Machine Learning‐Based New Earthquake Catalog Illuminates On‐Fault and Off‐Fault Seismicity Patterns at the Discovery Transform Fault, East Pacific Rise

Abstract

Oceanic transform faults connect spreading centers and are imprinted with previous tectonic events. However, their tectonic interactions are not well understood due to limited observations. The Discovery transform fault system at 4°S, East Pacific Rise (EPR), represents a young transform system, offering a unique opportunity to study the interplay between faulting and other tectonic events at an early phases of an oceanic transform system. Discovery regularly hosts M5–6 characteristic earthquakes, and the seafloor north of Discovery includes a 35 km‐long rift zone that records a complex history of rifting, faulting and volcanism, suggesting that the transform faults likely interact with regional tectonic activity. We apply a machine‐learning enabled workflow to locate 21,391 earthquakes recorded during a 1‐year ocean bottom seismometer experiment in 2008. Our results indicate that seismicity on the western Discovery fault is separated into seven patches with distinct aseismic and seismic slip modes. Additionally, we observe a patch of off‐fault seismicity near where seafloor abyssal hills intersect the rift zone. This seismicity may have been caused by varying opening rates as spreading rate decreases from north to south in the rift zone. Our findings suggest that the Discovery system is still evolving, and that system equilibrium has not been reached between rifting and faulting. These results reflect the complex yet rarely observed interactions between fault slip, plate rotation, and rifting which are likely ubiquitous at oceanic transform systems.

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

“…In recent years, deep-learning-based phase pickers/event detectors (e.g., Kriegerowski et al, 2019;Mousavi et al, 2020;Ross et al, 2018;Soto & Schurr, 2021; have been gaining increasing attention due to their picking accuracy being comparable to human analysts (Chai et al, 2020) and high efficiency. Their application has surged in recent years, including for delineating seismicity in fault zones, subduction zones, oceanic transform faults, and volcanoes (e.g., Chen et al, 2022;Garza-Girón et al, 2023;Gong et al, 2023;Jiang et al, 2022;Liu et al, 2023;Liu et al, 2024;Tan et al, 2021;Wilding et al, 2023;Zhang et al, 2024). However, it can be difficult to predict deep-learning models' performance for outof-distribution data that are not well represented by training data (Teney et al, 2022;Wenzel et al, 2022).…”