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Abstract

Summary

Currently, missing seismic data because of too sparse sampling or irregularities in acquisition greatly hinders accurate seismic processing and interpretation. Reconstruction of highly sparse, irregular data to dense data can therefore aid in processing and interpretation of far sparser, more efficient seismic surveys. Here, two methods to solve the reconstruction problem, that requires an inverse operator mapping sparse to dense data, are compared in both space-time and wavenumber-frequency domain. The deterministic inversion is efficiently solved by least squares optimisation using an numerically-efficient Python-based linear operator representation. An alternative method is the probabilistic approach that uses deep learning. Here, the specific deep learning architecture is a Recurrent Inference Machine (RIM), which is designed specifically to solve inverse problems given known forward operators. Our trained RIM proved to be able to map sparse to dense seismic data by reducing spatial aliasing in the wavenumber-frequency domain caused by sub-Nyquist spatial sampling in space-time domain. The RIM is therefore an improvement over deterministic inversion that could not undo spatial aliasing, leaving the benchmarking of RIMs to other deep learning architectures as a subject of ongoing study.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202011046
2020-12-08
2024-04-26

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202011046
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Reconstructing Missing Seismic Data through Deep Learning with Recurrent Inference Machines
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202011046
/content/papers/10.3997/2214-4609.202011046
/content/papers/10.3997/2214-4609.202011046
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