Characterizing and understanding the surface and subsurface is a challenge for many scientific areas.
The risk assessment of a contaminated place, using traditional procedures, implies soil and water sampling to proceed to chemical analysis to quantify heavy metals. This is very expensive and time-consuming task.
Remote sensing methods can be applied as previous steps of the traditional approaches to define places to create classification maps, to know where the most contaminated sites are.
Applied Geophysics investigates underground using a variety of non-invasive and non-destructive techniques such as ground-penetrating radar, magnetics, electrical resistivity tomography, electromagnetic induction, and seismics. Remote Sensing uses methods such as photogrammetry, LIDAR, GNSS, and satellite hyperspectral data to determine physical properties at a distance. Some Remote Sensing technologies can also give information from the subsurface or the interior of structures. Artificial Intelligence can be a useful tool to manage information using as inputs data provided by different methods that can help in the calculation of contamination maps.
Knowledge in these fields can be applied to a variety of research topics, combining all-together results of several fields like the mentioned above, using Artificial Intelligence. This can enable the development of integrated tools for optimized environmental management, enabling the automated identification of risk areas and promoting the reduction of sampling and operational costs, as well as reducing assessment times in the management of contaminated areas.
This approach has great potential for replication to other contamination problems, such as those produced by industrial waste, landfills, as well as intensive agriculture.
This session will collect the contributions from Applied Geophysics, Remote Sensing and Artificial Intelligence in the following topics:
- Environmental studies: characterization of the soil and water contamination by heavy metals in mining places, industrial waste, landfills and intensive agriculture.
- Innovations in data acquisition, processing approaches, and big data management of Geophysical, Remote Sensing and AI methods.
Efficient and accurate estimations of submarine gas reservoir porosity and gas saturation are essential for successful reservoir characterization. However, most classical rock-physics inversion methods for gas saturation (e.g., [1-2]) require pre-existing data, such as porosity, the mineral constituent ratio of the rock, or assumed empirical equations. These methods have limited applicability due to incomplete data and the unclear physical significance of the empirical equations, often yielding inversion results that deviate significantly from well log data and cannot be applied to non-well regions. Although an effective multi-parameter inversion method [3] exists that can estimate critical parameters from elastic impedance data, elastic impedance inversion requires high-resolution raw seismic gathers and complex procedures [4], making it more expensive for 2D/3D reservoir characterization.
To address this, a new method (US appl. 19/432,887) based on rock-physics theory for submarine gas reservoirs is proposed now. In this approach, gas saturation, porosity, the mineral composition of the rock matrix, and fluid-mixture properties are treated as independent variables, while density and P-wave velocity are treated as dependent variables. These four parameters are simultaneously inverted from density and P-wave velocity data.
Testing this method at Site 1245E on Hydrate Ridge along the Cascadia Margin [5] produced acceptable root-mean-square errors for gas saturation (0.0598) and porosity (0.0151), and the estimated mineral constituent proportions closely matched smear-slide analyses. Inversion tests at three additional gas-bearing sites [6-8] demonstrated that the proposed method outperforms previous approaches in accurately estimating porosity and gas saturation, with further validation using 2D density and P-wave velocity profiles from post-stack seismic inversion, which yielded porosity and gas-saturation profiles below the bottom-simulating reflector that align well with logging data.
[1] M., Collett, T.: Gas hydrate and free gas saturations estimated from velocity logs on Hydrate Ridge, offshore Oregon, USA. In: Proc Ocean Drill Prog Sci Results 204:1–25, 2006.
[2] Tinivella, U.: A method for estimating gas hydrate and free gas concentration in marine sediments, Boll Geofis Teor Appl, 40, 19–30, 1999.
[3] Yan, Y., Li, H., Hao, G., et al.: Simultaneous inversion of five physical parameters of submarine gas reservoir from synthetic elastic impedance for high-efficiency reserve evaluation, J Petrol Explor Prod Technol, 15, 68, 2025.
[4] Maurya, S. P.: Estimating elastic impedance from seismic inversion method: A case study from Nova Scotia field, Canada, Current Science, 116, 628–635, 2018.
[5] Tréhu, A. M., Bohrmann, G., Rack, F. R., Torres, M. E., et al., Proc ODP Init Repts, 204, 1–75, 2003.
[6] Riedel, M., Collett, T., Malone, M., Expedition 311 scientists: Site U1329, Proc IODP, 311, 107–2006, 2006.
[7] Collett, T., Riedel, M., Cochran, J., Boswell, R., Presley, J., Kumar, P., Sathe, A., Sethi, A., Lall, M., NGHP Expedition Scientists: National Gas Hydrate Program Expedition 01 Initial Report, Directorate General of Hydrocarbons, Ministry of Petroleum and Natural Gas: New Delhi, 2008.
[8] Paull C, Matsumoto R, Wallace P, et al. Proceedings of the Ocean Drilling Program, Initial Reports, 164, 1996.
How to cite: Yan, Y., Zhao, Z., and Hao, G.: Effective estimation of critical parameters in submarine gas reservoirs using P-wave velocity and density data for 2D/3D reservoir characterization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2255, https://doi.org/10.5194/egusphere-egu26-2255, 2026.
Contaminants of emerging concern (CECs) have received increasing attention due to their persistence and potential ecological risks in freshwater environments. However, their spatial patterns, source contributions, and transfer from aquatic systems to surrounding soils remain insufficiently understood. Moreover, CECs are often poorly regulated, partly because interactions between individual contaminants and groups of contaminants are complex, making it hard to assess the risks they may cause. Given their adverse effects on ecosystems and human health through direct and indirect exposure, this study presents a first attempt to predict the occurrence of CECs in soil in a highly agricultural area in Serbia using machine learning techniques.
The investigation was conducted along the Veliki Bački Canal in Vojvodina (Serbia), which presents one of the main water supplies for irrigation. Initially, concentrations of CECs in canal water were measured and their spatial distribution mapped for the most frequently detected substances, including 4-acetamidoantipyrine, acesulfame calcium, estradiol, venlafaxine, and carbamazepine. Based on these results, representative locations for soil sampling on agricultural land were selected, and two soil sampling campaigns were carried out, where 96 samples were collected.
The analysis revealed that the dominant soil contaminants were primarily of industrial origin, such as tributyl phosphate, dodecyl sulfate, 2,5-di-tert-butylhydroquinone, and triethylene glycol bis (2-ethylhexanoate). Using soil and terrain characteristics as predictor variables, three machine learning algorithms were trained and evaluated - Multiple Linear Regression, Support Vector Machine, and Random Forest. Random Forest models showed strong predictive capability, particularly for industrial contaminants, such as tributyl phosphate, with a coefficient of determination (R²) of 0.65 and a mean squared error of 38.45 ng/g. Only in one case, for the prediction of 2,5-di-tert-butylhydroquinone, the Multiple Linear Regression model outperformed Random Forest. Feature importance analysis indicated that soil sand content and flow accumulation were the most influential factors controlling contaminant distribution in soil.
Although model performance is constrained by limited soil sampling data, the proposed framework provides a robust foundation for predicting soil contamination patterns and supports improved risk assessment and monitoring strategies in freshwater-influenced agricultural landscapes.
How to cite: Radulović, M., Živaljević, B., Kireeva, M., and Mimić, G.: Predicting CEC concentration in soil using machine learning algorithms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7015, https://doi.org/10.5194/egusphere-egu26-7015, 2026.
Legacy landfill sites often contain buried trenches that are not well delineated, not documented to modern standards, with subsurface voids that can act as preferential pathways for water, sediment and contaminants. To evaluate whether geophysical methods can help identify these features in a landfill, we conducted integrated electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) survey along three collocated transects; at a closed 1950’s landfill site in Ontario, Canada. The survey used a multi-electrode ERT array and a 250 MHz GPR to image shallow structures associated with historical waste trenches and potential soil-pipe development.
The survey results reveal a consistent pattern: high-resistivity anomalies in ERT between 1-2 m depth align with wide, crossing hyperbolas in the respective GPR profiles. The high-resistivity anomalies are interpreted as waste trenches. Located between the identified trenches, elongated resistive zones corresponding to GPR troughs and dipping reflectors, are tentatively interpreted as sedimentary layers with possible soil-pipe-like connections. These results, overlaid with site monitoring data, will improve the overall clarity and allow a better understanding of these subsurface structures. Three trench-like structures with two connecting anomalies are imaged along each transect, demonstrating a repeating subsurface pattern. Deeper ERT anomalies (4-9 m) lack GPR counterparts due to the limited penetration of the 250 MHz antenna Further surveys will use different antenna types to achieve deeper resolutions.
The study results show that combining ERT resistivity contrasts with GPR hyperbola geometry, provides a reliable means of mapping buried trenches and potential erosion pathways at legacy landfill sites. Next steps include expanding the survey grid, forward modeling, integrating other geophysical methods that complements ERT and GPR, and developing 3D interpretations to support long-term environmental monitoring and risk assessment.
How to cite: Binod, A., Bank, C.-G., Ebie, E., and Ngueleu Kamangou, S.: Mapping the 3D subsurface structure of a legacy landfill using a multi-geophysical approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8035, https://doi.org/10.5194/egusphere-egu26-8035, 2026.
The South Caspian Basin, positioned within the dynamic Alpine-Himalayan orogenic belt, is distinguished by its intricate geological architecture, featuring a sedimentary cover exceeding 20 km and substantial lateral fluctuations in Moho depth. Due to the presence of these deep, massive geological bodies, standard potential field maps frequently fail to resolve fine tectonic nuances. However, the gravitational signals from deep structures often mask subtle features, creating ambiguities in interpreting the regional deformation. For the first time, we utilize the Improved Logistic (IL) filter on Bouguer gravity anomalies to clarify these structural uncertainties and accentuate density contrasts that are otherwise obscured in conventional datasets. The results of our analysis uncovered a complex network of lineaments, predominantly trending NE-SW and WNW-ESE, which provide a multi-scale perspective on the basin's tectonic framework. We successfully highlighted critical tectonic boundaries, such as the Turkmenbashi-Makhachkala fault, and corroborated the segmentation of major uplift zones like Godin and Safidrud. We believe that the Improved Logistic filter acts as a powerful mechanism for revealing subsurface architecture in basins blanketed by thick sedimentation, offering a refined structural model for hydrocarbon exploration and seismotectonic assessment in this complex region. Compared to standard gradient methods, this technique effectively equalizes the gravitational response from varying depths to better interpret deformation mechanisms driven by ongoing plate convergence.
How to cite: Gahramanov, M., Safarov, R., Maden, N., and Gadirov (Kadirov), F.: Structural delineation of the South Caspian Basin using edge enhancement techniques: Application of the Improved Logistic filter to gravity data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10532, https://doi.org/10.5194/egusphere-egu26-10532, 2026.
The São Domingos Mine (Mértola, Portugal) is an abandoned sulfide mine. Heavy metal (HMs) contamination of the soil extends for approximately 20 km along a watercourse connected to a reservoir and two international rivers. Assessing the contamination is a slow process involving the collection of soil samples for HMs analysis.
The INCOME (Inputs for a more sustainable region – Instruments for managing metal-contaminated areas) project is an interdisciplinary study that aims to determine faster ways to obtain contamination maps using Artificial Intelligence techniques combining data from Geophysics, Chemistry Analysis, and Remote Sensing.
This approach allows to improve the sustainability of management of contaminants, which will drive optimization and reduce resources spent on sampling and analysis phases. Furthermore, the model aims to provide important real-time information for decision-making related to pollution monitoring and management. It also has high potential for replication in other contaminated environments, such as landfills, industries, or even intensive agriculture.
This work presents the results of electromagnetic induction surveys conducted in several sectors of the São Domingos Mine. The results are analyzed graphically, through shape analysis and compared with patterns observed in visible satellite imagery. Furthermore, the values obtained for these shapes are also analyzed to attempt to establish correspondence with standard values of the physical parameters of the contaminating materials present in the mine.
Acknowledgments: The work is supported by the Promove Program of the “la Caixa” Foundation, in partnership with BPI and the Foundation for Science and Technology (FCT), in the scope of the project INCOME – Inputs para uma região mais sustentável: Instrumentos para a gestão de zonas contaminadas por metais (Inputs for a more sustainable region: Instruments for managing metal-contaminated areas), PD23-00013, and by national funds through FCT, in the framework of the UID/06107/2025 – Centro de Investigação em Ciência e Tecnologia para o Sistema Terra e Energia (CREATE – University of Évora), and in the frame of UID/00073/2025 and UID/PRR/00073/2025 projects of the R&D unit of Geosciences Center (University of Coimbra, Portugal).
How to cite: Oliveira, R. J., Teixeira, P., Rodrigues, G., Potes, M., Custódio, M., Catarino, A., Semedo, N., Borges, J. F., Costa, M. J., Palma, P., and Caldeira, B.: Electromagnetic induction characterization of soil contamination from the São Domingos Mine (Portugal), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4796, https://doi.org/10.5194/egusphere-egu26-4796, 2026.
Mining activities generate significant soil contamination, leaving behind a persistent legacy of potentially toxic elements (PTEs) in the environment. At the inactive São Domingos mine in southern Portugal, the legacy of mining is marked by acid mine drainage (AMD), which facilitates the release and dispersion of PTEs, creating ongoing risks to ecosystem integrity and public health. In this context, the present study aims to characterize and evaluate the risks of soil contamination by PTEs in a selected area of São Domingos, where 11 topsoil samples (0-20 cm; A2 to A12) were collected. The PTE analyzed were cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), nickel (Ni), zinc (Zn), and arsenic (As). Quantification was performed by inductively coupled plasma mass spectrometry (ICP-MS) after microwave-assisted digestion, according to the USEPA (2007) method. PTE concentrations were compared with Portuguese reference values for agricultural and industrial soils, respectively (Cd: 1.0/1.9 mg/kg; Cr: 160 mg/kg; Ni: 130/340 mg/kg; Pb: 45/120 mg/kg; Cu: 180/300 mg/kg; Zn: 340 mg/kg; As: 18 mg/kg; APA, 2019) and Canadian soil quality guidelines for agricultural and industrial soils, respectively (Cd: 1.4/22 mg/kg; Cr: 64/87 mg/kg; Ni: 45/200 mg/kg; Pb: 70/600 mg/kg; Cu: 63/91 mg/kg; Zn: 250/410 mg/kg; As: 12 mg/kg; CCME, 2018). Cadmium (0.29–0.43 mg/kg) and chromium (25.31–67.27 mg/kg) showed low concentrations, remaining below guideline values for agricultural and industrial soils. The Ni concentrations (Ni; 36.34–163.29 mg/kg) exceeded agricultural thresholds but remained below limits established for industrial land use at some sampled locations. In contrast, As (324.72–2612.97 mg/kg), Pb (153.51–5321.22 mg/kg), Cu (197.50–1307.09 mg/kg) and Zn (61.17–2743.12 mg/kg), exhibited the highest concentrations, largely exceeding both national and international guideline values for agricultural and industrial soils. The results revealed high spatial variability in PTE concentrations across the study area, a characteristic feature of mining-impacted environments, with the identification of located hotspots representing critical zones of environmental risk. The results highlighted distinct contamination patterns, identifying As, Pb, Cu, and Zn, as the primary contaminants. These contaminants are primarily associated with historical mining activities and acid mine drainage processes. These elements are characterized by high toxicity and persistence in soils and therefore constitute the main contributors to the environmental risk identified in the study area. These findings support the INCOME project’s environmental management framework and inform the development of integrated and sustainable strategies for the remediation of abandoned mining areas.
Funding: The work is supported by the Promove Program of the “la Caixa” Foundation, in partnership with BPI and the Foundation for Science and Technology (FCT), in the scope of the project INCOME – Inputs para uma região mais sustentável: Instrumentos para a gestão de zonas contaminadas por metais (Inputs for a more sustainable region: Instruments for managing metal-contaminated areas), PD23-00013, and by national funds through FCT, in the framework of the UID/06107/2025 – Centro de Investigação em Ciência e Tecnologia para o Sistema Terra e Energia (CREATE – University of Évora), and in the frame of UID/00073/2025.
How to cite: Custódio, M., Semedo, N., Catarino, A., Rodrigues, G., Teixeira, P., Potes, M., Caldeira, B., Costa, M. J., Oliveira, R. J., and Palma, P.: Spatial distribution and assessment of potentially toxic elements in an Iberian Pyrite Belt Mine: the case-study of São Domingos (Southern Portugal), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13845, https://doi.org/10.5194/egusphere-egu26-13845, 2026.
The São Domingos Mine (southern Portugal) is an abandoned sulphide mining area where metal contamination extends over approximately 20 km along a watercourse connected to a reservoir and two international rivers. Conventional soil contamination assessment relies on extensive field sampling and laboratory analysis, both of which are time-consuming and spatially limited.
Within the INCOME project (Inputs for a more sustainable region – Instruments for managing metal-contaminated areas), satellite-based remote sensing is explored as a means to parametrise the study area at a spatial resolution of approximately 30 m, helping to overcome the spatial limitations inherent to point-based laboratory measurements.
Multispectral and hyperspectral satellite data, including Sentinel-2 MSI, EnMAP and PRISMA observations, are used to characterise metal-contaminated surfaces. This combines the high spatial and temporal coverage of the MSI with the enhanced spectral resolution of the hyperspectral sensors, which is essential for identifying the absorption features associated with metals. Atmospheric correction based on radiative transfer modelling (6SV) ensures consistent surface reflectance products, and machine learning techniques are employed to correlate satellite-derived information with laboratory measurements of specific metals. Overall, this work presents the potential of integrated remote sensing approaches in supporting the more efficient monitoring and management of metal-contaminated areas.
Acknowledgments: The work is supported by the Promove Program of the “la Caixa” Foundation, in partnership with BPI and the Foundation for Science and Technology (FCT), in the scope of the project INCOME – Inputs para uma região mais sustentável: Instrumentos para a gestão de zonas contaminadas por metais (Inputs for a more sustainable region: Instruments for managing metal-contaminated areas), PD23-00013, and by national funds through FCT, in the framework of the UID/06107/2025 – Centro de Investigação em Ciência e Tecnologia para o Sistema Terra e Energia (CREATE – University of Évora), and in the frame of UID/00073/2025 and UID/PRR/00073/2025 projects of the R&D unit of Geosciences Center (University of Coimbra, Portugal).
How to cite: Rodrigues, G., Teixeira, P., Custódio, M., Oliveira, R., Catarino, A., Semedo, N., Potes, M., João Costa, M., Gonçalves, T., Palma, P., Salgueiro, P., Rato, L., and Caldeira, B.: Integrated multispectral and hyperspectral remote sensing for mapping metal contamination in the São Domingos mining area (southern Portugal), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14968, https://doi.org/10.5194/egusphere-egu26-14968, 2026.
The pore structure, which serves as a conduit for gas and leachate migration during municipal solid waste (MSW) landfilling, undergoes continuous changes in pore size and connectivity with waste degradation. In this study, we established two types of landfill simulation containers filled with either formless or fixed-shaped MSW particles. Computer tomography (CT) scanning technology was used to monitor the pore structures regularly in situ during degradation, and Avizo image processing software was used to extract the characteristic parameters of the pore structure. The results showed that the pore structures all had unimodal distributions. The pore structures of the formless synthetic MSW particles continued to shrink, the sizes of the nodal pores decreased, and the pore channels narrowed and shortened with degradation. In contrast, the pore structures of the fixed-shaped MSW particles continued to grow during degradation, the nodal pores enlarged, and the pore channels expanded. Specifically, the pore channel length increased by nearly 1000 μm. This finding indicated that changes in the pore structures of wastes could be determined by the supporting factors of waste particles and by the biodegradation of microorganisms. The pore structures grew when the supporting factors were predominant and shrank when microorganism biodegradation was predominant.
How to cite: Liu, X.: Changes in the pore structures of municipal solid waste samples with different abilities to provide support to the landfill structure during degradation: Analysis of synthetic waste using X-ray computed microtomography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15520, https://doi.org/10.5194/egusphere-egu26-15520, 2026.
