Reconstructing missing seismic data using Deep Learning

TL;DR

This paper compares deterministic and deep learning methods for reconstructing missing seismic data from sparse, irregularly sampled surveys, demonstrating deep learning's superior ability to reduce spatial aliasing.

Contribution

It introduces and benchmarks two deep learning architectures, RIM and U-Net, for seismic data reconstruction, highlighting their effectiveness over traditional inversion methods.

Findings

Deep learning models successfully reconstruct dense seismic data from sparse samples.

Recurrent Inference Machine and U-Net outperform deterministic inversion in reducing aliasing.

Deep learning shows promise but needs further development for large, multi-component datasets.

Abstract

In current seismic acquisition practice, there is an increasing drive for sparsely (in space) acquired data, often in irregular geometry. These surveys can trade off subsurface information for efficiency/cost - creating a problem of "missing seismic data" that can greatly hinder subsequent processing and interpretation. Reconstruction of regularly sampled dense data from highly sparse, irregular data can therefore aid in processing and interpretation of these far sparser, more efficient seismic surveys. Here, two methods are compared to solve the reconstruction problem in both space-time and wavenumber-frequency domain. This requires an operator that maps sparse to dense data: the operator is generally unknown, being the inverse of a known data sampling operator. As such, here the deterministic inversion is efficiently solved by least squares optimisation using a numerically efficient Python-based linear operator representation. An alternative approach is probabilistic and uses deep learning. Here, two deep learning architectures are benchmarked against each other and the deterministic approach; a Recurrent Inference Machine (RIM), which is designed specifically to solve inverse problems given known forward operators, and the well-known U-Net. The trained deep learning networks are capable of successfully mapping sparse to dense seismic data for a range of different datasets and decimation percentages, thereby significantly reducing spatial aliasing in the wavenumber-frequency domain. The deterministic inversion on the contrary, could not reconstruct the missing data and thus did not reduce the undesired spatial aliasing. Results show that the application of Deep Learning for seismic reconstruction is promising, but the treatment of large-volume, multi-component seismic datasets will require dedicated learning architectures not yet realisable with existing tools.