Computers & Geosciences最新文献

{"title":"AI-based geological subsurface reconstruction using sparse convolutional autoencoders","authors":"Rodrigo Uribe-Ventura ,&nbsp;Yoan Barriga-Berrios ,&nbsp;Jorge Barriga-Gamarra ,&nbsp;Patrice Baby ,&nbsp;Willem Viveen","doi":"10.1016/j.cageo.2025.105981","DOIUrl":"10.1016/j.cageo.2025.105981","url":null,"abstract":"<div><div>Subsurface reconstruction is critical for geological modeling and resource exploration. Conventional spatial interpolation methods are limited by stationarity and spatial isotropy assumptions, while advanced geostatistical techniques require specialized datasets. Deep learning approaches often need large datasets, which is impractical for geoscientific applications. This study presents an AI-based methodology using a sparse convolutional autoencoder for robust subsurface modeling under data constraints and integrating secondary data sources such as Vertical Electrical Sounding (VES) data. A four-stage testing framework was implemented: (1) emulating conventional interpolation for baseline performance; (2) reconstructing subsurface geometries from synthetic data; (3) incorporating geophysical constraints through VES forward modeling; and (4) validating the methodology using a real-world case study from the Huancayo tectonic basin in the Peruvian Andes, using 41 VES measurements across two cross-sections (12 and 14 km long). Results demonstrate that the proposed model effectively emulates kriging interpolation (mean squared error: 1.5 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>3</mn></mrow></math></span> to 1.2 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>3</mn></mrow></math></span> with 100–800 training examples) through transfer learning from an inverse-distance, pre-trained model. In subsurface reconstruction, the model outperforms kriging (37.4–61.7 % improvement across 1–15 % sampling densities) through its ability to adapt to non-stationary conditions. When incorporating synthetic VES data, the model effectively reconstructed subsurface geometries with error reduction from 4.1 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>1</mn></mrow></math></span> to 9.1 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>3</mn></mrow></math></span> as stations increased from 1 to 40, demonstrating diminishing returns beyond this point. Application to the Huancayo basin case study validated the model's practical applicability by successfully identifying previously unmapped features including the contact between basement and sedimentary infill, folds and faults. The methodology demonstrates the AI's capability to enhance geological understanding in complex tectonic settings, revealing subtle features and refining existing assumptions about subsurface architecture.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105981"},"PeriodicalIF":4.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quadrangular adaptive mesh for elastic wave simulation in smooth anisotropic media","authors":"Marius Rapenne ,&nbsp;Paul Cupillard ,&nbsp;Guillaume Caumon ,&nbsp;Corentin Gouache","doi":"10.1016/j.cageo.2025.105946","DOIUrl":"10.1016/j.cageo.2025.105946","url":null,"abstract":"<div><div>Smooth anisotropic media are often met when implementing effective medium theory, full waveform inversion or seismic imaging. However, computational overburden is often a recurring problem when working with high frequencies or when quantifying uncertainties. In this context, adaptive meshes constitute, in principle, an attractive representation to maximize simulation accuracy while minimizing the computational cost. However, such meshes are difficult to create in the context of smooth anisotropic media as the optimal local size of the elements is not clearly defined. In this work, we present a two-step algorithm to efficiently mesh these media for spectral element method (SEM) simulation in the 2D elastic case. Our algorithm yields quadrangular only meshes which adapt the size of the element to the local and directional S-wave velocity. It relies on a quadtree division introduced by Maréchal (2009) to divide the mesh until the size of each element edge is adapted to the local minimum wavelength that will be propagated. Then, a Laplacian smoothing is applied to further optimize the size of the elements, increasing the global time step and makes the SEM simulation faster while keeping a good accuracy and even improving it in some cases. An application of our method on a 2D section of the homogenized Groningen area shows that simulation time can be reduced by a factor up to 7.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"203 ","pages":"Article 105946"},"PeriodicalIF":4.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rock thin section image classification in low data scenarios using few-shot learning","authors":"Maofa Wang ,&nbsp;Wenheng Guo ,&nbsp;Fengshan Yang ,&nbsp;Bingchen Yan ,&nbsp;Yanlin Xu ,&nbsp;Jun Jiang ,&nbsp;Jingjing Huang","doi":"10.1016/j.cageo.2025.105962","DOIUrl":"10.1016/j.cageo.2025.105962","url":null,"abstract":"<div><div>Microscopic rock thin section image recognition is crucial in rock mineral analysis. Typically, deep learning models are used to automate expert knowledge, but the scarcity of samples in certain categories limits the available training data, affecting the performance of traditional deep learning models. This paper proposes a novel few-shot learning model to address the challenge of classifying rock thin section images under limited sample conditions. Based on advanced few-shot learning processes involving pre-training and meta-training, we first introduce a Cross Attention Feature Fusion (CAFF) module. This module generates new features by combining plane polarized light images (PPL) and cross-polarized light images (XPL) of rock thin sections under a microscope, integrating these with the original features through autonomous learning to obtain more comprehensive features. Secondly, we propose a Feature Selection (FS) module based on the prototypical network (ProtoNet). This module enhances the model’s classification capability by extracting key feature dimensions from two perspectives: intra-class representativeness and inter-class distinctiveness. Finally, using the pre-trained ResNet50 and Swim-Transformer on ImageNet-1000k as the backbone network, simulation experiments were conducted on the Nanjing University Rock Thin Section Teaching Dataset. Under the 5-Way 5-Shot few-shot learning task standard, the proposed ProtoNet-CAFF-FS model achieved an average classification accuracy of 96.70% and 99.16%, outperforming traditional modeling methods and demonstrating the effectiveness of the newly added modules.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"203 ","pages":"Article 105962"},"PeriodicalIF":4.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GPU algorithm for identifying the longest flow paths in catchments","authors":"Bartłomiej Kotyra","doi":"10.1016/j.cageo.2025.105961","DOIUrl":"10.1016/j.cageo.2025.105961","url":null,"abstract":"<div><div>The longest flow path is one of the key features of a catchment, commonly considered in hydrological analysis and modeling. Recent literature highlights that identifying the longest flow paths using existing software tools is time-consuming. Over the last few years, attempts have been made to develop more computationally efficient algorithms for this particular task. This paper extends previously published research and presents a new GPU algorithm designed for fast identification of the longest flow paths using DEM-derived flow directions. Performance measurements show significantly shorter execution times compared to other existing algorithms for the same task. Additionally, this algorithm is able to efficiently process multiple catchments in the same run, offering further performance improvements.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"203 ","pages":"Article 105961"},"PeriodicalIF":4.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smoke or cloud: Real-time satellite image segmentation in a wildfire data integration application","authors":"Sequoia Andrade ,&nbsp;Nastaran Shafiei ,&nbsp;Peter Mehlitz","doi":"10.1016/j.cageo.2025.105960","DOIUrl":"10.1016/j.cageo.2025.105960","url":null,"abstract":"<div><div>Advanced satellite data is increasingly used for wildfire detection and monitoring, yet near real-time hotspot data products from the GOES-R series often have low confidence due to aerosol contamination. Since aerosol contamination impacts the confidence of the GOES-R hot spot detection algorithm, regardless of contamination from fire-indicating smoke or false positive-indicating clouds, differentiating smoke from cloud has the potential to improve the accuracy of real-time hot spot detection. The primary contribution of this paper is a multi-class smoke and cloud segmentation model that classifies smoke, cloud, and neither pixels from GOES-R true color images in a real-time application. When selecting the final model, we perform an experiment to examine the impact self-supervised learning has on different model architectures. The final model is a U-Net model pre-trained on over 10,000 images using Barlow Twins self-supervised learning and fine-tuned using supervised learning, which exhibits comparable performance to the larger and slower ResUnet model. Our model improves upon existing satellite-based smoke segmentation, with 85% accuracy and 68% mean intersection-over-union on the test set. The model is deployed in an Open Data Integration for wildfire management (ODIN) application, allowing for real-time smoke and cloud detection to improve situational awareness regarding smoke location. From real-time image import to smoke-cloud segmentation display in the browser, the total run time is approximately 74 s, with 52 s total from the segmentation model pipeline.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105960"},"PeriodicalIF":4.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable clustering of PS-InSAR time series for ground deformation detection","authors":"Claudia Masciulli ,&nbsp;Giacomo Guiduzzi ,&nbsp;Donato Tiano ,&nbsp;Marta Zocchi ,&nbsp;Francesco Guerra ,&nbsp;Paolo Mazzanti ,&nbsp;Gabriele Scarascia Mugnozza","doi":"10.1016/j.cageo.2025.105959","DOIUrl":"10.1016/j.cageo.2025.105959","url":null,"abstract":"<div><div>Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) provides high-precision ground deformation measurements over wide areas. However, analyzing PS time series remains challenging due to complex temporal patterns and the need to consider comprehensive displacement fields to fully characterize ground deformation processes. This study evaluates and compares unsupervised clustering approaches for PS time series analysis, contrasting feature extraction techniques against raw time series methods. We developed an online optimization algorithm for cluster number determination and introduced a custom density-based score (MLRD) for evaluating clustering quality in sparse geospatial datasets. The approaches were tested on Sentinel-1-derived PS data from the landslide-prone Offida municipality (Marche region, Italy), where feature-based methodologies demonstrated superior performance, achieving improvements of one to two orders of magnitude in clustering quality metrics compared to conventional approaches. The multivariate analysis notably outperformed univariate methods, with optimal MLRD (<span><math><mrow><mn>2</mn><mo>.</mo><mn>59</mn><mi>⋅</mi><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></math></span>) and Calinski–Harabasz scores (194.73) at 50% explained variance, while preserving the physical interpretability of the results. This comprehensive analysis identified coherent deformation clusters extending beyond previously mapped landslide boundaries, demonstrating the effectiveness of multivariate clustering in detecting potentially unstable areas. This methodological framework advances PS time series analysis through robust pattern recognition while enhancing geohazard assessment capabilities, offering a robust foundation for identifying unstable areas and providing quantitative support for improving our understanding of complex ground deformation mechanisms.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"203 ","pages":"Article 105959"},"PeriodicalIF":4.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial bagging for predictive machine learning uncertainty quantification","authors":"Fehmi Özbayrak ,&nbsp;John T. Foster ,&nbsp;Michael J. Pyrcz","doi":"10.1016/j.cageo.2025.105947","DOIUrl":"10.1016/j.cageo.2025.105947","url":null,"abstract":"<div><div>Uncertainty quantification is a critical component in the interpretation of spatial phenomena, particularly within the geosciences, where incomplete subsurface data leads to various possible scenarios, making it crucial for risk assessment and decision-making. Traditional geostatistical methods have served as the cornerstone for uncertainty analysis; however, the incorporation of machine learning, particularly ensemble methods, offers a compelling augmentation, especially in handling complex and noisy datasets. Building on our previous work, which introduced a spatial bagging technique for enhancing prediction accuracy, this study extends the method to uncertainty quantification by applying a widely-used UQ metric from geostatistics.</div><div>Our approach employs a bootstrap method adjusted for effective sample size derived from spatial statistics, addressing the common issue of overfitting when dealing with dependent data. We demonstrate, through a series of synthetic datasets with varied noise levels and spatial structures, that our spatial bagging method not only outperforms standard bagging techniques in prediction accuracy but also provides superior uncertainty quantification. The robustness of the method against noise and its computational efficiency, particularly in spatially correlated data, positions it as a promising tool for geoscientists and others who require reliable uncertainty measures in spatial analysis.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"203 ","pages":"Article 105947"},"PeriodicalIF":4.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing raster DEM generalization with a quadric error metric approach","authors":"Richard Feciskanin,&nbsp;Jozef Minár","doi":"10.1016/j.cageo.2025.105963","DOIUrl":"10.1016/j.cageo.2025.105963","url":null,"abstract":"<div><div>Generalizing Digital Elevation Models (DEMs)—a process that simplifies data while preserving essential features—is crucial for efficient land surface analysis and revealing hierarchical structures of landforms. However, traditional methods often struggle to balance simplification with feature preservation. This paper presents a novel approach for generalizing raster-based DEMs using Quadric Error Metrics (QEM). Traditionally used for polygonal simplification, QEM has been uniquely adapted to operate directly on gridded data, which is required by most geomorphometric calculation and analysis tools. By minimizing geometric distortion, QEM effectively maintains significant land surface features, even at high levels of generalization, where the limitations of existing methods become evident. This was confirmed through a methods comparison, evaluating the generalization level using local roughness measurements based on the circular variance of aspect on four distinct areas that vary considerably in terms of landform type. The QEM approach's implicit evaluation of local surface properties ensures that significant features are preserved without the need for explicit feature detection or extensive parameter tuning. The method employs an adaptive error threshold to progressively remove smaller, non-essential landforms, providing flexible control over the generalization process. The proposed method has significant implications for various applications utilizing DEMs, particularly for analyses for which micro-scale features are undesirable noise, but preservation of the terrain skeleton is especially important. By offering a robust tool for DEM generalization, this research aims to enhance support for digital geomorphological mapping, but it can also be useful for a wider range of geoscientific research.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"202 ","pages":"Article 105963"},"PeriodicalIF":4.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Auto-Tuning for OpenMP Dynamic Scheduling applied to Full Waveform Inversion","authors":"Felipe H. Santos-da-Silva ,&nbsp;João B. Fernandes ,&nbsp;Idalmis M. Sardina ,&nbsp;Tiago Barros ,&nbsp;Samuel Xavier-de-Souza ,&nbsp;Italo A.S. Assis","doi":"10.1016/j.cageo.2025.105932","DOIUrl":"10.1016/j.cageo.2025.105932","url":null,"abstract":"<div><div>Full Waveform Inversion (FWI) is a widely used method in seismic data processing, capable of estimating models that represent the characteristics of the geological layers of the subsurface. Because it works with a massive amount of data, the execution of this method requires much time and computational resources, being restricted to large-scale computer systems such as supercomputers. Techniques such as FWI adapt well to parallel computing and can be parallelized in shared memory systems using the application programming interface (API) OpenMP. The management of parallel tasks can be performed through loop schedulers contained in OpenMP. The dynamic scheduler stands out for distributing predefined fixed-size chunk sizes to idle processing cores at runtime. It can better adapt to FWI, where data processing can be irregular. However, the relationship between the size of the chunk size and the runtime is unknown. Optimization techniques can employ meta-heuristics to explore the parameter search space, avoiding testing all possible solutions. Here, we propose a strategy to use the Parameter Auto-Tuning for Shared Memory Algorithms (PATSMA), with Coupled Simulated Annealing (CSA) as its optimization method, to automatically adjust the chunk size for the dynamic scheduling of wave propagation, one of the most expensive steps in FWI. Since testing each candidate chunk size in the complete FWI is unpractical, our approach consists of running a PATSMA where the objective function is the runtime of the first time iteration of the first seismic shot of the first FWI iteration. The resulting chunk size is then employed in all wave propagations involved in an FWI. We conducted tests to measure the runtime of an FWI using the proposed auto-tuning, varying the problem size and running on different computational environments, such as supercomputers and cloud computing instances. The results show that applying the proposed auto-tuning in an FWI reduces its runtime by up to 70.46% compared to standard OpenMP schedulers.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"202 ","pages":"Article 105932"},"PeriodicalIF":4.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyInvGeo: An open-source Python package for regularized linear inversion in geophysics","authors":"Naveen Gupta ,&nbsp;Nasser Kazemi","doi":"10.1016/j.cageo.2025.105948","DOIUrl":"10.1016/j.cageo.2025.105948","url":null,"abstract":"<div><div>We developed several algorithms to solve the generalized linear inversion problem. In real-world problems, the datasets are huge and direct inversion of data matrix is not possible. Iterative algorithms can provide the desired solution by iteratively updating the solution along the opposite direction of the gradient. Hence, we develop steepest descent with <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, Huber, Cauchy, and hybrid <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>/</mo><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> norms regularization, conjugate gradient with smoothness and sparsity constraints, FISTA, and alternating minimization algorithms. L-curve for the <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>−</mo><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> minimization and Generalized Cross Validation function for the <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>−</mo><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></math></span> minimization are used to provide the optimum regularization parameter. The numerical seismic deconvolution tests on synthetic single-channel data show the performances of the different algorithms and the parameter selections. Then, based on the performances of the algorithms on single channel data, we select the conjugate gradient with sparsity constraint and FISTA for deconvolution of Teapot dome 2D real data. We find that on 2D data, the FISTA method provides sparser solutions. However, through deconvolution of 3D seismic data, by increasing the dimensions and complexity of signals of interest, we show that the FISTA algorithm struggles to provide continuous and interpretable results. To address this issue, we introduce the Hoyer-squared norm to promote sparsity. Hoyer-squared norm is almost everywhere differentiable, scale-invariant, and contrary to <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> norm it does not equally shrink all the coefficients. The 3D deconvolution shows that the Hoyer-squared method outperforms FISTA and provides a continuous and interpretable solution. Finally, we develop a Hoyer-squared-based multiple suppression in the Radon domain and successfully test the algorithm on synthetic and real marine Gulf of Mexico data. The multiple suppression algorithm is based on the parabolic Radon transform. The Python package for the algorithms and numerical testes is included for reproducibility purposes and to facilitate the use of the algorithms on different problems.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"202 ","pages":"Article 105948"},"PeriodicalIF":4.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}