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引用次数: 0
摘要
本文研究了全波形反演(FWI),以训练神经网络的方式恢复孔弹性波方程的三个介质参数。我们通过交错网格方案将时域中的孔弹性波模拟重铸成一个递归神经网络(RNN)过程。此外,RNN 的参数与 FWI 的反演参数相吻合。本文提出了一种名为 Adam 的随机梯度优化器的 FWI 算法。目标函数相对于介质参数的梯度是通过自动微分计算出来的。针对三个介质参数,即固体密度、饱和基质的拉梅参数和干多孔基质的剪切模量,对 FWI 进行了数值计算。两个设计模型的数值计算表明,本文所述方法具有良好的成像能力。该方法可用于反演孔弹性波方程中更多的介质参数。
Poroelastic full-waveform inversion as training a neural network
In this paper, we investigate the full-waveform inversion (FWI) for recovering three media parameters of the poroelastic wave equations as training a neural network. We recast the poroelastic wave simulation in the time domain by the staggered-grid schemes into a process of recurrent neural networks (RNNs). Furthermore, the parameters of RNNs coincide with the inverted parameters in FWI. The algorithm of FWI with a stochastic gradient optimizer named Adam is proposed. The gradients of the objective function with respect to the media parameters are computed by the automatic differentiation. FWI is implemented numerically for three media parameters, i.e., solid density, Lamé parameter of of saturated matrix and shear modulus of dry porous matrix. The numerical computations with two designed models show the good imaging ability of the described method in this paper. It can be applied to invert more media parameters of the poroelastic wave equations.
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.