Pub Date : 2025-12-27DOI: 10.1016/j.aiig.2025.100183
Osama Massarweh , Abdul Salam Abd , Jens Schneider , Ahmad S. Abushaikha
Accurate prediction of reservoir permeability based on geostatistical modeling and history matching is often limited by spatial resolution and computational efficiency. To address this limitation, we developed a novel supervised machine learning (ML) approach employing feedforward neural networks (FFNNs) to predict spatial permeability distribution in heterogeneous carbonate reservoirs from production well rates. The ML model was trained on 25 black oil reservoir simulation cases derived from a geologically realistic representation of the Upper Kharaib Member in the United Arab Emirates. Input features for training included cell spatial coordinates , distances between cells and the closest wells, and corresponding time-weighted oil production rates extracted from simulation outputs for each well. The target output was the permeability at each cell. The grid consisted of 22,739 structured cells, and training scenarios considered different closest well counts ( 1, 5, 10, and 20). The prediction performance of the trained model was evaluated across 12 unseen test cases. The model achieved higher accuracy with increased well input (), demonstrating the potential of ML for efficient permeability estimation. This study highlights the effectiveness of integrating physical simulation outputs and spatial production patterns within a neural network structure for robust reservoir characterization.
{"title":"Application of machine learning for permeability prediction in heterogeneous carbonate reservoirs","authors":"Osama Massarweh , Abdul Salam Abd , Jens Schneider , Ahmad S. Abushaikha","doi":"10.1016/j.aiig.2025.100183","DOIUrl":"10.1016/j.aiig.2025.100183","url":null,"abstract":"<div><div>Accurate prediction of reservoir permeability based on geostatistical modeling and history matching is often limited by spatial resolution and computational efficiency. To address this limitation, we developed a novel supervised machine learning (ML) approach employing feedforward neural networks (FFNNs) to predict spatial permeability distribution in heterogeneous carbonate reservoirs from production well rates. The ML model was trained on 25 black oil reservoir simulation cases derived from a geologically realistic representation of the Upper Kharaib Member in the United Arab Emirates. Input features for training included cell spatial coordinates <span><math><mrow><mo>(</mo><msub><mrow><mi>x</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>,</mo><msub><mrow><mi>y</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>,</mo><msub><mrow><mi>z</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>)</mo></mrow></math></span>, distances between cells and the <span><math><mi>n</mi></math></span> closest wells, and corresponding time-weighted oil production rates extracted from simulation outputs for each well. The target output was the permeability at each cell. The grid consisted of 22,739 structured cells, and training scenarios considered different closest well counts (<span><math><mrow><mi>n</mi><mo>=</mo></mrow></math></span> 1, 5, 10, and 20). The prediction performance of the trained model was evaluated across 12 unseen test cases. The model achieved higher accuracy with increased well input (<span><math><mi>n</mi></math></span>), demonstrating the potential of ML for efficient permeability estimation. This study highlights the effectiveness of integrating physical simulation outputs and spatial production patterns within a neural network structure for robust reservoir characterization.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100183"},"PeriodicalIF":4.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884816","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}
Pub Date : 2025-12-09DOI: 10.1016/j.aiig.2025.100171
Jhonatan Rivera-Rivera , Héctor Aguilera , Marta Béjar-Pizarro , Carolina Guardiola-Albert , Pablo Ezquerro , Anna Barra
Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets. The database integrates InSAR measurement points (MPs) from 20 case studies in Spain, obtained from various institutional sources, and 32 geoenvironmental variables related to ground deformation, morphometry, geology, climate, and land use. The proposed classification strategy follows a hierarchical structure with two levels: first, distinguishing between landslides and subsidence; then, identifying the specific type within each main class (mining landslide, environmental landslide, constructive subsidence, mining subsidence, and piezometric subsidence). Several machine learning algorithms (Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting Machine, XGBoost, LightGBM, and CatBoost) and data configurations were tested, combining different spatial resolutions and class balancing techniques. The best performance (Cohen's Kappa = 0.78) was achieved with the hierarchical approach using the 200 m grid dataset, applying XGBoost for the parental and landslide models, and CatBoost for the subsidence model. Using this approach, 70 % de test sites achieved over 88 % correctly classified cells, 20 % had between 50 % and 83 %, and only one test case was entirely misclassified. The analysis of the most relevant variables indicates that annual mean precipitation, mining activity, buildings, landslide susceptibility, and slope are key factors. These results demonstrate the potential of the hierarchical approach to improve classification and lay the groundwork for future application at national and European scales, incorporating new training cases, process types, and continental data sources. In conclusion, this study presents, for the first time, a hierarchical machine learning model capable of accurately classifying ground deformation processes in Spain, with the aim of supporting territorial management and geohazard mitigation.
地面变形过程，如滑坡和下沉，会造成重大的社会、经济和环境影响。本研究旨在使用应用于基于insar的数据集的机器学习方法对西班牙的地面变形过程进行自动分类。该数据库整合了来自西班牙20个案例研究的InSAR测量点（MPs），这些数据来自不同的机构来源，以及与地面变形、地貌测量、地质、气候和土地利用相关的32个地球环境变量。提出的分类策略遵循两个层次的分层结构：第一，区分滑坡和沉降；然后，在每个主要类别中确定具体类型（采矿滑坡、环境滑坡、建设性沉陷、采矿沉陷和压力沉降）。结合不同的空间分辨率和类平衡技术，测试了几种机器学习算法（Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting machine, XGBoost， LightGBM和CatBoost）和数据配置。使用200米网格数据集的分层方法获得了最佳性能（Cohen’s Kappa = 0.78），对亲代和滑坡模型应用XGBoost，对沉降模型应用CatBoost。使用这种方法，70%的测试站点实现了超过88%的正确分类单元，20%的站点在50%到83%之间，并且只有一个测试用例完全被错误分类。对最相关变量的分析表明，年平均降水、采矿活动、建筑物、滑坡易感性和坡度是关键因素。这些结果显示了层次方法改进分类的潜力，并为将来在国家和欧洲范围内的应用奠定了基础，结合了新的培训案例、过程类型和大陆数据源。总之，本研究首次提出了一种分层机器学习模型，能够准确地对西班牙的地面变形过程进行分类，目的是支持领土管理和减轻地质灾害。
{"title":"Hierarchical machine learning for the automatic classification of surface deformation from SAR observations","authors":"Jhonatan Rivera-Rivera , Héctor Aguilera , Marta Béjar-Pizarro , Carolina Guardiola-Albert , Pablo Ezquerro , Anna Barra","doi":"10.1016/j.aiig.2025.100171","DOIUrl":"10.1016/j.aiig.2025.100171","url":null,"abstract":"<div><div>Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets. The database integrates InSAR measurement points (MPs) from 20 case studies in Spain, obtained from various institutional sources, and 32 geoenvironmental variables related to ground deformation, morphometry, geology, climate, and land use. The proposed classification strategy follows a hierarchical structure with two levels: first, distinguishing between landslides and subsidence; then, identifying the specific type within each main class (mining landslide, environmental landslide, constructive subsidence, mining subsidence, and piezometric subsidence). Several machine learning algorithms (Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting Machine, XGBoost, LightGBM, and CatBoost) and data configurations were tested, combining different spatial resolutions and class balancing techniques. The best performance (Cohen's Kappa = 0.78) was achieved with the hierarchical approach using the 200 m grid dataset, applying XGBoost for the parental and landslide models, and CatBoost for the subsidence model. Using this approach, 70 % de test sites achieved over 88 % correctly classified cells, 20 % had between 50 % and 83 %, and only one test case was entirely misclassified. The analysis of the most relevant variables indicates that annual mean precipitation, mining activity, buildings, landslide susceptibility, and slope are key factors. These results demonstrate the potential of the hierarchical approach to improve classification and lay the groundwork for future application at national and European scales, incorporating new training cases, process types, and continental data sources. In conclusion, this study presents, for the first time, a hierarchical machine learning model capable of accurately classifying ground deformation processes in Spain, with the aim of supporting territorial management and geohazard mitigation.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100171"},"PeriodicalIF":4.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841219","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}
Pub Date : 2025-12-03DOI: 10.1016/j.aiig.2025.100173
Reza Taherdangkoo , Thomas Nagel , Vladimir Tyurin , Chaofan Chen , Faramarz Doulati Ardejani , Christoph Butscher
Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly laboratory testing. To offer a more efficient alternative, an extreme gradient boosting (XGBoost) model, optimized using a hybrid particle swarm optimization and genetic algorithm (PSO–GA), was developed. This hybrid model estimates the SWR across a broad suction range, accounting for both drying and wetting paths, along with key soil parameters. The performance of the model was evaluated through various statistical analyses and by comparing the predicted gravimetric water content with experimental data. A backward feature elimination method was employed to assess the impact of various input parameters on model accuracy and to offer a simplified model for scenarios with limited data availability. Additionally, Monte Carlo simulations were conducted to quantify the inherent uncertainties associated with the dataset, XGBoost hyperparameters, and model performance. The hybrid PSO–GA XGBoost model effectively estimates the water retention of clayey soils during both drying and wetting cycles, proving to be an alternative to traditional soil mechanics correlations.
{"title":"Prediction of the soil–water retention curve of compacted clays using PSO–GA XGBoost","authors":"Reza Taherdangkoo , Thomas Nagel , Vladimir Tyurin , Chaofan Chen , Faramarz Doulati Ardejani , Christoph Butscher","doi":"10.1016/j.aiig.2025.100173","DOIUrl":"10.1016/j.aiig.2025.100173","url":null,"abstract":"<div><div>Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly laboratory testing. To offer a more efficient alternative, an extreme gradient boosting (XGBoost) model, optimized using a hybrid particle swarm optimization and genetic algorithm (PSO–GA), was developed. This hybrid model estimates the SWR across a broad suction range, accounting for both drying and wetting paths, along with key soil parameters. The performance of the model was evaluated through various statistical analyses and by comparing the predicted gravimetric water content with experimental data. A backward feature elimination method was employed to assess the impact of various input parameters on model accuracy and to offer a simplified model for scenarios with limited data availability. Additionally, Monte Carlo simulations were conducted to quantify the inherent uncertainties associated with the dataset, XGBoost hyperparameters, and model performance. The hybrid PSO–GA XGBoost model effectively estimates the water retention of clayey soils during both drying and wetting cycles, proving to be an alternative to traditional soil mechanics correlations.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100173"},"PeriodicalIF":4.2,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747577","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}
Pub Date : 2025-12-01DOI: 10.1016/j.aiig.2025.100172
Rodrigo Uribe-Ventura , Willem Viveen , Ferdinand Pineda-Ancco , César Beltrán-Castañon
Landslides constitute one of the most destructive geological hazards worldwide, causing thousands of casualties and billions in economic losses annually. To mitigate these risks, accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance. While recent advances in deep learning, particularly with transformer architectures and large pre-trained models like the Segment Anything Model (SAM), have shown promise, their application to landslide mapping is often hindered by high computational costs, prompt dependency, and challenges with data imbalance. To address these limitations, we propose GeoNeXt, a novel semantic segmentation architecture for intelligent landslide recognition. It combines a scalable, pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention (PSA) and Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. Through domain adaptation on the large-scale CAS landslide dataset, we refined the encoder's general pre-trained features to learn robust, landslide-specific features. GeoNeXt exhibited zero-shot transferability, achieving 74–78 % F1 and 64–66 % mIoU across three distinct test datasets from diverse regions, which were entirely excluded from the training process. Ablation studies on decoder variants validated the PSA-ASPP synergy, achieving a superior F1 of 90.39 % and mIoU of 83.18 % on the CAS dataset. Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods, achieving F1 scores of 94.25 %, 86.43 %, and 92.27 % (mIoU: 89.51 %, 78.21 %, 86.02 %) on the Bijie, Landslide4Sense, and GVLM datasets, respectively, with 10× fewer parameters than SAM-based methods and lower computational demands. We showed that modernized convolutions, paired with strategic training, were a viable alternative to resource-intensive transformers. This efficiency facilitated their use in operational intelligent landslide recognition and geohazard monitoring systems.
{"title":"GeoNeXt: Efficient landslide mapping using a pre-trained ConvNeXt V2 encoder with a PSA-ASPP decoder","authors":"Rodrigo Uribe-Ventura , Willem Viveen , Ferdinand Pineda-Ancco , César Beltrán-Castañon","doi":"10.1016/j.aiig.2025.100172","DOIUrl":"10.1016/j.aiig.2025.100172","url":null,"abstract":"<div><div>Landslides constitute one of the most destructive geological hazards worldwide, causing thousands of casualties and billions in economic losses annually. To mitigate these risks, accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance. While recent advances in deep learning, particularly with transformer architectures and large pre-trained models like the Segment Anything Model (SAM), have shown promise, their application to landslide mapping is often hindered by high computational costs, prompt dependency, and challenges with data imbalance. To address these limitations, we propose GeoNeXt, a novel semantic segmentation architecture for intelligent landslide recognition. It combines a scalable, pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention (PSA) and Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. Through domain adaptation on the large-scale CAS landslide dataset, we refined the encoder's general pre-trained features to learn robust, landslide-specific features. GeoNeXt exhibited zero-shot transferability, achieving 74–78 % F1 and 64–66 % mIoU across three distinct test datasets from diverse regions, which were entirely excluded from the training process. Ablation studies on decoder variants validated the PSA-ASPP synergy, achieving a superior F1 of 90.39 % and mIoU of 83.18 % on the CAS dataset. Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods, achieving F1 scores of 94.25 %, 86.43 %, and 92.27 % (mIoU: 89.51 %, 78.21 %, 86.02 %) on the Bijie, Landslide4Sense, and GVLM datasets, respectively, with 10× fewer parameters than SAM-based methods and lower computational demands. We showed that modernized convolutions, paired with strategic training, were a viable alternative to resource-intensive transformers. This efficiency facilitated their use in operational intelligent landslide recognition and geohazard monitoring systems.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100172"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683651","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}
Pub Date : 2025-12-01DOI: 10.1016/j.aiig.2025.100163
Gong Cheng , Syed Hussain , Yingdong Yang , Li Sun , Asad Atta , Cheng Huang , Guangqiang Li , Mohammad Naseer , Lingyi Liao
Recent advancements in remote sensing technology have made it easier to detect surface faults. Deep learning, especially convolutional models, offers new potential for automatic fault detection from remote sensing imagery. However, these models often struggle with segmentation accuracy due to their limitations in handling spatial hierarchies and short-range dependencies. They process data in local contexts, which is insufficient for tasks requiring an understanding of global structures, like fault detection. This leads to inaccurate boundary divisions and incomplete fault trace detections. To address these issues, the Convolution Holographic Reduced Representations-Based Unet (CHRRA-Unet) is introduced. This U-shaped network combines convolution and a novel attention-based transformer for remote sensing image segmentation. By extracting both local and global features, the CHRRA-Unet significantly improves the detection of geological faults in remote sensing images. By incorporating a convolutional module (CM) and holographic reduced representation attention (HRRA), local and global feature extraction is improved. To minimize computational complexity, the traditional Multi-Layer Perceptron (MLP) is replaced with the Local Perception Module (LPM). The Multi-Feature Conversion Module (MFCM) ensures an effective combination of feature maps during encoding and decoding, enhancing the network's ability to accurately detect fault traces. Extensive experiments show that CHRRA-Unet achieves a high accuracy rate of 97.20 % in remote sensing image segmentation, outperforming existing models and providing superior fault detection capabilities over current methods.
{"title":"Enhancing fault detection using CHRRA-Unet and focal loss functions for imbalanced data: A case study in Luoping county, Yunnan, China","authors":"Gong Cheng , Syed Hussain , Yingdong Yang , Li Sun , Asad Atta , Cheng Huang , Guangqiang Li , Mohammad Naseer , Lingyi Liao","doi":"10.1016/j.aiig.2025.100163","DOIUrl":"10.1016/j.aiig.2025.100163","url":null,"abstract":"<div><div>Recent advancements in remote sensing technology have made it easier to detect surface faults. Deep learning, especially convolutional models, offers new potential for automatic fault detection from remote sensing imagery. However, these models often struggle with segmentation accuracy due to their limitations in handling spatial hierarchies and short-range dependencies. They process data in local contexts, which is insufficient for tasks requiring an understanding of global structures, like fault detection. This leads to inaccurate boundary divisions and incomplete fault trace detections. To address these issues, the Convolution Holographic Reduced Representations-Based Unet (CHRRA-Unet) is introduced. This U-shaped network combines convolution and a novel attention-based transformer for remote sensing image segmentation. By extracting both local and global features, the CHRRA-Unet significantly improves the detection of geological faults in remote sensing images. By incorporating a convolutional module (CM) and holographic reduced representation attention (HRRA), local and global feature extraction is improved. To minimize computational complexity, the traditional Multi-Layer Perceptron (MLP) is replaced with the Local Perception Module (LPM). The Multi-Feature Conversion Module (MFCM) ensures an effective combination of feature maps during encoding and decoding, enhancing the network's ability to accurately detect fault traces. Extensive experiments show that CHRRA-Unet achieves a high accuracy rate of 97.20 % in remote sensing image segmentation, outperforming existing models and providing superior fault detection capabilities over current methods.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100163"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798249","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}
Pub Date : 2025-12-01DOI: 10.1016/j.aiig.2025.100166
Mohamad Rifai , Harintaka
Tropical lakes such as Lake Sentarum in Kalimantan, Indonesia, represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan. This lake serves as a sensitive indicator of climate change; however, its monitoring is often hindered by persistent cloud cover. This study evaluates the effectiveness of a Gradient Tree Boosting machine learning model integrated with multisource satellite data, including optical imagery, Sentinel-1 SAR, Sentinel-2, and high resolution NICFI data, in accurately mapping surface water dynamics. The Gradient Tree Boosting model was trained and validated using water and non water samples collected from annual imagery spanning 2019 to 2024, achieving validation accuracies ranging from 80 percent to 97 percent. Results demonstrate that Gradient Tree Boosting successfully integrates the strengths of each sensor, producing consistent annual water maps despite extreme hydrological fluctuations caused by El Niño and La Niña events. These findings highlight the model's potential application in water resource management, particularly in providing accurate baseline data to support adaptation planning for droughts and floods in climate vulnerable regions.
印度尼西亚加里曼丹的森塔鲁姆湖（Lake Sentarum）等热带湖泊代表了生态丰富、生物多样性高的生态系统，是加里曼丹岛上最大的湖泊。这个湖是气候变化的敏感指标；然而，它的监测经常受到持续云层覆盖的阻碍。本研究评估了结合多源卫星数据（包括光学图像、Sentinel-1 SAR、Sentinel-2和高分辨率NICFI数据）的Gradient Tree Boosting机器学习模型在精确绘制地表水动力学地图方面的有效性。梯度树增强模型使用从2019年至2024年的年度图像中收集的水和非水样本进行训练和验证，验证精度从80%到97%不等。结果表明，Gradient Tree Boosting成功地整合了每个传感器的优势，尽管El Niño和La Niña事件造成了极端的水文波动，但仍能生成一致的年度水图。这些发现突出了该模型在水资源管理中的潜在应用，特别是在提供准确的基线数据以支持气候脆弱地区干旱和洪水的适应规划方面。
{"title":"Unveiling climate-driven water surface dynamics in the largest tropical lake in Borneo: A machine learning approach using multi-source satellite imagery","authors":"Mohamad Rifai , Harintaka","doi":"10.1016/j.aiig.2025.100166","DOIUrl":"10.1016/j.aiig.2025.100166","url":null,"abstract":"<div><div>Tropical lakes such as Lake Sentarum in Kalimantan, Indonesia, represent ecologically rich ecosystems with high biodiversity and constitute the largest lake on the island of Kalimantan. This lake serves as a sensitive indicator of climate change; however, its monitoring is often hindered by persistent cloud cover. This study evaluates the effectiveness of a Gradient Tree Boosting machine learning model integrated with multisource satellite data, including optical imagery, Sentinel-1 SAR, Sentinel-2, and high resolution NICFI data, in accurately mapping surface water dynamics. The Gradient Tree Boosting model was trained and validated using water and non water samples collected from annual imagery spanning 2019 to 2024, achieving validation accuracies ranging from 80 percent to 97 percent. Results demonstrate that Gradient Tree Boosting successfully integrates the strengths of each sensor, producing consistent annual water maps despite extreme hydrological fluctuations caused by El Niño and La Niña events. These findings highlight the model's potential application in water resource management, particularly in providing accurate baseline data to support adaptation planning for droughts and floods in climate vulnerable regions.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100166"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623402","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}
Pub Date : 2025-11-29DOI: 10.1016/j.aiig.2025.100170
Hua Wang , Meng Li , Qiang Wang , Shaopeng Shi , Gengxiao Yang , Zhilong Fang , Aihua Tao , Meng Wang
Cement bond quality evaluations are essential for assessing zonal isolation between formation strata, providing crucial information for ensuring environmental and ecological safety in oil and gas exploitation, geothermal energy injection and geological carbon dioxide sequestration. In the past decade, the ultrasonic pulse-echo and pitch-catch logging techniques have emerged as effective and non-destructive methods for quantitatively evaluating bond quality at both the casing-cement and cement-formation interfaces. This review presents a comprehensive overview of recent advancements in cement bond quality assessment based on ultrasonic measurements. Key developments include automatic waveform quality assessment, inversion techniques for mud and cement impedance, tool trajectory corrections, separation of flexural and extensional mode waves, machine learning-based extraction and enhancement of TIE waveforms, and imaging of the cement-formation interface using the reverse time migration approach. The review thoroughly explores the methodological principles and applications of these techniques, supported by synthetic datasets, full-scale physical well experiments, and field well data. Considering the recent progress in machine learning and the growing availability of advanced computational resources, we highlight the most significant achievements and ongoing challenges in data processing, while discussing the potential advancements these techniques could offer in the near future.
{"title":"Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes","authors":"Hua Wang , Meng Li , Qiang Wang , Shaopeng Shi , Gengxiao Yang , Zhilong Fang , Aihua Tao , Meng Wang","doi":"10.1016/j.aiig.2025.100170","DOIUrl":"10.1016/j.aiig.2025.100170","url":null,"abstract":"<div><div>Cement bond quality evaluations are essential for assessing zonal isolation between formation strata, providing crucial information for ensuring environmental and ecological safety in oil and gas exploitation, geothermal energy injection and geological carbon dioxide sequestration. In the past decade, the ultrasonic pulse-echo and pitch-catch logging techniques have emerged as effective and non-destructive methods for quantitatively evaluating bond quality at both the casing-cement and cement-formation interfaces. This review presents a comprehensive overview of recent advancements in cement bond quality assessment based on ultrasonic measurements. Key developments include automatic waveform quality assessment, inversion techniques for mud and cement impedance, tool trajectory corrections, separation of flexural and extensional mode waves, machine learning-based extraction and enhancement of TIE waveforms, and imaging of the cement-formation interface using the reverse time migration approach. The review thoroughly explores the methodological principles and applications of these techniques, supported by synthetic datasets, full-scale physical well experiments, and field well data. Considering the recent progress in machine learning and the growing availability of advanced computational resources, we highlight the most significant achievements and ongoing challenges in data processing, while discussing the potential advancements these techniques could offer in the near future.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100170"},"PeriodicalIF":4.2,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145698039","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}
Pub Date : 2025-11-17DOI: 10.1016/j.aiig.2025.100169
Jiawen Liu , Yuxin Ye , Ziheng Li , Zhezhe Xing , Shuisheng Ye
Geographic Information System (GIS) layers contain both spatial precision and domain knowledge, making them valuable for mineral prospectivity analysis. This study proposes a task-oriented methodology to construct a mineral prospecting knowledge graph directly from GIS maps. The framework integrates ontology construction, spatiotemporal semantic embedding, and triple confidence evaluation. Ontologies are built from GIS layers through terminology extraction and alignment with existing standards, while spatial and temporal semantics are encoded using GeoSPARQL and the Geological Time Ontology. Graph Convolutional Networks (GCN) combined with the TransE embedding model are then applied to assess triple plausibility. A case study in the Eastern Tianshan region of Xinjiang verifies the effectiveness of the proposed method through semantic evaluation and graph-theoretic analysis. Guided by GIS, ontology construction significantly enhances the semantic fidelity and structural robustness of the prospecting knowledge graph, providing relatively reliable support for subsequent reasoning and predictive studies.
{"title":"Constructing regional mineral prospecting knowledge graph from GIS maps","authors":"Jiawen Liu , Yuxin Ye , Ziheng Li , Zhezhe Xing , Shuisheng Ye","doi":"10.1016/j.aiig.2025.100169","DOIUrl":"10.1016/j.aiig.2025.100169","url":null,"abstract":"<div><div>Geographic Information System (GIS) layers contain both spatial precision and domain knowledge, making them valuable for mineral prospectivity analysis. This study proposes a task-oriented methodology to construct a mineral prospecting knowledge graph directly from GIS maps. The framework integrates ontology construction, spatiotemporal semantic embedding, and triple confidence evaluation. Ontologies are built from GIS layers through terminology extraction and alignment with existing standards, while spatial and temporal semantics are encoded using GeoSPARQL and the Geological Time Ontology. Graph Convolutional Networks (GCN) combined with the TransE embedding model are then applied to assess triple plausibility. A case study in the Eastern Tianshan region of Xinjiang verifies the effectiveness of the proposed method through semantic evaluation and graph-theoretic analysis. Guided by GIS, ontology construction significantly enhances the semantic fidelity and structural robustness of the prospecting knowledge graph, providing relatively reliable support for subsequent reasoning and predictive studies.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100169"},"PeriodicalIF":4.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579224","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}
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio (SNR) and helps identify oil and minerals. Dictionary learning (DL) is a promising method for noise attenuation. The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold. However, the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR. Ramanujan’s sum(s) (RS) is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals. Hence, we propose a novel DL method with threshold estimation based on RS to improve the output SNR. In this work, we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising. We analyze the results of the proposed work on synthetically generated and field data sets. We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error. The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD, SURE-based, and adaptive soft-thresholding techniques.
{"title":"Geophysical data denoising using dictionary learning method with Ramanujan sums for oil and minerals exploration","authors":"Lakshmi Kuruguntla , Mamatha Bandaru , Dokku Tejaswi , Anup Kumar Mandpura , Sravan Kumar Sikhakoli , Vineela Chandra Dodda","doi":"10.1016/j.aiig.2025.100168","DOIUrl":"10.1016/j.aiig.2025.100168","url":null,"abstract":"<div><div>Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio (SNR) and helps identify oil and minerals. Dictionary learning (DL) is a promising method for noise attenuation. The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold. However, the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR. Ramanujan’s sum(s) (RS) is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals. Hence, we propose a novel DL method with threshold estimation based on RS to improve the output SNR. In this work, we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising. We analyze the results of the proposed work on synthetically generated and field data sets. We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error. The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD, SURE-based, and adaptive soft-thresholding techniques.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100168"},"PeriodicalIF":4.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519714","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}
Pub Date : 2025-11-11DOI: 10.1016/j.aiig.2025.100167
Jingyi Han , Xiumei Zhang , Yujuan Qi , Lin Liu
Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images, this paper integrates the YOLOv8 model with the Convolution Block Attention Module (CBAM). It proposes an intelligent method for detecting fractures and holes, as well as segmenting whole-wellbore images. Firstly, we develop a dataset sample of effective reservoir sections by integrating logging data and conducting data augmentation on fracture and hole samples in ultrasonic logging images. A standardized process procedure for the generation of new samples and model training has been proposed effectively. Subsequently, the improved YOLOv8 model undergoes a process of training and validation. The results indicate that the model achieves average accuracies of 0.910 and 0.884 in target detection and image segmentation tasks, respectively. These findings demonstrate a notable performance improvement compared to the original model. Furthermore, a sliding window strategy is proposed to tackle the challenges of high computational demands and insufficient accuracy in the intelligent processing of full-well ultrasonic images. To manage overlapping regions within the sliding window, we employ the Non-Maximum Suppression (NMS) principle for effective processing. Finally, the model has been tested on actual logging images and demonstrates an enhanced capability to identify irregular fractures and holes, which significantly improves the efficiency of geological feature recognition in the whole-well section ultrasonic logging images.
{"title":"Intelligent identification of fractures and holes in ultrasonic logging images based on the improved YOLOv8 model","authors":"Jingyi Han , Xiumei Zhang , Yujuan Qi , Lin Liu","doi":"10.1016/j.aiig.2025.100167","DOIUrl":"10.1016/j.aiig.2025.100167","url":null,"abstract":"<div><div>Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images, this paper integrates the YOLOv8 model with the Convolution Block Attention Module (CBAM). It proposes an intelligent method for detecting fractures and holes, as well as segmenting whole-wellbore images. Firstly, we develop a dataset sample of effective reservoir sections by integrating logging data and conducting data augmentation on fracture and hole samples in ultrasonic logging images. A standardized process procedure for the generation of new samples and model training has been proposed effectively. Subsequently, the improved YOLOv8 model undergoes a process of training and validation. The results indicate that the model achieves average accuracies of 0.910 and 0.884 in target detection and image segmentation tasks, respectively. These findings demonstrate a notable performance improvement compared to the original model. Furthermore, a sliding window strategy is proposed to tackle the challenges of high computational demands and insufficient accuracy in the intelligent processing of full-well ultrasonic images. To manage overlapping regions within the sliding window, we employ the Non-Maximum Suppression (NMS) principle for effective processing. Finally, the model has been tested on actual logging images and demonstrates an enhanced capability to identify irregular fractures and holes, which significantly improves the efficiency of geological feature recognition in the whole-well section ultrasonic logging images.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100167"},"PeriodicalIF":4.2,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519713","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}
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