Geometry features modeling of three-dimensional fault plane of Changning earthquake based on machine learning
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摘要:
结合长宁地区大量的地震精定位数据和其它研究成果，利用监督分类和聚类分析等机器学习算法，基于地震簇的形态特征和地震震源机制解，编写了一套自动化提取三维破裂面形态特征的程序，获取了长宁地区地震破裂面的精细结构，可为相关研究提供可参考的发震构造模型。结果显示通过聚类分析最终获取了四个地震簇，结合对应的震源机制解节面信息，最终拟合出四条破裂面，其中：长宁背斜上的破裂面沿狮子滩背斜下部的高速体呈NW−SE方向展布，破裂面平直，倾角较陡，倾向SE；建武向斜内部的三条破裂面，主要分布在向斜两翼，规模较小，走向分别为NW，NNE和NNW，从外部包围了建武向斜核部的高速体，破裂面的展布方向与该地区三个主要震源机制解节面的产状一致，其中新城镇附近的NNW向破裂面切割深度较深，约为20 km，且倾向ENE，倾角约为70°。此外，结合地质构造背景和速度结构等反演结果推断，地震破裂面主要存在于先期形成的构造薄弱带或断裂带，例如背斜的核部和向斜的翼部因节理面贯通所形成的薄弱带以及高速体周围的软弱带，在构造应力的加载和工业开采下更容易微破裂成核，形成典型的发震构造。
Abstract:In recent years, the seismicity of the Changning area in the Sichuan Province has increased significantly. Seismogenic models and seismogenic structures on the background of structural loading coupled with human activities have gradually become the focus of research in the field. Using abundant and accurate hypocenters in the Changning area, we established a program for automatically extracting morphological fault features by using machine learning algorithms including supervised classification and clustering. The method provides a reliable, detailed model of seismogenic faults for relative researches. As a result, four earthquake clusters were identified by clustering analysis, and four fracture planes were fit based on the distribution of hypocenters. The fracture plane on the Changning anticline spreads in NW-SE direction along a high velocity body beneath the Shizitan anticline. The fracture plane is straight with steep dip angle, and inclines SE. The three fracture planes in the inner part of the Jianwu syncline are mainly distributed in small scale on the limbs of the syncline with strike of NW, NNE, and NNW, respectively. They are also distributed in the periphery of the high-velocity body at the core of the Jianwu syncline, and their spreading directions are consistent with the strike of nodal planes of three main focal mechanism solutions in this area. Among these fracture planes, the Xincheng fracture plane extends deep to about 20 km and dips ENE with dip angle 70°. Based also on the geological tectonic settings and velocity structure, the fracture planes mainly exist in weak tectonic zones, such as the nucleus of the anticline and the limbs of the syncline. In particular, the fragile zone around the high-velocity body is more likely to rupture and nucleate under the loading of tectonic stress and industrial mining, forming new seismogenic structures.
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图 1 研究区域的构造地质背景和地震分布（改自易桂喜等，2019；Jiang et al，2020）
Figure 1. The tectonic geological background and earthquake distribution in the studied area （modified from Yi et al，2019；Jiang et al，2020）
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图 2 长宁地区地震精定位结果分布特征
（a） 构造和地震的三维分布特征；（b） EW向地震分布剖面图；（c） NS向地震分布剖面图
Figure 2. Distribution characteristics of precision earthquake location results in Changning area
（a） Three-dimensional distribution characteristics of structures and earthquakes；（b） Earthquake distribution profile along EW direction；（c） Earthquake distribution profile along NS direction
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图 4 地震聚类分析及能量粒子簇提取
（a） 利用轮廓系数评价不同聚类参数的聚类效果，以此获取最优聚类参数；（b） 利用密度聚类分析获取地震簇；（c） 利用K邻近分类对能量粒子进行分类；（d） 能量粒子簇分布特征
Figure 4. Earthquake clustering analysis and energy particle cluster extraction
（a） The Silhouette coefficient used to evaluate the clustering effect of different clustering parameters so as to obtain the optimal clustering parameters；（b） Acquisition of earthquake clusters by DBSCAN；（c） Classification of energy particles using KNN；（d） Distribution characteristics of energy particle clusters
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图 5 长宁地震震源机制解分类结果及对应的震源节面玫瑰图
Figure 5. Classification results of focal mechanism solutions of Changning earthquake and the corresponding nodal rose diagrams
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图 6 长宁地区地震破裂面的空间展布特征
Figure 6. Spatial distribution characteristics of fracture planes of earthquakes in Changning area
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图 7 S波（a）和P波（b）速度结构和发震构造的分布特征
Figure 7. Distribution characteristics of S-wave （a） and P-wave （b） velocity structures and seismogenic structures
表 1 长宁地区研究成果数据信息汇总
Table 1 Data information from the research results of Changning area
数据类型 数据描述 数据来源 构造背景 基于区域地质与地震资料，结合地表调查结果，获取的节理面、构造线和应力场演化数据 常祖峰等（2 020） 褶皱构造 利用页岩气勘探钻井和反射地震资料进行构造分析，恢复了长宁背斜形成过程和构造
地质背景He et al （2 019） 发震断层 使用高分辨率地震反射剖面结合地质、地震和大地测量数据来揭示发震断层的三维分布 Lu et al （2 021） 地震重定位 使用TomoDDMC方法联合反演后，重新定位2万1 711次地震，东西向、南北向和垂直方向的误差中值分别为0.201，0.232和0.633 km Zuo et al （2 020） 微地震目录 应用最新发展的迁移学习震相识别技术、震相自动关联和定位技术，获取长宁微震目录，水平定位平均误差为（1.45±0.028） km 赵明等（2 021） 历史地震震源机制 对全国地震进行了矩张量反演，获得2 008—2 019年M≥3.0地震的震源机制解 郭祥云等（2 022） 地震序列震源机制 使用CAP （cut and paste）波形反演方法计算长宁地震16次MS≥3.6地震的震源机制解 易桂喜等（2 019） 综合震源机制解 借助于根据同一地震的多个震源机制解确定其中心解的方法，给出与所有震源机制解差别最小的中心震源机制解 刘敬光等（2 019） 速度结构 利用双差地震层析成像方法，获得了长宁—兴文地区高分辨率三维地壳vP，vS和vP/vS模型及地震位置 Zuo et al （2 020） 
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常祖峰,张艳凤,王光明,张世民,毛泽斌,刘昌伟. 2020. 2019年四川长宁 MS6.0地震的地质构造成因:区域性构造节理贯通、破裂结果[J]. 地球学报,41(4):469–480. doi: 10.3975/cagsb.2020.030202 Chang Z F,Zhang Y F,Wang G M,Zhang S M,Mao Z B,Liu C W. 2020. The geological genesis of the 2019 Changning MS6.0 earthquake in Sichuan:Connecting and rupturing of regional structural joints[J]. Acta Geoscientica Sinica,41(4):469–480 (in Chinese).
宫悦,王宇玺,梁明剑,龙锋,赵敏. 2020. 2019年四川长宁6.0级地震序列时空演化特征及其地震构造环境研究[J]. 地震,40(4):90–102. doi: 10.12196/j.issn.1000-3274.2020.04.007 Gong Y,Wang Y X,Liang M J,Long F,Zhao M. 2020. Study on the spatio-temporal evolution characteristics and seismic structure environment of the 2019 M6.0 Changning Sichuan earthquake sequence[J]. Earthquake,40(4):90–102 (in Chinese).
郭祥云,蒋长胜,韩立波,尹海权,赵志远. 2022. 中国大陆及邻区震源机制数据集(2009—2021年)[DB/OL]. [2022-05-01].https://data.earthquake.cn/datashare/report.shtml?PAGEID=datasourcelist&dt=ff8080827e4d6cf5017f1f5b440d0019. Guo X Y,Jiang C S,Han L B,Yin H Q,Zhao Z Y. 2022. Focal mechanism data set in Chinese mainland and its adjacent area (2009−2021)[DB/OL]. [2022-05-01].https://data.earthquake.cn/datashare/report.shtml?PAGEID=datasourcelist&dt=ff8080827e4d6cf5017f1f5b440d0019 (in Chinese).
郭志,高星,路珍. 2020. 2019年6月17日四川长宁地震重定位及震源机制研究[J]. 地震学报,42(3):245–255. doi: 10.11939/jass.20190132 Guo Z,Gao X,Lu Z. 2020. Relocation and focal mechanism inversion for the Changning,Sichuan,earthquake on 17 June 2019[J]. Acta Seismologica Sinica,42(3):245–255 (in Chinese).
何登发,鲁人齐,黄涵宇,王晓山,姜华,张伟康. 2019. 长宁页岩气开发区地震的构造地质背景[J]. 石油勘探与开发,46(5):993–1006. doi: 10.11698/PED.2019.05.19 He D F,Lu R Q,Huang H Y,Wang X S,Jiang H,Zhang W K. 2019. Tectonic and geological background of the earthquake hazards in Changning shale gas development zone,Sichuan Basin,SW China[J]. Petroleum Exploration and Development,46(5):1051–1064.
胡晓辉,盛书中,万永革,卜玉菲,李振月. 2020. 2019年6月17日四川长宁地震序列震源机制与震源区震后构造应力场研究[J]. 地球物理学进展,35(5):1675–1681. doi: 10.6038/pg2020DD0378 Hu X H,Sheng S Z,Wan Y G,Bu Y F,Li Z Y. 2020. Study on focal mechanism and post-seismic tectonic stress field of the Changning,Sichuan,earthquake sequence on June 17th 2019[J]. Progress in Geophysics,35(5):1675–1681 (in Chinese).
胡幸平,崔效锋,张广伟,王甘娇,Zang A,史丙新,姜大伟. 2021. 长宁地区复杂地震活动的力学成因分析[J]. 地球物理学报,64(1):1–17. doi: 10.6038/cjg2021O0232 Hu X P,Cui X F,Zhang G W,Wang G J,Zang A,Shi B X,Jiang D W. 2021. Analysis on the mechanical causes of the complex seismicity in Changning area,China[J]. Chinese Journal of Geophysics,64(1):1–17 (in Chinese).
李大虎,詹艳,丁志峰,高家乙,吴萍萍,孟令媛,孙翔宇,张旭. 2021. 四川长宁 MS6.0地震震区上地壳速度结构特征与孕震环境[J]. 地球物理学报,64(1):18–35. doi: 10.6038/cjg2021O0241 Li D H,Zhan Y,Ding Z F,Gao J Y,Wu P P,Meng L Y,Sun X Y,Zhang X. 2021. Upper crustal velocity and seismogenic environment of the Changning MS6.0 earthquake region in Sichuan,China[J]. Chinese Journal of Geophysics,64(1):18–35 (in Chinese).
梁姗姗,徐志国,盛书中,张广伟,赵博,邹立晔. 2020. 2019年四川长宁6.0级地震主震及中强余震( MS≥4.0)的震源机制及其应力场[J]. 地震地质,42(3):547–561. doi: 10.3969/j.issn.0253-4967.2020.03.001 Liang S S,Xu Z G,Sheng S Z,Zhang G W,Zhao B,Zou L Y. 2020. Focal mechanism solutions and stress field of the 2019 Changning,Sichuan mainshock and its moderate-strong aftershocks ( MS≥4.0)[J]. Seismology and Geology,42(3):547–561 (in Chinese).
刘敬光,万永革,黄志斌,李振月,胡晓辉,李泽潇. 2019. 2019年6月17日四川长宁6.0级地震中心震源机制解及震源区构造应力场研究[J]. 震灾防御技术,14(3):677–685. doi: 10.11899/zzfy20190319 Liu J G,Wan Y G,Huang Z B,Li Z Y,Hu X H,Li Z X. 2019. Study on central focal mechanism and its surrounding tectonic stress field of the Changning M6.0 earthquake in Sichuan[J]. Technology for Earthquake Disaster Prevention,14(3):677–685 (in Chinese).
孙权,裴顺平,苏金蓉,刘雁冰,薛晓添,李佳蔚,李磊,左洪. 2021. 2019年6月17日四川长宁 MS6.0地震震源区三维速度结构[J]. 地球物理学报,64(1):36–53. doi: 10.6038/cjg2021O0246 Sun Q,Pei S P,Su J R,Liu Y B,Xue X T,Li J W,Li L,Zuo H. 2021. Three-dimensional seismic velocity structure across the 17 June 2019 Changning MS6.0 earthquake,Sichuan,China[J]. Chinese Journal of Geophysics,64(1):36–53 (in Chinese).
万永革. 2020. 震源机制与应力体系关系模拟研究[J]. 地球物理学报,63(6):2281–2296. doi: 10.6038/cjg2020M0472 Wan Y G. 2020. Simulation on relationship between stress regimes and focal mechanisms of earthquakes[J]. Chinese Journal of Geophysics,63(6):2281–2296 (in Chinese).
徐志国,梁姗姗,盛书中,张广伟,邹立晔,周元泽. 2020. 2019年四川长宁 MS6.0地震序列重定位和震源特征分析[J]. 地震学报,42(4):377–391. doi: 10.11939/jass.20190170 Xu Z G,Liang S S,Sheng S Z,Zhang G W,Zou L Y,Zhou Y Z. 2020. Relocation and source characteristics of the 2019 Changning MS6.0 earthquake sequence[J]. Acta Seismologica Sinica,42(4):377–391 (in Chinese).
易桂喜,龙锋,梁明剑,赵敏,王思维,宫悦,乔慧珍,苏金蓉. 2019. 2019年6月17日四川长宁 MS6.0地震序列震源机制解与发震构造分析[J]. 地球物理学报,62(9):3432–3447. doi: 10.6038/cjg2019N0297 Yi G X,Long F,Liang M J,Zhao M,Wang S W,Gong Y,Qiao H Z,Su J R. 2019. Focal mechanism solutions and seismogenic structure of the 17 June 2019 MS6.0 Sichuan Changning earthquake sequence[J]. Chinese Journal of Geophysics,62(9):3432–3447 (in Chinese).
赵明,唐淋,陈石,苏金蓉,张淼. 2021. 基于深度学习到时拾取自动构建长宁地震前震目录[J]. 地球物理学报,64(1):54–66. doi: 10.6038/cjg2021O0271 Zhao M,Tang L,Chen S,Su J R,Zhang M. 2021. Machine learning based automatic foreshock catalog building for the 2019 MS6.0 Changning,Sichuan earthquake[J]. Chinese Journal of Geophysics,64(1):54–66 (in Chinesc).
周志华. 2016. 机器学习[M]. 北京:清华大学出版社:114-115. Zhou Z H. 2016. Machine Learning[M]. Beijing:Tsinghua University Press:114-115 (in Chinesc).
Al-Zoubi M B,Rawi M A. 2008. An efficient approach for computing Silhouette coefficients[J]. J Comput Sci,4(3):252–255. doi: 10.3844/jcssp.2008.252.255
Bergen K J,Johnson P A,de Hoop M V,Beroza G C. 2019. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science,363(6433):1299. doi: 10.1126/science.aau0323
Brunsvik B,Morra G,Cambiotti G,Chiaraluce L,Di Stefano R,De Gori P,Yuen D A. 2021. Three-dimensional Paganica fault morphology obtained from hypocenter clustering (L’Aquila 2009 seismic sequence,Central Italy)[J]. Tectonophysics,804:228756. doi: 10.1016/j.tecto.2021.228756
Gong L W,Zhang H,Chen S,Chen L J. 2022. Three-dimensional modeling of the Xichang crust in Sichuan,China by machine learning[J]. Appl Sci,12(6):2955. doi: 10.3390/app12062955
Jia K,Zhou S Y,Zhuang J C,Jiang C S,Guo Y C,Gao Z H,Gao S S,Ogata Y,Song X D. 2020. Nonstationary background seismicity rate and evolution of stress changes in the Changning salt mining and shale-gas hydraulic fracturing region,Sichuan Basin,China[J]. Seismol Res Lett,91(4):2170–2181. doi: 10.1785/0220200092
Jiang C S,Han L B,Long F,Lai G J,Yin F L,Bi J M,Si Z Y. 2021. Spatiotemporal heterogeneity of b values revealed by a data-driven approach for the 17 June 2019 MS6.0 Changning earthquake sequence,Sichuan,China[J]. Nat Hazards Earth Syst Sci,21(7):2233–2244. doi: 10.5194/nhess-21-2233-2021
Jiang D W,Zhang S M,Ding R. 2020. Surface deformation and tectonic background of the 2019 MS6.0 Changning earthquake,Sichuan basin,SW China[J]. J Asian Earth Sci,200:104493. doi: 10.1016/j.jseaes.2020.104493
Kaven J O,Pollard D D. 2013. Geometry of crustal faults:Identification from seismicity and implications for slip and stress transfer models[J]. J Geophys Res: Solid Earth,118(9):5058–5070. doi: 10.1002/jgrb.50356
Keller J M,Gray M R,Givens J A. 1985. A fuzzy K-nearest neighbor algorithm[J]. IEEE Trans Syst Man Cybern,SMC-15(4):580–585. doi: 10.1109/TSMC.1985.6313426
Kong Q K,Trugman D T,Ross Z E,Bianco M J,Meade B J,Gerstoft P. 2019. Machine learning in seismology:Turning data into insights[J]. Seismol Res Lett,90(1):3–14. doi: 10.1785/0220180259
Kuang W H,Yuan C C,Zhang J. 2021. Real-time determination of earthquake focal mechanism via deep learning[J]. Nat Commun,12(1):1432. doi: 10.1038/s41467-021-21670-x
Li W,Ni S D,Zang C,Chu R S. 2020. Rupture directivity of the 2019 MW5.8 Changning,Sichuan,China,earthquake and implication for induced seismicity[J]. Bull Seismol Soc Am,110(5):2138–2153. doi: 10.1785/0120200013
Liu J Q,Zahradník J. 2020. The 2019 MW5.7 Changning earthquake,Sichuan Basin,China:A shallow doublet with different faulting styles[J]. Geophys Res Lett,47(4):e2019GL085408. doi: 10.1029/2019GL085408
Lu R Q,Xu X W,He D F,John S,Liu B,Wang F Y,Tan X B,Li Y Q. 2017. Seismotectonics of the 2013 Lushan MW6.7 earthquake:Inversion tectonics in the eastern margin of the Tibetan Plateau[J]. Geophys Res Lett,44(16):8236–8243. doi: 10.1002/2017GL074296
Lu R Q,He D F,Liu J Z,Tao W,Huang H Y,Xu F,Liu G S. 2021. Seismogenic faults of the Changning earthquake sequence constrained by high-resolution seismic profiles in the southwestern Sichuan Basin,China[J]. Seismol Res Lett,92(6):3757–3766. doi: 10.1785/0220200302
Ouillon G,Ducorbier C,Sornette D. 2008. Automatic reconstruction of fault networks from seismicity catalogs:Three-dimensional optimal anisotropic dynamic clustering[J]. J Geophys Res: Solid Earth,113(B1):B01306.
Reading A M,Cracknell M J,Bombardieri D J,Chalke T. 2015. Combining machine learning and geophysical inversion for applied geophysics[J]. ASEG Extended Abstracts,(1):1–5.
Ross Z E,Trugman D T,Hauksson E,Shearer P M. 2019. Searching for hidden earthquakes in southern California[J]. Science,364(6442):767–771. doi: 10.1126/science.aaw6888
Schubert E,Sander J,Ester M,Kriegel H P,Xu X W. 2017. DBSCAN revisited,revisited:Why and how you should (still) use DBSCAN[J]. ACM Trans Database Syst,42(3):19.
Sun X L,Yang P T,Zhang Z W. 2017. A study of earthquakes induced by water injection in the Changning salt mine area,SW China[J]. J Asian Earth Sci,136:102–109. doi: 10.1016/j.jseaes.2017.01.030
Trugman D T,Shearer P M. 2017. GrowClust:A hierarchical clustering algorithm for relative earthquake relocation,with application to the Spanish Springs and Sheldon,Nevada,earthquake sequences[J]. Seismol Res Lett,88(2A):379–391. doi: 10.1785/0220160188
Wang S,Jiang G Y,Weingarten M,Niu Y F. 2020. InSAR evidence indicates a link between fluid injection for salt mining and the 2019 Changning (China) earthquake sequence[J]. Geophys Res Lett,47(16):e2020GL087603. doi: 10.1029/2020GL087603
Wang S,Jiang G Y,Lei X L,Barbour A J,Tan X B,Xu C J,Xu X W. 2022. Three MW≥4.7 earthquakes within the Changning (China) shale gas field ruptured shallow faults intersecting with hydraulic fracturing wells[J]. J Geophys Res: Solid Earth,127(2):e2021JB022946. doi: 10.1029/2021JB022946
Yin J X,Li Z F,Denolle M A. 2021. Source time function clustering reveals patterns in earthquake dynamics[J]. Seismol Res Lett,92(4):2343–2353. doi: 10.1785/0220200403
Zhang B,Lei J S,Zhang G W. 2020. Seismic evidence for influences of deep fluids on the 2019 Changning MS6.0 earthquake,Sichuan basin,SW China[J]. J Asian Earth Sci,200:104492. doi: 10.1016/j.jseaes.2020.104492
Zhang Z W,Liang C T,Long F,Zhao M,Wang D. 2022. Spatiotemporal variations of focal mechanism solutions and stress field of the 2019 Changning MS6.0 earthquake sequence[J]. Front Earth Sci,9:797907. doi: 10.3389/feart.2021.797907
Zuo K Z,Zhao C P,Zhang H J. 2020. 3D crustal structure and seismicity characteristics of Changning-Xingwen area in the southwestern Sichuan Basin,China[J]. Bull Seismol Soc Ama,110(5):2154–2167. doi: 10.1785/0120200085
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