应用 XGBoost 模型从波形数据中提前预测地震震级

IF 1.9 4区地球科学 Q2 Earth and Planetary Sciences

Journal of Earth System Science Pub Date : 2023-12-22 DOI:10.1007/s12040-023-02210-1

Anushka Joshi, Vishnu Chalavadi, C. K. Mohan, Balasubramanian Raman

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Application of XGBoost model for early prediction of earthquake magnitude from waveform data

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来源期刊

Journal of Earth System Science 地学-地球科学综合

CiteScore

3.00

自引率

5.30%

发文量

226

审稿时长

6 months

期刊介绍： The Journal of Earth System Science, an International Journal, was earlier a part of the Proceedings of the Indian Academy of Sciences – Section A begun in 1934, and later split in 1978 into theme journals. This journal was published as Proceedings – Earth and Planetary Sciences since 1978, and in 2005 was renamed ‘Journal of Earth System Science’. The journal is highly inter-disciplinary and publishes scholarly research – new data, ideas, and conceptual advances – in Earth System Science. The focus is on the evolution of the Earth as a system: manuscripts describing changes of anthropogenic origin in a limited region are not considered unless they go beyond describing the changes to include an analysis of earth-system processes. The journal''s scope includes the solid earth (geosphere), the atmosphere, the hydrosphere (including cryosphere), and the biosphere; it also addresses related aspects of planetary and space sciences. Contributions pertaining to the Indian sub- continent and the surrounding Indian-Ocean region are particularly welcome. Given that a large number of manuscripts report either observations or model results for a limited domain, manuscripts intended for publication in JESS are expected to fulfill at least one of the following three criteria. The data should be of relevance and should be of statistically significant size and from a region from where such data are sparse. If the data are from a well-sampled region, the data size should be considerable and advance our knowledge of the region. A model study is carried out to explain observations reported either in the same manuscript or in the literature. The analysis, whether of data or with models, is novel and the inferences advance the current knowledge.