Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site

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We made a study in the backyard of an agrochemical plant using a small-loop, frequency-domain electromagnetic induction (EMI) system. Such systems are very sensitive to conductive structures buried at shallow depths. Frequently, they are only used to locate and delimit these structures by direct obs...

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Autores principales: Martinelli, P., Duplaá, M.C.
Formato: JOUR
Materias:
Electromagnetic induction
Numerical models
Soils
Direct observations
Electromagnetic inductions (EMI)
Frequency domains
Lateral variations
Modeling technique
Neighboring point
Smoothness constraints
Spatial filters
Frequency domain analysis
data acquisition
data inversion
electromagnetic method
excavation
inverse problem
numerical model
one-dimensional modeling
smoothing
visualization
wasteland
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00168033_v73_n4_pF143_Martinelli
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00168033_v73_n4_pF143_Martinelli
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spelling todo:paper_00168033_v73_n4_pF143_Martinelli2023-10-03T14:14:54Z Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site Martinelli, P. Duplaá, M.C. Electromagnetic induction Numerical models Soils Direct observations Electromagnetic inductions (EMI) Frequency domains Lateral variations Modeling technique Neighboring point Smoothness constraints Spatial filters Frequency domain analysis data acquisition data inversion electromagnetic method excavation inverse problem numerical model one-dimensional modeling smoothing visualization wasteland We made a study in the backyard of an agrochemical plant using a small-loop, frequency-domain electromagnetic induction (EMI) system. Such systems are very sensitive to conductive structures buried at shallow depths. Frequently, they are only used to locate and delimit these structures by direct observation of data. However, much more information can be obtained by applying numerical modeling techniques to the data. First we mapped an anomalous zone that indicates the possible presence of buried waste or some other underground contamination by visualizing data. Then we applied a 1D inversion method to the data from this zone. By joining 1D inversion results, this method builds 2D images of the subsoil structure below survey lines. Because the code applies smoothness constraints to the 1D inversions, the subsoil properties in these 2D images change gradually with depth. The code does not impose any correlation between the data or 1D models corresponding to neighboring points, so sharp lateral changes can appear. Several of them do not represent real features of the subsoil. We designed and applied two spatial filters to smooth the spurious lateral variations in our models. One correlates the data acquired at adjacent points prior to inversions. The other applies an analogous correlation to the inverse models obtained from the original data. Both filters greatly improve the quality of the 2D images. Compiling these results, we obtained a 3D model of the subsoil that characterizes the anomalous structure. Excavations made later at the site confirmed the results. © 2008 Society of Exploration Geophysicists. All rights reserved. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00168033_v73_n4_pF143_Martinelli
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Electromagnetic induction
Numerical models
Soils
Direct observations
Electromagnetic inductions (EMI)
Frequency domains
Lateral variations
Modeling technique
Neighboring point
Smoothness constraints
Spatial filters
Frequency domain analysis
data acquisition
data inversion
electromagnetic method
excavation
inverse problem
numerical model
one-dimensional modeling
smoothing
visualization
wasteland
spellingShingle Electromagnetic induction
Numerical models
Soils
Direct observations
Electromagnetic inductions (EMI)
Frequency domains
Lateral variations
Modeling technique
Neighboring point
Smoothness constraints
Spatial filters
Frequency domain analysis
data acquisition
data inversion
electromagnetic method
excavation
inverse problem
numerical model
one-dimensional modeling
smoothing
visualization
wasteland
Martinelli, P.
Duplaá, M.C.
Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site
topic_facet Electromagnetic induction
Numerical models
Soils
Direct observations
Electromagnetic inductions (EMI)
Frequency domains
Lateral variations
Modeling technique
Neighboring point
Smoothness constraints
Spatial filters
Frequency domain analysis
data acquisition
data inversion
electromagnetic method
excavation
inverse problem
numerical model
one-dimensional modeling
smoothing
visualization
wasteland
description We made a study in the backyard of an agrochemical plant using a small-loop, frequency-domain electromagnetic induction (EMI) system. Such systems are very sensitive to conductive structures buried at shallow depths. Frequently, they are only used to locate and delimit these structures by direct observation of data. However, much more information can be obtained by applying numerical modeling techniques to the data. First we mapped an anomalous zone that indicates the possible presence of buried waste or some other underground contamination by visualizing data. Then we applied a 1D inversion method to the data from this zone. By joining 1D inversion results, this method builds 2D images of the subsoil structure below survey lines. Because the code applies smoothness constraints to the 1D inversions, the subsoil properties in these 2D images change gradually with depth. The code does not impose any correlation between the data or 1D models corresponding to neighboring points, so sharp lateral changes can appear. Several of them do not represent real features of the subsoil. We designed and applied two spatial filters to smooth the spurious lateral variations in our models. One correlates the data acquired at adjacent points prior to inversions. The other applies an analogous correlation to the inverse models obtained from the original data. Both filters greatly improve the quality of the 2D images. Compiling these results, we obtained a 3D model of the subsoil that characterizes the anomalous structure. Excavations made later at the site confirmed the results. © 2008 Society of Exploration Geophysicists. All rights reserved.
format JOUR
author Martinelli, P.
Duplaá, M.C.
author_facet Martinelli, P.
Duplaá, M.C.
author_sort Martinelli, P.
title Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site
title_short Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site
title_full Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site
title_fullStr Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site
title_full_unstemmed Laterally filtered 1D inversions of small-loop, frequency-domain EMI data from a chemical waste site
title_sort laterally filtered 1d inversions of small-loop, frequency-domain emi data from a chemical waste site
url http://hdl.handle.net/20.500.12110/paper_00168033_v73_n4_pF143_Martinelli
work_keys_str_mv AT martinellip laterallyfiltered1dinversionsofsmallloopfrequencydomainemidatafromachemicalwastesite
AT duplaamc laterallyfiltered1dinversionsofsmallloopfrequencydomainemidatafromachemicalwastesite
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