LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow
LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow
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LOC-FLOW：基于端到端机器学习的高精度地震定位工作流程
DOI：
10.1785/0220220019
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发表时间：
2022
影响因子：
3.3
通讯作者：
Weiqiang Zhu
中科院分区：
文献类型：
--
作者：
Miao Zhang;Min Liu;Tian Feng;Ruijia Wang;Weiqiang Zhu
不断增加的地震数据网络和数量推动了对无缝自动工作流程的需求，以实现快速准确的地震检测和定位。近年来，基于机器学习（ML）的拾取器在泛化方面取得了显着的准确性和效率，因此可以显着提高先前开发的顺序定位方法的地震定位精度。然而，多个软件包之间不一致的输入或输出（I/O）格式往往限制了它们的交叉应用。为了减少格式障碍，我们将广泛使用的ML相位拾取器-PhaseNet-与几种流行的地震定位方法相结合，并开发了一种“免提”端到端基于ML的定位工作流程（名为LOC-FLOW），该工作流程可直接应用于连续波形，并在本地和区域范围内构建高精度地震目录。更新后的开源软件包组装了几个顺序算法，包括地震初至拾取（PhaseNet和STA/LTA），相位关联（真实的），绝对位置（VELEST和HYPOINVERSE）和双差相对位置（hypoDD和GrowClust）。我们提供不同的定位策略和I/O接口进行格式转换，形成无缝的地震定位工作流程。可以灵活地选择和/或组合不同的算法。作为一个例子，我们应用LOC-FLOW 2004年9月28日MW 6.0帕克菲尔德，加州，地震序列。LOC-FLOW完成了16天连续地震数据的地震相位拾取、关联、速度模型更新、台站校正、绝对定位和双差重定位。我们检测并定位了3.7次（即，4357）与来自北方加州地震数据中心的互相关双差位置的地震一样多。我们的研究表明，LOC-FLOW能够从连续的地震数据中高效无缝地构建高精度的地震目录。
The ever-increasing networks and quantity of seismic data drive the need for seamless and automatic workflows for rapid and accurate earthquake detection and location. In recent years, machine learning (ML)-based pickers have achieved remarkable accuracy and efficiency with generalization, and thus can significantly improve the earthquake location accuracy of previously developed sequential location methods. However, the inconsistent input or output (I/O) formats between multiple packages often limit their cross application. To reduce format barriers, we incorporated a widely used ML phase picker—PhaseNet—with several popular earthquake location methods and developed a “hands-free” end-to-end ML-based location workflow (named LOC-FLOW), which can be applied directly to continuous waveforms and build high-precision earthquake catalogs at local and regional scales. The renovated open-source package assembles several sequential algorithms including seismic first-arrival picking (PhaseNet and STA/LTA), phase association (REAL), absolute location (VELEST and HYPOINVERSE), and double-difference relative location (hypoDD and GrowClust). We provided different location strategies and I/O interfaces for format conversion to form a seamless earthquake location workflow. Different algorithms can be flexibly selected and/or combined. As an example, we apply LOC-FLOW to the 28 September 2004 Mw 6.0 Parkfield, California, earthquake sequence. LOC-FLOW accomplished seismic phase picking, association, velocity model updating, station correction, absolute location, and double-difference relocation for 16-day continuous seismic data. We detected and located 3.7 times (i.e., 4357) as many as earthquakes with cross-correlation double-difference locations from the Northern California Earthquake Data Center. Our study demonstrates that LOC-FLOW is capable of building high-precision earthquake catalogs efficiently and seamlessly from continuous seismic data.