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

Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation function(NCCF)has been widely adopted by using daily waveforms.However,it is still challenging to detect the shortterm clock drift and overcome the influence of local noise on NCCF.To address these challenges,we conduct a study on two temporary datasets,including an ocean-bottom-seismometer(OBS)dataset from the southern Mariana subduction zone and a dataset from a temporary dense network from the Weiyuan shale gas field,Sichuan,China.We first inspect the teleseismic and local event waveforms to evaluate the overall clock drift and data quality for both datasets.For the OBS dataset,NCCF using different time segments(3,6,and 12-h)beside daily waveforms data is computed to select the data length with optimal detection capability.Eventually,the 6-h segment is the preferred choice with high detection efficiency and low noise level.For the land dataset,higher drift detection is achieved by NCCF using the daily long waveforms.Meanwhile,we find that NCCF symmetry on the dense array is highly influenced by localized intense noise for large interstation distances(>1 km)but is well preserved for short interstation distances.The results have shown that the use of different segments of daily waveform data in the OBS dataset,and the careful selection of interstation distances in the land dataset substantially improved the NCCF results.All the clock drifts in both datasets are successfully corrected and verified with waveforms and NCCF.The newly developed strategies using short-segment NCCF help to overcome the existing issues to correct the clock drift of seismic data.

Spatial-temporal evolution of injection-induced earthquakes in the Weiyuan Area determined by machine-learning phase picker and waveform cross-correlation

Anthropogenic induced seismicity has been widely reported and investigated in many regions,including the shale gas fields in the Sichuan basin,where the frequency of earthquakes has increased substantially since the commencement of fracking in late 2014.However,the details of how earthquakes are induced remain poorly understood,partly due to lack of high-resolution spatial-temporal data documenting the evolution of such seismic events.Most previous studies have been based on a diffusive earthquake catalog constructed by routine methods.Here,however,we have constructed a high resolution catalog using a machine learning detector and waveform cross-correlation.Despite limited data,this new approach has detected one-third more earthquakes and improves the magnitude completeness of the catalog,illuminating the comprehensive spatial-temporal migration of the emerging seismicity in the target area.One of the clusters clearly delineates a potential unmapped fault trace that may have led to the Mw 5.2 in September 2019,by far the largest earthquake recorded in the region.The migration of the seismicity also demonstrates a pore-pressure diffusion front,suggesting additional constraints on the inducing mechanism of the region.The patterns of the highly clustered seismicity reconcile the causal link between the emerging seismicity and the activity of hydraulic fracturing in the region,facilitating continued investigation of the mechanisms of seismic induction and their associated risks.