Unary adaptive subtraction of joint multiple models with complex wavelet frames

Ventosa, Sergi; Rabeson, H.; Huard, Irène; Pica, A.; Duval, Laurent; Roy, Sylvain

doi:10.1190/segam2012-0440.1

Cited by 1 publication

(1 citation statement)

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“…The conventional physics‐based method employed in this study involved preconditioning of common shot gathers (CSGs) by separation of dips of primary signal and SI noise where possible, and rank‐reduction (Trickett et al., 2012) based denoising to improve the signal‐to‐noise ratio (S/N) of coherent events (both wanted signal and unwanted SI noise). Next, SI noise was modelled by a sparse τ– p inversion (Zhang & Wang, 2015) scheme and adapted to the input CSGs with a complex wavelet adaptation (Ventosa et al., 2012). The training and validation data sets consisted of 7000 and 1000 images, respectively.…”

Section: Field Data Demonstrationmentioning

confidence: 99%

Improving signal fidelity for deep learning‐based seismic interference noise attenuation

Sun

Hou²

2022

Geophysical Prospecting

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Deep learning has shown a considerable potential to significantly improve processing efficiency but has not yet been widely deployed to production projects of seismic signal separation such as seismic interference attenuation. The main reasons are: First, the industry has high standards for signal fidelity, which are critical for the success of subsequent seismic imaging, and deep neural network methods have not yet matched the required level; second, the network's interpretability issue has affected many geophysicists and sponsors' trust in the deep learning technique. To develop deep neural network methods towards the end of benefiting real-world production, we first attempt to better understand their performance, especially in how they make use of local and global features of the data. A novel quantitative research of the overall network model behaviour on synthetic data is conducted. We simulate three types of coherent seismic data components in the shot domain, blend them together and then train a network to separate them. In this process, random noise, a component having only learnable local features, is selectively injected into the network's training pairs. Three network models sharing the same architecture are trained individually, and they show distinctive behaviours when applied to the same test data. Step-by-step analysis of each of them reveals that training the network with additional random noise injected into both the input and the output channel of the desired signal can lead to a decent prediction of the coherent noise based on good learning of the global features and, in the meantime, preserve almost all the data information from being lost. We propose this key lesson we learnt as a new method to improve the network's signal fidelity for shot-domain seismic interference attenuation, which is essentially a signal separation task. Its effectiveness is demonstrated on field data from Africa with a comparison to a conventional physics-based seismic interference attenuation method used in production.

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Section: Field Data Demonstrationmentioning

confidence: 99%