Kavli Affiliate: Feng Wang
| First 5 Authors: Feng Wang, Bo Yang, Renfang Wang, Hong Qiu,
| Summary:
Deep learning techniques have been used to build velocity models (VMs) for
seismic traveltime tomography and have shown encouraging performance in recent
years. However, they need to generate labeled samples (i.e., pairs of input and
label) to train the deep neural network (NN) with end-to-end learning, and the
real labels for field data inversion are usually missing or very expensive.
Some traditional tomographic methods can be implemented quickly, but their
effectiveness is often limited by prior assumptions. To avoid generating
labeled samples, we propose a novel method by integrating deep learning and
dictionary learning to enhance the VMs with low resolution by using the
traditional tomography-least square method (LSQR). We first design a type of
shallow and simple NN to reduce computational cost followed by proposing a
two-step strategy to enhance the VMs with low resolution: (1) Warming up. An
initial dictionary is trained from the estimation by LSQR through dictionary
learning method; (2) Dictionary optimization. The initial dictionary obtained
in the warming-up step will be optimized by the NN, and then it will be used to
reconstruct high-resolution VMs with the reference slowness and the estimation
by LSQR. Furthermore, we design a loss function to minimize traveltime misfit
to ensure that NN training is label-free, and the optimized dictionary can be
obtained after each epoch of NN training. We demonstrate the effectiveness of
the proposed method through numerical tests.
| Search Query: ArXiv Query: search_query=au:”Feng Wang”&id_list=&start=0&max_results=3