Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatial-Temporal Densely Connected Convolutional Networks: An
  Application to CO2 Leakage Detection   In carbon capture and sequestration, building an effective monitoring method
is a crucial step to detect and respond to CO2 leakage. CO2 leakage detection
methods rely on geophysical observations and monitoring sensor network.
However, traditional methods usually require physical models to be interpreted
by experts, and the accuracy of these methods will be restricted by different
application conditions. In this paper, we develop a novel data-driven detection
method based on densely connected convolutional networks. Our detection method
learns a mapping relation between seismic data and the CO2 leakage mass. To
account for the spatial and temporal characteristics of seismic data, we design
a novel network architecture by combining 1-D and 2-D convolutional neural
networks together. To overcome the expensive computational cost, we further
apply a densely-connecting policy to our network architecture to reduce the
network parameters. We employ our detection method to synthetic seismic
datasets using Kimberlina model. The numerical results show that our leakage
detection method accurately detects the leakage mass. Therefore, our novel CO2
leakage detection method has great potential for monitoring CO2 storage.

9
The categories of this paper are: nlin
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph