A hybrid approach to seismic deblending: when physics meets
self-supervision
- URL: http://arxiv.org/abs/2205.15395v1
- Date: Mon, 30 May 2022 19:24:21 GMT
- Title: A hybrid approach to seismic deblending: when physics meets
self-supervision
- Authors: Nick Luiken and Matteo Ravasi and Claire E. Birnie
- Abstract summary: We introduce a new concept that consists of embedding a self-supervised denoising network into the Plug-and-Play framework.
A novel network is introduced whose design extends the blind-spot network architecture of [28 ] for partially correlated noise.
The network is then trained directly on the noisy input data at each step of the supervised time algorithm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To limit the time, cost, and environmental impact associated with the
acquisition of seismic data, in recent decades considerable effort has been put
into so-called simultaneous shooting acquisitions, where seismic sources are
fired at short time intervals between each other. As a consequence, waves
originating from consecutive shots are entangled within the seismic recordings,
yielding so-called blended data. For processing and imaging purposes, the data
generated by each individual shot must be retrieved. This process, called
deblending, is achieved by solving an inverse problem which is heavily
underdetermined. Conventional approaches rely on transformations that render
the blending noise into burst-like noise, whilst preserving the signal of
interest. Compressed sensing type regularization is then applied, where
sparsity in some domain is assumed for the signal of interest. The domain of
choice depends on the geometry of the acquisition and the properties of seismic
data within the chosen domain. In this work, we introduce a new concept that
consists of embedding a self-supervised denoising network into the
Plug-and-Play (PnP) framework. A novel network is introduced whose design
extends the blind-spot network architecture of [28 ] for partially coherent
noise (i.e., correlated in time). The network is then trained directly on the
noisy input data at each step of the PnP algorithm. By leveraging both the
underlying physics of the problem and the great denoising capabilities of our
blind-spot network, the proposed algorithm is shown to outperform an
industry-standard method whilst being comparable in terms of computational
cost. Moreover, being independent on the acquisition geometry, our method can
be easily applied to both marine and land data without any significant
modification.
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