Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seismic Bayesian evidential learning: Estimation and uncertainty
  quantification of sub-resolution reservoir properties   We present a framework that enables estimation of low-dimensional
sub-resolution reservoir properties directly from seismic data, without
requiring the solution of a high dimensional seismic inverse problem. Our
workflow is based on the Bayesian evidential learning approach and exploits
learning the direct relation between seismic data and reservoir properties to
efficiently estimate reservoir properties. The theoretical framework we develop
allows incorporation of non-linear statistical models for seismic estimation
problems. Uncertainty quantification is performed with Approximate Bayesian
Computation. With the help of a synthetic example of estimation of reservoir
net-to-gross and average fluid saturations in sub-resolution thin-sand
reservoir, several nuances are foregrounded regarding the applicability of
unsupervised and supervised learning methods for seismic estimation problems.
Finally, we demonstrate the efficacy of our approach by estimating posterior
uncertainty of reservoir net-to-gross in sub-resolution thin-sand reservoir
from an offshore delta dataset using 3D pre-stack seismic data.

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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: econ
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex