1887

Abstract

Summary

The requirement for accurate labels in supervised learning often forces us to train our networks using synthetic data. However, synthetic experiments do not reflect the realities of the field experiment, and we end up with poor performance of the trained neural network (NN) models at the inference stage. Thus, we describe a novel approach to enhance our NN model training with real data features (domain adaptation). This is accomplished by applying two operations on the input data to the NN model, whether they are from the synthetic or real data subset class: 1) The crosscorrelation of the input data section (i.e. shot gather or seismic image) with a fixed reference trace from that section. 2) The convolution of the resulting data with a randomly chosen auto correlated section of the other subset class. In the training stage, as expected, the input data are from the synthetic subset class and the auto-corrected sections are from the real subset class, and in the inference/application stage, it is the opposite. An example application on passive seismic data for microseismic event source location determination is used to demonstrate the power of this approach in improving the applicability of our trained models on real data.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202113262
2021-10-18
2024-03-29
Loading full text...

Full text loading...

References

  1. Araya-Polo, M., J.Jennings, A.Adler, and T.Dahlke
    , 2018, Deep-learning tomography: The Leading Edge, 37, 58–66.
    [Google Scholar]
  2. Kouw, W. M.
    , 2018, An introduction to domain adaptation and transfer learning: ArXiv, abs/1812.11806.
    [Google Scholar]
  3. Ovcharenko, O., V.Kazei, M.Kalita, D.Peter, and T.Alkhalifah
    , 2019, Deep learning for low-frequency extrapolation from multioffset seismic data: GEOPHYSICS, 84, R989–R1001.
    [Google Scholar]
  4. Staněk, F., and L.Eisner
    , 2017, Seismicity induced by hydraulic fracturing in shales: A bedding plane slip model: Journal of Geophysical Research: Solid Earth, 122, 7912–7926.
    [Google Scholar]
  5. Wang, H., T.Alkhalifah, and Q.Hao
    , 2020, Predict passive seismic events with a convolutional neural network: SEG Technical Program Expanded Abstracts 2020, 2140–2145.
    [Google Scholar]
  6. Wrona, T., I.Pan, R. L.Gawthorpe, and H.Fossen
    , 2018, Seismic facies analysis using machine learning: GEOPHYSICS, 83, O83–O95.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202113262
Loading
/content/papers/10.3997/2214-4609.202113262
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