On 3 August 2014, an Mw 6.2 earthquake occurred in Ludian, Yunnan Province, China (27.245° N 103.427° E). This damaging earthquake caused approximately 400 fatalities, 1800 injuries, and the destruction of at least 12,000 houses. Using air temperature data of the National Center for Environmental Prediction (NCEP) and the tidal force fluctuant analysis (TFFA) method, we derive the temperature variations in multiple air layers between before and after the Ludian earthquake. In the spatial range of 30° × 30° (12°–42° N, 88°–118° E) of China, a thermal anomaly appeared only on or near the epicenter before earthquake, and air was heated from the land, then uplifted by a heat flux, and then cooled and dissipated upon rising. With the approaching earthquake, the duration and range of the thermal anomaly during each tidal cycle was found to increase, and the amplitude of the thermal anomaly varied with the tidal force potential: air temperature was found to rise during the negative phase of the tidal force potential, to reach peak at its trough, and to attenuate when the tidal force potential was rising again. A significance test supports the hypothesis that the thermal anomalies are physically related to Ludian earthquakes rather than being coincidences. Based on these results, we argue that the change of air temperature could reflect the stress changes modulated under the tidal force. Moreover, unlike the thermal infrared remote sensing data, the air temperature data provided by NCEP are not affected by clouds, so it has a clear advantage for monitoring the pre-earthquake temperature variation in cloudy areas.

The application of certain mathematical–statistical methods can quantitatively identify and extract the abnormal characteristics from the observation data, and the comprehensive analysis of seismic multi-parameters can study and judge the risk of the tectonic regions better than a single parameter. In this study, the machine learning-based detection of seismic multi-parameters using the sliding extreme value relevancy method, based on the earthquake-corresponding relevancy spectrum, was calculated in the tectonic regions in the western Chinese mainland, and the R-value evaluation was completed. Multi-parameter data included the b value, M value (missing earthquakes), ƞ value (the relationship between seismic magnitude and frequency), D value (seismic hazard), Mf value (intensity factor), N value (earthquake frequency), and Rm value (modulation parameter). The temporal results showed that the high-value anomalies appeared before most target earthquakes during the training period. Moreover, some target earthquakes also occurred during the advantageous extrapolation period with high-value anomalies. The spatial results showed that some months before the target earthquakes, there was indeed a significant abnormal enhancement area that appeared near the epicenter, and the anomaly gradually disappeared after the earthquakes. This study demonstrated that machine learning techniques for detecting earthquake anomalies using seismic multi-parameter data were feasible.

The air temperature changes in the Palu MW7.5 earthquake in Indonesia on 28 September 2018 were analyzed, based on the additive tectonic stress caused by celestial tidal-generating forces (ATSCTF) and air temperature data from the National Center for Environmental Prediction (NCEP). This paper explored the variation characteristics of three-dimensional stratified of air temperature caused by seismic activity and the coupling relationship between air temperature changes and the tidal force. The background information for air temperature calculation was obtained from the tidal force changes, and the air temperature increment method was used to study the temperature evolution process of different periods in the study area. The results found that the tidal force acting on the critical state earthquake faults may be an important external factor inducing earthquakes, and there was indeed a significant air temperature increase anomaly during the Palu MW7.5 earthquake. The paper also summarized the abnormal characteristics of air temperature caused by seismic activity: the air temperature closer to the land’s surface has greater anomaly amplitude and a wider anomaly area than that of the upper air.

Earthquakes are the most dangerous natural disasters, and scholars try to predict them to protect lives and property. Recently, a long-term statistical analysis based on a “heating core” filter was applied to explore thermal anomalies related to earthquakes; however, some gaps are still present. Specifically, (1) whether there are differences in thermal anomalies generated by earthquakes of different magnitudes has not yet been discussed; and (2) thermal anomalies in high-spatial-resolution data are often distributed in spots, which is not convenient for statistics of thermal anomalies. To address these issues, in this study, we applied high-spatial-resolution thermal infrared data to explore the performance of the “heating core” for earthquake prediction at different magnitudes (i.e., 3, 3.5, 4, 4.5, and 5). The specific steps were as follows: first, the resampling and moving-window methods were applied to reduce the spatial resolution of the dataset and extract the suspected thermal anomalies; second, the “heating core” filter was used to eliminate thermal noise unrelated to the seismic activity in order to identify potential thermal anomalies; third, the time–distance–magnitude (TDM) windows were used to establish the correspondence between earthquakes and thermal anomalies; finally, the new 3D error diagram (false discovery rate, false negative rate, and space–time correlation window) and the significance test method were applied to investigate the performance under each minimum magnitude with training data, and the robustness was validated using a test dataset. The results show that the following: (1) there is no obvious difference in the thermal anomalies produced by earthquakes of different magnitudes under the conditions of a “heating core”, and (2) the best model with a “heating core” can predict earthquakes effectively within 200 km and within 20 days of thermal anomalies’ appearance. The binary prediction model with a “heating core” based on thermal infrared anomalies can provide some reference for earthquake prediction.

This study examines the three-dimensional layered air temperature variations associated with the Hualian M6.9 earthquake, which occurred on 18 September 2022, using data from the National Center for Environmental Prediction (NCEP) and extracting background information for air temperature through tidal forces. Changes in air temperature stratification revealed that near the epicenter, temperature anomalies began on 12 September and peaked on 13 September, with pronounced increases in magnitude and area. These anomalies followed a seismic thermal anomaly pattern, i.e., one with greater amplitude and a wider range near the land surface, decreasing with altitude until they dissipated, while the other exhibited high atmospheric temperature anomalies, i.e., the upper atmospheric warming exceeded that near the land surface, likely due to meteorological factors. Stable weather conditions and a low geomagnetic Kp index and Dst index during the research period supported the reliability of these findings. The deformation field recorded by InSAR reflects crustal deformation directly caused by fault slip and stress accumulation; moreover, the area with significantly higher air temperature coincides with the areas with the most concentrated deformation.

The Kamchatka Peninsula is one of the most seismically active regions in the world and experienced the Mw 8.8 earthquake in 2025. To examine whether tectonic processes were associated with enhanced atmospheric warming while minimizing external interference, this study analyzed multi-level air temperature fields from the NCEP (National Centers for Environmental Prediction) reanalysis data. A tidal-force-based background framework was used to characterize the vertical evolution of air temperature excesses before and after the mainshock. Results showed that a pronounced warming excess emerged near the epicentral area prior to the earthquake. The warming excess exhibited the strongest intensity and amplitude in the near-surface layer and decayed progressively with altitude, a pattern that may be consistent with the vertical attenuation expected for tectonically related thermal excesses. Sentinel-5P SO₂ observations showed enhanced volcanic SO₂ concentrations from mid-July 2025, before the Mw 8.8 mainshock. The SO₂ enhancement was observed in the volcanic regions of the study area, suggesting that the SO₂ signal may reflect enhanced regional degassing activity while also being influenced by volcanic emissions, plume dispersion, and atmospheric transport. Sentinel-1A InSAR observations derived from SAR images acquired on 23 July and 4 August 2025 represent cumulative coseismic line-of-sight (LOS) deformation associated with the Mw 8.8 mainshock, rather than preseismic subsidence. Overall, the air temperature excesses and SO₂ enhancement may provide auxiliary evidence for possible changes in the regional volcanic-tectonic system before the earthquake, whereas the InSAR result provides coseismic deformation context. These findings highlight the potential value of vertical air temperature structure for investigating possible earthquake-related thermal signals, but further studies based on multi-year statistical analyses of various observational datasets and additional earthquake cases are still required.