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Searching for Microseismic Precursors to the July 2020 Mw 7.8 Simeonof, Alaska Earthquake in a Machine-Learning Enhanced Catalog

Description: 

Examining patterns of small earthquakes within subduction zones has the potential to provide insights into the physical processes that control the initiation of great (magnitude M > 7) megathrust earthquakes. The use of machine learning has enhanced our ability to detect these small events. This study explores offshore seismic activity in the months leading up to the 22 July 2020 M_w 7.8 Simeonof earthquake in the Alaska-Aleutian Subduction Zone (AASZ) by using a locally-trained machine learning picking model to detect and catalog small seismicity. Detecting small earthquakes in subduction zones presents a challenge due to their offshore location, where land-based seismic networks have low sensitivity. Fortunately, recent advances in machine learning-driven detection methods enable the creation of more-complete seismic catalogs compared with traditional methods, even for offshore seismicity. We use an EQTransformer picking model trained on 6,764 earthquakes (~123,000 waveforms) recorded on land by the Alaska Amphibious Community Seismic Experiment to construct an earthquake catalog spanning October 2019 to July 2020. Using match filter, we will further expand the catalog, lowering the magnitude of completeness. Looking forward, this catalog, along with an improved understanding of earthquake locations in the AASZ (see Nolan et al., this session), will enable us to analyze microseismicity patterns relative to mainshock hypocenters and asperities, and reveal fault zone processes in the AASZ leading up to a major seismic event.

Session: Structure and Behavior of the Alaska-Aleutian Subduction Zone [Poster Session]

Type: Poster

Date: 5/3/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Coralis

Student Presenter: Yes

Invited Presentation: 

Authors