Underground Natural Gas Microleakage Detection With Hyperspectral Imagery Based on Temporal Features and Ensemble Learning-Reference-Cited by-同舟云学术

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Underground Natural Gas Microleakage Detection With Hyperspectral Imagery Based on Temporal Features and Ensemble Learning

Published:2024 Issue: Volume:17 Page:1191-1203
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Jiang Jinbao¹^ORCID,Pan Yingyang¹^ORCID,Li Kangning¹^ORCID,Wang Xinda¹^ORCID,Zhang Wenxuan¹^ORCID

Affiliation:

1. College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10325603.pdf?arnumber=10325603

Reference55 articles.

1. An optimized nonlinear grey Bernoulli prediction model and its application in natural gas production

2. Study on gas leakage along cement sheath interfaces in salt cavern gas storage well

3. Natural geological seepage of hydrocarbon gas in the Appalachian Basin and Midwest USA in relation to shale tectonic fracturing and past industrial hydrocarbon production

4. Methane concentrations in streams reveal gas leak discharges in regions of oil, gas, and coal development

5. Identifying plants under natural gas micro-leakage stress using hyperspectral remote sensing

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