Low-Frequency Seismic Data Reconstruction Using Deep-Learning Refined Deconvolution

Low-frequency (LF) data plays a key role in mitigating cycle-skipping in full waveform inversion (FWI). We propose a method to efficiently and accurately reconstruct LF seismic data for a large number of shot gathers based on multi-channel deconvolution (MD) and deep learning (DL). Specifically, we firstly propose a MD method to predict LF data for very limited shot gathers. Then, we use a deep neural network (called ‘acceleration network’) to learn the relation between a shot gather and its corresponding LF data, based on the labels provided by the MD method, enabling efficient prediction for all shot gathers. Next, another deep neural network (called ‘improvement network’) is proposed to improve the accuracy of the LF shot gathers predicted by the ‘acceleration network’. To do so, several horizontal layered velocity models are generated based on the statistical distribution of well logs, and several synthetic shot gathers with and without LF are generated by solving the acoustic wave equation. Based on these synthetic shot gathers, the MD predicted LF data and the corresponding true LF data form a data pair [predicted LF, true LF] for each shot gather, and these data pairs are used to train the ‘improvement network’. Finally, employing a cascade of ‘acceleration network’ and ‘improvement network’, we reconstruct the LF data of all shot gathers. Synthetic and field data examples verify that the proposed method exhibits superior accuracy compared to conventional MD method in LF data reconstruction.