人工神经网络非线性地震波形反演

地震波形反演是非线性问题，其目标函数阳多极值的函数，若用线性化的反演方法求解，常会遇到迭代收敛于目标函数局部最优等困难。本文研究能求得目标函数全局最优解的遗传算法训练人工神经网络的地震波形反演方法。考虑到遗传算法训练神经网络地震波形反演的未知能数量大，而通常的二进制编码遗传算法占用计算机内存量大，不能在较小内存的计算机上实现，故以可节省内存的０－１编码遗传算法训练神经网络，提出了加速网络收敛的方法

Nonlinear seismic waveform inversion using the artifiual neural network

The seismic waveform inversion is a nonlinear problem. Its objective functions arc functions with several extreme values. When solving the problem using the linear inversion method,the difficulty of thc iteration converging to tho local optimum of the objoctive function is often met. The paper proposes a seismie waveform inversion method using the attifical neural network trained by the genetic algorithm which can get thc overall optimum solution of the objective function. Since the unknown paramcters of the seismie waveform inversion using the neural network trained by the genetic algorithm are many,the usual binary-coded genetic algorithm can not be implemented on the computer with a little memory space, so the 0-1 coded genetic algorithm which can save memory space is used to train the neural network. Also,the method to specd the network convergence is prcsented.