Temporal filtering and time-lapse inversion of geoelectrical data for long-term monitoring with application to a chlorinated hydrocarbon contaminated site
(2022) In Geophysical Journal International 228(3). p.1648-1664- Abstract
We present a solution for long-term direct current resistivity and time-domain induced polarization (DCIP) monitoring, which consists of a monitoring system and the associated software that automates the data collection and processing. This paper describes the acquisition system that is used for remote data collection and then introduces the routines that have been developed for pre-processing of the monitoring data set. The collected data set is pre-processed using digital signal processing algorithms for outlier detection and removal; the resulting data set is then used for the inversion procedure. The suggested processing workflow is tested against a simulated time-lapse experiment and then applied to field data. The results from the... (More)
We present a solution for long-term direct current resistivity and time-domain induced polarization (DCIP) monitoring, which consists of a monitoring system and the associated software that automates the data collection and processing. This paper describes the acquisition system that is used for remote data collection and then introduces the routines that have been developed for pre-processing of the monitoring data set. The collected data set is pre-processed using digital signal processing algorithms for outlier detection and removal; the resulting data set is then used for the inversion procedure. The suggested processing workflow is tested against a simulated time-lapse experiment and then applied to field data. The results from the simulation show that the suggested approach is very efficient for detecting changes in the subsurface; however, there are some limitations when no a priori information is used. Furthermore, the mean weekly data sets that are generated from the daily collected data can resolve low-frequency changes, making the approach a good option for monitoring experiments where slow changes occur (i.e. leachates in landfills, internal erosion in dams, bioremediation). The workflow is then used to process a large data set containing 20 months of daily monitoring data from a field site where a pilot test of in situ bioremediation is taking place. Based on the time-series analysis of the inverted data sets, we can detect two portions of the ground that show different geophysical properties and that coincide with the locations where the different fluids were injected. The approach that we used in this paper provides consistency in the data processing and has the possibility to be applied to further real-time geophysical monitoring in the future.
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- author
- Nivorlis, Aristeidis LU ; Rossi, Matteo LU and Dahlin, Torleif LU
- organization
- publishing date
- 2022-03-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Electrical properties, Electrical resistivity tomography (ERT), Inverse theory, Numerical modelling, Time-series analysis
- in
- Geophysical Journal International
- volume
- 228
- issue
- 3
- pages
- 17 pages
- publisher
- Oxford University Press
- external identifiers
-
- scopus:85121141229
- ISSN
- 0956-540X
- DOI
- 10.1093/gji/ggab422
- project
- Characterisation and monitoring of in-situ remediation of chlorinated hydrocarbon contamination using an interdisciplinary approach
- language
- English
- LU publication?
- yes
- additional info
- This article has been accepted for publication in Geophysical Journal International ©: 2021 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
- id
- dfa43579-c7f0-48c4-9def-c2c8d81b926e
- date added to LUP
- 2022-01-13 16:48:19
- date last changed
- 2023-09-25 17:13:59
@article{dfa43579-c7f0-48c4-9def-c2c8d81b926e,
abstract = {{<p>We present a solution for long-term direct current resistivity and time-domain induced polarization (DCIP) monitoring, which consists of a monitoring system and the associated software that automates the data collection and processing. This paper describes the acquisition system that is used for remote data collection and then introduces the routines that have been developed for pre-processing of the monitoring data set. The collected data set is pre-processed using digital signal processing algorithms for outlier detection and removal; the resulting data set is then used for the inversion procedure. The suggested processing workflow is tested against a simulated time-lapse experiment and then applied to field data. The results from the simulation show that the suggested approach is very efficient for detecting changes in the subsurface; however, there are some limitations when no a priori information is used. Furthermore, the mean weekly data sets that are generated from the daily collected data can resolve low-frequency changes, making the approach a good option for monitoring experiments where slow changes occur (i.e. leachates in landfills, internal erosion in dams, bioremediation). The workflow is then used to process a large data set containing 20 months of daily monitoring data from a field site where a pilot test of in situ bioremediation is taking place. Based on the time-series analysis of the inverted data sets, we can detect two portions of the ground that show different geophysical properties and that coincide with the locations where the different fluids were injected. The approach that we used in this paper provides consistency in the data processing and has the possibility to be applied to further real-time geophysical monitoring in the future. </p>}},
author = {{Nivorlis, Aristeidis and Rossi, Matteo and Dahlin, Torleif}},
issn = {{0956-540X}},
keywords = {{Electrical properties; Electrical resistivity tomography (ERT); Inverse theory; Numerical modelling; Time-series analysis}},
language = {{eng}},
month = {{03}},
number = {{3}},
pages = {{1648--1664}},
publisher = {{Oxford University Press}},
series = {{Geophysical Journal International}},
title = {{Temporal filtering and time-lapse inversion of geoelectrical data for long-term monitoring with application to a chlorinated hydrocarbon contaminated site}},
url = {{https://lup.lub.lu.se/search/files/111930836/Nivorlis_et_al_2022_Temporal_filtering_and_time_lapse_inversion_of_geoelectrical_data_for_long_term_monitoring_with_application_to_a_chlorinated_hydrocarbon_contaminated_site_GJI_ggab422.pdf}},
doi = {{10.1093/gji/ggab422}},
volume = {{228}},
year = {{2022}},
}