基于自适应遗传算法的CSAMT一维反演

1D inversion of CSAMT data based on adaptive genetic algorithm

传统的CSAMT反演方法大多依赖于初始模型的选择,并且在反演过程中可能会因为出现病态矩阵而导致反演失败。本文提出了一种基于自适应遗传算法的CSAMT一维反演方法,该方法具有不依赖于初始模型的优点,并且反演过程中不会出现病态矩阵。首先,通过水平层状模型对标准遗传算法和自适应遗传算法进行比较,证明后者的改进效果;然后,在数据中加入随机噪声,证明其具有抗噪性;最后,将其运用到实测数据中,证明了该方法的实用性。

Most of conventional controlled source audio-magnetotelluric (CSAMT) data inversions depend on the initial model, and they may fail due to the presence of ill matrix. We propose an adaptive genetic algorithm to 1D inversion of CSAMT data in this paper. This algorithm does neither depend on the initial model nor on generate ill matrix in the process. First with a horizontal layered model we test standard genetic algorithm and adaptive genetic algorithm (AGA), and prove its improvement. Then adding some random noise to data, we test the algorithm and find its good anti-noise ability. Finally, applications in real data prove its practicability. © 2017, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.