SAIPy:利用深度学习进行单站地震监测的 Python 软件包

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Wei Li , Megha Chakraborty , Claudia Quinteros Cartaya , Jonas Köhler , Johannes Faber , Men-Andrin Meier , Georg Rümpker , Nishtha Srivastava
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引用次数: 0

摘要

近年来,随着深度学习方法在解决各种问题方面的应用,地震学取得了重大进展。这些技术已经展示了其从大量数据集中有效提取统计属性的卓越能力,在一定程度上超越了传统方法的能力。在本研究中,我们介绍了 SAIPy,这是一个开源 Python 软件包,专门用于通过实施深度学习快速处理地震数据。SAIPy 为多种地震学任务提供了解决方案,包括地震信号检测、地震相位拾取、初动极性识别和震级估计。我们介绍了 CREIME_RT 等以前发布的模型的升级版本,其识别地震的准确率超过 99.8%,震级估计的均方根误差为 0.38 单位。这些升级版模型的性能优于 Vision Transformer 网络等最先进的方法。SAIPy 提供了一个应用程序接口(API),可简化这些先进模型(包括 CREIME_RT、DynaPicker_v2 和 PolarCAP)与基准数据集的集成。据我们所知,它还引入了首个基于深度学习的全自动管道来处理连续波形。该软件包有望用于实时地震监测,以便及时采取行动减轻地震事件的影响。正在进行的开发工作旨在进一步提高 SAIPy 的性能,并纳入更多可增强勘探工作的功能。所有功能的详细说明见补充文件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SAIPy: A Python package for single-station earthquake monitoring using deep learning

Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open-source Python package specifically developed for fast seismic data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake signal detection, seismic phase picking, first motion polarity identification and magnitude estimation. We introduce upgraded versions of previously published models such as CREIME_RT capable of identifying earthquakes with an accuracy above 99.8% and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state-of-the-art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIME_RT, DynaPicker_v2, and PolarCAP, along with benchmark datasets. It also, to the best of our knowledge, introduces the first fully automated deep learning based pipeline to process continuous waveforms. The package has the potential to be used for real-time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to further enhance SAIPy’s performance and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem. A detailed description of all functions is available in a supplementary document.

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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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