Seismic-inversion method for nonlinear mapping multilevel well-seismic matching based on bidirectional long short-term memory networks

Abstract

In this paper, the recurrent neural network structure of a bidirectional long short-term memory network (Bi-LSTM) with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data. A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear mapping. The seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale, which then stepwise approach the logging curve in the high-frequency band. Finally, a seismic-inversion method of nonlinear mapping multilevel well-seismic matching based on the Bi-LSTM network is developed. The characteristic of this method is that by applying the multilevel well-seismic matching process, the seismic data are stepwise matched to the scale range that is consistent with the logging curve. Further, the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well-seismic matching process, such as the inconsistency in the scale of two types of data, accuracy in extracting the seismic wavelet of the well-side seismic traces, and multiplicity of solutions. Model test and practical application demonstrate that this method improves the vertical resolution of inversion results, and at the same time, the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect.