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Title: Contaminant source identification using semi-supervised machine learning

Journal Article · · Journal of Contaminant Hydrology
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Office of Environmental Management (EM)
Grant/Contract Number:
AC52-06NA25396; 11145687
OSTI ID:
1408837
Alternate ID(s):
OSTI ID: 1526792
Report Number(s):
LA-UR-17-23269; TRN: US1703077
Journal Information:
Journal of Contaminant Hydrology, Vol. 212; ISSN 0169-7722
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 33 works
Citation information provided by
Web of Science

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Cited By (5)

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering journal August 2018
A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes journal November 2018
Distributed non-negative matrix factorization with determination of the number of latent features journal February 2020
Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing journal October 2019
Targeted Source Detection for Environmental Data preprint January 2019

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Related Subjects

54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
Earth Sciences
Mathematics
Non-negative matrix factorization
Feature Extraction
Blind Source Separation
Robustness analysis
Semi-supervised learning
Groundwater contamination
Source identification
Advection-diffusion transport
Geochemical signatures