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引用次数: 0
摘要
解决地震预测问题面临着理论和实践两方面的困难。原因是地震的发生取决于许多因素,而这些因素又会产生各种异常现象,这些异常现象可作为地震的前兆。然而,由于地震发生过程的复杂性和地壳详细结构信息的缺乏,只有少数异常现象能够准确预示未来的地震事件。机器学习和深度学习的应用成果让人们看到了希望,如果将不同的因素结合起来,就有可能获得更准确的未来强震信息。为了确定地震最重要的征兆,并确定乌兹别克斯坦特定地震活跃地区(即费尔干纳洼地、科拉3号、科拉4号)强震的空间位置,实施了机器学习的随机森林算法和 LSTM、深度学习的 ANN 架构。
EARTHQUAKE PREDICTION USING ARTIFICIAL INTELLIGENCE IN THE FERGHANA DEPRESSION (UZBEKISTAN)
Solving the problem of predicting earthquakes faces difficulties of both theoretical and practical nature. The reason is that the occurrence of earthquakes depends on many factors, which give rise to various anomalies that are used as precursors. However, because of the complexity of the earthquake process and the unavailability of much information about the detailed structure of the Earth's crust, a small number of them can accurately indicate future seismic events. The results of the application of machine learning and deep learning give hope for the possibility of obtaining more accurate information about future strong earthquakes if disparate factors are combined. To determine the most important signs of an earthquake and determine the spatial location of strong earthquakes in a specific seismically active territory of Uzbekistan, namely, in the Fergana depression, the Cora 3, Cora 4, random forest algorithms of machine learning and LSTM, ANN architectures of deep learning were implemented.
期刊介绍:
Earthquake is a comprehensive academic journal hosted by the China Earthquake Network Centre (CENC) and edited by Mr. Wang Haitao, Director of CENC and a senior researcher. The purpose of the journal is to exchange research results on earthquake observation, earthquake precursors and strong earthquake mechanism and prediction, and to promote the exploration and research on earthquake forecasting and the application of its results in earthquake prevention and disaster reduction. The readers are mainly earthquake scientists and technicians engaged in the research of earthquake observation and analysis, earthquake precursor exploration, earthquake mechanism and prediction, as well as scientists and technicians in related scientific and technological fields.
Inclusion:
The Chinese core periodicals in the general list of the core periodicals
Chinese Science Citation Database (CSCD) Source Journals
Scopus (Netherlands) Source Journals
Japan Science and Technology Literature Database (JST) source journals