Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seismic data denoising and deblending using deep learning   An important step of seismic data processing is removing noise, including
interference due to simultaneous and blended sources, from the recorded data.
Traditional methods are time-consuming to apply as they often require manual
choosing of parameters to obtain good results. We use deep learning, with a
U-net model incorporating a ResNet architecture pretrained on ImageNet and
further trained on synthetic seismic data, to perform this task. The method is
applied to common offset gathers, with adjacent offset gathers of the gather
being denoised provided as additional input channels. Here we show that this
approach leads to a method that removes noise from several datasets recorded in
different parts of the world with moderate success. We find that providing
three adjacent offset gathers on either side of the gather being denoised is
most effective. As this method does not require parameters to be chosen, it is
more automated than traditional methods.

20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg