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| 1 | Template matching for simple waveforms with low signal-to-noise ratio and its application to icequake detection显示文摘Template matching is a useful method to detect seismic events through waveform similarity between two signals.The traditional template matching method works well in detecting small tectonic earthquakes.However,the method has some difficulty when the signals have relatively low signal-to-noise ratios(SNRs)and simple shapes,e.g.a sinusoidal function.In this study,we modify the traditional template matching approach for this situation.We first construct a virtual three-component seismic station using vertical-component waveforms recorded by three stations.Next,we select a template event from the virtual station,and apply the traditional template matching.We then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No.1 and compare the results with those from the short-term-averages over long-term-average(STA/LTA),the REST method,and traditional template matching method.It can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs. | Haichao Ma Risheng Chu Minhan Sheng Ziye Yu | 2020 | Earthquake Science2020，33，5： | 3 |
| 2 | Poisson's ratios and S-wave velocities of the Xishancun landslide,Sichuan,China,inferred from high-frequency receiver functions of local earthquakes显示文摘Landslides are recurrent geological phenomena on Earth that cause heavy casualties and property losses annually.In this study,we use the V_(p)-k stacking and nonlinear waveform inversion methods of high-frequency receiver functions extracted from local earthquakes,to sequentially invert Poisson’s ratios and S-wave velocities of the Quaternary Xishancun landslide,which is composed of three segments,i.e.,h1,h2,and h3 from bottom to top.Our results show that Poisson’s ratio values are generally higher than 0.33 and that the S-wave velocities vary from 0.1 to 0.9 km s^(-1).High Poisson’s ratios(>0.44)are mainly distributed in the juncture regions between different segments,as well as the western edge of h2.These zones show significant variation in landslide thickness and are potentially hazardous areas.Low velocities of 0.05–0.2 km s^(-1)with thicknesses of 10–30m are widely observed in the lower layer of the landslide.The high Poisson’s ratios and low-velocity layer may be related to water-rich materials in these areas.Our study suggests that the high-frequency receiver functions from local earthquakes can be used to delineate geotechnical structures,which is valuable for landslide stability analysis and hazard mitigation. | Zigen WEI Risheng CHU Zhiwei LI Minhan SHENG Qiu ZENG | 2021 | Science China Earth Sciences2021，64，7： | 3 |
| 3 | Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks,Sichuan,China显示文摘Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences.Increasing seismicity in shale gas blocks of the Sichuan Basin,China,has presented a serious challenge to monitoring and managing the seismicity itself.In this study,to detect events we apply a machine-learning-based phase picker(PhaseNet)to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks(SGB).Both P-and S-phases are picked and associated for location.We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model.Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog.Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center(CENC),which used only the sparsely distributed permanent stations.We also measure the local magnitude and achieve magnitude completeness of ML0.We relocate clusters of events,showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks.Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network.The algorithms can facilitate rapid characterization of earthquake sequences. | PengCheng Zhou William L.Ellsworth HongFeng Yang Yen Joe Tan Gregory C.Beroza MinHan Sheng RiSheng Chu | 2021 | Earth and Planetary Physics2021，5，6： | 1 |
| 4 | Causative fault and seismogenic mechanism of the 2010 Suining M5.0 earthquake from joint modeling of seismic and InSAR data显示文摘Although the Sichuan basin is a stable block with low historical seismicity,the Suining M5.0 earthquake on January31,2010 occurred near the center of the basin,causing casualty and substantial damage.Previous studies have shown that the earthquake is very shallow and may occur in the sedimentary cover rocks,but its causative fault has not been identified.Based on local broadband seismic waveform data as well as a pair of ALOS PALSAR ascending orbit data,we explore the seismogenic mechanism via further constraining the source depth and the ruptured fault.The earthquake caused ground uplift in the southeast of the epicenter area,with a maximum line of sight displacement of about 13.6 cm,much larger than the displacement caused by a M5 earthquake at a typical depth of 10 km,which indicates that the earthquake is very shallow.Through joint inversion of seismic waveform and InSAR data,we obtain the moment magnitude of Suining earthquake as MW4.5,with the strike,dip,and rake of its fault plane as 17°,66° and 90°,respectively,and the centroid depth less than 1 km,supporting that the earthquake occurred at the shallow part of a high angle thrust fault dipping to the southeast.It is further confirmed that the earthquake may be triggered by the diffusion of high-pressure fluid migrating from the underside gas reservoir. | Wangwang GU Sidao NI Shuofan WANG Baolong ZHANG Xinglin LEI Risheng CHU Aizhi GUO Qiang SHEN Hansheng WANG Liming JIANG Minhan SHENG Jiajun CHONG | 2023 | Science China Earth Sciences2023，66，8： | 0 |
| 5 | CRPN: A cascaded classification and regression DNN framework for seismic phase picking显示文摘Current deep neural networks(DNN)used for seismic phase picking are becoming more complex,which consumes much computing time without significant accuracy improvement.In this study,we introduce a cascaded classification and regression framework for seismic phase picking,named as the classification and regression phase net(CRPN),which contains two convolutional neural network(CNN)models with different complexity to meet the requirements of accuracy and efficiency.The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0,respectively.The second stage of CRPN is used for high precision classification and regression.The regression is designed to reduce the time difference between the probability maximum and the real arrival time.After being trained using 500,000 P and S phases,the CRPN can process 400 hours’seismic data per second,whose sampling rate is 1 Hz and 25 Hz for the two stages,respectively,on a Nvidia K2200 GPU,and pick 93%P and 89%S phases with the error being reduced by 0.1s after regression correction. | ZiyeYu Risheng Chu Weitao Wang Minhan Sheng | 2020 | Earthquake Science2020，33，2： | 0 |
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