Inversion of fault slip parameters based on DInSAR and automated fault model discretization method

doi:10.6046/gtzyyg.2016.03.05

Abstract
References
Related Articles
Metrics

Download: PDF(2802 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The inversion of geometry and motion parameters of the seismogenic fault based on elastic dislocation model and constrained by coseismic deformation data obtained by DInSAR has great significance for understanding the activity characteristics of the fault and assessing the risk of potential disasters. The existing inversion methods of fault slip parameters are mostly based on the uniform discretization with rectangular dislocation units or artificially setting discretizaiton, which will lead to "pseudo-slip" or the phenomenon that the slip distribution is too smooth to reflect the slip details on the fault plane. Therefore, the automated fault model discetization method is introduced in this paper, which takes into account the principle of the minimized residuals and the optimal smoothing scales of the model, so the reliable solution can be obtained under the effective constraints. The inversion of geometry and slip parameters of fault in Bam earthquake is taken as an example. The experimental results show that using the automated fault model discretization method to invert the motion parameters of the single fault can generate reliable results.

Keywords building extraction very high resolution imagery airborne LiDAR shadow cool-colored roof multi-level

TP79

Issue Date: 01 July 2016

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	WANG Xue
	LI Peijun
	JIANG Shasha
	LIU Jing
	SONG Benqin

Cite this article:

WANG Xue,LI Peijun,JIANG Shasha, et al. Inversion of fault slip parameters based on DInSAR and automated fault model discretization method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 25-30.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.03.05 OR https://www.gtzyyg.com/EN/Y2016/V28/I3/25

[1] 洪顺英,申旭辉,单新建,等.基于升降轨ASAR的于田Ms 7.3级地震同震形变场信息提取与分析[J].国土资源遥感,2010,22(4):98-102.doi:10.6046/gtzyyg.2010.04.20. Hong S Y,Shen X H,Shan X J,et al.The calculation and analysis of the co-seismic deformation field of Yutian Ms 7.3 earthquake basing on the ascending and descending orbit ASAR data[J].Remote Sensing for Land and Resource,2010,22(4):98-102.doi:10.6046/gtzyyg.2010.04.20.
[2] 罗旭巍,孙建宝,沈正康,等.基于InSAR同震形变观测反演2010年新西兰南岛Mw7.1 Darfield地震同震破裂分布[J].地球物理学报,2013,56(8):2613-2624. Luo X W,Sun J B,Shen Z K,et al.Co-seismic slip distribution of 2010 Darfield, New Zealand Mw7.1 earthquake inverted using InSAR measurements[J].Chinese Journal of Geophysics,2013,56(8):2613-2624.
[3] 薛莲,孙建宝,沈正康.2010年1月12日海地Mw7.0级地震InSAR同震形变观测及同震滑动分布反演[J].地震地质,2011,33(1):157-174. Xue L,Sun J B,Shen Z K.InSAR coseismic deformation observation of the Jan 12th, 2010 HaiTi earthquake and its coseismic slip distribution inversion[J].Seismology and Geology,2011,33(1):157-174.
[4] 周辉,冯光财,李志伟,等.利用InSAR资料反演缅甸Mw6.8地震断层滑动分布[J].地球物理学报,2013,56(9):3011-3021. Zhou H,Feng G C,Li Z W,et al.The fault slip distribution of the Myanmar Mw6.8 earthquake inferred from InSAR measurements[J].Chinese Journal of Geophysics,2013,56(9):3011-3021.
[5] 温扬茂,何平,许才军,等.联合Envisat和ALOS卫星影像确定L'Aquila地震震源机制[J].地球物理学报,2012,55(1):53-65. Wen Y M,He P,Xu C J,et al.Source parameters of the 2009 L'Aquila earthquake, Italy from Envisat and ALOS satellite SAR images[J].Chinese Journal of Geophysics,2012,55(1):53-65.
[6] Resor P G,Pollard D D,Wright T J,et al.Integrating high-precision aftershock locations and geodetic observations to model coseismic deformation associated with the 1995 Kozani-Grevena earthquake,Greece[J].Journal of Geophysical Research:Solid earth,2005,110(B9):B09402.
[7] Walters R J,Elliott J R,D'Agostino N,et al.The 2009 L'Aquila earthquake(central Italy):A source mechanism and implications for seismic hazard[J].Geophysical Research Letters,2009,36(17):L17312.
[8] 张国宏,屈春燕,汪驰升,等.基于GPS和InSAR反演汶川Mw7.9地震断层滑动分布[J].大地测量与地球动力学,2010,30(4):19-24. Zhang G H,Qu C Y,Wang C S,et al.Inversion of slip distribution of 2008 Wenchuan Mw7.9 earthquake constrained jointly by InSAR and GPS measurements[J].Journal of Geodesy and Geodynamics,2010,30(4):19-24.
[9] Barnhart W D,Lohman R B.Automated fault model discretization for inversions for coseismic slip distributions[J].Journal of Geophysical Research,2010,115(B10):B10419.
[10] Meade B J.Algorithms for the calculation of exact displacements,strains, and stresses for triangular dislocation elements in a uniform elastic half space[J].Computer and Geosciences,2007,33(8):1064-1075.
[11] Golub G H,Hansen P C,O'Leary D P.Tikhonov regularization and total least squares[J].SIAM Journal on Matrix Analysis and Applications,1999,21(1):185-194.
[12] Du Y J,Aydin A,Segall P.Comparison of various inversion techniques as applied to the determination of a geophysical deformation model for the 1983 Borah Peak earthquake[J].Bulletin of the Seismological Society of America,1992,82(4):1840-1866.
[13] Okada Y.Surface deformation due to shear and tensile faults in a half-space[J].Bulletin of the Seismological Society of America,1985,75(4):1135-1154.
[14] 凌勇,曾祺明,罗扬,等.巴姆地震变形场和应力场:I.用差分干涉雷达和Okada方法求解[J]. 岩石学报,2006,22(9):2367-2374. Ling Y,Zeng Q M,Luo Y,et al.Deformation and stress field of Bam earthquake:Ⅰ.InSAR and Okada calculation[J].Acta Petrologica Sinica,2006,22(9):2367-2374.

[1]	WU Fang, LI Yu, JIN Dingjian, LI Tianqi, GUO Hua, ZHANG Qijie. Application of 3D information extraction technology of ground obstacles in the flight trajectory planning of UAV airborne geophysical exploration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 286-292.
[2]	WU Yu, ZHANG Jun, LI Yixu, HUANG Kangyu. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
[3]	HU Xinyu, XU Zhanghua, CHEN Wenhui, CHEN Qiuxia, WANG Lin, LIU Hui, LIU Zhicai. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image[J]. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
[4]	LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[5]	QIU Yifan, CHAI Dengfeng. A deep learning method for Landsat image cloud detection without manually labeled data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 102-107.
[6]	Lei MENG, Chao LIN. Discussion on quality inspection and solution of DEM generated by airborne LiDAR technology[J]. Remote Sensing for Land & Resources, 2020, 32(1): 7-12.
[7]	Qi LI, Jianchao WANG, Yachao HAN, Zihong GAO, Yongjun ZHANG, Dingjian JIN. Potential evaluation of China’s coastal airborne LiDAR bathymetry based on CZMIL Nova[J]. Remote Sensing for Land & Resources, 2020, 32(1): 184-190.
[8]	Lei DU, Jie CHEN, Minmin LI, Xiongwei ZHENG, Jing LI, Zihong GAO. The application of airborne LiDAR technology to landslide survey: A case study of Zhangjiawan Village landslides in Three Gorges Reservoir area[J]. Remote Sensing for Land & Resources, 2019, 31(1): 180-186.
[9]	Li XUE, Shuwen YANG, Jijing MA, Xin JIA, Ruliu YAN. Automatic expansion extraction algorithm of remote sensing images[J]. Remote Sensing for Land & Resources, 2019, 31(1): 42-48.
[10]	Li YAN, Yao LI, Hong XIE. Automatic reconstruction of LoD3 city building model based on airborne and vehicle-mounted LiDAR data[J]. Remote Sensing for Land & Resources, 2018, 30(4): 97-101.
[11]	CUI Shiai, CHENG Bo, LIU Yueming. Research on methods of building area extraction from high resolution SAR image based on manifold learning[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 48-56.
[12]	FU Ying, GUO Qiaozhen, PAN Yingyang, WANG Dongchuan. Research on building extraction rules based on SPOT6 data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 65-69.
[13]	LI Jiajun, ZHONG Ruofei. Route design of light airborne LiDAR[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 97-103.
[14]	WANG Xudong, DUAN Fuzhou, QU Xinyuan, LI Dan, YU Panfeng. Building extraction based on UAV imagery data with the synergistic use of objected-based method and SVM classifier[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 97-103.
[15]	WANG Jinjie, DING Jianli, ZHANG Cheng, CHEN Wenqian. Method of water information extraction by improved SWI based on GF-1 satellite image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 29-35.

Viewed

Full text

Abstract

Cited

Shared

Discussed