基于改进粒子群优化的超顺磁效应多参数提取

Multi-Parameter Extraction of Superparamagnetic Effect Based on Improved Particle Swarm Optimization

时域电磁法中的超顺磁效应一般由磁异常体或磁化层引起,并在响应中后期出现呈近似-1次幂律衰减的慢扩散现象,同时也是地下磁性介质信息的重要表征。传统参数提取方法在对磁性环境的实测数据进行处理时,会由于忽略这种效应导致数据的错误解释。为了实现对含有超顺磁效应数据的准确解释,本文基于改进粒子群优化算法进行了超顺磁效应的多参数提取研究,通过引入Cole-Cole磁化率模型实现了层状超顺磁效应的数值模拟,证明了磁化率、电导率可以影响超顺磁响应的幅值与衰减斜率,并结合改进粒子速度和位置更新策略的粒子群优化算法实现了对磁化率、电导率等参数的提取。结果表明,本文的方法在对超顺磁效应多参数进行提取时最大相对误差不超过2%,验证了方法的有效性。

Superparamagnetic(SPM)effects in time-domain electromagnetic method is generally caused by magnetic anomalies or magnetic layers,and cause the slow diffusion phenomenon of approximately-1 power law decay in the middle and late stages of the response.Meanwhile,it is also an important representation of the underground magnetic media information.However,ignoring the SPM effects will lead to an incorrect data interpretation using traditional parameter extraction method to process the magnetic environment data.In order to interpret the SPM response accurately,this paper proposes a multi-parameter extraction of SPM effects based on the improved particle swarm optimization(PSO)algorithm.The numerical simulation of the layered SPM effect is realized based on the Cole-Cole susceptibility model,and magnetic susceptibility and conductivity can affect the amplitude and attenuation slope of SPM responses.We improve the PSO algorithm with the particle velocity and position update strategy to realize the extraction of magnetic susceptibility,conductivity and other parameters.The results show that the maximum relative error of the proposed method is less than 2%when extracting multiple parameters of SPM effect,which verifies the effectiveness of this method.