Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep-learning inversion: a next generation seismic velocity-model
  building method   Seismic velocity is one of the most important parameters used in seismic
exploration. Accurate velocity models are key prerequisites for reverse-time
migration and other high-resolution seismic imaging techniques. Such velocity
information has traditionally been derived by tomography or full-waveform
inversion (FWI), which are time consuming and computationally expensive, and
they rely heavily on human interaction and quality control. We investigate a
novel method based on the supervised deep fully convolutional neural network
(FCN) for velocity-model building (VMB) directly from raw seismograms. Unlike
the conventional inversion method based on physical models, the supervised
deep-learning methods are based on big-data training rather than
prior-knowledge assumptions. During the training stage, the network establishes
a nonlinear projection from the multi-shot seismic data to the corresponding
velocity models. During the prediction stage, the trained network can be used
to estimate the velocity models from the new input seismic data. One key
characteristic of the deep-learning method is that it can automatically extract
multi-layer useful features without the need for human-curated activities and
initial velocity setup. The data-driven method usually requires more time
during the training stage, and actual predictions take less time, with only
seconds needed. Therefore, the computational time of geophysical inversions,
including real-time inversions, can be dramatically reduced once a good
generalized network is built. By using numerical experiments on synthetic
models, the promising performances of our proposed method are shown in
comparison with conventional FWI even when the input data are in more realistic
scenarios. Discussions on the deep-learning methods, training dataset, lack of
low frequencies, and advantages and disadvantages of the new method are also
provided.

7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an