Shuai-Jing Ren

Long-term trends, seasonal fluctuations, and short-term disturbances are superimposed in land surface temperature (LST) time series, complicating the identification of persistent coal-fire thermal anomalies. Based on 1188 Landsat scenes of the Midong mining area, Xinjiang, China, acquired during 1990–2025, this study applied Seasonal-trend decomposition using LOESS (STL) to extract long-term temperature trends. The contemporaneous spatial mean of the trend components in non-mining areas was used as the background reference to construct the background-corrected temperature difference ΔLST. Thermal anomalies were identified using year-specific thresholds, and their spatiotemporal evolution and relationship with mining-land changes were analyzed in conjunction with land-use/land-cover (LULC) records. In 2020 and 2024, the matching rates with field-measured high-temperature points were 75.0% and 83.3%, respectively, for Z > 2, compared with 47.2% and 41.7% for Z > 3. To focus on thermal anomalies with larger standardized temperature deviations and to examine their long-term evolution, subsequent analyses used the Z > 3 results. Thermal-anomaly area peaked at approximately 1.7 km² in 2016 and subsequently declined overall, but anomalies were repeatedly detected at some locations within open-pit mining areas during 2016–2025. From 2014 onward, thermal anomalies in these mining areas accounted for more than 80% of the total identified anomaly area. The mining-land area ratio and the thermal-anomaly area ratio were positively correlated (Spearman’s r = 0.376, p = 0.0245), but mining-land area ratios were generally lower in 2016–2025 than in 2005–2015, while thermal-anomaly area ratios remained relatively high. At comparable mining-land area ratios, thermal-anomaly area ratios were generally higher in the later period. Long-term records of thermal anomalies and LULC reveal patterns of localized anomaly recurrence that are difficult to capture using annual area statistics or the current extent of mining land, providing a basis for determining long-term monitoring priorities in mining areas.

Coal spontaneous combustion fire is a major hazard threatening mine safety and production. Accurate identification of hidden fire sources is the key to preventing and controlling coal spontaneous combustion fires. Given that traditional detection methods are unable to accurately identify high-temperature zones, this study develops an acoustic-signal-based approach for reconstructing the temperature field associated with coal spontaneous combustion. By constructing a two-dimensional acoustic thermometry experimental system for temperature field measurement, multipath acoustic signals were collected in both the air medium and a loose coal medium, respectively. The variation characteristics of the acoustic spectrum, energy attenuation, and travel time were analyzed under different heat source temperatures, media, and sound source positions. The results show that the dominant frequencies of the received signals under different heat source temperatures are concentrated in the range of 1000–1300 Hz. An increase in temperature does not significantly alter the dominant frequency range, but it reduces the peak amplitude of the received signals. Compared with propagation in air, acoustic waves experience greater energy loss in loose coal. Their travel time is also generally longer, indicating that the pore structure alters the propagation path and influences the acoustic propagation process. To account for pore-structure-induced deviations between the actual acoustic propagation path and the geometric path, a path correction coefficient is introduced. Using the compensated correction model, the temperature fields under working conditions of 100 °C, 150 °C, and 200 °C were reconstructed, with peak temperatures of 91.507 °C, 140.554 °C, and 188.665 °C, respectively. The gap between the reconstructed results and those obtained in the air medium is significantly narrowed. This suggests the model effectively corrects path deviations due to pore structure and enhances acoustic reconstruction accuracy for non-uniform temperature fields in loose coal. These findings offer a methodological basis for acoustic temperature field reconstruction and hidden fire source detection in loose coal, with practical value for enhancing mine fire prevention efficiency.

The mine goaf is a caved space formed by the random accumulation of residual coal, broken rock fragments, and gangue, and is prone to disasters such as spontaneous combustion of residual coal and gas explosions. The accumulation state of loose media in the goaf affects the propagation variation in acoustic signals. Achieving acoustic detection in the goaf critically depends on the choice of the sound source signal. To study the acoustic behavior of typical unconsolidated goaf materials, coal, limestone, and coal gangue were taken as the test media. Industrial CT scanning and sound absorption tests, together with 3D reconstruction, were used to study the pore features and sound absorption behavior of various loose media. The results indicate that the pore structure of loose media exhibits pronounced multiscale characteristics. As the particle size of loose media increases, the pore structure exhibits a trend of increasing pore size accompanied by a decrease in the number of pores. Based on the analysis of mechanical parameters of the loose media, the elastic moduli of the three media are found to be on the order of 10³ to 10⁴ MPa, and the Poisson’s ratios are generally distributed in the range of 0.21–0.36. This indicates that all three media are rigid porous media. Sound absorption tests show that the maximum absorption coefficient of the three loose media decreases with particle size but increases with packing thickness. In rigid porous media, the acoustic absorption behavior of loose materials is governed by their pore structure variations. These findings lay a theoretical foundation for the acoustic identification of spontaneous coal combustion in goafs and present new technical avenues for mine safety and hazard management.