Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

TL;DR

This paper introduces CoBSI, an unsupervised deep learning method using coordinate-based neural networks for seismic shot gather interpolation in irregular land surveys, effectively estimating continuous seismic events without external training data.

Contribution

The paper proposes a novel coordinate-based neural network approach for seismic shot gather interpolation that works on irregular surveys without requiring large training datasets.

Findings

Successfully interpolates seismic data in irregular surveys

Outperforms traditional sparsity and low-rank methods

Validates on real and synthetic 3D datasets

Abstract

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation(CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Experimental results on real and synthetic 3D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.