A hybrid approach to seismic deblending: when physics meets self-supervision

Nick Luiken, Matteo Ravasi, Claire Emma Birnie

Keywords: Seismic deblending, self-supervised denoising, Plug-and-Play, inverse problems

TL;DR: A novel method is intrdouced that combines physics and self-supervised denoising to deblend seismic data

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 consecu-
tive 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 Laine et al. (2019) 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,
our algorithm is shown to outperform an industry-standard method whilst being
comparable in terms of computational cost. Moreover, being independent on the
acquisition geometry, it can be easily applied to both marine and land data without
any significant modification.