期刊,中国地质（英文） 2024年卷2期_国家学术搜索

期刊信息/Journal information

中国地质（英文）

出版周期：季刊

国际刊号：2096-5192

电子邮箱：chinageology@126.com

电　　话：010-58584267

邮政编码：100037

地　　址：北京市西城区阜外大街45号

中国地质（英文）/Journal China GeologyCSTPCDCSCD北大核心

查看更多>>本刊旨在围绕地球多圈层交互作用调查研究、能源和其他重要矿产资源调查评价、水文地质与水资源调查评价、山水林田湖草综合调查评价与生态保护修复、海洋地质调查、重要经济区与城市地质调查评价、国际地质合作研究等方面，与世界各国同行交流地质科学研究新问题和地质调查新发现，传播地质新理念，共同探讨信息化、网络化、数字化背景下，地质工作如何保持先行者的形象，持续高效地支撑全球经济发展和环境保护。依托中国地质调查创新发现的优势，设立原创性学术论文、综述性学术论文、最新研究进展、地质界简讯与新闻等四大栏目，重点刊发在解决重大资源环境和基础地质问题过程中前瞻性、引领性、颠覆性学术成果；重点刊发基础性、综合性、专题性综述性成果；重点刊发最新发现和最新地质调查数据等优秀成果。

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Exploring mechanism of hidden,steep obliquely inclined bedding landslides using a 3DEC model:A case study of the Shanyang landslide in Shaanxi Province,China

Jia-yun WangZi-long WuXiao-ya ShiLong-wei Yang...

303-314页

查看更多>>摘要：Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures(also referred to as obliquely inclined bedding slopes),where the apparent dip sliding is not readily visible.This phenomenon has become a focal point in landslide research.Yet,there is a lack of studies on the failure modes and mechanisms of hidden,steep obliquely inclined bedding slopes.This study investigated the Shanyang landslide in Shaanxi Province,China.Using field investigations,laboratory tests of geotechnical parameters,and the 3DEC software,this study developed a numerical model of the landslide to analyze the failure process of such slopes.The findings indicate that the Shanyang landslide primarily crept along a weak interlayer under the action of gravity.The landslide,initially following a dip angle with the support of a stable inclined rock mass,shifted direction under the influence of argillization in the weak interlayer,moving towards the apparent dip angle.The slide resistance effect of the karstic dissolution zone was increasingly significant during this process,with lateral friction being the primary resistance force.A reduction in the lateral friction due to karstic dissolution made the apparent dip sliding characteristics of the Shanyang landslide more pronounced.Notably,deformations such as bending and uplift at the slope's foot suggest that the main slide resistance shifts from lateral friction within the karstic dissolution zone to the slope foot's resistance force,leading to the eventual buckling failure of the landslide.This study unveils a novel failure mode of apparent dip creep-buckling in the Shanyang landslide,highlighting the critical role of lateral friction from the karstic dissolution zone in its failure mechanism.These insights offer a valuable reference for mitigating risks and preventing disasters related to obliquely inclined bedding landslides.

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万方数据
维普

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

Tao LiChen-chen XieChong XuWen-wen Qi...

315-329页

查看更多>>摘要：Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of 92.36％in the test set.In addition,14.95％of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

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万方数据
维普

Exploring deep learning for landslide mapping:A comprehensive review

Zhi-qiang YangWen-wen QiChong XuXiao-yi Shao...

330-350页

查看更多>>摘要：A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in high-resolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly data-driven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

原文链接:

万方数据
维普

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

Yuan TianYang-landuo DengMing-zhi ZhangXiao Pang...

351-364页

查看更多>>摘要：This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pre-trained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6％compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2％,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

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