Geostatistical Algorithms Research Articles

How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection

Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions or decisions. This lack of transparency is particularly problematic in critical fields such as healthcare, finance, and law, where the stakes are high. Consequently, there has been a surge in the development of explanation methods for DNNs. Typically, the effectiveness of these methods is assessed subjectively via human observation on the heatmaps or attribution maps generated by eXplanation AI (XAI) methods. In this paper, a novel GeoStatistics Explainable Artificial Intelligence (GSEAI) framework is proposed, which integrates spatial pattern analysis from Geostatistics with XAI algorithms to assess and compare XAI understandability. Global and local Moran’s I indices, commonly used to assess the spatial autocorrelation of geographic data, assist in comprehending the spatial distribution patterns of attribution maps produced by the XAI method, through measuring the levels of aggregation or dispersion. Interpreting and analyzing attribution maps by Moran’s I scattergram and LISA clustering maps provide an accurate global objective quantitative assessment of the spatial distribution of feature attribution and achieves a more understandable local interpretation. In this paper, we conduct experiments on aircraft detection in SAR images based on the widely used YOLOv5 network, and evaluate four mainstream XAI methods quantitatively and qualitatively. By using GSEAI to perform explanation analysis of the given DNN, we could gain more insights about the behavior of the network, to enhance the trustworthiness of DNN applications. To the best of our knowledge, this is the first time XAI has been integrated with geostatistical algorithms in SAR domain knowledge, which expands the analytical approaches of XAI and also promotes the development of XAI within SAR image analytics.

Using multiple-point geostatistics for geomodeling of a vein-type gold deposit

Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions.

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

AbstractDeveloping a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions

Surface ozone pollution, as a pressing environmental concern, has garnered widespread attention across China. Due to air mass transport, effective control of ozone pollution is highly dependent on collaborative efforts across neighboring regions. However, specific regions with strong internal interactions of ozone pollution are not yet well identified. Here, we introduced the Geospatial SHapley Additive exPlanation (GeoSHAP) approach, which primarily involves machine learning and geostatistical algorithms. Based on extensive atmospheric environmental monitoring data from 2017 to 2021, machine learning models were employed to train and predict ozone concentrations at the target location. The R2 values on the test sets of different scale regions all reached 0.98 in the overall condition, indicating that the core model has good accuracy and generalization ability. The results highlight key regions with high ozone geospatial relationship (OGR) index, predominantly located in the Northern District (ND), spanning the Fen-Wei Plain, the Loess Plateau, and the North China Plain, as well as within portions of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). Further investigation indicated that high geospatial relationships stem from a synergy between anthropogenic and natural factors, with anthropogenic factors serving as a pivotal element. This study revealed key regions with the most urgent need for joint control of anthropogenic sources to mitigate ozone pollution.

Compositional kriging analysis: A spatial interpolation method for distributions

Effective development of subsurface petroleum resources relies on the estimation of spatially distributed parameters at undrilled locations. Established geostatistical algorithms based on kriging exist for the estimation of scalar spatial variables such as porosity and permeability. This may not be suitable for the estimation of distributions of properties, such as grain size, whose whole distribution varies spatially. The current conventional approach is to fit a normal or log-normal distribution to the available data, and then to estimate the parameters of the distribution, the mean and standard deviation, spatially using kriging, taking care to consider any dependence between the mean and standard deviation. The assumption that all the grain size distributions can be approximated by a single distribution type is unsatisfactory, since datasets have very different-looking distributions, with variations in skewness, kurtosis, and modality. This paper presents an alternative approach that can handle significant variability in the distribution shape by separating the distribution into bins, like a histogram, and treating these bins as percentages in a composition. Compositional data needs to be mapped onto a simplex to overcome spurious correlations between those components, in addition, spatial estimation methods for compositional data have already been developed. However, the contribution to this field is the mapping of continuous data from distributions into a composition that enables the compositional kriging method to predict distributions at new locations. Moreover, the results showed the prediction distributions in the presence of varying modality, skewness, and kurtosis. Therefore, the grain size datasets in this paper have been working with the confidentiality restrictions so it explains the technique with a dataset of population ages from the US census 2010 for the state of Texas, which shows similar variability in distribution.

Production boreholes water quality evaluation using GIS based geostatistical algorithms in Windhoek

The acquaintance on the manifestation of production boreholes, its replenishment and chemical distinctive have distinct significance in semi-arid zone like Windhoek where production boreholes are partially source of water used for domestics. The goal of this research is to provide an overview for evaluation of production boreholes water quality in Windhoek by using GIS (Geographic Information System) and geostatistical algorithms. The production boreholes water quality parameters; pH, chloride, Iron, electrical conductivity and temperature were sampled and analyzed from production boreholes in Windhoek. Furthermore, maps of each parameter were produced by means of geostatistical (kriging) tactic. Moreover, semivariogram values were tested for different ordinary kriging models to ascertain the superlative fitted for the five water quality parameters and the superlative models were selected based on the average standard error, root mean square standardized error, root mean square error and mean square error. Five maps of the production boreholes water quality parameters were used to compute the production boreholes water quality map using the index technique. Therefore, pro-active measures must be taken into consideration to check the levels of pH, chloride, Iron and electrical conductivity and temperature to avert severe contamination and also to protect the production boreholes in Windhoek.

Estimation of porosity and volume of shale using artificial intelligence, case study of Kashafrud Gas Reservoir, NE Iran

The key problem in oil exploration and engineering is the lack of accurate and reliable data about the reservoir parameters of a field. Having a precise assessment of petrophysical properties can provide the ability to make decisions with a high degree of confidence about planning for production, exploitation, and further field development scenario. In this research, an artificial intelligence (AI)-based approach was developed to improve the estimation of reservoir parameters including porosity and volume of shale, which has a significant role in different stages of hydrocarbon exploration, in the Kashafrud Gas Reservoir in the northeast of Iran. For this purpose, we measured the petrophysical properties of 27 samples of the Kashafrud Formation. To increase the amount of data for employing a multilayer perceptron (MLP) artificial neural network (ANN), a geostatistical algorithm was used to increase the amount of laboratory measured data of porosity and volume of shale to 686 and 702, respectively. In addition, 2263 well-logging data from the same well were provided. The optimal MLP network with the topology of 6-7-1, and 6-8-1 was selected to estimate the porosity and shale volume with mean squared error (MSE) of 2.78731E−4, and 1.28701E−9, respectively. The training process was performed using two different sets of input data. In the first approach, all available well-logging data were used as input, ending up in high MSE. In the second approach, some selected well logs were used based on the results of sensitivity analysis which clearly improved the estimations. The ability of MLP networks made great improvements in the estimation of the both parameters up to 99.9%. The presence of valuable core data in this study significantly improved the process of comparison and conclusion. The final results prove that AI is a trusted method, also the potential of the ANN method for the reservoir characterization and evaluation associated problems should be taken into consideration. Due to the unavailability of core data along the whole wells, the application of intelligent methods, such as machine learning (ML) can be used to estimate the parameters in other oil or gas fields and wells.

Generative Adversarial Networks to incorporate the Training Image uncertainty in multiple-point statistics simulation

Multiple-point geostatistics (MPS) algorithms have been extensively used for geostatistical reservoir modeling. These techniques are known for building geostatistical simulations with curvilinear features, which are crucial for applications in flow simulation. One challenge in using MPS algorithms is obtaining a representative Training Image (TI). The result is that often one TI is used to condition the entire set of geostatistical simulations, and the TI uncertainty is disregarded. If the TI is uncertain, this uncertainty should be considered in the geostatistical simulations. Otherwise, the spatial uncertainty represented by the geostatistical simulations is underestimated. Incorporating the TI uncertainty requires building a data set or catalog of plausible TIs. In this context, we propose to use Generative Adversarial Networks (GANs) to build this catalog of TIs prior to the MPS simulation. Each TI generated by GAN is used to condition one realization built by the chosen MPS algorithm. The proposed workflow combines the strength of GAN to generate TIs with the strength of MPS algorithms to consider several sources of information to condition the simulations. A case study is shown, where the methodology is compared against the traditional MPS workflow, which uses a single TI. The results show that the proposed method successfully incorporated the TI uncertainty into the geostatistical workflow. The proposed method built numerical models with higher uncertainty and variability and, thus, provides a more realistic quantification of the space of uncertainty.

Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources

Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (1.7% for wash yield, 6.98% for ash yield, 4.94% for calorific value and 0.36% for seam thickness) when MLAs and geostatistical algorithms are merged through the hybrid spatial modeling approaches.

The Best Scenario for Geostatistical Modeling of Porosity in the Sarvak Reservoir in an Iranian Oil Field, Using Electrofacies, Seismic Facies, and Seismic Attributes

Summary The lateral and vertical variations in porosity significantly impact the reservoir quality and the volumetric calculations in heterogeneous reservoirs. With a case study from Iran’s Zagros Basin Sarvak reservoir in the Dezful Embayment, this paper aims to demonstrate an efficient methodology for distributing porosity. Four facies models (based on electrofacies analysis data and seismic facies) with different geostatistical algorithms were used to examine the effect of different facies types on porosity propagation. Both deterministic and stochastic methods are adopted to check the impact of geostatistical algorithms on porosity modeling in the static model. A total of 40 scenarios were run and validated for porosity distribution through a blind test procedure to check the reliability of the models. The study’s findings revealed high correlation values in the blind test data for all porosity realizations linked to seismic facies, ranging from 0.778 to 0.876. In addition, co-kriging to acoustic impedance (AI), as a secondary variable, increases the correlation coefficient in all related cases. Unlike deterministic algorithms, using stochastic methods reduces the uncertainty and causes the porosity model to have an identical histogram compared with the original data. This study introduced a comprehensive workflow for porosity distribution in the studied carbonate Sarvak reservoir, considering the electrofacies, and seismic facies, and applying different geostatistical algorithms. As a result, based on this workflow, simultaneously linking the porosity distribution to seismic facies, co-kriging to AI, and applying the sequential Gaussian simulation (SGS) algorithm result in the best spatial modeling of porosity.

Geological modeling of diagenetic logs of the Sarvak reservoir in Dezful Embayment, southwestern Iran: implications for geostatistical simulation and reservoir quality assessment

Reservoir quality in carbonate reservoirs is significantly influenced by diagenetic processes. Although diagenesis is studied as a common reservoir quality damaging/enhancing process in many previous studies, literature is limited about the spatial modeling of diagenesis processes using advanced geostatistical algorithms. In the current study, 3D models of the main diagenetic processes which affect the reservoir quality of the Sarvak reservoir in an Iranian oilfield located in the north Dezful Embayment were constructed using geostatistics. According to the petrographic studies, a total of 10 microfacies were identified. In addition, the significant diagenetic processes in this reservoir include dolomitization, cementation, dissolution, and compaction. In this study, diagenetic electrofacies were determined using the “multi-resolution graph clustering” method based on the quantitative results of the petrographic studies. The results of spatial modeling and provided average maps were used to investigate the lateral variation of those properties and their relationship with effective porosity. It shows that trends of the secondary porosity and velocity deviation log (VDL) maps are generally correlatable with the effective porosity maps confirming the impact of dissolution as a main significant diagenetic process on reservoir quality enhancement. The most impact of the dissolution on porosity is observed in Lower Sarvak-E2 zone where the correlation coefficient is 0.75. The correlation coefficient between porosity and VDL in some zones is high indicating the effect of diagenesis on reservoir quality as it exceeds 0.61 in Lower Sarvak-A1 zone. In the occurrence of dolomitization, it has dual constructive and destructive effects on the reservoir quality. The most constructive and destructive effects of dolomitization were observed in Lower Sarvak-E1 and Lower Sarvak-F zones in which the correlation coefficients were 0.476 and − 0.456, respectively. In addition, low porosity zones are correlatable with developing cementation, stylolites, and solution seams.

Conceptual cost estimation of highway bid unit prices using ordinary kriging

State-of-the-art encompasses the application of deterministic and geostatistical algorithms to quantify the effect of spatial variation of unit prices across different locations. No studies have evaluated the efficacy of the different combinations of variogram and kriging models when used to characterize the spatial variability of major highway bid unit prices, as well as how well they predict future unit prices at unsampled locations. In this paper, ordinary kriging was combined with three commonly used semivariograms (spherical, exponential, Gaussian) models one at a time to predict six years of the top five common highway bid items obtained from the Wisconsin Department of Transportation (WisDOT) and evaluated the prediction uncertainty for each bid item. The results show that the best-performing combination of ordinary kriging and semivariogram varied depending on the type of bid unit-price data. This study advances the state of practice by comparing the efficacy of various combinations of the variogram and ordinary kriging models in predicting highway bid unit prices and provides insight into the models’ behavior in describing bid unit price spatial heterogeneity. Transportation agencies can employ this methodology to model other highway bid items and project types that have not been experimented in this study.

Spatial Analysis of Aridity during Grapevine Growth Stages in Extremadura (Southwest Spain)

Aridity is a key determinant of agriculture worldwide due to rising temperatures, rainfall variability, and drought frequency and intensity, amongst other factors. The De Martonne aridity index is particularly useful to evaluate the spatial and temporal variations in aridity in agricultural regions for characterising the climate of these areas and evaluating their susceptibility to climate change. From the mean precipitation and maximum–minimum daily temperature values recorded at 108 weather stations over 32 years (1989–2020) in Extremadura (southwest Spain), spatial analysis of aridity was performed at different grapevine growth stages. The present study aimed to (1) determine the mean aridity conditions in Extremadura according to year and growth stage and (2) assess aridity in six grapevine-growing areas of Ribera del Guadiana de Extremadura (Spain) protected designation of origin (PDO). To visualise aridity patterns, maps were generated using a geographic information system and a multivariate regression geostatistical algorithm (ordinary kriging). The climate of Extremadura is primarily Mediterranean at the annual scale, and aridity widely varies from extremely humid at the dormancy stage to arid at the berry development and ripening stages. This variation shapes the conditions of the studied grapevine-growing region. Furthermore, large differences were noted amongst the sub-areas of the Rivera del Guadiana PDO at the initial and final grapevine growth stages, requiring differential crop management. In addition, analysis according to growth stage allowed us to identify the most vulnerable areas and periods to climate change and potential grapevine-growing areas highly suitable for this climate.

A comparative study of novel object-based geostatistical algorithm and direct sampling method on fracture network modeling

A comparative study of novel object-based geostatistical algorithm and direct sampling method on fracture network modeling

Potencial productivo agrícola y modelación del cambio climático en la cuenca del Papaloapan

The objective of this work was to make a proposal for potential land use and the projection of climate change scenarios in the Papaloapan basin, using the multi-criteria and multi-objective analysis technique to generate potential capabilities in the basin. From the greatest aptitude, the scenarios were generated using the methodology for Maximum Entropy. The hydrological region was delimited through cartographic material and climatic information was obtained from meteorological stations, geographic information and databases were collected from official secondary sources, the information was processed in the ArcGIS version 10.2.2 program, to obtain the geodatabases and geospatial matrices that served as cartographic input for the multi-criteria analysis. Based on the geospatial matrices and vector data, raster data were generated, which were used in the modeling with geo-statistical algorithms and potential zones were identified from a structured language. The result of the modeling regarding the suitability of the soil generated two classes with six levels of suitability: land for basic crops and for the cultivation of species of industrial importance and two climate change scenarios, current and future.

Streamlines Based Stochastic Methods and Reactive Transport Simulation Applied to Resource Estimation of Roll-Front Uranium Deposits Exploited by In-Situ Leaching

Roll-front uranium deposits are ore mineralizations that occur in sandstones or arkoses downstream from redox fronts or reduced/oxidized geochemical barriers. They are often bounded above and below by impermeable shaly/muddy layers making them ideal for in-situ leaching exploitation. Several stochastic simulations were previously investigated either to characterize the ore grade distribution within roll-front type deposits, or for describing geological processes involved in their formation. This work suggests some modifications/improvements of conventional geostatistical algorithms for honoring hydrodynamic constraints that govern fluid flows in ore bearing layers. In particular, instead of using the classical Euclidian or curvilinear (for Sgrid) distance for computing the variogram, it is proposed to calculate the variogram accounting for the time of flight (TOF) of water particles down the streamlines together with available well data. Non-deterministic streamline-based methods seem to provide more accurate interpolation results and resource estimation compared to a traditional geostatistical approach when applied to roll-front deposits.

COMPARISON OF PREDICTIVE MODELS IN DATA MINING AND IMPACTS OF AIR POLLUTION IN METROPOLITAN CITIES

In the past decades, as a result of the enormous environmental load, the air quality in the city has worsened in India. The remarkable increase in vehicle population and industries has led to the concentration of air pollutants in major metropolitan cities. The behavior of the air pollutants has a severs mash on human health and environment. To safeguard human health, the health risks posed by the increasing rate of air pollution on a wide scale must be projected and predicted. Monitoring and forecasting the vast amounts of data generated from numerous monitoring stations across the city has been a topic of debate. This led to the scientist to look for several predicting data mining techniques and big data analytics to monitor and predict the urban air quality. Data mining combines statistical, machine learning, and graphical approaches to extract information into a format that can be used in a variety of real-world applications. This study applies data mining to uncover the hidden knowledge of air pollution distribution in the voluminous data retrieved from monitoring stations National Air Monitoring Program (NAMP) station , National Ambient Air Quality (NAAQ) standards, Central Pollution Control Board. This article covers several ways to predicting urban air quality using data mining techniques such as linear regression, back propagation, and big data analytics such as Map reduction and Geostatistical algorithms

Research Status of and Trends in 3D Geological Property Modeling Methods: A Review

Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.

Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study’s results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R2, whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management.

A holistic methodology to study geochemical and geomorphological control of the distribution of potentially toxic elements in soil

Control of Potentially Toxic Elements (PTEs) in soil-polluted areas is needed to address the potential risk that pollution poses to public health and the environment. This study describes an innovative holistic methodology to assess the distribution of PTEs. It is based on the application of multi-variate statistical and geostatistical algorithms, soil pollution indices and geochemical & geomorphological/climate variables (element concentration, watercourses, winds, slope, orientation and visibility). The methodology proposed is exemplified through a comprehensive soil sampling in an area surrounding a former As-Hg mine that presents several sources of pollutants (abandoned mining spoil heaps, metallurgical waste, old chimneys, etc.). Factor analysis identified four main pollutants of concern: Hg, As, Pb and Sb. The mobility of the most abundant PTEs, especially As, and, to a minor extent, Hg, showed a clear influence of climatic/geomorphological variables. Moreover, the pollution indices confirmed that although the soils in the areas around the spoil heaps contain higher concentrations of the pollutants, the influence of the chimneys is present in the whole study area and depends on factors such as orientation and visibility. In contrast, the fingerprint of the spoil heaps showed a PTE distribution more associated with the slope factor and the presence of watercourses eroding the heaps. All things considered, the methodology proposed revealed PTE sources and distribution in a highly complex site and may therefore find application in similar scenarios of contamination.