4.5 Article

DynTriPy: A Python Package for Detecting Dynamic Earthquake Triggering Signals

Journal

SEISMOLOGICAL RESEARCH LETTERS
Volume 92, Issue 1, Pages 543-554

Publisher

SEISMOLOGICAL SOC AMER
DOI: 10.1785/0220200216

Keywords

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Funding

  1. National Natural Science Foundation of China [42074046]
  2. National Key R&D Program of China [2018YFC1503400]
  3. China Earthquake Science Experiment Project, China Earthquake Administration (CEA) [2017CESE0103, 2018CSES0102, 2019CSES0107]
  4. Hong Kong Special Administrative Region (HKSAR) Research Grant Council General Research Fund (GRF) [14305617]
  5. Chinese University of Hong Kong (CUHK) Direct Grant from the Faculty of Science, State Key Lab of Earthquake Dynamics, Institute of Geology, CEA [LED2017B07]

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DynTriPy is a robust Python package that automatically detects dynamic triggering signals, aiming to better understand earthquake interaction mechanisms and assess seismic hazards. It efficiently implements an algorithm suitable for processing big data and suppressing noise and background seismic activity changes. By processing data from multiple stations in parallel, it enables global monitoring of earthquake triggering.
Long-term and large-scale observations of dynamic earthquake triggering are urgently needed to understand the mechanism of earthquake interaction and assess seismic hazards. We developed a robust Python package termed DynTriPy to automatically detect dynamic triggering signals by distinguishing anomalous seismicity after the arrival of remote earthquakes. This package is an efficient implementation of the high-frequency power integral ratio algorithm, which is suitable for processing big data independent of earthquake catalogs or subjective judgments and can suppress the influence of noise and variations in the background seismicity. Finally, a confidence level of dynamic triggering (0-1) is statistically yielded. DynTriPy is designed to process data from multiple stations in parallel, taking advantage of rapidly expanding seismic arrays to monitor triggering on a global scale. Various data formats are supported, such as Seismic Analysis Code, mini Standard for Exchange of Earthquake Data (miniSEED), and SEED. To tune parameters more conveniently, we build a function to generate a database that stores power integrals in different time and frequency segments. All calculation functions possess a high-level parallel architecture, thoroughly capitalizing on available computational resources. We output and store the results of each function for continuous operation in the event of an unexpected interruption. The deployment of DynTriPy to data centers for real-time monitoring and investigating the sudden activation of any signal within a certain frequency scope has broad application prospects.

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