反演(地质)
计算机科学
电阻抗
声阻抗
卷积神经网络
残余物
反问题
地震反演
声学
地质学
地球物理学
人工智能
算法
地震学
工程类
物理
数学
光学
电气工程
数学分析
构造学
方位角
作者
J. Meng,S. Wang,Zhuang Wang,Chen Zhou,Limin Yang,G. Niu
标识
DOI:10.3997/2214-4609.202112610
摘要
Summary Post-stack acoustic impedance inversion has definitive geophysical significance.Traditional acoustic impedance inversion is based on convolutional model and requires low frequency model. When the definition of the initial model is not accurate, the impedance inversion is not credible. In addition, due to the filtering effect of seismic response, impedance inversion is band-limited.In this abstract, a method combining deep learning and transfer learning is used to inverse acoustic impedance. Independent of the initial model, the nonlinear relationship between seismic data and acoustic impedance can be established by CRNN. The network architecture trained by simulated data is finetuned with little logging data. Transfer learning not only overcomes the problem of less label data in field inversion, but also solves the approximation problem of convolutional model. We used two typical models with different geological characteristics to prove the effectiveness of the inversion method. This provides a new method for seismic inversion in field area.
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