China Geology最新文献

{"title":"Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China","authors":"Tao Li ,&nbsp;Chen-chen Xie ,&nbsp;Chong Xu ,&nbsp;Wen-wen Qi ,&nbsp;Yuan-dong Huang ,&nbsp;Lei Li","doi":"10.31035/cg2024064","DOIUrl":"https://doi.org/10.31035/cg2024064","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 315-329"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001125/pdfft?md5=e950175d353294f06218ffdbbc16a99d&pid=1-s2.0-S2096519224001125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"List of 85 typical catastrophic landslides from March 2004 to February 2024","authors":"Rui-chen Chen ,&nbsp;Yong-shuang Zhang","doi":"10.31035/cg2024079","DOIUrl":"https://doi.org/10.31035/cg2024079","url":null,"abstract":"","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 369-370"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001162/pdfft?md5=2509e2eea3bd4ca50635750a68bb52ce&pid=1-s2.0-S2096519224001162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Airblast evolution initiated by Wangjiayan landslides in the Ms 8.0 Wenchuan earthquake and its destructive capacity analysis","authors":"Yu-feng Wang ,&nbsp;Qian-gong Cheng ,&nbsp;Qi Zhu","doi":"10.31035/cg2023154","DOIUrl":"https://doi.org/10.31035/cg2023154","url":null,"abstract":"<div><p>Airblasts, as one common phenomenon accompanied by rapid movements of landslides or rock/snow avalanches, commonly result in catastrophic damages and are attracting more and more scientific attention. To quantitatively analyze the intensity of airblast initiated by landslides, the Wangjiayan landslide, occurred in the Wenchuan earthquake, is selected here with the landslide propagation and airblast evolution being studied using FLUENT by introducing the Voellmy rheological law. The results reveal that: (1) For the Wangjiayan landslide, its whole travelling duration is only 12 s with its maximum velocity reaching 36 m/s at t=10 s; (2) corresponding to the landslide propagation, the maximum velocity, 28 m/s, of the airblast initiated by the landslide also appears at t=10 s with its maximum pressure reaching 594.8 Pa, which is equivalent to violent storm; (3) under the attack of airblast, the load suffered by buildings in the airblast zone increases to 1300 Pa at <em>t</em>=9.4 s and sharply decreased to –7000 Pa as the rapid decrease of the velocity of the sliding mass at t=10 s, which is seriously unfavorable for buildings and might be the key reason for the destructive collapse of buildings in the airblast zone of the Wangjiayan landslide.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 237-247"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S209651922400106X/pdfft?md5=03791f872e1d5cc80f539788baaafb6d&pid=1-s2.0-S209651922400106X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extensive identification of landslide boundaries using remote sensing images and deep learning method","authors":"Chang-dong Li ,&nbsp;Peng-fei Feng ,&nbsp;Xi-hui Jiang ,&nbsp;Shuang Zhang ,&nbsp;Jie Meng ,&nbsp;Bing-chen Li","doi":"10.31035/cg2023148","DOIUrl":"https://doi.org/10.31035/cg2023148","url":null,"abstract":"<div><p>The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue. It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response. Therefore, the Skip Connection DeepLab neural network (SCDnn), a deep learning model based on 770 optical remote sensing images of landslide, is proposed to improve the accuracy of landslide boundary detection. The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features. SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block (ASPC) with a coding structure that reduces model complexity. The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8 and 0.9; while 52 images with MIoU values exceeding 0.9, which exceeds the identification accuracy of existing techniques. This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future investigations and applications in related domains.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 277-290"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001095/pdfft?md5=03dde05cc0f2e6426d5c83acef32e348&pid=1-s2.0-S2096519224001095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring mechanism of hidden, steep obliquely inclined bedding landslides using a 3DEC model: A case study of the Shanyang landslide in Shaanxi Province, China","authors":"Jia-yun Wang ,&nbsp;Zi-long Wu ,&nbsp;Xiao-ya Shi ,&nbsp;Long-wei Yang ,&nbsp;Rui-ping Liu ,&nbsp;Na Lu","doi":"10.31035/cg2024044","DOIUrl":"https://doi.org/10.31035/cg2024044","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 303-314"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001113/pdfft?md5=7951fe6b3e77f4acfd080700f89c5998&pid=1-s2.0-S2096519224001113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic simulation insights into friction weakening effect on rapid long-runout landslides: A case study of the Yigong landslide in the Tibetan Plateau, China","authors":"Zi-zheng Guo ,&nbsp;Xin-yong Zhou ,&nbsp;Da Huang ,&nbsp;Shi-jie Zhai ,&nbsp;Bi-xia Tian ,&nbsp;Guang-ming Li","doi":"10.31035/cg2023132","DOIUrl":"https://doi.org/10.31035/cg2023132","url":null,"abstract":"<div><p>This study proposed a novel friction law dependent on velocity, displacement and normal stress for kinematic analysis of runout process of rapid landslides. The well-known Yigong landslide occurring in the Tibetan Plateau of China was employed as the case, and the derived dynamic friction formula was included into the numerical simulation based on Particle Flow Code. Results showed that the friction decreased quickly from 0.64 (the peak) to 0.1 (the stead value) during the 5s-period after the sliding initiation, which explained the behavior of rapid movement of the landslide. The monitored balls set at different sections of the mass showed similar variation characteristics regarding the velocity, namely evident increase at the initial phase of the movement, followed by a fluctuation phase and then a stopping one. The peak velocity was more than 100 m/s and most particles had low velocities at 300s after the landslide initiation. The spreading distance of the landslide was calculated at the two-dimension (profile) and three-dimension scale, respectively. Compared with the simulation result without considering friction weakening effect, our results indicated a max distance of about 10 km from the initial unstable position, which fit better with the actual situation.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 222-236"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001058/pdfft?md5=e7b79c62567d112441745e05608deb9e&pid=1-s2.0-S2096519224001058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141244315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring deep learning for landslide mapping: A comprehensive review","authors":"Zhi-qiang Yang ,&nbsp;Wen-wen Qi ,&nbsp;Chong Xu ,&nbsp;Xiao-yi Shao","doi":"10.31035/cg2024032","DOIUrl":"https://doi.org/10.31035/cg2024032","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 330-350"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001137/pdfft?md5=d8721cc38a91ecb2b6fe4880aa3517ba&pid=1-s2.0-S2096519224001137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term displacement prediction for newly established monitoring slopes based on transfer learning","authors":"Yuan Tian ,&nbsp;Yang-landuo Deng ,&nbsp;Ming-zhi Zhang ,&nbsp;Xiao Pang ,&nbsp;Rui-ping Ma ,&nbsp;Jian-xue Zhang","doi":"10.31035/cg2024053","DOIUrl":"https://doi.org/10.31035/cg2024053","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 351-364"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001149/pdfft?md5=569962cd60bf7788418de8351bf60902&pid=1-s2.0-S2096519224001149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon emission reduction: Contribution of photovoltaic power and practice in China","authors":"Liang Wang ,&nbsp;Li-qiong Jia ,&nbsp;Geng Xie ,&nbsp;Xi-jie Chen ,&nbsp;Yang Liu","doi":"10.31035/cg2024078","DOIUrl":"https://doi.org/10.31035/cg2024078","url":null,"abstract":"","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 371-380"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001174/pdfft?md5=84cbd19efe8e89e090c1f4b32e2fa8c4&pid=1-s2.0-S2096519224001174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial structural characteristics of the Deda ancient landslide in the eastern Tibetan Plateau: Insights from Audio-frequency Magnetotellurics and the Microtremor Survey Method","authors":"Zhen-dong Qiu ,&nbsp;Chang-bao Guo ,&nbsp;Yi-ying Zhang ,&nbsp;Zhi-hua Yang ,&nbsp;Rui-an Wu ,&nbsp;Yi-qiu Yan ,&nbsp;Wen-kai Chen ,&nbsp;Feng Jin","doi":"10.31035/cg2023129","DOIUrl":"https://doi.org/10.31035/cg2023129","url":null,"abstract":"<div><p>It is of crucial importance to investigate the spatial structures of ancient landslides in the eastern Tibetan Plateau's alpine canyons as they could provide valuable insights into the evolutionary history of the landslides and indicate the potential for future reactivation. This study examines the Deda ancient landslide, situated in the Chalong-ranbu fault zone, where creep deformation suggests a complex underground structure. By integrating remote sensing, field surveys, Audio-frequency Magnetotellurics (AMT), and Microtremor Survey Method (MSM) techniques, along with engineering geological drilling for validation, to uncover the landslide's spatial features. The research indicates that a fault is developed in the upper part of the Deda ancient landslide, and the gully divides it into Deda landslide accumulation zone I and Deda landslide accumulation zone II in space. The distinctive geological characteristics detectable by MSM in the shallow subsurface and by AMT in deeper layers. The findings include the identification of two sliding zones in the Deda I landslide, the shallow sliding zone (DD-I-S1) depth is approximately 20 m, and the deep sliding zone (DD-I-S2) depth is 36.2–49.9 m. The sliding zone (DD-II-S1) depth of the Deda II landslide is 37.6–43.1 m. A novel MSM-based method for sliding zone identification is proposed, achieving less than 5% discrepancy in depth determination when compared with drilling data. These results provide a valuable reference for the spatial structural analysis of large-deep-seated landslides in geologically complex regions like the eastern Tibetan Plateau.</p></div>","PeriodicalId":45329,"journal":{"name":"China Geology","volume":"7 2","pages":"Pages 188-202"},"PeriodicalIF":4.5,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096519224001034/pdfft?md5=bed8c9f61aca111ac2e125392977e1b1&pid=1-s2.0-S2096519224001034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}