LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow

Creators: Zhang, Miao¹; Liu, Min¹; Feng, Tian^{1, 2}; Wang, Ruijia³; Zhu, Weiqiang⁴

1. Dalhousie University
2. China Earthquake Administration
3. Southern University of Science and Technology
4. California Institute of Technology

Abstract

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.

Copyright and License

Acknowledgement

The authors are grateful to the pioneers and research groups that developed the location packages and made them open access, which were cited accordingly in the main text. The authors thank the associate editor and two anonymous reviewers for their valuable comments. This work is supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN‐2019‐04297) and Ocean Frontier Institute Seed Fund (ALLRP‐559829‐20).

Data Availability

Waveform data, metadata, or data products for this study were accessed through the Northern California Earthquake Data Center (NCEDC), DOI: 10.7932/NCEDC. The maps in this article were made by the Generic Mapping Tools (Wessel et al., 2013) and the Matplotlib (Hunter, 2007). The newly developed FDTCC code is available at https://github.com/MinLiu19/FDTCC. The LOC‐FLOW is released and maintained at https://github.com/Dal-mzhang/LOC-FLOW. Detailed step‐by‐step explanations can be found in the CookBook and corresponding scripts in the package. All websites were last accessed in February 2022.

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