures and delineated shallow to intermediate sources of the Congo-Cruzeiro do Nordeste strike-slip shear zone system, the proposed structural boundary. This large set of shear zones are characterized by large mylonitic corridors with characteristic vertical foliation planes with moderately-plunging mineral lineations. Associated microstructures include bulging, subgrain rotation and recrystallization that, in association with the well-developed S–C fabrics and myrmekites indicate deformation at moderate temperature values (∼400–500 °C). Also, conspicuous fracture networks seen in plagioclase grains demonstrate that deformation also took place at the later developed brittle-ductile transition. Hence, the described contrasting rock ages and nature, geophysical signatures and deformational patterns is herein interpreted as the result of crustal agglutination during the Neoproterozoic, in which the Congo-Cruzeiro do Nordeste shear zone system would represent relics of a terrane boundary developed during the assembly of western Gondwana.

Digital Soil Mapping of Cadmium: Identifying Arable Land for Producing Winter Wheat with Low Concentrations of Cadmium

Intake of cadmium (Cd) via vegetable food poses a possible health risk. Cereals are one of the major sources of Cd, and the Cd concentration in the soil has a great effect on the levels in the grain. The aim of the study was to produce decision support for identification of areas suitable for low-Cd winter wheat production in the form of a detailed digital soil map covering an important agricultural region in southern Sweden. A two-step approach was used: (1) we increased the number of soil Cd observations by combining two sets of soil samples, one with laboratory Cd analyses (304 samples) and one with predicted Cd from a portable x-ray fluorescent (PXRF) sensor (2097 samples); and (2) a digital soil mapping (DSM) model (gradient boosting regression) was calibrated on all 2401 soil samples to create a soil Cd concentration map using a number of covariates, of which airborne gamma ray data was identified as the most important. In the first step, cross-validation of the PXRF model obtained a model efficiency (E) of 0.82 and mean absolute error (MAE) of 0.08 mg kg−1. The DSM model had an E of 0.69 and MAE of 0.11 mg kg−1. The map of predicted soil Cd concentrations were compared against 307 winter wheat (Triticum aestivum L.) grain samples with laboratory-analyzed Cd concentrations. Areas in the map with low soil Cd concentrations had a high frequency of lower grain Cd concentrations. The map thus seemed to have potential for finding areas suitable for production of low-Cd winter wheat; e.g., for baby food.

Airborne Radiometric Surveys and Machine Learning Algorithms for Revealing Soil Texture

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km2 agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

Prediction of topsoil stoniness using soil type information and airborne gamma-ray data

The stoniness of topsoil can have a significant impact on the cost-effectiveness and quality of work in mechanized forest operations. The operations and their models should be selected on a stand-specific basis, while the physical properties of the soil, including stoniness, to achieve maximum efficiency and to minimize the damage caused by heavy forest machinery. The aim of this study was to examine whether the stoniness of the topsoil can be predicted using the gamma-ray values available from geophysical data collected at low altitude and using soil type information. Stoniness was measured at several sites with various soil types, which were then divided into stoniness index classes (SICs) for further analysis by ordinal regression analysis using gamma-ray and soil type data. The predictions associated with SIC classification were 52% accurate and 79% acceptable (±1 class from the correct class), with kappa values of 0.55 and 0.72, respectively. The SIC prediction results were promising and showed the potential of gamma-ray and soil type data for estimating topsoil stoniness.

Footprint and height corrections for UAV-borne gamma-ray spectrometry studies

Advancements in the development of gamma-ray spectrometers (GRS) have led to small and lightweight spectrometers that can be used under unmanned aerial vehicles (UAVs). Airborne GRS measurements are used to determine radionuclide concentrations in the ground, among which the natural occurring radionuclides 40K, 238U, and 232Th. For successful applications of these GRS sensors, it is important that absolute values of concentrations can be measured. To extract these absolute radionuclide concentrations, airborne gamma-ray data has to be corrected for measurement height. However, the current analysis models are only valid for the height range of 50–250 m. The purpose of this study is to develop a procedure that correctly predicts the true radionuclide concentration in the ground when measuring in the UAV operating range of 0–40 m. An analytical model is developed to predict the radiation footprint as a function of height. This model is used as a tool to properly determine a source-detector geometry to be used in Monte-Carlo simulations of detector response at various elevations between 0 and 40 m. The analytical model predicts that the smallest achievable footprint at 10 m height lies between 22 and 91 m and between 40 and 140 m at 20 m height. By using Monte-Carlo simulations it is shown that the analytical model correctly predicts the reduction in full energy peak gamma-rays, but does not predict the Compton continuum of a spectrum as a function of height. Therefore, Monte-Carlo simulations should be used to predict the shape and intensity of gamma-ray spectra as a function of height. A finite set of Monte-Carlo simulations at intervals of 5 m were used for the analysis of GRS measurements at heights up to 35 m. The resulting radionuclide concentrations at every height agree with the radionuclide concentration measured on the ground.

Digital mapping of the soil thickness of loess deposits over a calcareous bedrock in central France

Soil thickness (ST) plays an important role in regulating soil processes, vegetation growth and land suitability. Therefore, it has been listed as one of twelve basic soil properties to be delivered in GlobalSoilMap project. However, ST prediction has been reported with poor performance in previous studies. Our case study is located in the intensive agriculture Beauce area, central France. In this region, the ST mainly depends on the thickness of loess (TOL) deposits over a calcareous bedrock. We attempted to test the TOL prediction by coupling a large soil dataset (10978 sampling sites) and 117 environmental covariates. After variable selection by recursive feature elimination, quantile regression forests (QRF) was employed for spatial modelling, as it was able to directly provide the 90% prediction intervals (PIs). Averaging a total of 50 models, generated by repeated stratified random sampling, showed a substantial model performance with mean R2 of 0.33, RMSE of 30.48 cm and bias of −1.20 cm. The prediction interval coverage percentage showed that 86.70% of the validation samples fall within the predefined 90% PIs, which also indicated the prediction uncertainty produced by QRF was reasonable. The relative variable importance indicated the importance of airborne gamma-ray radiometric data and Sentinel 2 products in TOL prediction. The produced TOL map with 90% PIs makes sense from a soil science and physiographic point of view. The final product can guide evidence-based decision making for agricultural land management, especially for irrigation in our case study.

Decomposition and reconstruction of ore-caused anomalies information by airborne gamma-ray spectrum based on fractal singular value decomposition method

Identification method research on ore-caused anomalies information is one of the core technologies of airborne gamma-ray spectrum data post-processing. This paper proposed a decomposition and reconstruction method of anomaly information called fractal singular value decomposition (FSVD), aiming at the current ubiquitous truncation error and boundary effect problems in filter design in ore-caused anomaly information identification methods in airborne gamma-ray spectrum. Based on the multi-fractal theory, this method decomposes the singular value of the specific nucleus activity data of the airborne gamma-ray spectrum under time series, and quantitatively calculates the fractal singular value truncation parameter according to residual square sum minimum principle. According to the calculation result, the original data is partially reconstructed to rebuild the ore-caused anomalies information. This paper used the 1:50000 airborne gamma-ray spectrum trial production flight data of a certain area in Inner Mongolia (the surveying area contains proved metal ore occurrences) to verify the method. The results show that: after the original signals are decomposed by FSVD, the high, medium and low singular values can represent the background, anomaly, and noise signal, respectively. At the same time, the false anomaly signals caused by flight error exist in high singular values. After reconstructing the decomposed signals according to median singular values, background noises can be effectively separated, the ore-caused anomaly in the known ore-caused anomalies can be identified and band-like false anomalies caused by time domain batches can be removed.

Study on application of entropy averaging method in airborne gamma-ray spectrum for local disturbance elimination

This paper briefly describes the basic principle and the entropy averaging method for local disturbance elimination in airborne gamma-ray spectrum. This method was employed to process t boundary of different lithologies and the identification of metal ore points. The results showed that applying the entropy averaging method to eliminate the interference of the airborne gamma-ray spectrum data, and selecting a larger window width, achieve more significant results compared with the ground gamma ray spectrum.

Airborne Gamma-Ray Spectroscopy for Modeling Cosmic Radiation and Effective Dose in the Lower Atmosphere

In this paper we present the results of a $\sim$5 hour airborne gamma-ray survey carried out over the Tyrrhenian sea in which the height range (77-3066) m has been investigated. Gamma-ray spectroscopy measurements have been performed by using the AGRS_16L detector, a module of four 4L NaI(Tl) crystals. The experimental setup was mounted on the Radgyro, a prototype aircraft designed for multisensorial acquisitions in the field of proximal remote sensing. By acquiring high-statistics spectra over the sea (i.e. in the absence of signals having geological origin) and by spanning a wide spectrum of altitudes it has been possible to split the measured count rate into a constant aircraft component and a cosmic component exponentially increasing with increasing height. The monitoring of the count rate having pure cosmic origin in the >3 MeV energy region allowed to infer the background count rates in the $^{40}$K, $^{214}$Bi and $^{208}$Tl photopeaks, which need to be subtracted in processing airborne gamma-ray data in order to estimate the potassium, uranium and thorium abundances in the ground. Moreover, a calibration procedure has been carried out by implementing the CARI-6P and EXPACS dosimetry tools, according to which the annual cosmic effective dose to human population has been linearly related to the measured cosmic count rates.

Enhancing the resolution of airborne gamma-ray data using horizontal gradients

The spatial resolution characteristics of airborne gamma-ray data are largely controlled by survey elevation and line separation. In the UK, although low nominal surveys altitudes may be permitted, regulatory zones with elevations in excess of 180m are required above conurbations. Since the data, typically in the form of grids, are evaluated alongside many other detailed geoscientific spatial datasets their absolute resolution limits, together with their spatial characteristics, become relevant. Here, using published software, we study the theoretical resolution characteristics of this form of survey data obtained with a line separation of 200m. Of particular interest is the airborne response behaviour when non-uniform distributions of radioactivity are encountered. Although ultimately a function of the radioelement-concentration contrast encountered, the calculations reveal that such zones are most difficult to identify when their scale length decreases below the scale of the line separation. This limited resolution then further decreases with elevation. In order to increase our ability to resolve the edges of non-uniform source regions we calculate the horizontal gradient magnitude (HGM) of the observed data. While the data used can be the estimated radioelement concentrations (potassium, thorium and uranium) or their ratios, we demonstrate that the total count is particularly suited to this type of analysis. The theoretical calculations are supported by an examination of survey data across a series of isolated bodies (offshore islands). This empirical study indicates the practical limits to resolution when using the horizontal gradient and these are governed by the survey line separation. The HGM response provides an enhanced mapping of the edges of zones associated with a contrast in flux behaviour. The edges are detected using the maxima in the response and these can be additionally examined using grid curvature analysis. The technique is assessed using recent survey data containing geological, soil and environmental influences. The results demonstrate the spatially pervasive nature of flux contrasts associated with soil and environmental contributions which potentially mask, or perturb, the underlying bedrock geological response.

Automated pattern recognition to support geological mapping and exploration target generation – A case study from southern Namibia

This paper demonstrates a methodology for the automatic joint interpretation of high resolution airborne geophysical and space-borne remote sensing data to support geological mapping in a largely automated, fast and objective manner. At the request of the Geological Survey of Namibia (GSN), part of the Gordonia Subprovince of the Namaqua Metamorphic Belt situated in southern Namibia was selected for this study.All data – covering an area of 120km by 100km in size – were gridded, with a spacing of adjacent data points of only 200m. The data points were coincident for all data sets. Published criteria were used to characterize the airborne magnetic data and to establish a set of attributes suitable for the recognition of linear features and their pattern within the study area. This multi-attribute analysis of the airborne magnetic data provided the magnetic lineament pattern of the study area.To obtain a (pseudo-) lithology map of the area, the high resolution airborne gamma-ray data were integrated with selected Landsat band data using unsupervised fuzzy partitioning clustering. The outcome of this unsupervised clustering is a classified (zonal) map which in terms of the power of spatial resolution is superior to any regional geological mapping. The classified zones are then assigned geological/geophysical parameters and attributes known from the study area, e.g. lithology, physical rock properties, age, chemical composition, geophysical field characteristics, etc. This information is obtained from the examination of archived geological reports, borehole logs, any kind of existing geological/geophysical data and maps as well as ground truth controls where deemed necessary.To obtain a confidence measure validating the unsupervised fuzzy clustering results and receive a quality criterion of the classified zones, stepwise linear discriminant analysis was chosen. Only a small percentage (8%) of the samples was misclassified by discriminant analysis when compared to the result obtained from unsupervised fuzzy clustering. Furthermore, a comparison of the aposterior probability of class assignment with the trustworthiness values provided by fuzzy clustering also indicates only slight differences. These observed differences can be explained by the exponential class probability term which tends to deliver either fairly high or low probability values.The methodology and results presented here demonstrate that automated objective pattern recognition can essentially contribute to geological mapping of large study areas and mineral exploration target generation. This methodology is considered well suited to a number of African countries whose large territories have recently been covered by high resolution airborne geophysical data, but where existing geological mapping is poor, incomplete or outdated.

Geophysical interpretation of U, Th, and rare earth element mineralization of the Bokan Mountain peralkaline granite complex, Prince of Wales Island, southeast Alaska

A prospectivity map for rare earth element (REE) mineralization at the Bokan Mountain peralkaline granite complex, Prince of Wales Island, southeastern Alaska, was calculated from high-resolution airborne gamma-ray data. The map displays areas with similar radioelement concentrations as those over the Dotson REE-vein-dike system, which is characterized by moderately high %K, eU, and eTh (%K, percent potassium; eU, equivalent parts per million uranium; and eTh, equivalent parts per million thorium). Gamma-ray concentrations of rocks that share a similar range as those over the Dotson zone are inferred to locate high concentrations of REE-bearing minerals. An approximately 1300-m-long prospective tract corresponds to shallowly exposed locations of the Dotson zone. Prospective areas of REE mineralization also occur in continuous swaths along the outer edge of the pluton, over known but undeveloped REE occurrences, and within discrete regions in the older Paleozoic country rocks. Detailed mineralogical examinations of samples from the Dotson zone provide a means to understand the possible causes of the airborne Th and U anomalies and their relation to REE minerals. Thorium is sited primarily in thorite. Uranium also occurs in thorite and in a complex suite of [Formula: see text] oxide minerals, which include fergusonite, polycrase, and aeschynite. These oxides, along with Y-silicates, are the chief heavy REE (HREE)-bearing minerals. Hence, the eU anomalies, in particular, may indicate other occurrences of similar HREE-enrichment. Uranium and Th chemistry along the Dotson zone showed elevated U and total REEs east of the Camp Creek fault, which suggested the potential for increased HREEs based on their association with U-oxide minerals. A uranium prospectivity map, based on signatures present over the Ross-Adams mine area, was characterized by extremely high radioelement values. Known uranium deposits were identified in the U-prospectivity map, but the largest tract occurs over a radioelement-rich granite phase within the pluton that is likely not related to mineralization. Neither mineralization type displays a well-defined airborne magnetic signature.

Environmental radioactivity in the UK: the airborne geophysical view of dose rate estimates

This study considers UK airborne gamma-ray data obtained through a series of high spatial resolution, low altitude surveys over the past decade. The ground concentrations of the naturally occurring radionuclides Potassium, Thorium and Uranium are converted to air absorbed dose rates and these are used to assess terrestrial exposure levels from both natural and technologically enhanced sources. The high resolution airborne information is also assessed alongside existing knowledge from soil sampling and ground-based measurements of exposure levels. The surveys have sampled an extensive number of the UK lithological bedrock formations and the statistical information provides examples of low dose rate lithologies (the formations that characterise much of southern England) to the highest sustained values associated with granitic terrains. The maximum dose rates (e.g. >300 nGy h−1) encountered across the sampled granitic terrains are found to vary by a factor of 2. Excluding granitic terrains, the most spatially extensive dose rates (>50 nGy h−1) are found in association with the Mercia Mudstone Group (Triassic argillaceous mudstones) of eastern England. Geological associations between high dose rate and high radon values are also noted. Recent studies of the datasets have revealed the extent of source rock (i.e. bedrock) flux attenuation by soil moisture in conjunction with the density and porosity of the temperate latitude soils found in the UK. The presence or absence of soil cover (and associated presence or absence of attenuation) appears to account for a range of localised variations in the exposure levels encountered. The hypothesis is supported by a study of an extensive combined data set of dose rates obtained from soil sampling and by airborne geophysical survey. With no attenuation factors applied, except those intrinsic to the airborne estimates, a bias to high values of between 10 and 15 nGy h−1 is observed in the soil data. A wide range of technologically enhanced, localised contributions to dose rate values are also apparent in the data sets. Two detailed examples are provided that reveal the detectability of site-scale environmental impacts due to former industrial activities and the high dose values (>500 nGy h−1) that are associated with former, small-scale Uranium mining operations.

Applying airborne γ-ray and DEM-derived attributes to the local improvement of the existing individual-tree growth model for diameter increment

Airborne gamma-ray data (γ-ray), measuring gamma radiation naturally emitted from the earth's crust has proved useful for predicting the character and distribution of soil properties in forested landscapes. In addition, digital elevation models (DEMs) provide a reliable source of information regarding the hydrological properties of soils. The growth potential of a forest stand is an important parameter in forest management and planning, so that accurate prediction of growth is needed. The present study looked into applying γ-ray data in combination with DEM-derived attributes for improving the existing individual-tree growth model. To adapt the national model to local conditions, the data of 1118 sample trees and 9987 tally located within 197 sample plots in South-eastern Finland were used. Trees were distributed subjectively to reflect the stands' composition and structure. The main aim was to test the suitability of airborne γ-ray in combination with DEM-derived attributes for localizing a general parametric prediction model for the trees' growth (diameter at breast height increment for the period of five years). Linear mixed effect procedures were used to fit models derived from the γ-ray and DEM. Of the various models constructed for comparison purposes, the best result was obtained with broadleaved trees, followed by Scot pine (Pinus sylvestris L.) while Norway spruce (Picea abies L.) revealed little improvement. The improvement was found to be more accurate in less fertile site types (Vaccinum type (VT) and Calluna type (CT)) as well as on mineral soils. The result was found to be effective in reducing the root mean square error (RMSE) and the bias.

Regional Regolith Parameter Prediction Using the Proxy of Airborne Gamma Ray Spectrometry

Interdependence of regolith density, moisture, and chemistry parameters, as well as their influence on natural gamma ray emissions, led us to investigate systematically whether statistical models could be inferred between gamma spectrometric data and regolith parameters. A series of soil sample parameters were analyzed vs. airborne gamma ray data through multiple linear regressions. Among the approximately 20 regolith parameters modeled (chemical, textural, and mineralogical), about 50% (equally distributed in each category) were found to be predictable, with acceptable error (Radj2 > 0.5 and p value < 5%). With an independent set of texture analyses, we validated two of the predicted parameters (sand and clay contents) with fairly low residuals, with standard deviations of 22 and 16%, respectively. Further statistical investigations revealed why a large number of soil parameters could successfully be modeled in this sedimentary environment. We showed that the main gamma emitters are hosted in weathering products and leached detrital materials. Two main mineral assemblages correlate with gamma variables: (i) fine‐grained weathering clays (with correlated Al, Fe, Mn, Mg, Pb, and V elements), and (ii) residual K‐rich minerals, interpreted as feldspar and/or muscovite (correlated with Na and Sr). Additionally, two chemical elements, Si and Ca, have specific behaviors and can scarcely be characterized by the gamma data: they apparently mitigate Ɣ signatures.

Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites

Partitioning cluster algorithms have proven to be powerful tools for data-driven integration of large geoscientific databases. We used fuzzy Gustafson-Kessel cluster analysis to integrate Landsat imagery, airborne radiometric, and regional geochemical data to aid in the interpretation of a multimethod database. The survey area extends over [Formula: see text] and is located in the Northern Cape Province, South Africa. We carefully selected five variables for cluster analysis to avoid the clustering results being dominated by spatially high-correlated data sets that were present in our database. Unlike other, more popular cluster algorithms, such as k-means or fuzzy c-means, the Gustafson-Kessel algorithm requires no preclustering data processing, such as scaling or adjustment of histographic data distributions. The outcome of cluster analysis was a classified map that delineates prominent near-to-surface structures. To add value to the classified map, we compared the detected structures to mapped geology and additional geophysical ground-truthing data. We were able to associate the structures detected by cluster analysis to geophysical and geological information thus obtaining a pseudolithology map. The latter outlined an area with increased mineral potential where manganese mineralization, i.e., psilomelane, had been located.

Impact of the pre-processings on the fractal properties of the airborne gamma ray measurements: a case study from Hoggar (Algeria)

The analysis of gamma ray measurements provides valuable information about the basement’s lithospherical model and the radioactive elements contained in the subsurface. To be exploited, the raw data need pre-processing operations which consist of corrections of the Compton effect, altimetry and the aircraft background, and 2D interpolations. These transformations may modify the data’s stochastic component which reflects heterogeneities of the investigated medium. In this study, a fractal analysis is carried out on airborne gamma ray data measured in the Hoggar’s area (Algeria). First, it is shown that the analyzed data exhibit fractal properties. Due to the dependence of the latter on the spatial location of the measures, the data can be considered as paths of multifractional Brownian motions. Another result worth noting is that the fractal nature of the raw data is not modified by the pre-processing operations. The fractal analysis can be then performed on the raw spectrometric measurements without submitting them to any pre-processing; thus, the stochastic component of the measurements is kept intact.

Discriminação de áreas de espesso regolito do leste do Estado do Amazonas usando estatística multivariada, algoritmo hiperespectral e modelagem de dados espaciais

Areas com espessos regolitos estao amplamente distribuidas na regiao amazonica. Nas ultimas decadas, as pesquisas sobre geologia de regolitos no Brasil tem se concentrado, principalmente, nos aspectos geoquimicos e mineralogicos e seus possiveis usos para a exploracao de recursos minerais. Embora a utilizacao da aerogeofisica e de metodos de integracao de dados tenham se ampliado na avaliacao de recursos minerais, a aplicacao em terrenos regoliticos ainda e recente. O objetivo deste artigo e mostrar o potencial de uma abordagem sistematica, incluindo o uso de aerogamaespectrometria regional, modelo digital de elevacao e processamento digital de imagens, para a identificacao de areas de espesso regolito com potencial interesse economico em uma area pouco explorada da porcao leste do Estado do Amazonas. A aplicacao de tecnicas de estatistica multivariada (Analise por Principais Componentes e K-medias) em dados aerogamaespectrometricos permitiu a individualizacao de regolitos frente as respostas provenientes de unidades geologicas da regiao. Como guias para a identificacao de areas de espesso regolito, os seguintes padroes foram observados: baixos valores de K (facilmente lixiviado durante o intemperismo) e da razao K/eU; altos valores de eTh, da razao eTh/K e medios de eU, alem de maiores altitudes (platos). Aplicou-se o algoritmo originalmente desenvolvido para o sensoriamento remoto hiperespectral, denominado Spectral Angle Mapper (SAM), e tecnicas de modelagens espaciais (logicas booleana e fuzzy) para a producao de mapas de favorabilidade para a ocorrencia de espesso regolito que foram comparados com mapa geologico em escala 1:250.000. As relacoes quantitativas entre conjuntos de dados geofisicos e altimetricos mostraram grande capacidade para o link entre interpretacoes geologicas e geofisicas, indicando-se areas potenciais para direcionar a prospeccao mineral na area de estudo.

Airborne gamma-ray and magnetic anomaly signatures of serpentinite in relation to soil geochemistry, northern California

Serpentinized ultramafic rocks and associated soils in northern California are characterized by high concentrations of Cr and Ni, low levels of radioelements (K, Th, and U) and high amounts of ferrimagnetic minerals (primarily magnetite). Geophysical attributes over ultramafic rocks, which include airborne gamma-ray and magnetic anomaly data, are quantified and provide indirect measurements on the relative abundance of radioelements and magnetic minerals, respectively. Attributes are defined through a statistical modeling approach and the results are portrayed as probabilities in chart and map form. Two predictive models are presented, including one derived from the aeromagnetic anomaly data and one from a combination of the airborne K, Th and U gamma-ray data. Both models distinguish preferential values within the aerogeophysical data that coincide with mapped and potentially unmapped ultramafic rocks. The magnetic predictive model shows positive probabilities associated with magnetic anomaly highs and, to a lesser degree, anomaly lows, which accurately locate many known ultramafic outcrops, but more interestingly, locate potentially unmapped ultramafic rocks, possible extensions of ultramafic bodies that dip into the shallow subsurface, as well as prospective buried ultramafic rocks. The airborne radiometric model shows positive probabilities in association with anomalously low gamma radiation measurements over ultramafic rock, which is similar to that produced by gabbro, metavolcanic rock, and water bodies. All of these features share the characteristic of being depleted in K, Th and U. Gabbro is the only rock type in the study area that shares similar magnetic properties with the ultramafic rock. The aerogeophysical model results are compared to the distribution of ultramafic outcrops and to Cr, Ni, K, Th and U concentrations and magnetic susceptibility measurements from soil samples. Analysis of the soil data indicates high positive correlation between magnetic susceptibilities and concentration of Cr and Ni. Although the study focused on characterizing the geophysical properties of ultramafic rocks and associated soils, it has also yielded information on other rock types in addition to ultramafic rocks, which can also locally host naturally-occurring asbestos; specifically, gabbro and metavolcanic rocks.