Injection‐Induced Earthquakes on Complex Fault Zones of the Raton Basin Illuminated by Machine‐Learning Phase Picker and Dense Nodal Array. Issue 14 (13th July 2020)

• Record Type: Journal Article
• Authors: Wang, Ruijia; Schmandt, Brandon; Zhang, Miao; Glasgow, Margaret; Kiser, Eric; Rysanek, Sarah; Stairs, Ryan
• Is Part Of: Geophysical research letters. Volume 47:Issue 14(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-13
• Subjects: machine‐learning -- nodal array -- induced seismicity -- focal mechanism -- earthquake detection and location -- statistical analysis; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2020GL088168
• Languages: English
• ISSNs: 0094-8276

Abstract

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. Plain Language Summary: Earthquakes induced by waste‐water injection are widely observed worldwide and have been occurring in the Raton Basin (located at the border of New Mexico and Colorado) for two decades. We deployed 96 short period seismic stations in the summer of 2018 to investigate the faults in the southern section of Raton Basin. Earthquake detection was performed with state‐of‐the‐art machine‐learning techniques, which led to a catalog with ~10, 000 earthquakes with magnitude ranging from −2.2 to 3. Clusters of earthquakes were investigated in detail. We found that the orientation of the faults varies within the study region and that induced earthquakes may exhibit spatial‐temporal‐magnitude clustering just like tectonic ones. Successful application of the automated catalog‐building workflow also sheds light on the power of "hands‐free" processing of large‐volume seismic data. Key Points: A machine‐learning phase picker and dense nodal array enabled location of ~10, 000 earthquakes in a month Hypocenter patterns and moment tensors vary among clusters, unveiling reactivation of complex basement faults Raton Basin seismicity exhibits frequency‐magnitude distribution and spatiotemporal evolution comparable to tectonic events