Full-waveform inversion (FWI) is a widely used technique in seismic processing to produce high resolution earth models iteratively improved an earth model using a sequence of linearized local inversions to solve a fully non-linear problem. Deep Neural Networks (DNN) are a subset of machine learning algorithms that are efficient in learning non-linear functionals between input and output pairs. The learning process within DNN involves iteratively updating network neuron weights to best approximate input-to-output mapping. There is clearly a similarity between FWI and DNN for optimization applications. I propose casting FWI as a DNN problem and implement a novel approach which learns pseudo-spectral data-driven FWI. I test this methodology by training a DNN on 1D data and then apply this to previously unseen data. Initial results achieved promising levels of accuracy, although not fully reconstructing the model. Future work will investigate deeper DNNs for better generalization and the application to real data.


Article metrics loading...

Loading full text...

Full text loading...


  1. Alom, M.Z., T.M.Taha, C.Yakopcic, S.Westberg, M.Hasan, B.C.Van Esesn, A.A.S.Awwal and V.K.Asari
    2018. The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. arXiv.
    [Google Scholar]
  2. Biondi, B.L.
    2006. 3D seismic imaging. Society of Exploration Geophysicists.
    [Google Scholar]
  3. Hastie, T., J.Friedman and R.Tibshirani
    2001. The Elements of Statistical Learning. Springer Series in Statistics.
    [Google Scholar]
  4. Hornik, K., M.Stinchcombe and H.White
    1989. Multilayer feedforward networks are universal approximator. Neural Networks. 2, pp.359–366.
    [Google Scholar]
  5. Michaels, P. and R.B.Smith
    1992. Recurrent neural network representation of the inelastic wave equation and full waveform inversion without local minimaIn: 62nd Ann. Internat. Mtg. Society of Exploration Geophysicists, pp. 22–25.
    [Google Scholar]
  6. Richardson, A. and A.Geophysical
    2018. Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques.
    [Google Scholar]
  7. Schmidhuber, J.
    2015. Deep Learning in neural networks: An overview. Neural Networks. 61.
    [Google Scholar]
  8. Tarantola, A.
    1984. Inversion of seismic reflection data in the acoustic approximation. Geophysics. 49(8), pp.1259–1266.
    [Google Scholar]

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