Detecting Slow Slip Events From Seafloor Pressure Data Using Machine Learning

Detecting slow slip events (SSEs) at offshore subduction zones is important to understand the slip behavior on offshore subduction megathrusts, where tsunamis can be generated. The most widely used method to detect SSEs is to measure the vertical seafloor deformation caused by SSEs using seafloor pressure data. However, due to the small signal-to-noise ratio and instrumental drift, such detection is very difficult. In this study, we trained a machine learning model using synthetic data to detect SSEs and applied it to real pressure data in New Zealand between 2014 and 2015. Our method detected five events, two of which are confirmed by the onshore GPS records. Besides, our model performs better than the traditional matched filter method. We conclude that machine learning could be used to detect SSEs in real seafloor pressure data. The method can be applied to other regions, especially where near trench GPS is not available.