Generalized Seismic Phase Detection with Deep Learning

震级(天文学) 计算机科学 地震学 安静的 波形 卷积神经网络 深度学习 噪音(视频) 集合(抽象数据类型) 航程(航空) 数据集 地震模拟 灵敏度(控制系统) 地质学 相(物质) 匹配(统计) 人工智能 物理 电信 工程类 统计 图像(数学) 航空航天工程 量子力学 数学 程序设计语言 电子工程 雷达 天文
作者
Zachary E. Ross,Men‐Andrin Meier,Egill Hauksson,Thomas H. Heaton
出处
期刊:Bulletin of the Seismological Society of America [Seismological Society of America]
卷期号:108 (5A): 2894-2901 被引量:302
标识
DOI:10.1785/0120180080
摘要

To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a century ago. Recently, it has become possible to search continuous waveform archives for replicas of previously recorded events (template matching), which has led to at least an order of magnitude increase in the number of detected earthquakes and greatly sharpened our view of geological structures. Earthquake catalogs produced in this fashion, however, are heavily biased in that they are completely blind to events for which no templates are available, such as in previously quiet regions or for very large magnitude events. Here we show that with deep learning we can overcome such biases without sacrificing detection sensitivity. We trained a convolutional neural network (ConvNet) on the vast hand-labeled data archives of the Southern California Seismic Network to detect seismic body wave phases. We show that the ConvNet is extremely sensitive and robust in detecting phases, even when masked by high background noise, and when the ConvNet is applied to new data that is not represented in the training set (in particular, very large magnitude events). This generalized phase detection (GPD) framework will significantly improve earthquake monitoring and catalogs, which form the underlying basis for a wide range of basic and applied seismological research.
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