摘要
在震源参数反演理论研究中,地表形变与震源参数之间为复杂多维的非线性关系,针对传统泰勒级数展开的精度评定方法可能无法适用于震源参数的精度评定问题,本文将Bootstrap方法引入到震源参数非线性反演及精度评定研究中.通过对GPS地表形变观测数据实施Bootstrap重采样获取自助样本,使用遗传算法(Genetic Algorithm,GA)搜索震源参数,设计并给出了震源参数精度评定的Bootstrap方法计算流程.将本文方法用于6个模拟地震、Amatrice地震及Visso地震实验中,通过反演震源参数、获取参数的置信区间及中误差,并与Jackknife方法、Monte Carlo方法进行对比分析.实验结果表明,通过执行本文精度评定方法能够获取比Jackknife方法更加可靠的震源参数置信区间以及更加精确的精度信息.实验验证了将Bootstrap方法用于震源参数精度评定的有效性和可靠性,为研究震源参数精度评定理论研究提供了一种新的采样思路.
There is a complex and multi-dimensional nonlinear relationship between the surface deformation and the earthquake source parameters in the theories of source parameter inversion.Since the traditional precision estimation methods based on the Taylor series expansion may be inapplicable in the earthquake source parameters case,this paper introduces the Bootstrap resampling method into the nonlinear inversion and precision estimation of earthquake source parameters.The Bootstrap samples are obtained by resampling the GPS surface deformation measurement data,and the Genetic Algorithm(GA)is applied to search the optimal source parameters.The calculation procedures of the Bootstrap method for precision estimation of the source parameters are designed.Applying the algorithm proposed in this paper into six simulated earthquakes,the Amatrice earthquake and the Visso earthquake experiments,obtaining the confidence intervals and calculating the standard error,the proposed method is then compared with the Jackknife method and the Monte Carlo method in inversing the earthquake source parameters.The experimental results show that,the precision estimation method proposed can obtain more reliable confidence intervals and more accurate precision information of earthquake source parameters than the Jackknife method.This verifies the reliability and effectiveness of the Bootstrap method for evaluating the precision of source parameters,and also provides a new sampling method for the theoretical research of source parameter precision estimation.
作者
王乐洋
李志强
WANG LeYang;LI ZhiQiang(Faculty of Geomatics,East China University of Technology,Nanchang 330013,China;School of Surveying and Geo-Informatics,Shandong Jianzhu University,Jinan 250101,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2021年第6期2001-2016,共16页
Chinese Journal of Geophysics
基金
国家自然科学基金(41874001,41664001)
国家重点研发计划(2016YFB0501405)联合资助.
