{"id":867966,"date":"2022-08-08T10:00:03","date_gmt":"2022-08-08T17:00:03","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2023-01-09T07:18:21","modified_gmt":"2023-01-09T15:18:21","slug":"source-attribution-and-emissions-quantification-for-methane-leak-detection-a-non-linear-bayesian-regression-approach","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/source-attribution-and-emissions-quantification-for-methane-leak-detection-a-non-linear-bayesian-regression-approach\/","title":{"rendered":"Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Approach"},"content":{"rendered":"<p><span dir=\"ltr\" role=\"presentation\">Methane leak detection and remediation efforts are critical <\/span><span dir=\"ltr\" role=\"presentation\">for combating climate change due to methane\u2019s role as a potent green-<\/span><span dir=\"ltr\" role=\"presentation\">house gas. In this work, we consider the problem of source attribution <\/span><span dir=\"ltr\" role=\"presentation\">and leak quantification: given a set of methane ground sensor readings, <\/span><span dir=\"ltr\" role=\"presentation\">our goal is to determine the sources of the leaks and quantify their size <\/span><span dir=\"ltr\" role=\"presentation\">in order to enable prompt remediation efforts and to assess the environ-<\/span><span dir=\"ltr\" role=\"presentation\">mental impact of such emissions. Previous works considering a Bayesian <\/span><span dir=\"ltr\" role=\"presentation\">inversion framework have focused on the over-determined (more sensors <\/span><span dir=\"ltr\" role=\"presentation\">than sources) regime and a linear dependence of methane concentration <\/span><span dir=\"ltr\" role=\"presentation\">on the leak rates. In this paper, we focus on the opposite, industry-<\/span><span dir=\"ltr\" role=\"presentation\">relevant regime of few sources per sensor (under-determined regime) and <\/span><span dir=\"ltr\" role=\"presentation\">consider a non-linear dependence on the leak rates. We find the model to <\/span><span dir=\"ltr\" role=\"presentation\">be robust in determining the location of the major emission sources, and <\/span><span dir=\"ltr\" role=\"presentation\">their leak rate quantification, especially when the signal strength from <\/span><span dir=\"ltr\" role=\"presentation\">the source at a sensor location is high.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Methane leak detection and remediation efforts are critical for combating climate change due to methane\u2019s role as a potent green-house gas. In this work, we consider the problem of source attribution and leak quantification: given a set of methane ground sensor readings, our goal is to determine the sources of the leaks and quantify their [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data 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