A new method for coseismic offset detection from GPS coordinate time series

Abstract

Currently, the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes. This is followed by the process of differencing the average GPS coordinate time series data, with a time interval of 3 to 5 days before and after the earthquake. In the face of the huge amount of GPS coordinate time series data today, the conventional approach of relying on earthquake catalog data to assist in obtaining coseismic offset signals has become increasingly burdensome. To address this problem, we propose a new method for automatically detecting coseismic offset signals in GPS coordinate time series without an extra earthquake catalog for reference. Firstly, we pre-process the GPS coordinate time series data for filtering out stations with significant observations missing and detecting and removing outliers. Secondly, we eliminate other signals and errors in the GPS coordinate time series, such as trend and seasonal signals, leaving the coseismic offset signals as the primary signal. The resulting coordinate time series is then modeled using the first-order difference and data stacking method. The modeling method enables automatic detection of the coseismic offset signals in the GPS coordinate time series. The aforementioned method is applied to automatically detect coseismic offset signals using simulated data and the Searles Valley GPS data in California, USA. The results demonstrate the efficacy of our proposed method, successfully detecting coseismic offsets from vast amounts of GPS coordinate time series data.