skip to main content

DOE PAGESDOE PAGES

Title: Identification of release sources in advection–diffusion system by machine learning combined with Green’s function inverse method

The identification of sources of advection–diffusion transport is based usually on solving complex ill-posed inverse models against the available state-variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influences of the original sources. Importantly, the number of these original release sources is typically unknown, which hinders reliability of the classical inverse-model analyses. To address this challenge, we present a novel hybrid method for identification of the unknown number of release sources. Our hybrid method, called HNMF, couples unsupervised learning based on Non-negative Matrix Factorization (NMF) and inverse-analysis Green’s functions method. HNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green’s function of advection–diffusion equation to identify their characteristics. In the paper, we introduce the method and demonstrate that it is capable of identifying the advection velocity and dispersivity of the medium as well as the unknown number, locations, and properties of various sets of synthetic release sources with different space and time dependencies, based only on the recorded data. HNMF can be applied directly to any problem controlled by a partial-differential parabolic equation where mixtures of anmore » unknown number of sources are measured at multiple locations.« less
Authors:
 [1] ;  [1] ;  [1] ; ORCiD logo [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-27231
Journal ID: ISSN 0307-904X
Grant/Contract Number:
AC52-06NA25396; 11145687; 20180060
Type:
Accepted Manuscript
Journal Name:
Applied Mathematical Modelling
Additional Journal Information:
Journal Volume: 60; Journal Issue: C; Journal ID: ISSN 0307-904X
Publisher:
Elsevier
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Office of Environmental Management (EM); USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Environmental Protection; Information Science
OSTI Identifier:
1459817

Stanev, Valentin G., Iliev, Filip L., Hansen, Scott, Vesselinov, Velimir V., and Alexandrov, Boian S.. Identification of release sources in advection–diffusion system by machine learning combined with Green’s function inverse method. United States: N. p., Web. doi:10.1016/j.apm.2018.03.006.
Stanev, Valentin G., Iliev, Filip L., Hansen, Scott, Vesselinov, Velimir V., & Alexandrov, Boian S.. Identification of release sources in advection–diffusion system by machine learning combined with Green’s function inverse method. United States. doi:10.1016/j.apm.2018.03.006.
Stanev, Valentin G., Iliev, Filip L., Hansen, Scott, Vesselinov, Velimir V., and Alexandrov, Boian S.. 2018. "Identification of release sources in advection–diffusion system by machine learning combined with Green’s function inverse method". United States. doi:10.1016/j.apm.2018.03.006. https://www.osti.gov/servlets/purl/1459817.
@article{osti_1459817,
title = {Identification of release sources in advection–diffusion system by machine learning combined with Green’s function inverse method},
author = {Stanev, Valentin G. and Iliev, Filip L. and Hansen, Scott and Vesselinov, Velimir V. and Alexandrov, Boian S.},
abstractNote = {The identification of sources of advection–diffusion transport is based usually on solving complex ill-posed inverse models against the available state-variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influences of the original sources. Importantly, the number of these original release sources is typically unknown, which hinders reliability of the classical inverse-model analyses. To address this challenge, we present a novel hybrid method for identification of the unknown number of release sources. Our hybrid method, called HNMF, couples unsupervised learning based on Non-negative Matrix Factorization (NMF) and inverse-analysis Green’s functions method. HNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green’s function of advection–diffusion equation to identify their characteristics. In the paper, we introduce the method and demonstrate that it is capable of identifying the advection velocity and dispersivity of the medium as well as the unknown number, locations, and properties of various sets of synthetic release sources with different space and time dependencies, based only on the recorded data. HNMF can be applied directly to any problem controlled by a partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations.},
doi = {10.1016/j.apm.2018.03.006},
journal = {Applied Mathematical Modelling},
number = C,
volume = 60,
place = {United States},
year = {2018},
month = {3}
}