通过多目标智能优化算法研究微震震源定位存在的模型组合合理性未阐明、易陷入局部最优解、定位结果波动性较大等问题。为解决这些问题,首先在到时差模型与到时差商模型基础上设计4个不同的微震震源定位数学模型,两两组合构建6个多目标...通过多目标智能优化算法研究微震震源定位存在的模型组合合理性未阐明、易陷入局部最优解、定位结果波动性较大等问题。为解决这些问题,首先在到时差模型与到时差商模型基础上设计4个不同的微震震源定位数学模型,两两组合构建6个多目标优化定位模型;再设计3组基于不同台网形状(三维多面体、二维长方形、一维直线型)的微震震源正演仿真实验和1组工程数据验证实验,并引入多目标蚁狮优化(multi-objective ant lion optimization,MOALO)算法求解这些模型;最后采用多个统计指标评判各个模型组合定位效果的优劣。结果表明,数学模型组合(TDA-P1,TDQA)结合MOALO算法的多目标优化定位策略能够得到较高的微震震源定位精度,且模型稳健性较好,优于其他模型组合和传统多目标定位方法,在微震监测领域具有一定的应用价值。展开更多
We have developed a type of L-shaped single-component geophone array as a single station(L-array station)for surface microseismic monitoring.The L-array station consists of two orthogonal sensor arrays,each being a li...We have developed a type of L-shaped single-component geophone array as a single station(L-array station)for surface microseismic monitoring.The L-array station consists of two orthogonal sensor arrays,each being a linear array of single-component sensors.L-array stations can be used to accurately estimate the polarization of first arrivals without amplitude picking.In a synthetic example,we first use segmentally iterative ray tracing(SIRT)method and forward model to calculate the travel time and polarization of first arrivals at a set of L-array stations.Then,for each L-array station,the relative delay times of first arrivals along sensor arrays are used to estimate the polarization vector.The small errors in estimated polarization vectors show the reliability and robustness of polarization estimation based on L-array stations.We then use reverse-time ray-tracing(RTRT)method to locate the source position based on estimated polarizations at a set of L-array stations.Very small errors in inverted source location and origin time indicate the great potential of L-array stations for source localization applications in surface microseismic monitoring.展开更多
文摘通过多目标智能优化算法研究微震震源定位存在的模型组合合理性未阐明、易陷入局部最优解、定位结果波动性较大等问题。为解决这些问题,首先在到时差模型与到时差商模型基础上设计4个不同的微震震源定位数学模型,两两组合构建6个多目标优化定位模型;再设计3组基于不同台网形状(三维多面体、二维长方形、一维直线型)的微震震源正演仿真实验和1组工程数据验证实验,并引入多目标蚁狮优化(multi-objective ant lion optimization,MOALO)算法求解这些模型;最后采用多个统计指标评判各个模型组合定位效果的优劣。结果表明,数学模型组合(TDA-P1,TDQA)结合MOALO算法的多目标优化定位策略能够得到较高的微震震源定位精度,且模型稳健性较好,优于其他模型组合和传统多目标定位方法,在微震监测领域具有一定的应用价值。
基金Project(KYCX17_0500)supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province,ChinaProjects(2013/B17020664X,2014B17614)supported by the Fundamental Research Funds for the Central Universities,China+2 种基金Project(41174043)supported by the National Natural Science Foundation of ChinaProject supported by the Funds from China Scholarship Council(CSC)Project(487237)supported by the NSERC Discovery Grant for LIU Qin-ya。
文摘We have developed a type of L-shaped single-component geophone array as a single station(L-array station)for surface microseismic monitoring.The L-array station consists of two orthogonal sensor arrays,each being a linear array of single-component sensors.L-array stations can be used to accurately estimate the polarization of first arrivals without amplitude picking.In a synthetic example,we first use segmentally iterative ray tracing(SIRT)method and forward model to calculate the travel time and polarization of first arrivals at a set of L-array stations.Then,for each L-array station,the relative delay times of first arrivals along sensor arrays are used to estimate the polarization vector.The small errors in estimated polarization vectors show the reliability and robustness of polarization estimation based on L-array stations.We then use reverse-time ray-tracing(RTRT)method to locate the source position based on estimated polarizations at a set of L-array stations.Very small errors in inverted source location and origin time indicate the great potential of L-array stations for source localization applications in surface microseismic monitoring.
