SSA2py: A High-Performance Python Implementation of the Source-Scanning Algorithm for Spatiotemporal Seismic Source Imaging

Abstract

Abstract This article introduces the first version of SSA2py (v.1.0)—an open-source package designed to implement the source-scanning algorithm (SSA). SSA2py is a Python-based, high-performance-oriented package that incorporates the SSA method, which has been effectively applied to numerous earthquakes for imaging the spatiotemporal behavior of the seismic source. The software supports a wide range of data and metadata resources. These include the International Federation of Digital Seismograph Networks Web Services, the SeedLink protocol, and others, ensuring optimal access to waveforms and station metadata. Furthermore, the code may evaluate the quality of accessible waveforms using signal analysis methods, allowing for the most appropriate data selection. The SSA method has been computationally optimized using multiprocessing techniques for efficient central processing unit and graphic processing units executions, enabling considerably accelerated computational processes even for large-scale grid searches. The program is also designed to provide statistical and methodological uncertainties for the executed cases through jackknife, bootstrap, and backprojection array response function tests. After appropriate tuning by the user, SSA2py can be used for detailed earthquake source studies that backprojection technique typically serves as a complementary output to the source inversion result or as a near-real-time tool for successful and quick identification of the style and complexity of the earthquake rupture. With a wide and flexible configuration, the user has complete control over all calculating aspects of SSA2py. This article provides a detailed description of the structure and capabilities of this new package, and its reliability is demonstrated through targeted applications to the 2004 Mw 6.0 Parkfield and 2019 Mw 7.1 Ridgecrest earthquakes. Furthermore, the computational efficiency of SSA2py is validated through rigorous performance tests.