Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimation of groundwater storage from seismic data using deep learning   Convolutional neural networks can provide a potential framework to
characterize groundwater storage from seismic data. Estimation of key
components such as the amount of groundwater stored in an aquifer and delineate
water-table level, from active-source seismic data are investigated in this
study. The data to train, validate, and test the neural networks are obtained
by solving wave propagation in a coupled poroviscoelastic-elastic media. A
discontinuous Galerkin method is applied to model wave propagation whereas a
deep convolutional neural network is used for the parameter estimation problem.
In the numerical experiment, the primary unknowns are the amount of stored
groundwater and water-table level, and are estimated, while the remaining
parameters, assumed to be of less of interest, are marginalized in the
convolutional neural networks-based solution. Results, obtained through
synthetic data, illustrate the potential of deep learning methods to extract
additional aquifer information from seismic data, which otherwise would be
impossible based on a set of reflection seismic sections or velocity tomograms.

15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat