Spectral decomposition and multifrequency joint amplitude-variation-with-offset inversion based on the neural network

The conventional amplitude-variation-with-offset (AVO) inver-sion method based on the amplitude attribute of prestack gathers calculates the elastic parameters of underground media through the variation of amplitude with offset. However, when the under-ground medium is a thin interlayer, the tuning effect will occur, which is the aliasing phenomenon of amplitude at the reflection interface. The tuning effect makes the conventional AVO inver-sion method based on amplitude attributes difficult to solve the problem of thin interlayer recognition. In addition, the same re-flection interface will have different AVO characteristics at differ-ent frequencies, whereas the frequency factor is not included in conventional AVO inversion methods. Two-stage neural network approaches based on deep learning are combined to improve the resolution of thin interlayers and to accurately invert the elastic parameters. For the first-stage neural network, a fully connected network is used to solve the inversion spectral decomposition problem. It can eliminate the thin interlayer tuning effect, effec-tively improve the resolution, and obtain reflection coefficients at different frequencies. For the second-stage neural network, a multichannel convolutional neural network is used to establish the mapping relationship between multifrequency reflection co-efficients and elastic parameters, so that the multifrequency joint inversion of the elastic parameters could be realized. This pro-cedure is applied to synthetic data (with and without noise) to indicate the resistance to noise interference of the two-stage deep-learning method. Compared with the method of directly pre-dicting elastic parameters using seismic data and the conventional AVO inversion method, the two-stage deep-learning method can describe the elastic parameters of thin interbeds more accurately. The same procedure is applied to the field data, and the inversion results indicate that they can well match with the well-logging data. Hence, it is promising for practical applications.