A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

Abstract

Within the field of seismic data acquisition with active sources, the technique of acquiring simultaneous data, also known as blended data, offers operational advantages. The preferred processing of blended data starts with a step of deblending, that is separation of the data acquired by the different sources, to produce data that mimic data from a conventional seismic acquisition and can be effectively processed by standard methods. Recently, deep learning methods based on the deep neural network have been applied to the deblending task with promising results, in particular using an iterative approach. We propose an enhancement to deblending with an iterative deep neural network, whereby we modify the training stage of the deep neural network in order to achieve better performance through the iterations. We refer to the method that only uses the blended data as the input data as the general training method. Our new multi-data training method allows the deep neural network to be trained by the data set with the input patches composed of blended data, noisy data with low amplitude crosstalk noise, and unblended data, which can improve the ability of the deep neural network to remove crosstalk noise and protect weak signal. Based on such an extended training data set, the multi-data training method embedded in the iterative separation framework can result in different outputs at different iterations and converge to the best result in a shorter iteration number. Transfer learning can further improve the generalization and separation efficacy of our proposed method to deblend the simultaneous-source data. Our proposed method is tested on two synthetic data and two field data to prove the effectiveness and superiority in the deblending of the simultaneous-source data compared with the general training method, generic noise attenuation network and low-rank matrix factorization methods.

A blended wavefield separation method for seismic exploration based on improved GoogLeNet

“…However, the short time intervals between different sources results in the phenomenon of wavefield mixing, which greatly reduces the SNR of seismic data and the quality of the section plane. Therefore, the effective separation of blended data generated by multiple sources is a significant part of simultaneous acquisition [ 2 – 4 ]. The existing data deblending methods are mostly based on the continuity characteristics of the useful signal and the random distribution characteristics of blended noise in the non-shot-gather domain.…”

Focal deblending: Marine data processing experiences

“…(2022) use iterative inversion and a multi‐resolution U‐Net to take advantage of the multiscale nature of seismic data. Deep neural network–based approaches can also be used for gradient denoising in iterative schemes, both in a supervised (K. Wang, Mao, et al., 2022; K. Wang & Hu, 2022) and in an unsupervised (K. Wang, Hu, et al., 2022) fashion.…”

Untitled

“…However, the short time intervals between different sources results in the phenomenon of wavefield mixing, which greatly reduces the SNR of seismic data and the quality of the section plane. Therefore, the effective separation of blended data generated by multiple sources is a significant part of simultaneous acquisition [2][3][4]. The existing data deblending methods are mostly based on the continuity characteristics of the useful signal and the random distribution characteristics of blended noise in the non-shot-gather domain.…”