Response time correction of slow-response sensor data by deconvolution of the growth-law equation
<p>Accurate high-resolution measurements are essential to improve our understanding of environmental processes. Several chemical sensors relying on membrane separation extraction techniques have slow response times due to a dependence on equilibrium partitioning across the membrane separating...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-08-01
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| Series: | Geoscientific Instrumentation, Methods and Data Systems |
| Online Access: | https://gi.copernicus.org/articles/11/293/2022/gi-11-293-2022.pdf |
| _version_ | 1798037852697657344 |
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| author | K. O. Dølven J. Vierinen R. Grilli J. Triest B. Ferré |
| author_facet | K. O. Dølven J. Vierinen R. Grilli J. Triest B. Ferré |
| author_sort | K. O. Dølven |
| collection | DOAJ |
| description | <p>Accurate high-resolution measurements are essential to improve our understanding of environmental processes. Several chemical sensors relying on membrane separation extraction techniques have slow response times due to a dependence on equilibrium partitioning across the membrane separating the measured medium (i.e., a measuring chamber) and the medium of interest (i.e., a solvent). We present a new technique for deconvolving slow-sensor-response signals using statistical inverse theory; applying a weighted linear least-squares estimator with the growth law as a measurement model.
The solution is regularized using model sparsity, assuming changes in the measured quantity occur with a certain time step, which can be selected based on domain-specific knowledge or L-curve analysis. The advantage of this method is that it (1) models error propagation, providing an explicit uncertainty estimate of the response-time-corrected signal; (2) enables evaluation of the solution self consistency; and (3) only requires instrument accuracy, response time, and data as input parameters. Functionality of the technique is demonstrated using simulated, laboratory, and field measurements. In the field experiment, the coefficient of determination (<span class="inline-formula"><i>R</i><sup>2</sup></span>) of a slow-response methane sensor in comparison with an alternative fast-response sensor significantly improved from 0.18 to 0.91 after signal deconvolution. This shows how the proposed method can open up a considerably wider set of applications for sensors and methods suffering from slow response times due to a reliance on the efficacy of diffusion processes.</p> |
| first_indexed | 2024-04-11T21:32:13Z |
| format | Article |
| id | doaj.art-6711f908cd5549e58ce986a83bd08dfe |
| institution | Directory Open Access Journal |
| issn | 2193-0856 2193-0864 |
| language | English |
| last_indexed | 2024-04-11T21:32:13Z |
| publishDate | 2022-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Instrumentation, Methods and Data Systems |
| spelling | doaj.art-6711f908cd5549e58ce986a83bd08dfe2022-12-22T04:01:55ZengCopernicus PublicationsGeoscientific Instrumentation, Methods and Data Systems2193-08562193-08642022-08-011129330610.5194/gi-11-293-2022Response time correction of slow-response sensor data by deconvolution of the growth-law equationK. O. Dølven0J. Vierinen1R. Grilli2J. Triest3B. Ferré4Centre for Arctic Gas Hydrate, Environment, and Climate, UiT The Arctic University of Norway, Tromsø, NorwayInstitute for Physics and Technology, UiT The Arctic University of Norway, Tromsø, NorwayCNRS, University of Grenoble Alpes, IRD, Grenoble INP, 38000 Grenoble, France4H-JENA engineering GmbH, Wischhofstrasse 1–3, 24148 Kiel, GermanyCentre for Arctic Gas Hydrate, Environment, and Climate, UiT The Arctic University of Norway, Tromsø, Norway<p>Accurate high-resolution measurements are essential to improve our understanding of environmental processes. Several chemical sensors relying on membrane separation extraction techniques have slow response times due to a dependence on equilibrium partitioning across the membrane separating the measured medium (i.e., a measuring chamber) and the medium of interest (i.e., a solvent). We present a new technique for deconvolving slow-sensor-response signals using statistical inverse theory; applying a weighted linear least-squares estimator with the growth law as a measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occur with a certain time step, which can be selected based on domain-specific knowledge or L-curve analysis. The advantage of this method is that it (1) models error propagation, providing an explicit uncertainty estimate of the response-time-corrected signal; (2) enables evaluation of the solution self consistency; and (3) only requires instrument accuracy, response time, and data as input parameters. Functionality of the technique is demonstrated using simulated, laboratory, and field measurements. In the field experiment, the coefficient of determination (<span class="inline-formula"><i>R</i><sup>2</sup></span>) of a slow-response methane sensor in comparison with an alternative fast-response sensor significantly improved from 0.18 to 0.91 after signal deconvolution. This shows how the proposed method can open up a considerably wider set of applications for sensors and methods suffering from slow response times due to a reliance on the efficacy of diffusion processes.</p>https://gi.copernicus.org/articles/11/293/2022/gi-11-293-2022.pdf |
| spellingShingle | K. O. Dølven J. Vierinen R. Grilli J. Triest B. Ferré Response time correction of slow-response sensor data by deconvolution of the growth-law equation Geoscientific Instrumentation, Methods and Data Systems |
| title | Response time correction of slow-response sensor data by deconvolution of the growth-law equation |
| title_full | Response time correction of slow-response sensor data by deconvolution of the growth-law equation |
| title_fullStr | Response time correction of slow-response sensor data by deconvolution of the growth-law equation |
| title_full_unstemmed | Response time correction of slow-response sensor data by deconvolution of the growth-law equation |
| title_short | Response time correction of slow-response sensor data by deconvolution of the growth-law equation |
| title_sort | response time correction of slow response sensor data by deconvolution of the growth law equation |
| url | https://gi.copernicus.org/articles/11/293/2022/gi-11-293-2022.pdf |
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