Artificial Intelligence in Geosciences最新文献_第2页

Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes 基于套管井超声测量的水泥胶结评价的最新进展与挑战

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-11-29 DOI: 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.

水泥胶结质量评价是地层间层间隔离评价的重要内容，为油气开采、地热能注入和地质二氧化碳封存等环境生态安全提供重要信息。在过去的十年中，超声波脉冲回波和井距捕捉测井技术已经成为定量评估套管-水泥和水泥-地层界面胶结质量的有效且非破坏性的方法。本文综述了基于超声测量的水泥胶结质量评价的最新进展。关键的发展包括自动波形质量评估、泥浆和水泥阻抗反演技术、工具轨迹校正、弯曲和伸展波分离、基于机器学习的TIE波形提取和增强，以及使用逆时偏移方法对水泥-地层界面进行成像。在综合数据集、全尺寸物理井实验和现场井数据的支持下，本文深入探讨了这些技术的方法原理和应用。考虑到机器学习的最新进展和先进计算资源的日益可用性，我们强调了数据处理中最重要的成就和持续的挑战，同时讨论了这些技术在不久的将来可能提供的潜在进步。

{"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}

引用次数: 0

Constructing regional mineral prospecting knowledge graph from GIS maps 利用GIS地图构建区域找矿知识图谱

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-11-17 DOI: 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.

地理信息系统（GIS）层包含空间精度和领域知识，使其在矿产找矿分析中具有重要价值。本文提出了一种面向任务的方法，直接从GIS地图中构建找矿知识图谱。该框架集成了本体构建、时空语义嵌入和三重置信度评估。本体通过术语提取和与现有标准对齐从GIS层构建，而空间和时间语义使用GeoSPARQL和地质时间本体进行编码。然后将图卷积网络（GCN）与TransE嵌入模型相结合，进行三重可信性评估。以新疆东天山地区为例，通过语义评价和图论分析验证了该方法的有效性。在GIS的指导下，本体构建显著提高了勘探知识图的语义保真度和结构鲁棒性，为后续推理和预测研究提供了相对可靠的支持。

{"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}

引用次数: 0

Geophysical data denoising using dictionary learning method with Ramanujan sums for oil and minerals exploration 基于Ramanujan和的字典学习方法的物探数据去噪

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-11-14 DOI: 10.1016/j.aiig.2025.100168

Lakshmi Kuruguntla , Mamatha Bandaru , Dokku Tejaswi , Anup Kumar Mandpura , Sravan Kumar Sikhakoli , Vineela Chandra Dodda

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.

在地震勘探中，去噪是一个重要的预处理步骤，可以提高信噪比，帮助识别石油和矿物。字典学习（DL）是一种很有前途的降噪方法。该算法利用过完备字典从地震数据中提取稀疏特征，并基于阈值进行去噪。然而，深度学习中阈值的选择对去噪效果和输出信噪比的提高影响很大。拉马努金求和（RS）是一种表现出导数行为的信号处理工具，在信号的边缘检测和噪声估计中得到了应用。因此，我们提出了一种新的基于RS的阈值估计的深度学习方法，以提高输出信噪比。在这项工作中，我们基于RS估计地震数据的噪声方差，并将其作为DL方法进行去噪的阈值。我们分析了在综合生成和现场数据集上提出的工作的结果。我们在广泛的信噪比值范围内对有噪声的地震数据进行模拟，并使用性能指标信噪比和均方误差将去噪结果制成表格。结果表明，与MAD、基于sure和自适应软阈值技术相比，该方法具有更高的信噪比和更小的均方误差。

{"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}

引用次数: 0

Intelligent identification of fractures and holes in ultrasonic logging images based on the improved YOLOv8 model 基于改进YOLOv8模型的超声测井图像缝孔智能识别

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-11-11 DOI: 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.

针对全井筒超声图像中地质特征的智能识别需求，本文将YOLOv8模型与卷积块注意模块（Convolution Block Attention Module， CBAM）相结合。提出了一种智能的裂缝、井眼检测及全井图像分割方法。首先，对测井资料进行整合，并对超声测井图像中的裂缝和孔样进行数据增强，得到有效储层剖面数据集样本；提出了一种用于新样本生成和模型训练的标准化流程。随后，改进的YOLOv8模型经历了一个训练和验证过程。结果表明，该模型在目标检测和图像分割任务中的平均准确率分别为0.910和0.884。这些发现表明，与原始模型相比，性能有了显著提高。此外，针对全井超声图像智能处理中计算量大、精度不高的问题，提出了滑动窗口策略。为了管理滑动窗口内的重叠区域，我们采用非最大抑制（NMS）原则进行有效的处理。最后，在实际测井图像上进行了验证，结果表明该模型对不规则裂缝和井眼的识别能力增强，显著提高了全井段超声测井图像的地质特征识别效率。

{"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}

引用次数: 0

AI-based approaches for wetland mapping and classification: A review of current practices and future perspectives 基于人工智能的湿地制图与分类方法：现状与展望

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-11-05 DOI: 10.1016/j.aiig.2025.100165

Kai Cheng , Cong Zhang , Yaocheng Fan , Hongli Diao , Shibin Xia

Wetlands are critical ecosystems that provide essential ecological, hydrological, and socio-economic services, such as water purification, climate regulation, and biodiversity conservation. However, effective wetland management faces significant challenges, particularly in the analysis and classification of complex wetland environments. Traditional methods of wetland monitoring often suffer from limitations in spatial coverage, temporal resolution, and data processing efficiency. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning techniques, have been increasingly integrated with remote sensing technologies, offering a powerful solution to these challenges. AI has demonstrated significant potential in automating large-scale remote sensing data analysis, enabling the extraction of detailed spatial information, and enhancing the accuracy and efficiency of wetland mapping and classification. Bibliometric analysis indicates a growing body of research, with notable contributions from China and the United States, though regional disparities and a lack of diverse datasets remain key issues. Despite the success of AI in wetland monitoring, challenges persist in addressing environmental heterogeneity, mixed pixels, and data quality. This review synthesizes the current state of AI-based approaches in wetland mapping and classification, identifies trends and gaps, and outlines future research directions, emphasizing the need for interdisciplinary collaboration and integration of multi-source data to advance AI applications in wetland conservation.

湿地是重要的生态系统，提供必要的生态、水文和社会经济服务，如水净化、气候调节和生物多样性保护。然而，有效的湿地管理面临着重大挑战，特别是在复杂湿地环境的分析和分类方面。传统的湿地监测方法在空间覆盖、时间分辨率和数据处理效率等方面存在局限性。人工智能（AI）的最新进展，特别是机器学习和深度学习技术，已越来越多地与遥感技术相结合，为应对这些挑战提供了有力的解决方案。人工智能在自动化大规模遥感数据分析、提取详细空间信息以及提高湿地制图和分类的准确性和效率方面显示出巨大的潜力。文献计量分析表明，尽管区域差异和缺乏多样化的数据集仍然是关键问题，但中国和美国的研究成果正在不断增加。尽管人工智能在湿地监测方面取得了成功，但在解决环境异质性、混合像素和数据质量方面仍然存在挑战。本文综述了基于人工智能的湿地制图和分类方法的现状，指出了趋势和差距，并概述了未来的研究方向，强调需要跨学科合作和多源数据的整合来推进人工智能在湿地保护中的应用。

{"title":"AI-based approaches for wetland mapping and classification: A review of current practices and future perspectives","authors":"Kai Cheng , Cong Zhang , Yaocheng Fan , Hongli Diao , Shibin Xia","doi":"10.1016/j.aiig.2025.100165","DOIUrl":"10.1016/j.aiig.2025.100165","url":null,"abstract":"<div><div>Wetlands are critical ecosystems that provide essential ecological, hydrological, and socio-economic services, such as water purification, climate regulation, and biodiversity conservation. However, effective wetland management faces significant challenges, particularly in the analysis and classification of complex wetland environments. Traditional methods of wetland monitoring often suffer from limitations in spatial coverage, temporal resolution, and data processing efficiency. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning techniques, have been increasingly integrated with remote sensing technologies, offering a powerful solution to these challenges. AI has demonstrated significant potential in automating large-scale remote sensing data analysis, enabling the extraction of detailed spatial information, and enhancing the accuracy and efficiency of wetland mapping and classification. Bibliometric analysis indicates a growing body of research, with notable contributions from China and the United States, though regional disparities and a lack of diverse datasets remain key issues. Despite the success of AI in wetland monitoring, challenges persist in addressing environmental heterogeneity, mixed pixels, and data quality. This review synthesizes the current state of AI-based approaches in wetland mapping and classification, identifies trends and gaps, and outlines future research directions, emphasizing the need for interdisciplinary collaboration and integration of multi-source data to advance AI applications in wetland conservation.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100165"},"PeriodicalIF":4.2,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465152","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}

引用次数: 0

Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework 使用新的贝叶斯深度学习框架量化矿物预测的不确定性

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-11-04 DOI: 10.1016/j.aiig.2025.100164

Yue Liu

Mineral resource exploration increasingly demands not only accurate prospectivity maps but also reliable measures of confidence to guide high-stakes decisions. In this study, a novel Bayesian deep learning (BDL) framework was introduced, which embeds probabilistic inference within a deep neural network to jointly predict mineralization potential and quantify uncertainty. Two posterior approximation strategies, Metropolis–Hastings (MH) sampling and variational inference (VI), are implemented to estimate model weights as distributions rather than as fixed values, enabling decomposition of predictive uncertainty into aleatoric and epistemic components. When applied to eleven ore-controlling features in the Nanling tungsten polymetallic region (China), both MH-based and VI-based BDL models demonstrate strong classification performance while revealing contrasting spatial patterns and uncertainty patterns. Correlation studies across probability bands confirm that MH sampling captures a broader spread of uncertainty at the cost of greater computational demand, while VI delivers greater efficiency but risks underestimating uncertainty. The results highlight trade-offs between accuracy, interpretability, and computational load, demonstrating that MH-based BDL offers more robust uncertainty assessments, whereas VI-based BDL places greater emphasis on efficiency. By providing spatially explicit probability and uncertainty maps, this framework advances risk-aware mineral exploration, enabling practitioners to target areas of high potential with low uncertainty and to identify regions warranting additional data acquisition.

矿产资源勘探不仅需要精确的勘探地图，而且需要可靠的信心措施来指导高风险的决策。在本研究中，引入了一种新的贝叶斯深度学习（BDL）框架，该框架将概率推理嵌入到深度神经网络中，以联合预测矿化潜力和量化不确定性。采用Metropolis-Hastings （MH）抽样和变分推理（VI）两种后验逼近策略，将模型权重估计为分布而不是固定值，从而将预测不确定性分解为任意分量和认知分量。将其应用于南岭钨多金属区11个控矿特征，结果表明，基于h和vi的BDL模型在揭示空间格局和不确定性格局的同时具有较强的分类能力。跨概率带的相关性研究证实，MH采样以更大的计算需求为代价，捕获了更广泛的不确定性，而VI提供了更高的效率，但存在低估不确定性的风险。结果强调了准确性、可解释性和计算负载之间的权衡，表明基于mh的BDL提供了更强大的不确定性评估，而基于vi的BDL更强调效率。通过提供空间上明确的概率和不确定性地图，该框架促进了风险意识的矿产勘探，使从业者能够以低不确定性瞄准高潜力区域，并确定需要额外数据采集的区域。

{"title":"Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework","authors":"Yue Liu","doi":"10.1016/j.aiig.2025.100164","DOIUrl":"10.1016/j.aiig.2025.100164","url":null,"abstract":"<div><div>Mineral resource exploration increasingly demands not only accurate prospectivity maps but also reliable measures of confidence to guide high-stakes decisions. In this study, a novel Bayesian deep learning (BDL) framework was introduced, which embeds probabilistic inference within a deep neural network to jointly predict mineralization potential and quantify uncertainty. Two posterior approximation strategies, Metropolis–Hastings (MH) sampling and variational inference (VI), are implemented to estimate model weights as distributions rather than as fixed values, enabling decomposition of predictive uncertainty into aleatoric and epistemic components. When applied to eleven ore-controlling features in the Nanling tungsten polymetallic region (China), both MH-based and VI-based BDL models demonstrate strong classification performance while revealing contrasting spatial patterns and uncertainty patterns. Correlation studies across probability bands confirm that MH sampling captures a broader spread of uncertainty at the cost of greater computational demand, while VI delivers greater efficiency but risks underestimating uncertainty. The results highlight trade-offs between accuracy, interpretability, and computational load, demonstrating that MH-based BDL offers more robust uncertainty assessments, whereas VI-based BDL places greater emphasis on efficiency. By providing spatially explicit probability and uncertainty maps, this framework advances risk-aware mineral exploration, enabling practitioners to target areas of high potential with low uncertainty and to identify regions warranting additional data acquisition.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100164"},"PeriodicalIF":4.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519710","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}

引用次数: 0

Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms 基于XGBoost和基于突变的遗传算法的各向异性粘土中开沉箱锚杆不排水上扬能力预测

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-10-26 DOI: 10.1016/j.aiig.2025.100162

Rungroad Suppakul , Wittaya Jitchaijaroen , Suraparb Keawsawasvong , Sutasinee Intui , Shinya Inazumi

This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis (FELA) and a hybrid machine learning framework. The FELA simulations investigate the influence of the radius ratio (R/B), anisotropic ratio (r_e), interface roughness factor (α), and inclination angle (β). Specifically, the results reveal that increasing β significantly enhances N_c, especially as soil behavior approaches isotropy. Higher α improves resistance at steeper inclinations by mobilizing greater interface shear. N_c increases with r_e, reflecting enhanced strength under isotropic conditions. To enhance predictive accuracy and generalization, a hybrid machine learning model was developed by integrating Extreme Gradient Boosting (XGBoost) with Genetic Algorithm (GA) and Mutation-Based Genetic Algorithm (MGA) for hyperparameter tuning. Among the models, MGA-XGBoost outperformed GA-XGBoost, achieving higher predictive accuracy (R² = 0.996 training, 0.993 testing). Furthermore, SHAP analysis consistently identified anisotropic ratio (r_e) as the most influential factor in predicting uplift capacity, followed by interface roughness factor (α), inclination angle (β), and radius ratio (R/B). The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries, offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.

本研究使用有限元极限分析（FELA）和混合机器学习框架评估了嵌入各向异性粘土中的开放式沉箱锚杆的不排水上拔能力。通过FELA模拟研究了半径比（R/B）、各向异性比（re）、界面粗糙度因子（α）和倾角（β）的影响。具体而言，结果表明，增加β显著提高Nc，特别是当土壤行为接近各向同性时。较高的α通过调动更大的界面剪切来提高陡坡阻力。Nc随re的增加而增加，反映了各向同性条件下强度的增强。为了提高预测精度和泛化能力，将极端梯度增强（XGBoost）与遗传算法（GA）和基于突变的遗传算法（MGA）相结合，建立了一种混合机器学习模型。其中，MGA-XGBoost优于GA-XGBoost，预测准确率更高（训练R2 = 0.996，检验R2 = 0.993）。此外，SHAP分析一致认为各向异性比（re）是预测抬升能力的最重要因素，其次是界面粗糙度系数（α）、倾角（β）和半径比（R/B）。所提出的框架可作为可扩展的决策支持工具，适用于各种土壤类型和基础几何形状，为各向异性粘性土壤的抗隆起设计提供更有效和数据驱动的方法。

{"title":"Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms","authors":"Rungroad Suppakul , Wittaya Jitchaijaroen , Suraparb Keawsawasvong , Sutasinee Intui , Shinya Inazumi","doi":"10.1016/j.aiig.2025.100162","DOIUrl":"10.1016/j.aiig.2025.100162","url":null,"abstract":"<div><div>This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis (FELA) and a hybrid machine learning framework. The FELA simulations investigate the influence of the radius ratio (<em>R/B</em>), anisotropic ratio (<em>r</em><sub><em>e</em></sub>), interface roughness factor (<em>α</em>), and inclination angle (<em>β</em>). Specifically, the results reveal that increasing <em>β</em> significantly enhances <em>N</em><sub><em>c</em></sub>, especially as soil behavior approaches isotropy. Higher <em>α</em> improves resistance at steeper inclinations by mobilizing greater interface shear. <em>N</em><sub><em>c</em></sub> increases with <em>r</em><sub><em>e</em></sub>, reflecting enhanced strength under isotropic conditions. To enhance predictive accuracy and generalization, a hybrid machine learning model was developed by integrating Extreme Gradient Boosting (XGBoost) with Genetic Algorithm (GA) and Mutation-Based Genetic Algorithm (MGA) for hyperparameter tuning. Among the models, MGA-XGBoost outperformed GA-XGBoost, achieving higher predictive accuracy (R<sup>2</sup> = 0.996 training, 0.993 testing). Furthermore, SHAP analysis consistently identified anisotropic ratio (<em>r</em><sub><em>e</em></sub>) as the most influential factor in predicting uplift capacity, followed by interface roughness factor (<em>α</em>), inclination angle (<em>β</em>), and radius ratio (<em>R/B</em>). The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries, offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100162"},"PeriodicalIF":4.2,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415648","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}

引用次数: 0

Deciphering influential features in the seismic catalog for large earthquake occurrence from a machine learning perspective 从机器学习的角度解读大地震发生的地震目录中的影响特征

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-10-26 DOI: 10.1016/j.aiig.2025.100161

Jinsu Jang , Byung-Dal So , David A. Yuen , Sung-Joon Chang

The spatiotemporal distribution and magnitude of seismicity collected over decades are crucial for understanding the stress interactions underlying large earthquakes. In this study, machine learning (ML) explainers identify and rank the features that distinguish Large Earthquake Occurrence (LEO) from non-LEO spatiotemporal windows. Seventy-eight statistics related to time, latitude, longitude, depth, and magnitude were extracted from the earthquake catalog (Global Centroid Moment Tensor) to produce 202,706 spatiotemporally discretized windows. ML explainers trained on these windows revealed the maximum magnitude (M_max) as the most influential feature. Classification performance improved when the maximum inter-event time, the average inter-event time, and the minimum ratio of focal depth to magnitude were jointly trained with M_max. The top five features showed weak-to-moderate correlations, providing complementary information to the ML explainers. Our explainable ML framework can be extended to different earthquake catalogs, including those with focal mechanisms and small-magnitude events.

几十年来收集的地震活动的时空分布和震级对于理解大地震背后的应力相互作用至关重要。在本研究中，机器学习（ML）解释器识别并排序区分大地震发生（LEO）和非LEO时空窗口的特征。从地震目录（全球质心矩张量）中提取78个与时间、纬度、经度、深度和震级相关的统计量，产生202,706个时空离散窗口。在这些窗口上训练的ML解释器显示最大幅度（Mmax）是最具影响力的特征。当最大事件间隔时间、平均事件间隔时间和最小震级比与Mmax联合训练时，分类性能得到提高。前五个特征显示出弱到中等的相关性，为ML解释器提供了补充信息。我们的可解释的ML框架可以扩展到不同的地震目录，包括那些具有震源机制和小震级事件。

{"title":"Deciphering influential features in the seismic catalog for large earthquake occurrence from a machine learning perspective","authors":"Jinsu Jang , Byung-Dal So , David A. Yuen , Sung-Joon Chang","doi":"10.1016/j.aiig.2025.100161","DOIUrl":"10.1016/j.aiig.2025.100161","url":null,"abstract":"<div><div>The spatiotemporal distribution and magnitude of seismicity collected over decades are crucial for understanding the stress interactions underlying large earthquakes. In this study, machine learning (ML) explainers identify and rank the features that distinguish Large Earthquake Occurrence (LEO) from non-LEO spatiotemporal windows. Seventy-eight statistics related to time, latitude, longitude, depth, and magnitude were extracted from the earthquake catalog (Global Centroid Moment Tensor) to produce 202,706 spatiotemporally discretized windows. ML explainers trained on these windows revealed the maximum magnitude (<em>M</em><sub><em>max</em></sub>) as the most influential feature. Classification performance improved when the maximum inter-event time, the average inter-event time, and the minimum ratio of focal depth to magnitude were jointly trained with <em>M</em><sub><em>max</em></sub>. The top five features showed weak-to-moderate correlations, providing complementary information to the ML explainers. Our explainable ML framework can be extended to different earthquake catalogs, including those with focal mechanisms and small-magnitude events.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100161"},"PeriodicalIF":4.2,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465151","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}

引用次数: 0

Unsupervised hierarchical sequence stratigraphy framework of carbonate successions 碳酸盐岩序列的无监督层序地层学格架

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-10-10 DOI: 10.1016/j.aiig.2025.100160

Márcio Vinicius Santana Dantas , Kaio Henrique Masse Vieira , Thomás Jung Spier , José Arthur Oliveira Santos , Alan Cabral Trindade Prado , Danilo Vomlel , Mariana Leite , Felipe Alves Farias , Daniel Galvão Carnier Fragoso , Humberto Reis , Gabriel Coutinho , Douglas G. Macharet

Performing the high-resolution stratigraphic analysis may be challenging and time-consuming if one has to work with large datasets. Moreover, sedimentary records have signals of different frequencies and intrinsic noise, resulting in a complex signature that is difficult to identify only through eyes-based analysis. This work proposes identifying transgressive-regressive (T-R) sequences from carbonate facies successions of three South American basins: (i) São Francisco Basin - Brazil, (ii) Santos Basin - Brazil, and (iii) Salta Basin - Argentina. We applied a hidden Markov model in an unsupervised approach followed by a Score-Based Recommender System that automatically finds medium or low-frequency sedimentary cycles from high-frequency ones. Our method is applied to facies identified using Fullbore Formation Microimager (FMI) logs, outcrop description, and composite logs from carbonate intervals. The automatic recommendation results showed better long-distance correlations between medium- to low-frequency sedimentary cycles, whereas the hidden Markov model method successfully identified high-resolution (high-frequency) transgressive and regressive systems tracts from the given facies successions. Our workflow offers advances in the automated analyses and construction of lower- to higher-rank stratigraphic framework and short to long-distance stratigraphic correlation, allowing for large-scale automated processing of the basin dataset. Our approach in this work fits the unsupervised learning framework, as we require no previous input of stratigraphical analysis in the basin. The results provide solutions for prospecting any sediment-hosted mineral resource, especially for the oil and gas industry, offering support for subsurface geological characterization, whether at the exploration scale or for reservoir zoning during production development.

如果必须处理大型数据集，那么进行高分辨率地层分析可能是具有挑战性和耗时的。此外，沉积记录具有不同频率的信号和固有噪声，这导致了复杂的特征，仅通过肉眼分析很难识别。本文提出了从三个南美盆地(1)巴西s o Francisco盆地、(2)巴西Santos盆地和(3)阿根廷Salta盆地的碳酸盐岩相序列中识别海侵-退（T-R）层序的方法。我们在无监督的方法中应用了隐马尔可夫模型，然后是基于分数的推荐系统，该系统自动从高频沉积旋回中发现中低频沉积旋回。我们的方法应用于通过全孔地层微成像仪（FMI）测井、露头描述和碳酸盐岩层段的复合测井来识别相。自动推荐结果显示中低频沉积旋回之间具有较好的长距离相关性，而隐马尔可夫模型方法则成功地从给定的相序列中识别出高分辨率（高频）海侵和海退体系域。我们的工作流程在低阶到高阶地层格架的自动化分析和构建以及短距离到长距离地层对比方面取得了进展，从而允许对盆地数据集进行大规模的自动化处理。我们在这项工作中的方法适合无监督学习框架，因为我们不需要事先输入盆地的地层分析。研究结果为任何含沉积物矿产资源的勘探提供了解决方案，特别是对油气行业来说，无论是在勘探规模上还是在生产开发过程中进行储层划分，都为地下地质特征提供了支持。

{"title":"Unsupervised hierarchical sequence stratigraphy framework of carbonate successions","authors":"Márcio Vinicius Santana Dantas , Kaio Henrique Masse Vieira , Thomás Jung Spier , José Arthur Oliveira Santos , Alan Cabral Trindade Prado , Danilo Vomlel , Mariana Leite , Felipe Alves Farias , Daniel Galvão Carnier Fragoso , Humberto Reis , Gabriel Coutinho , Douglas G. Macharet","doi":"10.1016/j.aiig.2025.100160","DOIUrl":"10.1016/j.aiig.2025.100160","url":null,"abstract":"<div><div>Performing the high-resolution stratigraphic analysis may be challenging and time-consuming if one has to work with large datasets. Moreover, sedimentary records have signals of different frequencies and intrinsic noise, resulting in a complex signature that is difficult to identify only through eyes-based analysis. This work proposes identifying transgressive-regressive (T-R) sequences from carbonate facies successions of three South American basins: (i) São Francisco Basin - Brazil, (ii) Santos Basin - Brazil, and (iii) Salta Basin - Argentina. We applied a hidden Markov model in an unsupervised approach followed by a Score-Based Recommender System that automatically finds medium or low-frequency sedimentary cycles from high-frequency ones. Our method is applied to facies identified using Fullbore Formation Microimager (FMI) logs, outcrop description, and composite logs from carbonate intervals. The automatic recommendation results showed better long-distance correlations between medium- to low-frequency sedimentary cycles, whereas the hidden Markov model method successfully identified high-resolution (high-frequency) transgressive and regressive systems tracts from the given facies successions. Our workflow offers advances in the automated analyses and construction of lower- to higher-rank stratigraphic framework and short to long-distance stratigraphic correlation, allowing for large-scale automated processing of the basin dataset. Our approach in this work fits the unsupervised learning framework, as we require no previous input of stratigraphical analysis in the basin. The results provide solutions for prospecting any sediment-hosted mineral resource, especially for the oil and gas industry, offering support for subsurface geological characterization, whether at the exploration scale or for reservoir zoning during production development.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100160"},"PeriodicalIF":4.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264973","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}

引用次数: 0

Advancements in Sinkhole Remediation: Field data-driven Sinkhole grout volume prediction model via machine learning-based regression Analysis 天坑修复的进展：基于机器学习的回归分析的现场数据驱动的天坑灌浆量预测模型

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-10-10 DOI: 10.1016/j.aiig.2025.100159

Bubryur Kim , Yuvaraj Natarajan , K.R. Sri Preethaa , V. Danushkumar , Ryan Shamet , Jiannan Chen , Rui Xie , Timothy Copeland , Boo Hyun Nam , Jinwoo An

Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based Equitable Distribution Method (TEDM), were applied to distribute grout influence across CPTs, with TEDM demonstrating superior predictive performance. Synthetic data augmentation using spline methodology further improved model robustness. A high-degree polynomial regression model, optimized with ridge regularization, achieved high accuracy (R² = 0.95; PEV = 0.94) and significantly outperformed existing linear and logarithmic models. Results confirm that lower SRR values correlate with higher grout demand, and the proposed model reliably captures these nonlinear relationships. This research advances sinkhole remediation practice by providing a data-driven, accurate, and generalizable framework for grout volume estimation, enabling more efficient resource allocation and improved project outcomes.

在喀斯特地区，天坑的形成造成了严重的地质灾害，在那里，不可预测的地下侵蚀往往需要昂贵的灌浆来稳定。由于空间变异性、场地特定条件和传统经验方法的局限性，准确估计浆液体积仍然是一个持续的挑战。该研究引入了一种新的基于机器学习的浆液体积预测回归模型，该模型集成了锥贯入试验（CPT）得出的天坑阻力比（SRR）值、CPT与注浆点（GPs）之间的空间相关性以及佛罗里达州六个天坑现场记录的浆液体积。采用近端分配法（PAM）、公平分配法（EDM）和基于阈值的公平分配法（TEDM）三种数据转换方法对不同cpt的灌浆影响进行了分布，TEDM显示出较好的预测性能。采用样条法对合成数据进行增强，进一步提高了模型的鲁棒性。采用脊正则化优化的高次多项式回归模型获得了较高的精度（R2 = 0.95; PEV = 0.94），显著优于现有的线性和对数模型。结果证实，较低的SRR值与较高的浆液需求相关，并且所提出的模型可靠地捕获了这些非线性关系。本研究通过提供一个数据驱动的、准确的、可推广的灌浆量估算框架来推进天坑修复实践，从而实现更有效的资源分配和改善项目成果。

{"title":"Advancements in Sinkhole Remediation: Field data-driven Sinkhole grout volume prediction model via machine learning-based regression Analysis","authors":"Bubryur Kim , Yuvaraj Natarajan , K.R. Sri Preethaa , V. Danushkumar , Ryan Shamet , Jiannan Chen , Rui Xie , Timothy Copeland , Boo Hyun Nam , Jinwoo An","doi":"10.1016/j.aiig.2025.100159","DOIUrl":"10.1016/j.aiig.2025.100159","url":null,"abstract":"<div><div>Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based Equitable Distribution Method (TEDM), were applied to distribute grout influence across CPTs, with TEDM demonstrating superior predictive performance. Synthetic data augmentation using spline methodology further improved model robustness. A high-degree polynomial regression model, optimized with ridge regularization, achieved high accuracy (R<sup>2</sup> = 0.95; PEV = 0.94) and significantly outperformed existing linear and logarithmic models. Results confirm that lower SRR values correlate with higher grout demand, and the proposed model reliably captures these nonlinear relationships. This research advances sinkhole remediation practice by providing a data-driven, accurate, and generalizable framework for grout volume estimation, enabling more efficient resource allocation and improved project outcomes.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100159"},"PeriodicalIF":4.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320144","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}

引用次数: 0