A Silent Build-up in Seismic Energy Precedes Slow Slip Failure in the Cascadia Subduction Zone

TL;DR

This study identifies a seismic energy build-up pattern preceding slow slip failures in Cascadia, using machine learning to analyze seismic signals and reveal a cycle that resembles laboratory observations.

Contribution

The paper introduces a machine learning approach to detect seismic energy patterns that predict slow slip events in Cascadia, linking field data with laboratory findings.

Findings

Seismic energy patterns follow a 14-month cycle before failure.

A recurrent seismic energy build-up indicates impending slow slip events.

Patterns in seismic energy resemble laboratory slow slip observations.

Abstract

We report on slow earthquakes in Northern Cascadia, and show that continuous seismic energy in the subduction zone follows specific patterns leading to failure. We rely on machine learning models to map characteristic energy signals from low-amplitude seismic waves to the timing of slow slip events. We find that patterns in seismic energy follow the 14-month slow slip cycle. Our results point towards a recurrent build-up in seismic energy as the fault approaches failure. This behavior shares a striking resemblance with our previous observations from slow slips in the laboratory.