Description: Foreshocks could provide valuable information for seismogenic process. Similar to aftershocks and swarms, foreshocks are usually clustered in time and space, which could result in low magnitude earthquakes being overwhelmed by the coda waves of other earthquakes. In addition, the limited observation condition could result in the loss of small earthquakes in local catalog. The waveform correlation technique has been proved to be an effective method to detect missing small earthquakes and widely used in foreshock and aftershock sequence detection. On January 19, 2020, the M〈sub〉S〈/sub〉6.4 earthquake occurred in Jiashi, Xinjiang. About 45 hours before the earthquake, the M〈sub〉S〈/sub〉5.4 earthquake occurred in the focal area, which formed a typical foreshock sequence. In this study, we collected the continuous waveforms between December 1, 2019 and January 31, 2020 from 6 stations located within 100 km of the mainshock. With 791 relocated earthquakes (including 33 foreshocks) as templates, 4664 earthquakes (including 261 foreshocks) were detected and located by the Match Locate method. The complete magnitude of the catalog was reduced from M〈sub〉L〈/sub〉1.6 to M〈sub〉L〈/sub〉1.0. With the enhanced catalog, we observe that the foreshock sequence has lower b-value than aftershocks and lacks small-magnitude (M〈sub〉L〈/sub〉〈0.3) events, which are generally consistent with other studies. According to the spatial and temporal evolution of the earthquake sequence, seismicity becomes relative quiet within 26 days before the Jiashi M〈sub〉S〈/sub〉5.4 earthquake and 2 days before the mainshock. In addition, the earthquakes during the two days before the mainshock migrated to the mainshock epicenter.

Description: Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors. This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets, to address cases when partial overlaps and/or temporal gaps exist. A new Power Exponential Knothe Model (PEKM) fits and fuses overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses any temporal gaps in the series. Taking the city of Wuhan (China) as experiment area, COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019) and Sentinel-1 (2019–2021) SAR datasets were fused to map long-term surface deformation over the last decade. An independent 2011–2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison. The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The correlation coefficient of the overall results is 0.78, which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve. The experimental results are consistent with existing studies of surface deformation at Wuhan, demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.