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arXiv 2408.02129physics.geo-ph
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地震 magnitude 依赖于地震历史,如神经网络分析所揭示

Earthquake magnitudes depend on seismic history, as revealed by a neural network analysis

Tel-Aviv University(特拉维夫大学);Google Research, Google(谷歌研究,谷歌);The Hebrew University of Jerusalem(耶路撒冷希伯来大学)

Neri Berman, Oleg Zlydenko, Oren Gilon, Yossi Matias, Yohai Bar-Sinai

AI总结:

本文通过神经网络分析证明地震 magnitude 可由历史地震数据预测,提出 MAGNET 模型在加州、日本和新西兰数据中实现 0.07 bit 的信息增益。

摘要:

地震发生 notoriously 难以预测。虽然某些时空统计特征可由点过程模型较好捕捉,但未来事件 magnitude 的知识甚少,且深究是否能提前预测 magnitude。大多数操作模型假设地震 magnitude 遵循时间不变的 Gutenberg-Richter 分布,将 magnitude 视为与地震历史无关。本文通过证明标准震中目录包含未来地震 magnitude 信息,挑战传统分离假设,并提出 MAGNET 模型,利用多编码器神经网络与 LSTM 单元处理地震历史时空模式,生成概率 magnitude 预测,显示比 GR 模型有信息增益,控制检测偏差后仍具优势。

英文摘要:

Earthquake occurrence is notoriously difficult to predict. While some aspects of their spatiotemporal statistics can be relatively well captured by point-process models, very little is known regarding the magnitude of future events, and it is deeply debated whether it is possible to predict the magnitude of an earthquake before it starts. Most operational forecasting models assume that earthquake magnitudes follow a time-independent Gutenberg-Richter (GR) distribution, effectively treating magnitudes as independent of seismic history. We address this fundamental question by demonstrating that standard hypocenter catalogs carry information about future earthquake magnitudes, making them more predictable than previously considered. We present MAGNET (MAGnitude Neural EsTimation model), which uses a multi-encoder neural network architecture with LSTM units to process spatiotemporal patterns in seismic history. By analyzing hypocenter locations, occurrence times, and magnitudes of past events, MAGNET generates probabilistic magnitude forecasts that demonstrate information gains in predicting magnitudes of future events over GR-based models, after controlling for detection artifacts. Our model achieves an information gain of approximately 0.07 bit per earthquake on average over the GR benchmark in Southern California, Japan, and New Zealand catalogs, with this advantage persisting. These results demonstrate that hypocentral earthquake catalogs contain extractable information about future magnitudes, challenging the conventional separability assumption in earthquake forecasting and offering new approaches for seismic hazard assessment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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