Applied Computing and Geosciences最新文献

{"title":"Resistivity imaging and uncertainty assessment of volcanic covered sedimentary basins of India derived from a new strategy","authors":"Jit Varish Tiwari ,&nbsp;Kuldeep Sarkar ,&nbsp;Upendra K. Singh","doi":"10.1016/j.acags.2025.100244","DOIUrl":"10.1016/j.acags.2025.100244","url":null,"abstract":"<div><div>In basalt-covered areas like Saurashtra, India, the Deccan Traps are a significant part of the Indian lithosphere with notable geophysical anomalies and tectono-thermal history dating back to the Mesozoic. Magnetotellurics (MT) is commonly used to image subtrappean Tertiary and Quaternary strata in these regions. We assessed the Improved Wolf Optimization (IWOA) strategy, inspired by whale hunting behavior, to enhance the electrical resistivity structure in basalt-covered regions without relying on seismic and borehole data. Initially tested on theoretical/synthetic MT datasets representing geological scenarios, IWOA was then applied to field data from hydrocarbon potential basins: (i) trap-covered areas, yielding reliable subsurface models with MT alone, and (ii) traps overlain by conductive Tertiary sediments. Instead of selecting the global model with the lowest error, we used Bayesian posterior probability density function (PDF) to reconstruct models. This approach considers models with PDF values above 68.27 % confidence interval, constructing an average model from these models with lesser uncertainty. Our analysis revealed a thick subtrappean Tertiary sedimentary layer over volcanic cover in the Cambay basin. The method also identified two layers: a highly conductive layer likely alluvium and a major resistive layer probably due to volcanic deposits. These findings align with geological stratigraphy and drill samples, demonstrating that IWOA provides a reliable and superior subsurface model.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100244"},"PeriodicalIF":2.6,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction","authors":"Xinyi Wang ,&nbsp;Weihua Hua ,&nbsp;Xiuguo Liu ,&nbsp;Peng Li ,&nbsp;Guohe Li","doi":"10.1016/j.acags.2025.100239","DOIUrl":"10.1016/j.acags.2025.100239","url":null,"abstract":"<div><div>Three-dimensional (3D) geological models are crucial for a comprehensive understanding of regional geological formations. Deep learning-based 3D reconstruction technologies offer highly automated approaches for recognizing complex data patterns and generating realistic reconstruction results. The application of these methods in the reconstruction of surface geological bodies is particularly significant in the context of advancing the construction of digital mines nationwide. Neural Radiance Fields (NeRF) have been employed to generate 3D scenes by training models on images captured from different viewpoints. However, parallax errors across viewpoints may lead to misalignment or overlapping of details in the generated images, especially in regions with complex geometric structures. These errors can hinder the model's ability to accurately reconstruct surface details, resulting in substantial distortions in the final output. To address this issue and reduce artifacts and noise in the reconstructed 3D surface geological model, this study explores the use of NeRF for geologic body reconstruction. We propose an enhanced method, GPE-DNeRF, which integrates depth information with Gaussian positional encoding to achieve high-quality reconstruction of geological surfaces. The performance of the proposed method is evaluated, and comparative analyses are conducted with the SfM-MVS and NeRF methods. The GPE-DNeRF method demonstrates a strong capability to eliminate artifacts and retain detailed terrain features, thereby enhancing reconstruction quality and ensuring a closer alignment with actual surface geological conditions.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100239"},"PeriodicalIF":2.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments","authors":"Joshua M. Rosera ,&nbsp;Graham W. Lederer ,&nbsp;John H. Schuenemeyer","doi":"10.1016/j.acags.2025.100240","DOIUrl":"10.1016/j.acags.2025.100240","url":null,"abstract":"<div><div>Correlations between grade and tonnage exist in mineral resource data compiled from published reports, but they are not always addressed during quantitative assessment of undiscovered mineral resources. Failure to account for correlated grade and tonnage distributions can result in geologically unrealistic assessment results. Current software tools simulate univariate ore tonnage and multivariate resource grades of undiscovered deposits independently. As a result, analysts are forced to rely on <em>ad-hoc</em> solutions to minimize the correlation issues by: 1) creating subsets of data with restricted criteria; 2) truncating grade and tonnage distributions; and 3) testing model robustness using exploratory data analysis. While these methods represent pragmatic solutions, the statistical solutions presented here provide additional options to address real correlations in grade and tonnage data used for mineral resource assessments. We present a modified version of the MapMark4 package in R that introduces two alternatives for modeling grade and tonnage distributions, consisting of a multivariate solution that accounts for correlations between ore tonnage and metal grades and an empirical solution that utilizes simple random sampling with replacement to reproduce coupled grades and tonnages from the input data. We present simulations for contained ore and metal for three case studies representing tungsten skarn, komatiite-hosted nickel, and sediment-hosted carbonate amagmatic zinc-lead (Mississippi Valley-type) deposits. Employing the methods presented here yields quantitative mineral resource assessment results that more closely reflect the empirical distributions of grades and tonnages observed in nature and expands the applicability of these tools for ongoing critical mineral resource assessments.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100240"},"PeriodicalIF":2.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic Intelligence Tool: an extensive multipurpose software for seismic signal analysis","authors":"Andrea Bono","doi":"10.1016/j.acags.2025.100241","DOIUrl":"10.1016/j.acags.2025.100241","url":null,"abstract":"<div><div>Over the past five years, the <em>Istituto Nazionale di Geofisica e Vulcanologia</em> (INGV) has started a technological transformation of its real-time seismic monitoring capabilities. This comprehensive restructuring initiative represents a pivotal moment in the Institute's commitment to advancing seismic research and enhancing public safety. At the heart of this transformation lies the development and deployment of the integrated system known as <em>Caravel</em>. Caravel stands as a testament to INGV's dedication to cutting-edge seismic monitoring technology and its mission to provide timely and accurate seismic information to researchers, emergency responders, and the general public. It represents a leap forward in real-time seismic monitoring, integrating state-of-the-art technologies and methodologies to detect, analyze, and disseminate seismic data with unprecedented efficiency and precision. This development reflects INGV's commitment to staying at the forefront of seismic research and hazard mitigation. This integrated system not only improves the accuracy of earthquake detection but also enhances our ability to rapidly assess the potential impact of seismic events, enabling more informed decision-making during emergency situations. <em>Seismic Intelligence Tool (SIT)</em> emerges as a software <em>fork</em> from one of Caravel's components previously known as <em>PickFX</em>. The reason behind this fork is to share with the scientific community a robust, multi-platform and freely accessible data analysis tool that adheres to current standards for representing seismic data while removing all INGV specific customizations from PickFX. The decision to fork the original software and release SIT underscores a commitment to democratizing access to advanced seismic analysis tools. By offering this resource at no cost, the scientific community gains access to a platform that is fully compatible with contemporary seismic data representation standards and that can become very powerful with time and cooperation. This endeavor not only promotes open access to critical seismic research tools but also facilitates collaboration and knowledge sharing among researchers, ultimately contributing to advancements in our understanding of seismic activity and its implications.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100241"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the impacts of salt precipitation on soil hydraulic properties at the field lysimeter scale","authors":"Qian Liu ,&nbsp;Yanfeng Liu ,&nbsp;Menggui Jin ,&nbsp;Jinlong Zhou ,&nbsp;Paul A. Ferré","doi":"10.1016/j.acags.2025.100238","DOIUrl":"10.1016/j.acags.2025.100238","url":null,"abstract":"<div><div>Previous bench-scale investigations have demonstrated that salt precipitation reduces soil saturated hydraulic conductivity (<em>K</em>_s) due to the clogging effect. However, this conclusion may be confounded by the boundary effects inherent to the physical model. While existing field-scale studies have primarily focused on water-solute migration by directly assuming that salt precipitation reduces <em>K</em>_s, systematic investigations examining how salt crystallization alters soil hydraulic properties remain scarce. This study employed a time-windowed inverse method to analyze one set of data from four lysimeters supplied through the bottom with NaCl solution at concentrations of 3, 30, 100, and 250 g/L under field condition, aiming to examine: (1) whether the salt precipitation impacts the soil hydraulic properties; and (2) whether the degree of this impact depends on the water salinity. Results in each column showed that the inverse-derived <em>K</em>_s unexpectedly increased by more than 50 % at the intermediate time and then decreased to its early-time value. This trend in inverse-derived <em>K</em>_s showed a strong positive correlation with the ambient evaporation rate. Based on measurements of bottom fluxes and ambient evaporation, these opposing trends in inverse-derived <em>K</em>_s are primarily ascribed to the actual <em>K</em>_s change caused by the salt precipitation, rather than variations in salt crust-soil surface hydraulic connectivity (which also affect effective <em>K</em>_s). These findings highlight the need for future experiments to investigate salt precipitation-induced soil pore structure changes under varying evaporation intensities and across multiple scales.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100238"},"PeriodicalIF":2.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing expert decision-making in geosteering: Statistical insights from a large-scale controlled experiment","authors":"Yasaman Cheraghi ,&nbsp;Sergey Alyaev ,&nbsp;Reidar B. Bratvold ,&nbsp;Aojie Hong ,&nbsp;Igor Kuvaev ,&nbsp;Stephen Clark ,&nbsp;Andrei Zhuravlev","doi":"10.1016/j.acags.2025.100237","DOIUrl":"10.1016/j.acags.2025.100237","url":null,"abstract":"<div><div>Geosteering is a sequential decision-making process used in the oil and gas industry which adjusts and controls the drilling trajectory of a well in real time, aimed at maximizing values derived from hydrocarbon production operations. For layered geological formations, Stratigraphy-Based Steering (SBS) has emerged as a popular approach to generate decision-supporting information to guide steering horizontal wells. This method involves the interpretation of log data measure while drilling, the development of a geomodel around the wellbore based on the log interpretation, and the use of the geomodel to guide well placement decisions. However, the main challenge in geosteering is that it is often not approached as a structured decision-making process. Consequently, essential decision quality elements—such as defining clear objectives and their trade-offs, alternatives, and properly quantifying uncertainties—are often missing. This issue causes a lack of unique and standard guidelines for geosteering practices.</div><div>This paper presents an analysis of data collected from 349 participants of a controlled geosteering experiment – the Rogii Geosteering World Cup (GWC) 2021. The data consists of log interpretations and geosteering decisions made by the participants, acting as geosteerers, for two wells representing conventional and unconventional drilling operations. More than 10,000 snapshots were recorded, consisting of interpretations of log data for each participant's well and corresponding decisions, every 2 min. These snapshots form a comprehensive database that is useful and valuable to provide insights into the decision-making process of the geosteerers and learning for improving geosteering decision-making. The dataset utilized in this study is openly accessible and published alongside the paper.</div><div>The novelties and key contributions of this paper are (1) a statistical analysis of recorded data to investigate causation and correlation between geosteering decisions and the quality of well placements, (2) revealing the factors that contribute to good geosteering decisions and well placements and (3) evaluating the extent to which good well placements are the result of interpretation and decision-making skills versus luck. By conducting a comprehensive statistical analysis of the recorded data, this study provides insights into the geosteering decision-making process and identifies key factors that are likely to contribute to favorable outcomes.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100237"},"PeriodicalIF":2.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia","authors":"I.N. Gómez-Miranda ,&nbsp;C. Restrepo-Estrada ,&nbsp;A. Builes-Jaramillo ,&nbsp;João Porto de Albuquerque","doi":"10.1016/j.acags.2025.100226","DOIUrl":"10.1016/j.acags.2025.100226","url":null,"abstract":"<div><div>Landslides, a global phenomenon, significantly impact economies and societies, especially in densely populated areas. Effective mitigation requires awareness of landslide risks, yet temporal links between occurrences are often neglected, challenging model performance due to non-stationary triggering and predisposing factors. This study presents a novel landslide susceptibility model that incorporates spatial and temporal dependencies, including landslide recurrence. We applied AI models — Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Trees, Random Forest, and Support Vector Machine (SVM) — to a case study in Medellín, a mountainous city in northwest Colombia. Using heuristic methods, we evaluated geological and geomorphological characteristics to identify high-risk areas. Integrating temporal data from four consecutive periods allowed us to enhance estimation robustness by incorporating random effects. Our findings identify slope, stream distance, geology, geomorphology, and mean annual precipitation as key factors influencing landslide susceptibility in Medellín. The SVM model demonstrated superior performance with an accuracy of 85%, closely aligning with previous studies. This research underscores the importance of temporal dynamics in landslide susceptibility assessments, improving prediction accuracy and supporting more effective risk management.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100226"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land use and land cover classification for change detection studies using convolutional neural network","authors":"V. Pushpalatha ,&nbsp;P.B. Mallikarjuna ,&nbsp;H.N. Mahendra ,&nbsp;S. Rama Subramoniam ,&nbsp;S. Mallikarjunaswamy","doi":"10.1016/j.acags.2025.100227","DOIUrl":"10.1016/j.acags.2025.100227","url":null,"abstract":"<div><div>Efficient land use land cover (LULC) classification is crucial for environmental monitoring, urban planning, and resource management. This study investigates LULC changes in Nanjangud taluk, Mysuru district, Karnataka, India, using remote sensing (RS) and geographic information systems (GIS). This paper mainly focuses on the classification and change detection analysis of LULC in 2010 and 2020 using linear imaging self-scanning sensor-III (LISS-III) remote sensing images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning method for LULC classification. The main objective of the research work is to perform an efficient LULC classification for the change detection study of the Nanjagud taluk using the classified maps of the years 2010 and 2020. The experimental results indicate that the proposed classification method is outperformed, with an overall accuracy of 94.08% for the 2010 data and 95.30% for the 2020 data. Further, change detection analysis has been carried out using classified maps and the results show that built-up areas increased by 8.34 sq. km (0.83%), agricultural land expanded by 2.21 sq. km (0.23%), and water bodies grew by 3.31 sq. km (0.35%). Conversely, forest cover declined by 1.49 sq. km (0.15%), and other land uses reduced by 11.93 sq. km (1.22%) over the decade.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100227"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pymaginverse: A python package for global geomagnetic field modeling","authors":"Frenk Out ,&nbsp;Maximilian Schanner ,&nbsp;Liz van Grinsven ,&nbsp;Monika Korte ,&nbsp;Lennart V. de Groot","doi":"10.1016/j.acags.2025.100222","DOIUrl":"10.1016/j.acags.2025.100222","url":null,"abstract":"<div><div>Data-based geomagnetic models are key for mapping the global field, predicting the movement of magnetic poles, understanding the complex processes happening in the outer core, and describing the global expression of magnetic field reversals. There exists a wide range of models, which differ in a priori assumptions and methods for spatio-temporal interpolation. A frequently used modeling procedure is based on regularized least squares (RLS) spherical harmonic analysis, which has been used since the 1980s. The first version of this algorithm has been written in Fortran and inspired many different research groups to produce versions of the algorithm in other programming languages, either published open-access or only accessible within the institute. To open up the research field and allow for reproducibility of results between existing versions, we provide a user-friendly open-source Python version of this popular algorithm. We complement this method with an overview on background literature – concerning Maxwells equations, spherical harmonics, cubic B-Splines, and regularization – that forms the basis for RLS geomagnetic models. We included six spatial and two temporal damping methods from literature to further smooth the magnetic field in space and time. Computational resources are kept to a minimum by employing the banded structure of the normal equations involved and incorporating C-code (with Cython) for matrix formation, enabling a massive speed-up. This ensures that the algorithm can be executed on a simple laptop, and is as fast as its Fortran predecessor. Four tutorials with ample examples show how to employ the new lightweight and quick algorithm. With this properly documented open-source Python algorithm, we have the intention to encourage current and new users to employ and further develop the method.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100222"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic variogram inference using pre-trained Convolutional Neural Networks","authors":"Mokdad Karim ,&nbsp;Koushavand Behrang ,&nbsp;Boisvert Jeff","doi":"10.1016/j.acags.2025.100219","DOIUrl":"10.1016/j.acags.2025.100219","url":null,"abstract":"<div><div>A novel approach is presented for inferring covariance functions from sparse data using Convolutional Neural Networks (CNNs). Two workflows are proposed: (1) direct prediction of variogram model parameters, and (2) prediction of experimental variogram values at specified lag distances, which are smooth and easily autofit. Workflow 1 achieves an r-squared of 0.80, while Workflow 2 attains a higher r-squared of 0.96. Data augmentation through rotation improves robustness, and can be used to examine variogram uncertainty; the distribution for each predicted parameter can be obtained and used in uncertainty modeling. The CNNs are pre-trained, ensuring minimal computational time and fully automated processing. The workflows are applicable to sparse or dense data but are currently limited to 2D normal score variograms.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100219"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}