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
detecting change-points in a piecewise 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 using 36 GPS stations in southwest Japan via the co-occurrence
of non-volcanic tremors, hypothesis tests and fault estimation.