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Multi-domain diffraction identification: A supervised deep learning technique for seismic diffraction classification
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DOI：10.1016/j.cageo.2021.104845.png)
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Abstract
En 中文
The seismic wavefield arises from interactions of a source wavefield with subsurface heterogeneities as the wavefield propagates through the earth. In a conventional seismic processing workflow, the reflected portion of the wavefield is enhanced at the expense of the rest of the wavefield. While this is useful, considerable information which is contained outside of the reflections is lost. To alleviate this issue, we propose a deep learning technique which aims to separate the wavefield into three of the wavefield components: reflections, diffractions, and noise. This technique involves first performing several data domain transformations on the input and applying these as input classes on a pixel-by-pixel basis to guide the neural network. A simple separation is performed to remove the reflections and noise, allowing the diffractions to be processed independently. This technique, called Multi-domain diffraction identification, gives a high standard classification of the diffractions in a fraction of the time and computational cost of plane-wave destruction. These diffractions have then been removed from the data and compared with separation results from plane-wave destruction, showing similarities in the diffractions and demonstrating the denoising capability of the method.
Keywords:
Seismic
Processing
Diffraction
Imaging
Separation
Neural networks
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