Machine learning(ML)efficiently and accurately processes dense seismic array data,improving earthquake catalog creation,which is crucial for understanding earthquake sequences and fault systems;analyzing its reliabili...Machine learning(ML)efficiently and accurately processes dense seismic array data,improving earthquake catalog creation,which is crucial for understanding earthquake sequences and fault systems;analyzing its reliability is also essential.An M5.8 earthquake struck Alxa Left Banner,Inner Mongolia,China on April 15,2015,a region with limited CENC monitoring capabilities,making analysis challenging.However,abundant data from ChinArray provided valuable observations for assessing the event.This study leveraged ChinArray data from the 2015 Alxa Left Banner earthquake sequence,employing machine learning(specifically PhaseNet,a deep learning method,and GaMMA,a Bayesian approach)for automated seismic phase picking,association,and location analysis.Our generated catalog,comprising 10,432 phases from 708 events,is roughly ten times larger than the CENC catalog,encompassing all CENC events with strong consistency.A slight magnitude overestimation is observed only at lower magnitudes.Furthermore,the catalog adheres to the Gutenberg-Richter and Omori laws spatially,temporally,and in magnitude distribution,demonstrating its high reliability.Double-difference tomography refined locations for 366 events,yielding a more compact spatial distribution with horizontal errors within 100m,vertical errors within 300m,and travel-time residuals within 0.05s.Depths predominantly range from 10-30km.Aftershocks align primarily NEE,with the mainshock east of the aftershock zone.The near-vertical main fault plane dips northwestward,exhibiting a Y-shaped branching structure,converging at depth and expanding towards the surface.FOCMEC analysis,using first motion and amplitude ratios,yielded focal mechanism solutions for 10 events,including the mainshock.These solutions consistently indicate a strike-slip mechanism with a minor extensional component.Integrating the earthquake sequence's spatial distribution and focal mechanisms suggests the seismogenic structure is a negative flower structure,consistent with the Dengkou-Benjing fault.Comparing the CENC and ML-generated catalogs using the maximum curvature(MAXC)method reveals a 0.6 decrease in completeness magnitude(M_(C)).However,magnitude-frequency distribution discrepancies above the MAXC-estimated M_(C)suggest MAXC may underestimate both M_(C)and the b-value.This study analyzes the 2015 Alxa Left Banner M5.8 earthquake using a reliable,MLgenerated earthquake catalog,revealing detailed information about the sequence,faulting structure,aftershock distribution,and stress characteristics.展开更多
In this study, a classic survey adjustment computation method was used for data obtained in the Inner Mongolia and Ningxia gravimetric networks between September 2013 and April 2015 so as to investigate the variation ...In this study, a classic survey adjustment computation method was used for data obtained in the Inner Mongolia and Ningxia gravimetric networks between September 2013 and April 2015 so as to investigate the variation of gravity before the Alxa Zuoqi M5.8 earthquake. The relationship between gravity variation and the Alxa Zuoqi M5.8 earthquake was analyzed. The results showed that： （1） the severe variation in gravity field at the test sites before the Alxa Zuoqi M5.8 earthquake, as well as the subsequent accelerated rising, might be an earthquake precursor; （2） the Alxa Zuoqi M5.8 earthquake occurred at the turning point where the high-gravity gradient zone changed from the NE direction to NW.展开更多
基金funded by the Inner Mongolia Natural Science Foundation(No.2024MS04021)the Science and Technology Plan of Inner Mongolia Autonomous Region(No.2023YFSH0004)the Director Fund of the Inner Mongolia Autonomous Region Seismological Bureau(No.2023GG01,No.2023GG02,No.2023MS05,No.2023QN13)。
文摘Machine learning(ML)efficiently and accurately processes dense seismic array data,improving earthquake catalog creation,which is crucial for understanding earthquake sequences and fault systems;analyzing its reliability is also essential.An M5.8 earthquake struck Alxa Left Banner,Inner Mongolia,China on April 15,2015,a region with limited CENC monitoring capabilities,making analysis challenging.However,abundant data from ChinArray provided valuable observations for assessing the event.This study leveraged ChinArray data from the 2015 Alxa Left Banner earthquake sequence,employing machine learning(specifically PhaseNet,a deep learning method,and GaMMA,a Bayesian approach)for automated seismic phase picking,association,and location analysis.Our generated catalog,comprising 10,432 phases from 708 events,is roughly ten times larger than the CENC catalog,encompassing all CENC events with strong consistency.A slight magnitude overestimation is observed only at lower magnitudes.Furthermore,the catalog adheres to the Gutenberg-Richter and Omori laws spatially,temporally,and in magnitude distribution,demonstrating its high reliability.Double-difference tomography refined locations for 366 events,yielding a more compact spatial distribution with horizontal errors within 100m,vertical errors within 300m,and travel-time residuals within 0.05s.Depths predominantly range from 10-30km.Aftershocks align primarily NEE,with the mainshock east of the aftershock zone.The near-vertical main fault plane dips northwestward,exhibiting a Y-shaped branching structure,converging at depth and expanding towards the surface.FOCMEC analysis,using first motion and amplitude ratios,yielded focal mechanism solutions for 10 events,including the mainshock.These solutions consistently indicate a strike-slip mechanism with a minor extensional component.Integrating the earthquake sequence's spatial distribution and focal mechanisms suggests the seismogenic structure is a negative flower structure,consistent with the Dengkou-Benjing fault.Comparing the CENC and ML-generated catalogs using the maximum curvature(MAXC)method reveals a 0.6 decrease in completeness magnitude(M_(C)).However,magnitude-frequency distribution discrepancies above the MAXC-estimated M_(C)suggest MAXC may underestimate both M_(C)and the b-value.This study analyzes the 2015 Alxa Left Banner M5.8 earthquake using a reliable,MLgenerated earthquake catalog,revealing detailed information about the sequence,faulting structure,aftershock distribution,and stress characteristics.
基金supported by the China Earthquake Administration Earthquake Tracking Task Orientation(2016020202,2016010216,and 2016010220)the“Three Combination”project of the China Earthquake Administration(163201)+2 种基金the National Natural Science Foundation of China(41204058,41474064,and 41374088)the special earthquake research,China Earthquake Administration(201508009-08)the Director,Foundation of Institute of Seismology,China Earthquake Administration(IS201326123)
文摘In this study, a classic survey adjustment computation method was used for data obtained in the Inner Mongolia and Ningxia gravimetric networks between September 2013 and April 2015 so as to investigate the variation of gravity before the Alxa Zuoqi M5.8 earthquake. The relationship between gravity variation and the Alxa Zuoqi M5.8 earthquake was analyzed. The results showed that： （1） the severe variation in gravity field at the test sites before the Alxa Zuoqi M5.8 earthquake, as well as the subsequent accelerated rising, might be an earthquake precursor; （2） the Alxa Zuoqi M5.8 earthquake occurred at the turning point where the high-gravity gradient zone changed from the NE direction to NW.
