2019 May 07

Location: Coors Tech

Attending

Jihyun Andy Thomas Bin Iga

Key takeaways

  • We discussed the SEG abstract that interests Khalid (Wu McMechan, 2018)
  • Novel aspect: updating weights of CNN in each iteration of inversion
  • Abstract mixes geophysics and machine learning terminology in confusing ways
  • Unsure how or why CNN improves inversion

Problems

  • Example uses a really good initial model - does the algorithm work with a poor initial model?
  • So many layers! 26! Does the CNN need to be so big?
  • Abstract has few details

Comments

  • Train a CNN to input a vector of ones and output an initial velocity model
    • 26 hidden layers
  • For FWI where forward modeling data given velocity model is d(v) and gradient w.r.t. weights is g, the objective function to be minimized is C(v) = ||d(v)-d_observed||^2
  • If CNN network is G(w)=v where w are weights of the CNN and v is the velocity model, then the objective function becomes C(w) = ||d(G(w))-d_observed||^2
  • Minimize the new objective function by adjusting the weights w of the CNN
  • Paper under review

Topics of discussion for next week

  • Further discussion on this abstract
  • Thomas’s TasNet usage

Comments