贝叶斯有限断层破裂分布模型反演研究进展

魏国光; 陈克杰; 朱海; 柴海山

doi:10.19975/j.dqyxx.2022-080

贝叶斯有限断层破裂分布模型反演研究进展

doi: 10.19975/j.dqyxx.2022-080

魏国光^1,,
陈克杰^{1, 2, 3, ,},
朱海¹,
柴海山¹

1.
南方科技大学地球与空间科学系，深圳 518055
2.
南方科技大学风险分析预测与管控研究院，深圳 518055
3.
广东省地球物理高精度成像技术重点实验室（南方科技大学），深圳 518055

基金项目: 国家自然科学基金资助项目（42074024）；广东省地球物理高精度成像技术重点实验室资助项目（2022B1212010002）；中国科协青年人才托举工程

详细信息

作者简介:
魏国光，男，博士研究生. 主要从事大地测量地震断层破裂反演研究. Email：guoguang_wei@126.com

通讯作者:
陈克杰，男，研究员，博士生导师. 主要从事大地测量震源参数反演、链生灾害智能预测等方面的研究. Email：chenkj@sustech.edu.cn

中图分类号: P315, P228
计量
- 文章访问数: 47
- HTML全文浏览量: 23
- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2022-12-19
- 录用日期: 2023-02-14
- 修回日期: 2023-02-13
- 网络出版日期: 2023-02-21

Review of Bayesian finite-fault source model inversion

Wei Guoguang^1
,,
Chen Kejie^{1, 2, 3
, ,},
Zhu Hai¹,
Chai Haishan¹

1.
Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China
2.
Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen 518055, China
3.
Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, Southern University of Science and Technology, Shenzhen 518055, China

Funds: Supported by the National Natural Science Foundation of China (Grant No. 42074024), Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology (Grant No. 2022B1212010002), and the Young Talent Promotion Project of the China Association for Science and Technology

摘要

摘要: 精确的有限断层破裂分布模型对于研究震源物理机制、评估地震灾害等具有重要意义. 目前，有限断层反演通常采用线性最小二乘方法，但该方法存在一定局限性：（1）不易评估完全的参数空间，因而不利于评估非高斯分布的参数不确定性；（2）为了提高反演稳定性，在反演中通常施加断层滑移平滑约束（正则化），但平滑强度的确定具有一定主观性；（3）断层几何设置不同使得反演结果不尽相同；（4）难以顾及地球速度模型不确定等. 与之对应，通过确定参数概率密度分布，贝叶斯反演提供了所有参数总体最优解，同时刻画不同参数之间协方差大小，可以有效克服上述问题. 特别是过去十余年间，随着计算机算力飞速提升，贝叶斯反演得到了越来越多应用. 通过阐释贝叶斯有限断层反演理论与技术，本文试图梳理近年来贝叶斯有限断层反演成果，最后展望贝叶斯有限断层反演发展趋势.
- 破裂分布 /
- 线性反演 /
- 贝叶斯推断 /
- 概率密度分布
Abstract: A finite-fault earthquake slip model can characterize the kinematics of rupture, which is essential for earthquake mechanism studies and seismic hazard assessments. The finite-fault model of an earthquake can be inverted from a wide range of geodetic measurements and seismic recordings. The linear least squares method minimizes misfits between observations and forward modeling and is a common approach in finite-fault inversion problems. This method may not yield the most plausible finite-fault model and has four main limitations. First, total parameter spaces are challenging to explore, and non-Gaussian parameter uncertainties cannot be evaluated. Second, to improve the stability of the fault slip inversion, fault slip smoothing operators (regularization techniques) are usually applied; however, determining the strength of smoothing a fault slip distribution is subjective. Third, the fault geometry needs to be predetermined, and different fault geometry settings can result in varying inversion results. Fourth, it is difficult to account for uncertainties in forward modeling because of imprecise Earth velocity models. In contrast, Bayesian inversion determines the probability density distribution of the model parameters, providing a globally optimized solution to all model parameters and characterizing trade-offs between pairs of model parameters. Therefore, the Bayesian approach effectively overcomes the problems encountered in linear inversion. With the rapid improvement in computer technology, Bayesian inversion has become highly developed, especially in the past decade. This review reports the results of recent Bayesian finite-fault inversion studies and explains the theory and methodology of Bayesian finite-fault inversion.
- rupture kinematics /
- linear inversion /
- Bayesian inference /
- probability density distribution

HTML全文

图 1 贝叶斯有限断层运动学反演主要流程

Figure 1. The process of Bayesian kinematic finite-fault inversion

下载: 全尺寸图片幻灯片

图 2 基于贝叶斯推断的2011年东日本大地震破裂分布. 背景颜色表示滑移值大小，黑色箭头表示滑移方向. 等值线表示地震破裂前端，间隔为10 s（修改自Minson et al., 2014）

Figure 2. The 2011 Tohoku-oki Japan earthquake rupture distribution based on Bayesian inference. The background color represents the size of the slip value, and the black arrows indicate the direction of the slip. The contour lines represent the front end of the earthquake rupture, with a spacing of 10 s (modified from Minson et al., 2014)

下载: 全尺寸图片幻灯片

图 3 在三维弹性结构下，不考虑或考虑弹性结构不确定性的滑移反演结果对比. （a）目标滑移模型. 当真实地壳结构具有沿走向划分的不均匀分布时，（b）和（c）分别显示了不考虑和考虑弹性结构不确定性的滑移分布估计值和相应的标准差. 当真实地壳结构具有沿与走向垂直划分的不均匀分布时，（d）和（e）分别显示了不考虑和考虑弹性结构不确定性的滑移分布估计值和相应的标准差. 真实地壳结构和假定地壳结构的示意图采用颜色编码，颜色越浅表示介质越接近（修改自Ragon and Simons, 2021）

Figure 3. Comparison of the inversion results for the slip without or with considering the uncertainty of the elastic structure under the three-dimensional elastic structure. (a) The target slip model. When the true crustal structure has a heterogenous distribution along the strike direction, (b) and (c) show the estimated slip distribution and corresponding standard deviation, respectively, without and with considering the uncertainty in the elastic structure. When the true crustal structure has a heterogenous distribution perpendicular to the strike direction, (d) and (e) show the estimated slip distribution and corresponding standard deviation, respectively, without and with considering the uncertainty in the elastic structure. The illustrations of the true and assumed crustal structures are coded by grayscale, with the light gray indicating the compliant earth medium (modified from Ragon and Simons, 2021)

下载: 全尺寸图片幻灯片

图 4 基于贝叶斯推断的断层几何和滑移分布同步反演. （a）由（b ）所示的断层滑移分布正演合成的50个三分量位移测站分布，三角形测站和正方形测站分别具有1 cm 和 2 cm的噪声水平；（c）基于图（a）中合成的观测值，采用贝叶斯反演获得的断层几何和滑移分布；（d）在图（c）中4个截面A、B、C和D处，贝叶斯推断的断层倾斜估计值（红色）与输入参考值（蓝色）的对比（修改自Wei et al., 2023）

Figure 4. The simultaneous inversion of fault geometry and slip distribution based on Bayesian inference. (a) The distribution of 50 three-component displacement stations synthesized from the forward-modeled fault slip distribution is shown in (b), with triangle stations and square stations having noise levels of 1 cm and 2 cm, respectively; (c) the fault geometry and slip distribution obtained using Bayesian inversion from the observation values synthesized in (a); and (d) the comparison of Bayesian inferred fault dip estimates (red) with input reference values (blue) at four planes, A, B, C and D, in (c) (modified from Wei et al., 2023)

下载: 全尺寸图片幻灯片

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