Reconstructing missing seismic data using Deep Learning
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 spacetime and wavenumberfrequency 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 Pythonbased 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 wellknown UNet. 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 wavenumberfrequency 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 largevolume, multicomponent seismic datasets will require dedicated learning architectures not yet realisable with existing tools.
 Publication:

arXiv eprints
 Pub Date:
 January 2021
 DOI:
 10.48550/arXiv.2101.09554
 arXiv:
 arXiv:2101.09554
 Bibcode:
 2021arXiv210109554K
 Keywords:

 Physics  Geophysics;
 Physics  Data Analysis;
 Statistics and Probability
 EPrint:
 36 pages, 9 figures