Abstract
ABSTRACTDuring seismic data acquisition, receivers with finer trace intervals can be distributed to record seismic signals. The distance between neighbouring shots, on the other hand, can be significant, resulting in regularly missing shots in the common receiver gathers, which may cause significant spatial aliasing and reduce the precision of later seismic imaging. Traditional interpolation algorithms have several issues and limitations for seismic data with spatial aliasing due to prior assumptions and human–computer interactions. As a result, we present a new deep learning method that includes the generation of adaptive training data and the design of a consistent convolution kernel. Deep learning has the ability to characterize seismic data in a nonlinear manner that is ideal for accurate seismic interpolation. Because the common shot gathers and common receiver gathers have similar features due to the spatial reciprocity of Green's function of the wave equation if all shots have the same physical signatures, the common shot gathers with a refined trace interval are adaptively extracted as the training dataset to guarantee the interpolation performance in the common receiver gathers. With regularly subsampled data as input an improved U‐Net is created and trained to match the desired output, that is, the completed data. The trained network can be deployed to the test common receiver gathers to rebuild regularly missing shots. We develop a novel consistent convolution kernel to ensure high accuracy of missing shot reconstruction while accounting for differences between prestack unmigrated seismic data and images. Using numerically subsampled synthetic and field data, the effectiveness and validity of the developed consistent convolution kernel and the upgraded U‐Net with adaptive training data for missing shot interpolation are demonstrated.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.