Spatiotemporal Correlation Characteristics Between Thermal Infrared Remote Sensing Obtained Surface Thermal Anomalies and Reconstructed 4-D Temperature Fields of Underground Coal Fires

Underground coal fires are global catastrophes that result in energy waste, carbon emission, and eco-environment pollution. Remote sensing (RS) detection is essential for underground coal fire extinguishing engineering, and the most used is thermal infrared (TIR) RS. It can well obtain the thermal anomalies of land surface temperature (LST), which is the most direct surface feature of underground coal fires. However, most studies using TIR RS simply delineate underground fire sources vertically according to LST anomalies, which has relatively little impact when initially determining coal fire area locations on the large scale. As for the precise location of small-scale subsurface fire sources, the deviation between subsurface fire source locations inferred and real locations could lead to errors or even mistakes to fire extinguishing engineering. There is a lack of subsurface fire source evolution model reconstruction method, and the spatiotemporal correlations characteristic of LST thermal anomalies and underground fire sources have not yet been discussed. To this end, taking Miquan coalfield (Western China) as an example, a 3-D empirical Bayesian Kriging (EBK3D) method is first proposed to reconstruct the 4-D temperature fields of underground fire sources. Then, the feasibility of the vertical correspondence approach to inferring small-scale subsurface fire sources through LST thermal anomalies detected by unmanned aerial vehicle TIR RS and satellite TIR RS is analyzed. Finally, the spatiotemporal correlation characteristic of LST thermal anomalies and subsurface fire sources is analyzed. As the results show, it is feasible to reconstruct the underground fire source evolution model by the EBK3D method. The reconstructed 4-D temperature fields can dynamically reflect the evolutionary states of underground fire sources in three time periods, with cross-validated root mean square errors of 52.2 degrees C, 49.6 degrees C, and 37.1 degrees C and R-2 of linear regressions of 0.925, 0.9145, and 0.8429, respectively. The LST thermal anomalies show a significant spatiotemporal delay with respect to the subsurface fire source evolution. This makes the locations of the underground fire sources traced by the vertical correspondence method deviate from the real ones. The offsets of underground fire sources relative to surface thermal anomalies in the coal seam strike and dip directions for different time periods at depths of (T1: -44.43 m, T2: -27.72 m, and T3: -20.04 m) are (T1: 73.80 m, T2: 52.33 m, and T3: 45.06 m), and (T1: 16.79 m, T2: 17.27 m, and T3: 24.82 m), respectively. R-2's for the linear regression model of the offset averages in three directions versus time and fire source size are (0.9247, 0.7949, and 0.9564) and (0.8739, 0.85 and 0.9152), respectively.

Automated summary

Underground coal fires are global catastrophes that have significant effects on energy, carbon emissions, and the environment. Remote sensing detection, especially thermal infrared remote sensing, is important for underground coal fire extinguishing engineering. However, there is a lack of precise location methods for small-scale subsurface fire sources, and the spatiotemporal correlations between thermal anomalies and underground fire sources have not been thoroughly discussed. This study proposes a 3-D empirical Bayesian Kriging method to reconstruct the temperature fields of underground fire sources and analyzes the feasibility of inferring subsurface fire sources using thermal anomalies detected by unmanned aerial vehicle and satellite thermal infrared remote sensing. The study also analyzes the spatiotemporal correlation characteristic between thermal anomalies and subsurface fire sources.