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
采用通用引物PCR扩增法,测定了辽东湾海域的白色霞水母(Cyanea nozakii)螅状体、碟状体及水母体的18S以及ITS-5.8S r DNA序列,同时利用Gene Bank数据库中已有同源序列对其进行序列分析及系统分析。结果显示,白色霞水母3个个体的18S和ITS...采用通用引物PCR扩增法,测定了辽东湾海域的白色霞水母(Cyanea nozakii)螅状体、碟状体及水母体的18S以及ITS-5.8S r DNA序列,同时利用Gene Bank数据库中已有同源序列对其进行序列分析及系统分析。结果显示,白色霞水母3个个体的18S和ITS-5.8S r DNA序列完全一致。白色霞水母样品的ITS-5.8S r DNA序列与Gen Bank中未知真核生物的序列高度相似(≥99%),推测该物种可能是早期发育阶段(卵、浮浪幼虫或碟状体)的白色霞水母样品。霞水母属不同种间18S r DNA序列经比对后同源序列长度为1709bp,多态位点数33个;比对后ITS1同源序列长度为368bp,其中变异位点203个,简约信息位点数178个,单变异位点21个。基于18S r DNA基因序列的霞水母属种内和种间平均遗传距离分别为0、0.008,而基于ITS1序列的霞水母属种内和种间平均遗传距离分别为0.019、0.284。基于ITS1的种间遗传距离是种内遗传距离的15倍,适合于进行物种鉴定。NJ系统树的结果也表明同种的不同个体各自聚枝,其聚类结果大致与形态分类吻合。研究表明,ITS基因片段在霞水母不同种间变异较大,更适于大型水母种类鉴定、检测及属内种间水平的系统进化研究。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.
文摘采用通用引物PCR扩增法,测定了辽东湾海域的白色霞水母(Cyanea nozakii)螅状体、碟状体及水母体的18S以及ITS-5.8S r DNA序列,同时利用Gene Bank数据库中已有同源序列对其进行序列分析及系统分析。结果显示,白色霞水母3个个体的18S和ITS-5.8S r DNA序列完全一致。白色霞水母样品的ITS-5.8S r DNA序列与Gen Bank中未知真核生物的序列高度相似(≥99%),推测该物种可能是早期发育阶段(卵、浮浪幼虫或碟状体)的白色霞水母样品。霞水母属不同种间18S r DNA序列经比对后同源序列长度为1709bp,多态位点数33个;比对后ITS1同源序列长度为368bp,其中变异位点203个,简约信息位点数178个,单变异位点21个。基于18S r DNA基因序列的霞水母属种内和种间平均遗传距离分别为0、0.008,而基于ITS1序列的霞水母属种内和种间平均遗传距离分别为0.019、0.284。基于ITS1的种间遗传距离是种内遗传距离的15倍,适合于进行物种鉴定。NJ系统树的结果也表明同种的不同个体各自聚枝,其聚类结果大致与形态分类吻合。研究表明,ITS基因片段在霞水母不同种间变异较大,更适于大型水母种类鉴定、检测及属内种间水平的系统进化研究。
