Injection-Induced Earthquakes on Complex Fault Zones of the Raton Basin Illuminated by Machine-Learning Phase Picker and Dense Nodal Array

Ruijia Wang, Brandon Schmandt, Miao Zhang, Margaret Glasgow, Eric Kiser, Sarah Rysanek, Ryan Stairs

Research output: Contribution to journalArticle

Abstract

Seismicity in the Raton Basin over the past two decades suggests reactivation of basement faults due to waste-water injection. In the summer of 2018, 96 short period three-component nodal instruments were installed in a highly active region of the basin for a month. A machine-learning based phase picker (PhaseNet) was adopted and identified millions of picks, which were associated into events using an automated algorithm—REAL (Rapid Earthquake Association and Location). After hypocenter relocation with hypoDD, the earthquake catalog contains 9,259 ML −2.2 to 3 earthquakes focused at depths of 4–6 km. Magnitude of completeness (Mc) varies from −1 at nighttime to −0.5 in daytime, likely reflecting noise variation modulated by wind. The clustered hypocenters with variable depths and focal mechanisms suggest a complex network of basement faults. Frequency-magnitude statistics and the spatiotemporal evolution of seismicity are comparable to tectonic systems.

Original languageEnglish (US)
Article numbere2020GL088168
JournalGeophysical Research Letters
Volume47
Issue number14
DOIs
StatePublished - Jul 28 2020

Keywords

  • earthquake detection and location
  • focal mechanism
  • induced seismicity
  • machine-learning
  • nodal array
  • statistical analysis

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)

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