Velocity model building by deep learning: From general synthetics to field data application
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Accepted Manuscript
Type
Conference PaperKAUST Department
Earth Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Date
2020-09-30Online Publication Date
2020-09-30Print Publication Date
2020-09-30Permanent link to this record
http://hdl.handle.net/10754/665479
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Velocity model building is not straightforward in geologically complex environments. We train a convolutional neural network (CNN) to map full wavefields to smooth subsurface parameter distributions to address the problem. Specifically, cubes of neighboring CMP gathers are mapped into in 1D vertical profiles to simplify the training phase and to make it easier to utilize well logs in future applications. We train the CNN using a total of one hundred thousand random subsurface models generated on-the-fly and the corresponding synthetic data. The application of the trained CNN on synthetic and real data admitted reasonably accurate models representing mostly the low wavenumber features of the true models.Citation
Kazei, V., Ovcharenko, O., & Alkhalifah, T. (2020). Velocity model building by deep learning: From general synthetics to field data application. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3428324.1Sponsors
We thank Saudi Aramco for its support and CGG for sharingthe broadseis field data. The research reported in this publication was supported by funding from King Abdullah Universityof Science and Technology (KAUST), Thuwal, 23955-6900,Saudi ArabiaPublisher
Society of Exploration GeophysicistsAdditional Links
https://library.seg.org/doi/10.1190/segam2020-3428324.1ae974a485f413a2113503eed53cd6c53
10.1190/segam2020-3428324.1