基于InSAR同震形变场的新疆逆冲型地震烈度评估

doi:10.3969/j.issn.0253-4967.2025.05.20240005

地震地质 ›› 2025, Vol. 47 ›› Issue (5): 1396-1415.DOI: 10.3969/j.issn.0253-4967.2025.05.20240005

基于InSAR同震形变场的新疆逆冲型地震烈度评估

王顺¹⁾(), 姚远²^,³⁾, 高明星¹⁾()

1)新疆大学, 地质与矿业工程学院, 乌鲁木齐 830017
2)新疆帕米尔陆内俯冲国家野外科学观测研究站, 北京 100029
3)中国地震局乌鲁木齐中亚地震研究所, 乌鲁木齐 830011

收稿日期:2024-01-09 修回日期:2024-03-17 出版日期:2025-10-20 发布日期:2025-11-11
通讯作者: *高明星, 女, 1981年生, 博士, 副教授, 主要从事构造地貌、活动构造研究, E-mail: postdegree@163.com。
作者简介:
王顺, 男, 1995年生, 现为新疆大学地质与矿业工程学院资源与环境专业在读硕士研究生, 研究方向为InSAR技术及其在地震中的应用, E-mail: wangshun0516@126.com。
基金资助:
国家重点研发计划重点项目(2022YFC3003700); 新疆维吾尔自治区科技厅自然科学基金(2022D01C361); 国家自然科学基金(42462023); 新疆维吾尔自治区高层次人才项目(TCBR202105); 新疆大学博士启动基金(620320044)

RESEARCH ON INTENSITY EVALUATION OF XINJIANG THRUST-TYPE EARTHQUAKES BASED ON INSAR COSEISMIC DEFORMATION FIELD

WANG Shun¹⁾(), YAO Yuan²^,³⁾, GAO Ming-xing¹⁾()

1)School of Geology and Mining Engineering, Xinjiang University, Urumqi 830017, China
2)Xinjiang Pamir Intracontinental Subduction National Field Observation and Research Station, Beijing 100029, China
3)Urumqi Institute of Central Asia Earthquake, China Earthquake Administration, Urumqi 830011, China

Received:2024-01-09 Revised:2024-03-17 Online:2025-10-20 Published:2025-11-11

摘要/Abstract

摘要：

为探索地震烈度与地震发生后地表形变的关系, 解决震后快速评估新疆逆冲型地震烈度的难题, 文中以新疆2015年皮山6.5级、 2017年精河6.6级、 2020年伽师6.4级3个逆冲型地震为例, 通过对比研究不同地震的InSAR同震形变场与实际调查地震烈度数据, 发现同震形变场与地震烈度之间存在较高的相关性。研究结果表明: 1)InSAR技术能够有效地识别出震区的同震形变场, 为震后的地震烈度评估提供了重要的数据支持, 对评价房屋结构类型单一和缺少强震动台站观测数据区域的烈度评估提供了重要依据; 2)利用人口聚集区的同震形变进行烈度评估的手段, 以及利用历史地震事件的烈度-形变关系模型和同震形变场大小估计当前地震的烈度等级的方法可在地震烈度早期评估中发挥重要作用, 有助于我们今后调查地震烈度等级和烈度影响的范围判断; 3)利用AHP-熵权法对InSAR形变量、库仑应力变化值、人口密度、震源距离、沉积层厚度这5个因子进行加权叠加并进行烈度评估研究可提高地震烈度评估结果的可靠性, 该方法可为地震烈度评估提供一种新的思路。同时, 文中还讨论了InSAR同震形变场的逆冲型地震烈度评估方法的局限性, 为今后的研究提供了参考。

关键词: D-InSAR, 同震形变, 地震烈度

Abstract:

Accurately and rapidly assessing seismic intensity following an earthquake is essential for effective emergency response, targeted disaster relief, and scientifically informed post-disaster reconstruction. This need is particularly acute in seismically active and often remote regions such as Xinjiang, China. Situated in the interior of Eurasia, Xinjiang is characterized by complex geological structures, where compressional forces from the north and south dominate tectonic activity across the Tianshan, Pamir, and other mountain ranges. Such tectonic environment produces frequent strong earthquakes, most of which are thrust events. Compared with strike-slip and normal faulting, thrust earthquakes are associated with shallow fault dips and may be linked to near-horizontal detachments. Fault displacement is typically absorbed by distributed fold deformation along the fault and attenuates rapidly, often producing little or no surface rupture. These characteristics complicate the interpretation of coseismic rupture processes and the spatial distribution of earthquake damage. Combined with the region's rugged terrain and sparse infrastructure, thrust earthquakes pose a serious threat to lives and property in Xinjiang.

High-quality, rapid post-earthquake intensity assessments are therefore critical to reducing earthquake impacts. Intensity maps are a primary basis for emergency rescue, recovery, and reconstruction. Traditional field investigations of intensity, however, require considerable human and material resources, pose safety risks to investigators, and are influenced by subjective judgment in assessing building damage. Additionally, since the widespread implementation of seismic-resistant housing projects in Xinjiang after 2003, the uniformity of residential building types has further limited the effectiveness of on-site evaluations.

With the advancement of remote sensing technology, Interferometric Synthetic Aperture Radar(InSAR)has emerged as a powerful tool for surface deformation monitoring and disaster assessment. Its all-weather, all-day imaging capabilities, unaffected by conditions such as rain or snow, make Differential InSAR(D-InSAR) an important technique for monitoring earthquake-induced surface deformation. To explore the relationship between seismic intensity and coseismic deformation and to address the challenge of rapid thrust-earthquake intensity assessment in Xinjiang, this study investigates three thrust earthquakes: the 2015 Pishan M_S6.5, the 2017 Jinghe M_S6.6, and the 2020 Jiashi M_S6.4 events.

Comparisons between InSAR-derived coseismic deformation fields and field-surveyed seismic intensities reveal a strong correlation. In population centers, deformation of 0.5~1.5cm corresponds to intensity Ⅶ, while deformation exceeding 1.5cm corresponds to intensity Ⅷ. Using these relationships, a linear regression model was developed between deformation and intensity levels. Furthermore, based on both a single-factor evaluation(coseismic deformation) and a multi-factor framework that integrates InSAR deformation, coseismic stress changes, population density, source distance, and sedimentary thickness, intensity assessments were performed using the AHP-entropy weight method.

The results indicate that:

(1)D-InSAR can rapidly monitor large-scale surface deformation after an earthquake, providing comprehensive and accurate coseismic deformation patterns. Unlike traditional methods dependent on sparse seismic station data, InSAR directly reflects the spatial distribution of regional deformation and supplies valuable geological background information for seismic intensity evaluation, especially in regions with limited building-type diversity or seismic station coverage.

(2)There is a clear relationship between seismic intensity and coseismic deformation. Mapping deformation fields onto intensity scales allows for the rapid estimation of earthquake intensity levels. Using historical deformation-intensity relationships enhances early evaluations of both the intensity grade and its spatial extent in future earthquakes.

(3)Multi-factor evaluation combining InSAR deformation with stress change, population density, focal distance, and sediment thickness improves the reliability of seismic intensity assessments compared to single-factor approaches. This method integrates both natural factors(e.g. geology, topography) and socioeconomic factors(e.g. population distribution), thereby capturing the complexity and diversity of earthquake impacts.

Overall, the AHP-entropy weight-based multi-factor evaluation framework demonstrates strong potential for application in earthquake risk assessment, disaster prevention and mitigation. At the same time, this study discusses the limitations of applying InSAR for thrust-earthquake intensity evaluation, offering insights for future research. The findings support more accurate and rapid post-earthquake assessments and highlight the value of InSAR technology in evaluating strong earthquake intensity in Xinjiang.

Key words: D-InSAR, coseismicdeformation, seismic intensity

王顺, 姚远, 高明星. 基于InSAR同震形变场的新疆逆冲型地震烈度评估[J]. 地震地质, 2025, 47(5): 1396-1415.

WANG Shun, YAO Yuan, GAO Ming-xing. RESEARCH ON INTENSITY EVALUATION OF XINJIANG THRUST-TYPE EARTHQUAKES BASED ON INSAR COSEISMIC DEFORMATION FIELD[J]. SEISMOLOGY AND GEOLOGY, 2025, 47(5): 1396-1415.

图/表 15

表 1 不同研究机构给出的震源机制解

Table1 The focal mechanism solutions reported by different research institutions

名称	机构	震源机制解				矩心深度/km
名称	机构	节面	走向/(°)	倾角/(°)	滑动角/(°)	矩心深度/km
皮山地震	中国地震台网中心	节面Ⅰ	314	68	97	10
	中国地震台网中心	节面Ⅱ	115	23	72	10
	GCMT	节面Ⅰ	294	68	92	15.6
	GCMT	节面Ⅱ	109	22	85	15.6
	USGS	节面Ⅰ	303	68	97	10
	USGS	节面Ⅱ	104	23	73	10
精河地震	中国地震台网中心	节面Ⅰ	76	44	80	23
	中国地震台网中心	节面Ⅱ	269	47	99	23
	GCMT	节面Ⅰ	101	44	118	28
	GCMT	节面Ⅱ	244	52	66	28
	USGS	节面Ⅰ	92	60	92	20
	USGS	节面Ⅱ	269	30	87	20
伽师地震	中国地震台网中心	节面Ⅰ	182	35	32	16
	中国地震台网中心	节面Ⅱ	65	72	121	16
	GCMT	节面Ⅰ	196	37	30	12
	GCMT	节面Ⅱ	81	72	123	12
	USGS	节面Ⅰ	221	20	72	20
	USGS	节面Ⅱ	60	71	96	20

图 1 地震构造背景

Fig. 1 The tectonic background of seismic structure.

表 2 SAR数据

Table2 SAR data

地震	轨道信息	轨道方向	极化方式	主影像日期	从影像日期	时间基线	垂直基线
皮山地震	136	降轨	VV	2015-06-24	2015-07-18	24	31
精河地震	63	降轨	VV	2017-08-07	2017-08-19	12	-80
伽师地震	129	升轨	VV	2020-01-16	2020-01-28	12	11

图 2 皮山地震的同震场与地震烈度对比图(据姚远等, 2016修改)

Fig. 2 Comparison of coseismic field to seismic intensity in Pishan earthquake (modified from YAO Yuan et al., 2016).

图 3 精河地震的同震场与地震烈度对比图(据常想德等, 2017修改)

Fig. 3 Comparison of coseismic field to seismic intensity in Jinghe earthquake (modified from CHANG Xiang-de et al., 2017).

图 4 伽师地震的同震场与地震烈度对比图(据马建等, 2020修改)

Fig. 4 Comparison of coseismic field to seismic intensity in Jiashi earthquake(modified from MA Jian et al., 2020).

表 3 线性回归分析结果

Table3 Results of linear regression analysis

结果	非标准化系数		标准化系数 Beta	t	VIF	R²	调整R²	F
结果	B	标准误差	标准化系数 Beta	t	VIF	R²	调整R²	F
常数	7.184	0.04	0.569	179.79	1	0.323	0.319	80.766(P=0.000^*)
形变量/cm	0.138	0.015		8.987

图 5 各因子归一化图 a 皮山地震形变量归一化; b 伽师地震形变量归一化; c 皮山地震库仑应力变化归一化; d 伽师地震库仑应力变化归一化; e 皮山地震人口密度归一化;f 伽师地震人口密度归一化; g 皮山地震震源距离归一化; f 伽师地震震源距离归一化; i 皮山地震沉积层厚度归一化; j 伽师地震沉积层厚度归一化

Fig. 5 Normalization of each factor.

表 4 因子矩阵

Table4 Factor matrix

因子	同震形变量	库仑应力	人口密度	沉积层厚度	震源距离
同震形变量	1	1	3	3	3
库仑应力	1	1	3	3	3
人口密度	13	13	1	1	1
沉积层厚度	13	13	1	1	1
震源距离	13	13	1	1	1
合计	3	3	9	9	9

表 5 AHP层次分析结果

Table5 AHP hierarchical analysis result

因子	特征向量	权重值/%	最大特征根	CI值
同震形变量	1.67	33.33	5	0
库仑应力	1.67	33.33
人口密度	0.56	11.11
沉积层厚度	0.56	11.11
震源距离	0.56	11.11

表 6 一致性检验结果

Table6 Consistency examination

最大特征根	CI值	RI值	CR值	一致性检验结果
5	0	1.11	0	通过

表 7 AHP-熵权法权重结果

Table7 Weight results with AHP-entropy weight method

方法权重	形变量/%	库仑应力变化/%	沉积层厚度/%	人口密度/%	震源距离/%
层次分析法	33.33	33.33	11.11	11.11	11.11
熵权法	27.39	34.19	2.72	34.42	1.28
组合权重	32.56	36.38	5.93	21.07	4.06

图 6 基于AHP-熵权法的皮山地震烈度评估

Fig. 6 Evaluation of Pishan earthquake intensity based on AHP-entropy weight method.

图 7 基于AHP-熵权法的伽师地震烈度评估

Fig. 7 Evaluation of Jiashi earthquake intensity based on AHP-entropy weight method.

表 8 调查烈度与评估烈度面积对比

Table8 Comparison of area between investigation intensity and assessment intensity

序号	名称	烈度等级	调查烈度面积/km²	单因子评估烈度面积/km²	多因子评估烈度面积/km²
1	皮山地震	Ⅷ	1110	993	284
		Ⅶ	3410	575	1027
2	精河地震	Ⅷ	979	771
		Ⅶ	3190
3	伽师地震	Ⅷ	257	668	615
		Ⅶ	2397	1166	1451

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基于InSAR同震形变场的新疆逆冲型地震烈度评估

RESEARCH ON INTENSITY EVALUATION OF XINJIANG THRUST-TYPE EARTHQUAKES BASED ON INSAR COSEISMIC DEFORMATION FIELD

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