基于改进粒子群算法的大地电磁阻抗张量分解方法  

Tensor decomposition method of magnetotelluric impedance based on particle swarm optimization

作　　者：陈先洁 王绪本[1] 李德伟[1] 谢卓良 乃国茹 CHEN Xianjie;WANG Xuben;LI Dewei;XIE Zhuoliang;NAI Guoru(School of Geophysics, Chengdu University of Technology,Chengdu 610000,China)

机构地区：[1]成都理工大学地球物理学院,成都610000

出　　处：《物探化探计算技术》2021年第5期620-627,共8页Computing Techniques For Geophysical and Geochemical Exploration

基　　金：国家自然科学基金项目(41674078)。

摘　　要：为消除由局部不均匀异常体造成的局部畸变的影响,大地电磁数据往往需要进行畸变校正。Groom-Bailey(GB)分解法,是大地电磁阻抗张量分解方法中应用最为广泛的一种。传统的GB分解方法多采用线性最优化方法,其计算精度低,速度慢。对此,这里将粒子群优化算法引入GB分解法中,同时对标准粒子群算法进行改进,提高算法的性能,并利用改进算法实现大地电磁阻抗张量分解。编程实现改进粒子群算法,通过测试函数验证改进算法的可行性。利用合成的单点单频数据和3D/2D模型正演数据进行分解试验,证明改进算法能够有效校正局部异常造成的畸变影响,并通过对实际资料数据进行分解处理,表明了方法的实际应用效果。In order to eliminate the influence of local distortion caused by local non-uniform anomalies,distortion correction has always been required for magnetotelluric data.The Groom-Bailey decomposition method is the most widely used one of the earth magneto-impedance tensor decomposition method,which is referred to as GB decomposition.The traditional GB decomposition method mostly uses linear optimization method,which has low calculation accuracy and slow speed.In this regard,this paper introduces the particle swarm optimization algorithm into the GB decomposition method,and at the same time improves the standard particle swarm optimization algorithm to improve the performance of the algorithm,and the improved algorithm is used to achieve the decomposition of the tectonic electromagnetic impedance tensor.Programming to achieve improved particle swarm optimization algorithm,and verify the feasibility of the improved algorithm through the test function.The synthetic single-point single-frequency data and the 3D/2D model forward data were used to perform decomposition experiments.It was proved that the improved algorithm can effectively correct the distortion caused by local anomalies,and the actual data was decomposed to show the practical application of the method.

关 键 词：大地电磁 GB分解 粒子群算法 优化算法 

分 类 号：P631.2[天文地球—地质矿产勘探]