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Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas., This research was supported in part by the National Natural Science Foundation of China (Grant No.41874044 and Grant No. 42004011), in part by project G2HOTSPOTS (PID2021-122142OB- I00) from the MCIN /AEI /10.13039 /501100011033 /FEDER, UE and in part by China Postdoctoral Science Foundation (Grant No. 2020M671646). At the same time, the research was also funded by the Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project (B20046) and National Key R&D Program of China (Grant No. 2022YFE0102600)., Peer Reviewed, Postprint (published version)

The present study investigates the effect of hydrogen addition to methane-air jet flame based on Sandia flame D. For this purpose, RANS simulations with a global four-step mechanism were first compared with other reduced (1-step, 2-step) and detailed mechanisms (DRM-19, GRI-Mech 3.0) using the standard k-e model. The accuracy of the simulations was further verified based on the LES wall-adapting local eddy-viscosity (WALE) model. The turbulence-chemistry interaction was described by the eddy dissipation concept (EDC) model. All simulations were carried out with Open FOAM. Simulation results with two main turbulence models, RANS and LES, were compared with available experimental data. The results showed the good agreement between experimental data and simulations for the selected flame using the global four-step mechanism. Other reduced and detailed chemical mechanisms were not attractive because the accuracy and computational cost were insufficient. CH4-H2 blending jet flame (5% CH4+20% H2) based on flame D geometry was simulated to verify that RANS and LES can have similar and reasonable results. After that, the influence of different mixture proportions was analyzed using the RANS model and the global four-step mechanism. It was observed that the addition of hydrogen accelerates the propagation and attenuation of jet flame under conditions of the same geometry and constant Re number. But it allowed the flame to maintain the same peak temperature while reducing carbon dioxide emission and oxygen consumption. © 2022, Scipedia S.L. All rights reserved., Peer Reviewed, Postprint (published version)