The MW5.5 earthquake on August 6,2023,in Pingyuan,Shandong,China:A rupture on a buried fault

On August 6,2023,a magnitude MW5.5 earthquake struck Pingyuan County,Dezhou City,Shandong Province,China.This event was significant as no large earthquakes had been recorded in the region for over a century,and no active fault had been previously identified.This study collects 1309 P-wave arrival times and 866 S-wave arrival times from 74 seismic stations less than 200 km to the epicenter to constrain the spatial distribution of the mainshock and its 125 early aftershocks by the double difference earthquake relocation method,and selects 864 P-waveforms from 288 stations located within 800 km of the epicenter to constrain the focal mechanism solution of the mainshock through centroid moment tensor inversion.The relocation and the inversion indicate,the Pingyuan MW5.5 earthquake was caused by a rupture on a buried fault,likely an extensive segment of the Gaotang fault.This buried fault exhibited a dip of approximately 75°to the northwest,with a strike of 222°,similar to the Gaotang fault.The rupture initiated at the depth of 18.6 km and propagated upward and northeastward.However,the ground surface was not broken.The total duration of the rupture was~6.0 s,releasing the scalar moment of 2.5895×1017 N·m,equivalent to MW5.54.The moment rate reached the maximum only 1.4 seconds after the rupture initiation,and the 90%scalar moment was released in the first 4.6 s.In the first 1.4 seconds of the rupture process,the rupture velocity was estimated to be 2.6 km/s,slower than the local S-wave velocity.As the rupture neared its end,the rupture velocity decreased significantly.This study provides valuable insights into the seismic characteristics of the Pingyuan MW5.5 earthquake,shedding light on the previously unidentified buried fault responsible for the seismic activity in the region.Understanding the behavior of such faults is crucial for assessing seismic hazards and enhancing earthquake preparedness in the future.

The Research on the Impact Factors of the Comprehensive Evaluation of Communication Unit Camps Based on Analytic Hierarchy Process

Currently, the comprehensive assessment of the communication troops’ camp planning project is primarily qualitative, with limited quantitative evaluation. Drawing upon the relevant spirit of the Military Commission’s documents and leveraging the author’s own work experience in branch offices, this article thoroughly explores the factors influencing the comprehensive assessment of the project and proposes quantitative representation methods for these factors. Utilizing the Analytic Hierarchy Process (AHP), a hierarchical structure model and judgment matrix for the evaluation factors of the communication troops’ camp construction planning project are constructed, enabling the determination of the weightage of each factor. This provides a certain level of support and reference for the project approval and management by branch offices, while also offering valuable insights for the approval and management of camp planning and construction projects in other types of troops and battlefield projects.

Research on the Construction of Aerospace Equipment Maintenance Support Chain Models

The current space launch missions are intense, and the utilization of equipment is frequent, demanding increasingly higher responsiveness and capability in maintenance and support. The aerospace equipment maintenance and support chain relies on aerospace equipment maintenance and support facilities, deploying various maintenance and support resources rationally according to specific requirements and principles, ultimately forming a unidirectional functional chain or network from the supply side to the demand side. This system helps address the “bottleneck” issue in the generation of aerospace equipment support capability and significantly improves the level of aerospace equipment maintenance and support. The model construction is a prerequisite for analyzing the formation and operation mechanism of the chain, and identifying factors affecting the efficiency and effectiveness of maintenance and support. With consideration of the particularity of aerospace equipment maintenance and support, the paper extensively investigates the construction of the aerospace equipment maintenance and support chain model by drawing on research achievements in modern supply chain and logistics theories, as well as model construction methods. It develops a structural diagram-based chain model, with symbols as key elements, and establishes an evaluation indicator system, providing insights into understanding and grasping the composition of the aerospace equipment maintenance and support chain effectively. Furthermore, it offers a reference for solving other equipment support chains’ construction and optimization problems.

USTC-Pickers:a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.

DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.

P-wave velocity structure beneath reservoirs and surrounding areas in the lower Jinsha River

The lower reaches of the Jinsha River are rich in hydropower resources because of the high mountains,deep valleys,and swift currents in this area.This region also features complex tectonic structures and frequent earthquakes.After the impoundment of the reservoirs,seismic activity increased significantly.Therefore,it is necessary to study the P-wave velocity structure and earthquake locations in the lower reaches of the Jinsha River and surrounds,thus providing seismological support for subsequent earthquake prevention and disaster reduction work in reservoir areas.In this study,we selected the data of 7.670 seismic events recorded by the seismic networks in Sichuan.Yunnan,and Chongqing and the temporary seismic arrays deployed nearby.We then applied the double-difference tomography method to this data,to obtain the P-wave velocity structure and earthquake locations in the lower reaches of the Jinsha River and surrounds.The results showed that the Jinsha River basin has a complex lateral P-wave velocity structure.Seismic events are mainly distributed in the transition zones between high-and low-velocity anomalies,and seismic events are particularly intense in the Xiluodu and Baihetan reservoir areas.Vertical cross-sections through the Xiangjiaba and Xiluodu reservoir areas revealed an apparent high-velocity anomaly at approximately 6 km depth:this high-velocity anomaly plays a role in stress accumulation,with few earthquakes distributed inside the high-velocity body.After the impoundment of the Baihetan reservoir,the number of earthquakes in the reservoir area increased significantly.The seismic events in the reservoir area north of 27°N were related to the enhanced activity of nearby faults after impoundment:the earthquakes in the reservoir area south of 27°N were probably induced by additional loads(or regional stress changes),and the multiple microseismic events may have been caused by rock rupture near the main faults under high pore pressure.

Preface to the special issue of Artificial Intelligence in Seismology

Seismology is a data-intensive and data-driven science.The rapid growth of seismometer density and data size calls for more efficient and effective processing tools.In recent years,artificial intelligence(AI)has been increasingly used in various areas of seismology.Among them,earthquake monitoring is likely the one most impacted(Kong QK et al.,2019;Mousavi and Beroza,2022).Popular seismic phase picking models and workflows like PhaseNet,EQTransformer,RISP,PALM,LOC-FLOW,QUAKE-FLOW(Zhu WQ and Beroza,2019;Mousavi et al.,2020;Liao SR et al.,2021;Zhou YJ et al.,2021;Zhang M et al.,2022;Zhu WQ et al.,2023)have been proposed and widely used.Also,AI algorithms for association(Ross et al.,2019;Yu ZY and Wang WT,2022),polarity determination and focal mechanism inversion(Ross et al.,2018;Zhang J et al.,2023;Li S et al.,2023),earthquake discrimination(Li ZF et al.,2018;Linville et al.,2019;Miao FJ et al.,2020)have emerged.

High-precision relocation of the aftershock sequence of the January 8,2022,M_(S)6.9 Menyuan earthquake

The 2022 Menyuan M_(S)6.9 earthquake,which occurred on January 8,is the most destructive earthquake to occur near the Lenglongling(LLL)fault since the 2016 Menyuan M_(S)6.4 earthquake.We relocated the mainshock and aftershocks with phase arrival time observations for three days after the mainshock from the Qinghai Seismic Network using the double-difference method.The total length and width of the aftershock sequence are approximately 32 km and 5 km,respectively,and the aftershocks are mainly concentrated at a depth of 7-12 km.The relocated sequence can be divided into 18 km west and 13 km east segments with a boundary approximately 5 km east of the mainshock,where aftershocks are sparse.The east and west fault structures revealed by aftershock locations differ significantly.The west fault strikes EW and inclines to the south at a 71°-90°angle,whereas the east fault strikes 133°and has a smaller dip angle.Elastic strain accumulates at conjunctions of faults with different slip rates where it is prone to large earthquakes.Based on surface traces of faults,the distribution of relocated earthquake sequence and surface ruptures,the mainshock was determined to have occurred at the conjunction of the Tuolaishan(TLS)fault and LLL fault,and the west and east segments of the aftershock sequence were on the TLS fault and LLL fault,respectively.Aftershocks migrate in the early and late stages of the earthquake sequence.In the first 1.5 h after the mainshock,aftershocks expand westward from the mainshock.In the late stage,seismicity on the northeast side of the east fault is higher than that in other regions.The migration rate of the west segment of the aftershock sequence is approximately 4.5 km/decade and the afterslip may exist in the source region.

Crustal velocity structures beneath North China revealed by ambient noise tomography

We collected continuous noise waveform data from January 2007 to February 2008 recorded by 190 broadband and 10 very broadband stations of the North China Seismic Array. The study region is divided into grid with interval 0.25°×0.25°, and group velocity distribution maps between 4 s and 30 s are obtained using ambient noise tomography method. The lateral resolution is estimated to be 20-50 km for most of the study area. We construct a 3-D S wave velocity model by inverting the pure path dispersion curve at each grid using a genetic algorithm with smoothing constraint. The crustal structure observed in the model includes sedimentary basins such as North China basin, Yanqing-Huailai basin and Datong basin. A well-defined low velocity zone is observed in the Beijing-Tianjin-Tangshan region in 22-30 km depth range, which may be related to the upwelling of hot mantle material. The high velocity zone near Datong, Shuozhou and Qingshuihe within the depth range of 1-23 km reveals stable characteristics of Ordos block. The Taihangshan front fault extends to 12 km depth at least.

Comparison of the earthquake detection abilities of PhaseNet and EQTransformer with the Yangbi and Maduo earthquakes

PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide.To evaluate the generaliz-ation ability of the two models and provide insights for the development of new models,this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences.It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions,the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes.PhaseNet has a higher recall than EQTransformer,but the recall of both models is reduced by 13%-56%when compared with the results rep-orted in the original papers.The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection perfor-mance.In designing earthquake detection models,attention should be paid to not only the balance of depth,width,and architecture but also to the quality and quantity of the training datasets.In addition,noise datasets should be incorporated during training.According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes,the Yangbi earthquake exhibited foreshock,while the Maduo earthquake showed no foreshock activity,indicating that the two earthquakes’nucleation processes were different.

A high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter

We present a high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi sequence.The catalog has a time range of 2021-05-01 to 2021-05-28,and contains~8,000 well located events.It captures the features of the whole foreshock sequence and the early aftershocks.We designed a detection strategy incorporating both an artificial intelligent(AI)picker and a matched filter algorithm.Here,we adopt a hybrid AI method incorporating convolutional and recurrent neural network(CNN&RNN)for event detection and phase picking respectively(i.e.CERP),a light-weight AI picker that can be trained with small volume of data.CERP is first trained with detections from a STA/LTA and Kurtosis-based method called PAL,and then construct a rather complete template set of~4,000 events.Finally,the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation,which finally results in precise relocation.This process gives 9,026 detections,among which 7,943 events can be well relocated.The catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity evolution.The main features are:(1)the foreshock sequence images simple fault geometry with consistent strike,but also show a variable event depth along strike;(2)the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast;(3)complex seismicity are developed in the post-seismic period,indicating complex triggering mechanisms.Thus,our catalog provides a reliable basis for further investigations,such as b-value studies,rupture process,and triggering relations.

Shallow crustal velocity structures revealed by active source tomography and fault activities of the Mianning–Xichang segment of the Anninghe fault zone, Southwest China

The Anninghe fault is a large left-lateral strike-slip fault in southwestern China. It has controlled deposition and magmatic activities since the Proterozoic, and seismic activity occurs frequently. The Mianning-Xichang segment of the Anninghe fault is a seismic gap that has been locked by high stress. Many studies suggest that this segment has great potential for large earthquakes(magnitude >7). We obtained three vertical velocity profiles of the Anninghe fault(between Mianning and Xichang) based on the inversion of P-wave first arrival times. The travel time data were picked from seismograms generated by methane gaseous sources and recorded by three linearly distributed across-fault dense arrays. The inversion results show that the P-wave velocity structures at depths of 0-2 km corresponds well with the local lithology. The Quaternary sediments have low seismic velocities, whereas the igneous rocks,metamorphic rocks, and bedrock have high seismic velocities. We then further discuss the fault activities of the two fault branches of the Anninghe fault in the study region based on small earthquakes(magnitudes between ML 0.5 and ML 2.5) detected by the Xichang array.The eastern fault branch is more active than the western branch and that the fault activities in the eastern branch are different in the northern and southern segments at the border of 28°21′N. The high-resolution models obtained are essential for future earthquake rupture simulations and hazard assessments of the Anninghe fault zone. Future studies of velocity models at greater depths may further explain the complex fault activities in the study region.

S-wave velocity structure in the SE Tibetan plateau

We use observations recorded by 23 permanent and 99 temporary stations in the SE Tibetan plateau to obtain the S-wave velocity structure along two profiles by applying joint inversion with receiver functions and surface waves. The two profiles cross West Yunnan block （WYB）, the Central Yunnan sub-block （CYB）, South China block （SCB）, and Nanpanjiang basin （NPB）. The profile at -25°N shows that the Moho interface in the CYB is deeper than those in the WYB and the NPB, and the topography and Moho depth have clear correspondence. Beneath the Xiaojiang fault zone （XJF）, there exists a crustal low-velocity zone （LYZ）, crossing the XJF and expanding eastward into the SCB. The NPB is shown to be of relatively high velocity. We speculate that the eastward extrusion of the Tibetan plateau may pass through the XJF and affect its eastern region, and is resisted by the rigid NPB, which has high velocity. This may be the main cause of the crustal thickening and uplift of the topography. In the Tengchong volcanic area, the crust is shown to have alternate high- and low-velocity layers, and the upper mantle is shown to be of low velocity. We consider that the magma which exists in the crust is from the upper mantle and that the complex crustal velocity structure is related to magmatic differentiation. Between the Tengchong volcanic area and the XJF, the crustal velocity is relatively high. Combining these observations with other geophysical evi- dence, it is indicated that rock strength is high and defor- mation is weak in this area, which is why the level of seismicity is quite low. The profile at ~ 23~N shows that the variation of the Moho depth is small from the eastern rigid block to the western active block with a wide range of LVZs. We consider that deformation to the south of the SE Tibetan Plateau is weak.

Preface to the special issue on the M_(S)6.4 Yangbi (Yunnan) and M_(S)7.4 Maduo (Qinghai) earthquakes

The Tibetan Plateau and the Himalayas,which are the highest mountains in the world,were created by the collision of the Indian and Eurasian plates.Earthquakes pose significant hazards in these mountainous regions as demonstrated by recent large earthquakes,including the M_(S)6.4/M_(W)6.1 Yangbi earthquake in Yunnan province on May 21,2021 and the M_(S)7.4/M_(W)7.4 Maduo earthquake in Qinghai province on May 22,2021.The Yangbi earthquake occurred near the northwestern extension of the Red River fault in the southeastern part of the Tibetan Plateau whereas the Maduo earthquake occurred in the Bayan Har block in the northeastern part of the plateau.Both earthquakes are related to strike-slip faults and provide unique opportunities to learn more about the lateral extrusion and escape of the southeastern edge of the Tibetan Plateau.

Seismic phase picking in China Seismic Array using a deep convolutional neuron network

Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography.Manual picking is considered as the most accurate way to access the arrival times but time consuming.Many automatic picking methods were proposed in the past decades,but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals.As the increasing deployment of large seismic array,the existing methods can not meet the requirements of quick and accurate phase picking.In this study,we applied a phase picking algorithm developed on the base of deep convolutional neuron network(PickNet)to pick seismic phase arrivals in ChinArray-Phase III.The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array.The raw picked travel-time data shows a large variation deviated from the traveltime curves.The absolute location residual is a key criteria for travel-time data selection.Besides,we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet.This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake.

2022年青海门源Mw 6.6地震的发震断层及孕震构造模式

2022年1月8日青海门源盆地北缘发生Mw 6.6地震,震源机制反演表明此次地震属于左旋走滑事件.震后10 d内,近600个余震被检测到,最大余震为M 5.1级.此次地震发生在祁连-海原左旋走滑断裂系统的冷龙岭段,该断裂段全长127 km,由古地震研究确定的特征地震大小在Mw 7.3~7.5.为了更为全面理解此次地震的震源机制以及当地孕震模式,我们分析了地震波形,获取了主震和17个Ms≥3.0余震的震源机制与矩心深度.利用升、降轨道SAR数据获取的像元偏移数据和同震干涉相位(interferometric synthetic aperture radar,InSAR)确定了两条地表破裂带的位置,并利用InSAR数据反演了主震的滑动模型.研究发现,此次地震破裂带对应于冷龙岭断裂西段和托莱山断裂的阶区,发震断层存在3个形变中心,最大滑动量约为4 m,出现在冷龙岭断裂上,形变中心深度为4 km.滑动模型显示释放了累计能量~1.58×1019 Nm,约合矩震级Mw 6.68,与本文利用地震学方法得到的Mw 6.58接近.结合区域活动构造特征、1986和2016年两次门源地震的位置及震源机制,推断祁连-海原断裂非对称花状构造结构可能是祁连山地区一种重要的应变卸载模式.同震滑动驱动下的库仑应力变化分析显示72.2%的余震分布符合同震触发效应.考虑到当前余震主要向东南扩展,库仑应力升高的破裂区西南部仍存在相对较高的地震风险,需要进一步关注.

The high-resolution community velocity model V2.0 of southwest China,constructed by joint body and surface wave tomography of data recorded at temporary dense arrays

The Sichuan-Yunnan area is located at the southeastern margin of the Tibetan Plateau,where tectonic movement is strong with deep and large faults distributed in a staggered manner,which results in strong seismic activities and severe earthquake hazards.Since the 21st century,several earthquakes of magnitude 7.0 or above occurred in this region,which have caused huge casualties and economic losses,especially the 2008 M_(s)8.0 Wenchuan earthquake.At present,earthquake monitoring and source parameter inversion,strong earthquake hazard analysis and disaster assessment are still the focus of seismological researches in the Sichuan-Yunnan region.Regional high-precision 3D community velocity models are fundamental for these studies.In this paper,by assembling seismic observations at permanent seismic stations and several temporary dense seismic arrays in this region,we obtained about 7.06 million body wave travel time data(including absolute and differential travel times)using a newly developed artificial intelligence body wave arrival time picking method and about 100,000 Rayleigh wave phase velocity dispersion data in the period range of 5-50 s from ambient noise cross-correlation technique.Based on this abundant dataset,we obtained the three-dimensional high resolution V_p and V_(s)model in the crust and uppermost mantle of southwest(SW)China by adopting the joint body and surface wave travel time tomography method considering the topography effect starting from the first version of community velocity model in SW China(SWChina CVM-1.0).Compared to SWChina CVM-1.0,this newly determined velocity model has higher resolution and better data fitness.It is accepted by the China Seismic Experimental Site as the second version of the community velocity model in SW China(SWChina CVM-2.0).The new model shows strong lateral heterogeneities in the shallow crust.Two disconnected low velocity zones are observed in the middle to lower crust,which is located in the Songpan-Ganzi block and the northern Chuandian block to the west of the Longmenshan-Lijiang-Xiaojinhe fault,and beneath the Xiaojiang fault zone,respectively.The inner zone of the Emeishan large igneous province(ELIP)exhibits a high velocity anomaly,which separates the two aforementioned low velocity anomalies.Low velocity anomaly is also shown beneath the Tengchong volcano.The velocity structures in the vicinity of the 2008 M_(s)8.0 Wenchuan earthquake,the 2013 M_(s)7.0Lushan earthquake and the 2017 M_(s)7.0 Jiuzhaigou earthquake mainly show high V_(p)and V_(s)anomalies and the mainshocks are basically located at the transition zone between the high and low velocity anomalies.Along with the segmentation characteristics of seismic activity,we suggest that areas with significant changes in velocity structures,especially in active fault zones,might have a greater potential to generate moderate to strong earthquakes.

Preliminary analysis on the source properties and seismogenic structure of the 2017 M_s7.0 Jiuzhaigou earthquake

At GMT time 13:19, August 8, 2017, an Ms7.0 earthquake struck the Jiuzhaigou region in Sichuan Province, China,causing severe damages and casualties. To investigate the source properties, seismogenic structures, and seismic hazards, we systematically analyzed the tectonic environment, crustal velocity structure in the source region, source parameters and rupture process, Coulomb failure stress changes, and 3-D features of the rupture plane of the Jiuzhaigou earthquake. Our results indicate the following:(1) The Jiuzhaigou earthquake occurred on an unmarked fault belonging to the transition zone of the east Kunlun fault system and is located northwest of the Huya fault.(2) Both the mainshock and aftershock rupture zones are located in a region where crustal seismic velocity changes dramatically. Southeast to the source region, shear wave velocity at the middle to lower crust is significantly low, but it rapidly increases northeastward and lies close to the background velocity across the rupture fault.(3) The aftershock zone is narrow and distributes along the northwest-southeast trend, and most aftershocks occur within a depth range of 5–20 km.(4) The focal mechanism of the Jiuzhaigou earthquake indicates a left-lateral strike-slip fault, with strike, dip, and rake angles of 152°, 74° and 8°, respectively. The hypocenter depth measures 20 km, whereas the centroid depth is about 6 km. The co-seismic rupture mainly concentrates at depths of 3–13 km, with a moment magnitude(M_w) of 6.5.(5) The co-seismic rupture also strengthens the Coulomb failure stress at the two ends of the rupture fault and the east segment of the Tazang fault. Aftershocks relocation results together with geological surveys indicate that the causative fault is a near vertical fault with notable spatial variations: dip angle varies within 66°–89° from northwest to southeast and the average dip angle measures ~84°. The results of this work are of fundamental importance for further studies on the source characteristics, tectonic environment, and seismic hazard evaluation of the Jiuzhaigou earthquake.

EL-Picker:基于集成学习的余震P波初动实时拾取方法

在实时地震监测中,地震P波(primary wave)的初动拾取任务具有至关重要的作用,其有助于地震应急响应的及时实施.虽然此前在该领域已开展了大量的研究,但是如何从地震分布密集并且充满噪声的监测波形中有效地识别出P波仍然是一个具有挑战性的任务.例如对于大地震的余震监测,实践中使用的普遍方法仍依赖于专家辅助标注.本文针对地震实时监测任务,基于集成学习策略,提出一个全新的技术框架——EL-Picker,实现从连续地震波形中自主拾取P波的初动到时.具体而言,EL-Picker包含3个模块,即触发器、分类器和精化器.其中,分类器模块借鉴集成学习策略,实现对多个个体学习器的整合,提升整体模型性能.基于汶川Ms8.0地震的余震数据集进行的大量实验,我们发现EL-Picker不仅较好地实现P波初动拾取效果,并且多诊断出120%被人工遗漏的地震P波.同时,实验结果也启发我们探索如何针对不同的地震站台选取个性化的个体学习器构建分类器模块.此外,我们进一步地讨论了被人工遗漏的地震波形的规律特点,用于指导人工地震标注.这些发现清晰地验证了EL-Picker框架的鲁棒性、时效性、灵活性以及稳定性.

Aftershock sequence relocation of the 2021 M_(S)7.4 Maduo Earthquake, Qinghai, China

The 2021 Qinghai Maduo M_(S)7.4 earthquake was one of the strongest earthquakes that occurred in the Bayan Har block of the Tibetan Plateau during the past 30 years,which spatially filled in the gap of strong earthquake in the eastern section of the northern block boundary.In this study,the aftershock sequence within 8 days after the mainshock was relocated by double difference algorithm.The results show that the total length of the aftershock zone is approximately 170 km;the mainshock epicenter is located in the center of the aftershock zone,indicating a bilateral rupture.The aftershocks are mainly distributed along NWW direction with an overall strike of 285°.The focal depth profiles indicate that the seismogenic fault is nearly vertical and dips to southwest or northeast in different sections,indicating a complex geometry.There is an aftershock gap located to the southeast of the mainshock epicenter with a scale of approximately 20 km.At the eastern end of the aftershock zone,horsetaillike branch faults show the terminal effect of a large strike-slip fault.There is a NW-trending aftershock zone on the north side of the western section,which may be a branch fault triggered by the mainshock.The location of the aftershock sequence is close to the eastern section of the Kunlun Mountain Pass-Jiangcuo(KMPJ)fault.The sequence overlaps well with surface trace of the KMPJ fault.We speculate that the KMPJ fault is the main seismogenic fault of the M_(S)7.4 Maduo earthquake.