1887

Abstract

Summary

We present a machine learning setup that can estimate a velocity model from raw seismic shot gathers without the need for an initial velocity model. Our setup is based on a convolutional neural network (CNN) trained on pairs of random generated synthetic velocity models and corresponding forward modelled synthetic shot gathers. The network is trained to predict the correct velocity model for a given input shot gather. We evaluate the performance of the trained network on both synthetic and real seismic data, and observe that the system is able to estimate background velocity trends directly from the raw shot gathers without need for preprocessing or preconditioning. Once trained, the network is very fast to run, and can deliver a velocity model in seconds running on a single GPU. The preciscion and resolution of the estimated velocity models is not on par with state of the art velocity model building techniques such as FWI and/or reflection tomography, but shows that machine learning can robustly extract meaningful velocity information from raw shot gathers, and that there might be potential in using such methods for velocity model building.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201900039
2019-04-04
2020-07-04
Loading full text...

Full text loading...

References

  1. Araya-Polo, M., Jennings, J., Adler, A., Dahlke, T.
    [2018] Deep-learning tomography, The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  2. Sun, H., Demanet, L.
    [2018], Low frequency extrapolation with deep learningSEG Technical Program Expanded Abstracts 2018: pp. 2712–2716.
    [Google Scholar]
  3. Wang, W., Yang, F., Ma, J.
    [2018] Velocity model building with a modified fully convolutional network, SEG Technical Program Expanded Abstracts 2018: pp. 2086–2090
    [Google Scholar]
  4. Wu, Y., McMechan, G.
    [2018] Feature-capturing full waveform inversion using a convolutional neural network, SEG Technical Program Expanded abstracts 2018: pp. 2061–2065
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201900039
Loading
/content/papers/10.3997/2214-4609.201900039
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error