L1 Trend Filtering‐Based Detection of Short‐Term Slow Slip Events: Application to a GNSS Array in Southwest Japan

Abstract

AbstractThe discovery of slow slip events (SSEs) based on the installation of dense geodetic observation networks has provided important clues to understanding the process of stress release and accumulation in subduction zones. Because SSEs with durations of days to weeks do not often result in sufficient displacements that can be visually inspected, refined automated detection methods are required to understand the occurrence of SSEs. In this study, we propose a new method based on which SSEs can be detected in observations derived by a Global Navigation Satellite System (GNSS) array by using l1 trend filtering, a variation of sparse estimation, in conjunction with combined ‐value techniques. The sparse estimation technique and data‐driven determination of hyperparameters are utilized in the proposed method to identify candidates of the event onsets. In addition, combined ‐value techniques are used to provide confidence values for the detections. The synthetic test demonstrated that the new method can detect 22 events of total 23 events and has only 1% false detections with the detection threshold 68%. The proposed method was then applied to daily displacements obtained at 39 GNSS stations in the Nankai subduction zone in western Shikoku, Southwest Japan. The results revealed that, in addition to all known events, new 12 events can be detected with the proposed method. Finally, we found the number of low‐frequency earthquakes in the target region increased around at the onsets of potential events.