{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T12:47:42Z","timestamp":1785847662621,"version":"3.56.0"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52101341"],"award-info":[{"award-number":["52101341"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["T22-505\/19-N"],"award-info":[{"award-number":["T22-505\/19-N"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020KF018"],"award-info":[{"award-number":["ZR2020KF018"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB4000901-03"],"award-info":[{"award-number":["2021YFB4000901-03"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M662469"],"award-info":[{"award-number":["2019M662469"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115416","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:12:59Z","timestamp":1782133979000},"page":"115416","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P3","title":["Physics-assisted deep probability learning for natural gas leakage detection from infrared cameras without anomaly data"],"prefix":"10.1016","volume":"181","author":[{"given":"Junjie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zonghao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihao","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjiang","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115416_bib1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1020281327116","article-title":"An introduction to MCMC for machine learning","volume":"50","author":"Andrieu","year":"2003","journal-title":"Mach. Learn."},{"key":"10.1016\/j.engappai.2026.115416_bib2","series-title":"ICPR International Workshops and Challenges: Virtual Event, January 10\u201315, 2021, Proceedings, Part II","first-page":"608","article-title":"DEEPPBM: deep probabilistic background model estimation from video sequences, pattern recognition","author":"Behnaz","year":"2021"},{"key":"10.1016\/j.engappai.2026.115416_bib3","doi-asserted-by":"crossref","first-page":"8122","DOI":"10.1109\/TII.2021.3064845","article-title":"Tensor-based approach for liquefied natural gas leakage detection from surveillance thermal cameras: a feasibility study in rural areas","volume":"17","author":"Bin","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.115416_bib4","series-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"key":"10.1016\/j.engappai.2026.115416_bib5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.cosrev.2014.04.001","article-title":"Traditional and recent approaches in background modeling for foreground detection: an overview","volume":"11","author":"Bouwmans","year":"2014","journal-title":"Computer science review"},{"key":"10.1016\/j.engappai.2026.115416_bib6","doi-asserted-by":"crossref","first-page":"6240","DOI":"10.1109\/TII.2019.2891521","article-title":"The gas leak detection based on a wireless monitoring system","volume":"15","author":"Dong","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.115416_bib7","series-title":"International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a bayesian approximation: representing model uncertainty in deep learning","author":"Gal","year":"2016"},{"key":"10.1016\/j.engappai.2026.115416_bib8","series-title":"Graph Moving Object Segmentation, 44","first-page":"2485","author":"Giraldo","year":"2020"},{"key":"10.1016\/j.engappai.2026.115416_bib9","series-title":"2012 American Control Conference (ACC)","first-page":"4305","article-title":"Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation","author":"Godbehere","year":"2012"},{"key":"10.1016\/j.engappai.2026.115416_bib10","series-title":"2012 Ieee Conference on Computer Vision and Pattern Recognition","first-page":"1568","article-title":"Incremental gradient on the grassmannian for online foreground and background separation in subsampled video","author":"He","year":"2012"},{"key":"10.1016\/j.engappai.2026.115416_bib11","series-title":"2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE","first-page":"38","article-title":"Background segmentation with feedback: the pixel-based adaptive segmenter","author":"Hofmann","year":"2012"},{"key":"10.1016\/j.engappai.2026.115416_bib12","series-title":"2019 Chinese Control and Decision Conference (CCDC)","first-page":"329","article-title":"A VOCs gas detection algorithm based on infrared thermal imaging","author":"Hong","year":"2019"},{"key":"10.1016\/j.engappai.2026.115416_bib13","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/TCSVT.2017.2711659","article-title":"WeSamBE: a weight-sample-based method for background subtraction","volume":"28","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.115416_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2021.112809","article-title":"MethaNet\u2013An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery","volume":"269","author":"Jongaramrungruang","year":"2022","journal-title":"Rem. Sens. Environ."},{"key":"10.1016\/j.engappai.2026.115416_bib15","series-title":"Computer Vision and Distributed Processing","first-page":"135","article-title":"An improved adaptive background mixture model for real-time tracking with shadow detection, video-based surveillance systems","author":"KaewTraKulPong","year":"2002"},{"issue":"5","key":"10.1016\/j.engappai.2026.115416_bib16","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1038\/s43017-020-0046-x","article-title":"The first decade of scientific insights from the Deepwater Horizon oil release","volume":"1","author":"Kujawinski","year":"2020","journal-title":"Nature Reviews Earth & Environment"},{"key":"10.1016\/j.engappai.2026.115416_bib17","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.ijhydene.2024.05.410","article-title":"Real time hydrogen plume spatiotemporal evolution forecasting by using deep probabilistic spatial-temporal neural network","volume":"72","author":"Li","year":"2024","journal-title":"Int. J. Hydrogen Energy"},{"key":"10.1016\/j.engappai.2026.115416_bib18","series-title":"European Conference on Computer Vision","first-page":"270","article-title":"Contourlet residual for prompt learning enhanced infrared image super-resolution","author":"Li","year":"2024"},{"key":"10.1016\/j.engappai.2026.115416_bib19","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"7534","article-title":"Difiisr: a diffusion model with gradient guidance for infrared image super-resolution","author":"Li","year":"2025"},{"key":"10.1016\/j.engappai.2026.115416_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2020.107478","article-title":"CFD simulations on high-buoyancy gas dispersion in the wake of an isolated cubic building using steady RANS model and LES","volume":"188","author":"Lin","year":"2021","journal-title":"Build. Environ."},{"key":"10.1016\/j.engappai.2026.115416_bib21","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/JAS.2024.124878","article-title":"PromptFusion: harmonized semantic prompt learning for infrared and visible image fusion","volume":"12","author":"Liu","year":"2024","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.engappai.2026.115416_bib22","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2226","article-title":"Dcevo: discriminative cross-dimensional evolutionary learning for infrared and visible image fusion","author":"Liu","year":"2025"},{"key":"10.1016\/j.engappai.2026.115416_bib23","doi-asserted-by":"crossref","first-page":"3670","DOI":"10.1109\/TII.2021.3120027","article-title":"Visual early leakage detection for industrial surveillance environments","volume":"18","author":"Lyu","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.115416_bib24","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1109\/TII.2019.2938527","article-title":"Spatiotemporal anomaly detection using deep learning for real-time video surveillance","volume":"16","author":"Nawaratne","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.115416_bib25","series-title":"Bayesian Learning for Neural Networks","author":"Neal","year":"2012"},{"key":"10.1016\/j.engappai.2026.115416_bib26","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1038\/s41467-022-29709-3","article-title":"Methane emissions from US low production oil and natural gas well sites","volume":"13","author":"Omara","year":"2022","journal-title":"Nat. Commun."},{"issue":"52","key":"10.1016\/j.engappai.2026.115416_bib27","doi-asserted-by":"crossref","first-page":"26376","DOI":"10.1073\/pnas.1908712116","article-title":"Satellite observations reveal extreme methane leakage from a natural gas well blowout","volume":"116","author":"Pandey","year":"2019","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.engappai.2026.115416_bib28","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1126\/science.aaw4741","article-title":"Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations","volume":"367","author":"Raissi","year":"2020","journal-title":"Science"},{"key":"10.1016\/j.engappai.2026.115416_bib29","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1021\/acs.est.6b03906","article-title":"Are optical gas imaging technologies effective for methane leak detection?","volume":"51","author":"Ravikumar","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.115416_bib30","first-page":"2044","article-title":"An anomaly detection method for satellites using monte carlo dropout","volume":"59","author":"Sadr","year":"2022","journal-title":"IEEE Trans. Aero. Electron. Syst."},{"key":"10.1016\/j.engappai.2026.115416_bib31","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1002\/ese3.380","article-title":"Natural gas: a transition fuel for sustainable energy system transformation?","volume":"7","author":"Safari","year":"2019","journal-title":"Energy Sci. Eng."},{"key":"10.1016\/j.engappai.2026.115416_bib32","article-title":"Advanced RANS model for simulating high-pressure gas leaks and dispersion dynamics","volume":"98","author":"Santolin","year":"2025","journal-title":"J. Loss Prev. Process. Ind."},{"key":"10.1016\/j.engappai.2026.115416_bib33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TMI.2019.2919951","article-title":"Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT","volume":"39","author":"Seeb\u00f6ck","year":"2019","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.engappai.2026.115416_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106780","article-title":"Real-time leak detection using an infrared camera and faster R-CNN technique","volume":"135","author":"Shi","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.engappai.2026.115416_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2020.119572","article-title":"Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach","volume":"219","author":"Shi","year":"2021","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115416_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.133201","article-title":"Real-time natural gas release forecasting by using physics-guided deep learning probability model","volume":"368","author":"Shi","year":"2022","journal-title":"J. Clean. Prod."},{"issue":"1","key":"10.1016\/j.engappai.2026.115416_bib37","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TIP.2014.2378053","article-title":"SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity","volume":"24","author":"St-Charles","year":"2015","journal-title":"IEEE Transactions on Image Processing"},{"issue":"10","key":"10.1016\/j.engappai.2026.115416_bib38","doi-asserted-by":"crossref","first-page":"4768","DOI":"10.1109\/TIP.2016.2598691","article-title":"Universal Background Subtraction Using Word Consensus Models","volume":"25","author":"St-Charles","year":"2016","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.engappai.2026.115416_bib39","series-title":"1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)","first-page":"246","article-title":"Adaptive background mixture models for real-time tracking, proceedings","author":"Stauffer","year":"1999"},{"key":"10.1016\/j.engappai.2026.115416_bib40","article-title":"VideoGasNet: deep learning for natural gas methane leak classification using an infrared camera","volume":"238","author":"Wang","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115416_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2019.113998","article-title":"Machine vision for natural gas methane emissions detection using an infrared camera","volume":"257","author":"Wang","year":"2020","journal-title":"Appl. Energy"},{"key":"10.1016\/j.engappai.2026.115416_bib42","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TSMC.2020.2968516","article-title":"Anomaly detection based on convolutional recurrent autoencoder for IoT time series","volume":"52","author":"Yin","year":"2022","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.115416_bib43","series-title":"Backgroundsubtractorcnt: a Fast Background Subtraction Algorithm","author":"Zeevi","year":"2016"},{"key":"10.1016\/j.engappai.2026.115416_bib44","article-title":"Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data","author":"Zhang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115416_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2024.114898","article-title":"Hydrogen jet and diffusion modeling by physics-informed graph neural network","volume":"207","author":"Zhang","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.engappai.2026.115416_bib46","series-title":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004","first-page":"28","article-title":"Improved adaptive Gaussian mixture model for background subtraction","author":"Zivkovic","year":"2004"},{"key":"10.1016\/j.engappai.2026.115416_bib47","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","article-title":"Efficient adaptive density estimation per image pixel for the task of background subtraction","volume":"27","author":"Zivkovic","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017008?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017008?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T12:05:43Z","timestamp":1785845143000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626017008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":47,"alternative-id":["S0952197626017008"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115416","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Physics-assisted deep probability learning for natural gas leakage detection from infrared cameras without anomaly data","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115416","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"115416"}}