Skip to content

Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.

License

Notifications You must be signed in to change notification settings

vkazei/deeplogs

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

45 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

deeplogs

Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.

This repository reproduces the results of the papers:

Kazei, V., Ovcharenko, O., Plotnitskii, P., Zhang, X., Peter, D. & Alkhalifah, T. "Mapping full seismic waveforms to vertical velocity profiles by deep learning", Geophysics, in moderate revision (2020) [https://repository.kaust.edu.sa/handle/10754/656082]

Kazei, V., Ovcharenko, O., Plotnitskii, P., Zhang, X., Peter, D. & Alkhalifah, T. "Deep learning tomography by mapping full seismic waveforms to vertical velocity profiles", EAGE Annual meeting, 2020 Run:

data/velocity_logs_from_seismic.ipynb

Common-midpoint gathers are used to build a velocity log at the central midpoint location. This allows us to utilize relevant traces for inversion and exploit the regualrity of sampling in typical active seismic acquisition.

With deep learning and regularly sampled data inversion can be set up as a search for mapping from data cubes to 1D vertical velocity profiles. Which is a lot easier to learn compared to mapping to the whole velocity models (2D or 3D).

cmp_to_log

We generate a set of pseudo-random models for training by cropping and skewing: cmp_to_log

Velocity model is then retrieved as an assembly of depth profiles. Deep learning models are naturally stochastic, so we train as set of five to provide initial uncertainty estimates: cmp_to_log

New training data is generated on-the-fly to save space and boost generalization power. Full-waveform inversion can help refine the results. cmp_to_log