Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks

Abstract

The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing; deep learning methods have attracted significant attention in seismic data reconstruction. One barrier associated with these deep-learning based reconstruction methods is the need for large training datasets, which are difficult to acquire owing to physical or financial constraints in practice. A novel method for the recovery of incomplete seismic data without the need of training datasets was developed. Seismic prior is implicitly captured based on the particular CNN structure choice, referred to as the “deep-seismic-prior-based”. The learned network weights are the parameters that represent seismic data, and as the convolutional filter weights are shared for spatial invariance, the CNN structure can function as a regularizer to guide the network learning. The reconstruction is realized during the iterative process by minimizing the mean square error (MSE) between the network output and the original corrupted seismic data. Our method could handle both irregular and regular seismic data, and testing its performance using both synthetic and field data showed it was more advantageous compared with the singular spectrum analysis (SSA) and de-aliased Cadzow methods employed in the reconstruction of irregular and regular data, respectively. The experimental results showed that the proposed method provided better reconstruction performance than the SSA and Cadzow methods.

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

“…This problem is very similar to compressed sensing as seismic data reconstruction is performed using sub-sampled data. In [134], a UNNP-based algorithm named DSPRecon was proposed for the reconstruction of seismic data. A U-Net with skip connections was used in the proposed UNNP framework that learns the deep prior for seismic image reconstruction.…”

EWR‐Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet

“…Chai et al (2020) applied a convolution neural network based on an encoder-decoder style U-Net (Ronneberger et al, 2015) to regularize regular/irregular seismic data, which showed better performance than the fast-generalized Fourier transform method (Naghizadeh & Sacchi, 2009) on irregular seismic data. Liu et al (2021) proposed a U-Netbased deep-seismic-prior method. This method does not require a large data set and can complete regularization with only one undersampled seismic data set.…”

Depthwise separable convolution Unet for 3D seismic data interpolation

“…In recent years, deep learning has achieved outstanding successes in a variety of domains, including computer vision (Ferdian et al, 2020;Manor and Geva, 2015) and medical image processing (Li et al, 2021;Tavoosi et al, 2021), with its powerful representing ability. In the field of geophysics, deep learning methods have also been applied to many research directions recently, such as seismic inversion (Shahbazi et al, 2020;Wu et al, 2020;, fault analysis (Wu et al, 2019;Lin et al, 2022;Wang et al, 2022;Zhu et al, 2022), denoising (Qiu et al, 2022;Jiang et al, 2022;Yang et al, 2021;Yang et al, 2022), and interpolation (Liu et al, 2021;Yu and Wu, 2022).…”