查看更多>>摘要：Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities. An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on August 8, 2017, which was served as material source for debris flow in later years. Debris flow appears frequently which are seriously endangering the safety of people's lives and properties. Even the earliest debris flow appeared in areas where no case ever reported before. The debris flow susceptibility evaluation (DFSE) is used for predicting the areas prone to debris flow, which is urgently required to avoid hazards and help to guide the strategy of preventive measures. Therefore, this work employs debris flow in Jiuzhaigou to reveal the characteristics of disaster-pregnant environment and to explore the application of machine learning in DFSE. Some new viewpoints are suggested: (i) Material density factor of debris flow is first adopted in this work, and it is proved to be a critical factor for triggering debris flows by sensitivity analysis method. (ii) Deep neural network and convolutional neural network (CNN) achieve relatively good area under the curve (AUC) values and are 0.021-0.024 higher than traditional machine learning methods. (iii) Watershed units combined with CNN-based model can achieve more accurate, reliable and practical susceptibility map. This work provides an idea for prevention of debris flow in mountainous lands.
查看更多>>摘要：The Chinese Loess Plateau, a region of remarkable ecological and economic value, grapples with significant water management challenges due to its distinctive geology and climate. This perspective offers a short review of the eco-environmental protection measures undertaken in the Loess Plateau, underscoring the transformative impacts of initiatives such as the "Grain for Green" project. However, it also highlights the enduring challenges, including land degradation, water resources issues, socio-economic inequities, and the implications of climate change. Particularly, water management emerges as a pivotal issue with far-reaching repercussions for soil conservation, biodiversity, and human livelihoods. The paper concludes by proposing future actions, emphasizing the necessity for policy modifications, novel initiatives, and research to tackle these challenges and foster sustainable development in the Loess Plateau. The insights gained from this region could offer invaluable lessons for other regions confronted with similar challenges, thereby contributing to global efforts to mitigate desertification and champion sustainable development.
查看更多>>摘要：Backscatter electron analysis from scanning electron microscopes (BSE-SEM) produces high-resolution image data of both rock samples and thin-sections, showing detailed structural and geochemical (mineralogical) information. This allows an in-depth exploration of the rock microstructures and the coupled chemical characteristics in the BSE-SEM image to be made using image processing techniques. Although image processing is a powerful tool for revealing the more subtle data "hidden" in a picture, it is not a commonly employed method in geoscientific microstructural analysis. Here, we briefly introduce the general principles of image processing, and further discuss its application in studying rock microstructures using BSE-SEM image data.
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