Artificial Intelligence in Geosciences最新文献

{"title":"Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes","authors":"Hua Wang ,&nbsp;Meng Li ,&nbsp;Qiang Wang ,&nbsp;Shaopeng Shi ,&nbsp;Gengxiao Yang ,&nbsp;Zhilong Fang ,&nbsp;Aihua Tao ,&nbsp;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":"2026-06-01","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}

{"title":"Fast sparse representation impedance inversion method based on online adaptive reservoir characterization","authors":"Zhaoxing Xu,&nbsp;Peng Liu,&nbsp;Qinqin Wu","doi":"10.1016/j.aiig.2026.100197","DOIUrl":"10.1016/j.aiig.2026.100197","url":null,"abstract":"<div><div>Seismic impedance inversion is a key technique for extracting reservoir information from seismic data. Traditional model-driven inversion methods often prove inadequate when dealing with complex reservoirs, which has led to the growing adoption of data-driven sparse representation constrained inversion approaches. These methods typically employ redundant dictionary learning to adaptively extract feature information from logging data for effective inversion constraints. Although they excel in enhancing the vertical resolution and accuracy of inversion results, they still suffer from limitations such as high computational complexity and a lack of horizontal feature constraints, resulting in insufficient horizontal continuity, overall accuracy, and computational efficiency. To address these issues, this paper proposes a fast sparse representation-based impedance inversion method using online adaptive reservoir features. Based on logging and seismic data, the method employs an online dictionary learning strategy to adaptively extract both vertical and horizontal reservoir characteristics for sparse representation inversion constraints. To further improve computational efficiency, orthogonal dictionary learning is introduced to reduce computational costs. Ultimately, an impedance inversion method is developed based on online orthogonal dictionary learning that simultaneously imposes adaptive joint constraints on both vertical and horizontal features. Experimental results demonstrate that the proposed method not only achieves high accuracy and high-resolution inversion results but also offers significant advantages in computational efficiency.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100197"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396653","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":"DTPP:An efficient depthwise separable TCN for seismic phase picking","authors":"Shuai Lv ,&nbsp;Yuxiang Peng","doi":"10.1016/j.aiig.2026.100189","DOIUrl":"10.1016/j.aiig.2026.100189","url":null,"abstract":"<div><div>With the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years. However, existing models face challenges in balancing picking accuracy with computational efficiency for real-time applications. To address this issue, we propose DTPP, a novel seismic phase picking network that integrates depthwise separable convolution and temporal dilated convolution. The model adopts a backbone-feature fusion-decoder architecture, utilizing depthwise separable convolution and dilated convolution to significantly expand the receptive field while reducing computational complexity. We trained the model on the STEAD dataset and evaluated its performance on the global GEEDataset V1.0(84,782 independent samples after excluding overlapping STEAD data to ensure fair cross-dataset evaluation). Experimental results demonstrate that DTPP achieves a P-wave recall of 0.877, F1 score of 0.878, and average P/S F1 score of 0.714, ranking first among all comparison models. Meanwhile, DTPP maintains high computational efficiency with only 0.25M parameters, 0.98 MB model size, and 3ms single-sample inference time per batch, making it suitable for real-time seismic monitoring applications. The proposed method provides an effective solution to the accuracy-efficiency trade-off problem in seismic phase picking tasks.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100189"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037971","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":"Application of machine learning for permeability prediction in heterogeneous carbonate reservoirs","authors":"Osama Massarweh ,&nbsp;Abdul Salam Abd ,&nbsp;Jens Schneider ,&nbsp;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":"2026-03-01","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}

{"title":"A data-driven approach to earthquake early warning: Multicomponent site-spectra prediction using deep neural networks","authors":"Ahmed A. Torky ,&nbsp;Susumu Ohno","doi":"10.1016/j.aiig.2026.100195","DOIUrl":"10.1016/j.aiig.2026.100195","url":null,"abstract":"<div><div>This paper presents a hybrid deep learning framework for earthquake early warning (EEW) that leverages front-site observations to predict target-site spectral characteristics—specifically Fourier amplitude spectra (FAS) and 5% damped pseudo-velocity response spectra (pSᵥ) in real time. In its current form, the framework is site-specific, as the front-site/target-site pairs used for training and evaluation are fixed. By integrating a convolutional neural network (CNN) front end with a long short-term memory (LSTM) sequence model, our approach captures both spatial frequency content and temporal correlations without requiring explicit source, path, or detailed geological inputs. Trained on a diverse corpus of historic accelerograms, the CNN-LSTM network learns cross-spectral and multicomponent dependencies and region-specific site effects, yielding rapid, physically consistent spectral estimates. We evaluate its performance across five case studies, demonstrating that our model not only reduces prediction error relative to established GMPEs for both FAS and pSᵥ, but also preserves spectral shape and cross-period correlations essential for reliable EEW. The developed technique is capable of estimating target-sites through very low latency inference, providing real-time capabilities. Compared to traditional GMPE-based warnings, our data-driven method achieves substantially faster issuance and improved shaking intensity forecasts. We conclude by outlining avenues for embedding sites’ distance and physics-informed constraints, expanding observation datasets, and enhancing model usefulness in seismic demand prediction which are key steps toward rapid EEW systems.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100195"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396251","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":"Enhancing fault detection using CHRRA-Unet and focal loss functions for imbalanced data: A case study in Luoping county, Yunnan, China","authors":"Gong Cheng ,&nbsp;Syed Hussain ,&nbsp;Yingdong Yang ,&nbsp;Li Sun ,&nbsp;Asad Atta ,&nbsp;Cheng Huang ,&nbsp;Guangqiang Li ,&nbsp;Mohammad Naseer ,&nbsp;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":"2026-03-01","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}

{"title":"Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation data","authors":"Sara Kellal ,&nbsp;Davy Nandito ,&nbsp;Ammar El-Husseiny ,&nbsp;Amjed Hassan ,&nbsp;Sherif M. Hanafy","doi":"10.1016/j.aiig.2026.100193","DOIUrl":"10.1016/j.aiig.2026.100193","url":null,"abstract":"<div><div>Nuclear Magnetic Resonance (NMR) has proven to be a powerful tool for in-situ permeability quantification however, it typically requires laboratory calibration, and its accuracy is strongly influenced by rock type and pore system heterogeneity. Existing NMR-based permeability studies are often limited by small datasets, commonly restricted to a single lithology (sandstone or carbonate), and rarely investigate whether permeability prediction is more reliable using the full transverse relaxation time (T₂) distribution (spectrum) or NMR-derived parameters (e.g., minimum, maximum, peak T₂ … etc). As a result, the generalization of existing formulations across diverse geological settings remains limited. In this study, we address these gaps by developing machine learning models trained on a large and heterogeneous dataset of 308 core samples, including both sandstones and carbonates from the US, France, Middle East, and China. The dataset spans wide porosity (0.10–32.91%) and permeability (0.0003–15,400 mD) ranges, ensuring applicability across heterogeneous rock systems. Two algorithms, namely: Multilayer Perceptron (MLP) and eXtreme Gradient Boosting (XGB), are evaluated for predicting matrix permeability from NMR data. Model performance is compared using three input configurations: (i) NMR parameters extracted from the T₂ distribution, (ii) the full T₂ spectrum, and (iii) a combination of both extracted parameters and the full spectrum. This comparison assists in evaluating the added value of the full relaxation distribution, which captures pore-scale information that may be overlooked in simplified parameters. The results show that XGB consistently outperformed MLP, with the best performance achieved when combining the full T₂ spectrum and extracted parameters, yielding an R² of 0.86 and a root mean square error (RMSE) of 0.51 in log permeability prediction (corresponding to approximately 3 mD). Incorporating lithology (rock type: carbonate versus sandstone) as an input has only a minor effect on XGB performance, suggesting prior lithological classification is not strictly required for accurate permeability prediction. These results indicate that the proposed approach can be generalized across sedimentary rocks and applied to both sandstone and carbonate reservoirs.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100193"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188454","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":"An adaptable hybrid method for lossless airborne lidar data compression","authors":"Ahmed Kotb ,&nbsp;Marwa S. Moustafa ,&nbsp;Safaa Hassan ,&nbsp;Hesham Hassan","doi":"10.1016/j.aiig.2026.100185","DOIUrl":"10.1016/j.aiig.2026.100185","url":null,"abstract":"<div><div>Light Detection and Ranging (LIDAR) point clouds provide high precision spatial data but impose significant storage and transmission challenges, often exceeding one gigabyte per square kilometer. This paper introduces a novel hierarchical framework for lossless LiDAR data compression, designed to address these issues through a three-stage approach: class-aware segmentation, adaptive algorithm selection, and hierarchical compression. The framework begins by partitioning point clouds into semantic classes (e.g., ground, vegetation, buildings) using an SVM-based classifier with a radial basis function kernel, enabling targeted compression that exploits intra-class redundancies. The adaptive algorithm selection stage employs a density-based matcher to choose optimal compression algorithms for each class, ensuring efficiency across varying point densities and terrain types. Finally, hierarchical compression merges class-specific compressed files and applies a secondary compression using WinRAR for enhanced efficiency. Evaluated on ten openly available benchmark LiDAR datasets, the proposed method consistently outperforms state-of-the-art lossless compression techniques, such as LASzip, achieving file size reductions to 12.76 % of the original for high-density point clouds and 22.51 % for low-density ones. While compression and decompression times are higher than some alternatives, the framework's superior storage savings and perfect fidelity make it ideal for large-scale LiDAR data archiving and exchange.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100185"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884817","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":"An FCM-based microseismic phase arrival picking method and application","authors":"Zhiqiang Lan ,&nbsp;Yaqi Zhang ,&nbsp;Yaojun Wang ,&nbsp;Keyu Chen ,&nbsp;Haoxiang Yang ,&nbsp;Yinzhu Chen ,&nbsp;Yangyang Yu","doi":"10.1016/j.aiig.2026.100188","DOIUrl":"10.1016/j.aiig.2026.100188","url":null,"abstract":"<div><div>Artificial intelligence-based methods for picking microseismic phase arrivals have been widely adopted. However, these methods are frequently challenged by complex and dynamic monitoring scenarios, where various types of environmental noise mask low-energy microseismic signals. Moreover, the paucity of labelled data often impairs the reliability and accuracy of their results. To address these issues, this study proposes a novel supervised learning framework named FC-Net, which integrates automatic labelling via Fuzzy C-means clustering (FCM) with the U-Net architecture. Specifically, the FCM algorithm is employed to derive the probabilistic distributions of microseismic phase arrival times, which are then used as training labels for model training. The proposed FC-Net is equipped with soft attention gates (AGs) and recurrent-residual convolution units (RRCUs), which effectively enhance the network's ability to focus on key seismic features. The arrival time is determined as the moment when the predicted probability exceeds a predefined threshold for the first arrival pick. Evaluated on a field dataset collected from Southwest China, FC-Net is demonstrated to outperform the conventional U-Net method. The experimental results demonstrate that FC-Net achieves adaptive labeling, enhances the detection rate of microseismic events, and improves the precision of phase arrival picking. Furthermore, it exhibits strong generalization performance across microseismic events with varying signal-to-noise ratios (SNRs).</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100188"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977632","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":"Explainable flood damage assessment using multi-atrous self-attention and vision-language integration","authors":"Ilhan Aydin,&nbsp;Emre Güçlü,&nbsp;Taha Kubilay Şener,&nbsp;Erhan Akin","doi":"10.1016/j.aiig.2026.100192","DOIUrl":"10.1016/j.aiig.2026.100192","url":null,"abstract":"<div><div>Flood disasters triggered by excessive rainfall cause severe damage to infrastructure and pose significant risks to human life. Within the context of disaster management, accurately identifying affected structures and providing interpretable analytical results are of critical importance. This study proposes a new disaster analysis framework that integrates the Multi-Atrous Self-Attention (MASA) mechanism, which is designed to capture multi-scale spatial features effectively, with vision-language models for explainable flood assessment. The proposed approach consists of two main components. The first component performs segmentation to detect and quantify flood-affected structures, while the second component employs a fine-tuned vision language model to generate natural language descriptions of the disaster scene. The MASA module processes image-mask pairs from the FloodNet dataset to segment disaster related structures, whereas the LoRA (Low Rank Adaptation) enhanced BLIP-2 (Bootstrapped Language Image Pre-training) model learns image-text pairs from the LADI-v2 dataset to produce textual disaster descriptions. Through this dual stage structure, the system provides both quantitative and linguistic outputs, enabling interpretable flood impact assessment. Experimental results demonstrate that the proposed MASA-based segmentation model achieves a mean Intersection over Union (mIoU) of 73.78 % on FloodNet, outperforming state-of-the-art segmentation models. Furthermore, the LoRA-fine-tuned BLIP-2 model achieves a BLEU score of 80.77 % on the LADI-v2 dataset, indicating fluent, contextually relevant, and semantically coherent textual outputs. The proposed system contributes to disaster analysis by enhancing explainability and interpretability in flood damage assessment.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"7 1","pages":"Article 100192"},"PeriodicalIF":4.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188453","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}