Knut Ola Dølven, J. Vierinen, R. Grilli, J. Triest, B. Ferré
{"title":"用生长律方程反褶积法校正慢响应传感器数据的响应时间","authors":"Knut Ola Dølven, J. Vierinen, R. Grilli, J. Triest, B. Ferré","doi":"10.5194/gi-2021-28","DOIUrl":null,"url":null,"abstract":"Abstract. 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 measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occurs 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 solutions 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 (R2) 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.\n","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Response time correction of slow response sensor data by deconvolution of the growth-law equation\",\"authors\":\"Knut Ola Dølven, J. Vierinen, R. Grilli, J. Triest, B. Ferré\",\"doi\":\"10.5194/gi-2021-28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. 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 measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occurs 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 solutions 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 (R2) 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.\\n\",\"PeriodicalId\":48742,\"journal\":{\"name\":\"Geoscientific Instrumentation Methods and Data Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2021-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscientific Instrumentation Methods and Data Systems\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.5194/gi-2021-28\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscientific Instrumentation Methods and Data Systems","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/gi-2021-28","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Response time correction of slow response sensor data by deconvolution of the growth-law equation
Abstract. 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 measurement model. The solution is regularized using model sparsity, assuming changes in the measured quantity occurs 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 solutions 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 (R2) 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.
期刊介绍:
Geoscientific Instrumentation, Methods and Data Systems (GI) is an open-access interdisciplinary electronic journal for swift publication of original articles and short communications in the area of geoscientific instruments. It covers three main areas: (i) atmospheric and geospace sciences, (ii) earth science, and (iii) ocean science. A unique feature of the journal is the emphasis on synergy between science and technology that facilitates advances in GI. These advances include but are not limited to the following:
concepts, design, and description of instrumentation and data systems;
retrieval techniques of scientific products from measurements;
calibration and data quality assessment;
uncertainty in measurements;
newly developed and planned research platforms and community instrumentation capabilities;
major national and international field campaigns and observational research programs;
new observational strategies to address societal needs in areas such as monitoring climate change and preventing natural disasters;
networking of instruments for enhancing high temporal and spatial resolution of observations.
GI has an innovative two-stage publication process involving the scientific discussion forum Geoscientific Instrumentation, Methods and Data Systems Discussions (GID), which has been designed to do the following:
foster scientific discussion;
maximize the effectiveness and transparency of scientific quality assurance;
enable rapid publication;
make scientific publications freely accessible.