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

doi:10.1029/2020GL088168

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 journal › Article › peer-review

16 Scopus citations

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 M_L −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 language	English (US)
Article number	e2020GL088168
Journal	Geophysical Research Letters
Volume	47
Issue number	14
DOIs	https://doi.org/10.1029/2020GL088168
State	Published - Jul 28 2020
Externally published	Yes

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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10.1029/2020GL088168

Cite this

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

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.",

keywords = "earthquake detection and location, focal mechanism, induced seismicity, machine-learning, nodal array, statistical analysis",

author = "Ruijia Wang and Brandon Schmandt and Miao Zhang and Margaret Glasgow and Eric Kiser and Sarah Rysanek and Ryan Stairs",

note = "Funding Information: The Vermejo Park Ranch, especially staff member Sara Holm, facilitated land access. Justin Wilgus collected the well injection data, which can be accessed from The Oil Conservation Division of the New Mexico Energy Minerals and Natural Resources Department. This research was supported by NSF EAR‐1554908. Funding Information: The Vermejo Park Ranch, especially staff member Sara Holm, facilitated land access. Justin Wilgus collected the well injection data, which can be accessed from The Oil Conservation Division of the New Mexico Energy Minerals and Natural Resources Department. This research was supported by NSF EAR-1554908. Publisher Copyright: {\textcopyright}2020. American Geophysical Union. All Rights Reserved.",

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AU - Zhang, Miao

AU - Glasgow, Margaret

AU - Kiser, Eric

AU - Rysanek, Sarah

AU - Stairs, Ryan

N1 - Funding Information: The Vermejo Park Ranch, especially staff member Sara Holm, facilitated land access. Justin Wilgus collected the well injection data, which can be accessed from The Oil Conservation Division of the New Mexico Energy Minerals and Natural Resources Department. This research was supported by NSF EAR‐1554908. Funding Information: The Vermejo Park Ranch, especially staff member Sara Holm, facilitated land access. Justin Wilgus collected the well injection data, which can be accessed from The Oil Conservation Division of the New Mexico Energy Minerals and Natural Resources Department. This research was supported by NSF EAR-1554908. Publisher Copyright: ©2020. American Geophysical Union. All Rights Reserved.

PY - 2020/7/28

Y1 - 2020/7/28

N2 - 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.

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

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Keywords

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