Evaluating and correcting short-term clock drift in data from temporary seismic deployments

Temporary seismic network deployments are quite common both in land and offshore. The acquired data have significantly helped improve our understanding of earthquake processes and internal structure of the Earth. However, some temporary stations, especially these all-in-one units without external GPS timing system, suffer from incorrect timing record and thus pose a challenge to fully utilize the valuable data. To inspect and fix such time problems, ambient noise cross-correlation function (NCCF) is widely adopted by using daily waveforms. However, it is difficult to identify short-term time drift after stacking the NCCF output for several days to months. To detect such clock errors, travel times of local and distant earthquakes are utilized along with NCCF. We apply such a strategy on an Ocean Bottom Seismograph (OBS) dataset from southern Mariana subduction zone and a dataset from a temporary dense network from Weiyuan shale gas field, Sichuan, China. By inspecting travel times from local and distant events, we identify a very short-term clock drift (~25 sec) on the OBS data that was not detectable using NCCF only. To overcome the problem, short segments (3, 6, 12 hours) of daily wavefrom data is inspected as clock errors become stable within the selected segments. In addition, the data quality is carefully inspected with impact of different interstation distance and period band on NCCF. In particular, we find that the 6-hour segment with a period band of 2-5 sec is able to detect and correct short term changes, including linear drift. For the dense array data, we observe that NCCF symmetry is well-preserved for short interstation distance (within 1 km) but becomes distorted for larger interstation distances. Therefore, we split our dense array (79 stations) into 16 groups with a maximum interstation distance of 500 meters and 1-2 sec period band was selected after testing. Short data segments improve the time-drift detection efficiency in NCCF results, which is consistent for both local and distant events. In a nutshell, the carefull selection of data length and NCCF parameters can be helpful to identify and correct the time drift errors of temporary seismic stations.