Automated Detection of Short-term Slow Slip Events in Southwest Japan

TL;DR

This paper introduces SSAID, a new statistical method for automatically detecting short-term slow slip events in GPS data, improving early earthquake hazard assessment in southwest Japan.

Contribution

The study presents SSAID, a novel approach that effectively detects short-term SSEs by identifying change-points, outperforming existing methods in noisy synthetic and real GPS data.

Findings

SSAID accurately detects short-term SSEs in synthetic data.

SSAID outperforms existing detection methods in noisy conditions.

Application to real GPS data reveals its effectiveness in southwest Japan.

Abstract

Inferring from the occurrence pattern of slow slip events (SSEs) the probability of triggering a damaging earthquake within the nearby velocity weakening portion of the plate interface is critical for hazard mitigation. Although robust methods exist to detect long-term SSEs consistently and efficiently, detecting short-term SSEs remains a challenge. In this study, we propose a novel statistical approach, called singular spectrum analysis isolate-detect (SSAID), for automatically estimating the start and end times of short-term SSEs in GPS data. The method recasts the problem of detecting SSEs as that of identifying change-points in a piecewise non-linear signal. This is achieved by obscuring the deviation from piecewise-linearity in the underlying SSE signals using added noise. We verify its effectiveness on a range of model-generated synthetic SSE data with different noise levels, and demonstrate its superior performance compared to two existing methods. We illustrate its capability in detecting short-term SSEs in observed GPS data from 36 stations in southwest Japan via the co-occurrence of non-volcanic tremors, hypothesis tests and fault estimation.