Exploring Thermal Anomalies using Continuous Wavelet Transform Approach

Exploring Thermal Anomalies using Continuous Wavelet Transform Approach

Earthquake, Thermal, Anomaly, Detection, CWT

This study analyzes surface temperature time series to identify thermal anomalies to detect the strong earthquake (Mw > 6) on Lombok Islandusing the datasetfrom NASA satellites (LOK). We exploreanomaly detection Continuous Wavelet Transform (CWT) method with a 4th-order complex Gaussian wavelet (cgau4), which allows for detailed time–frequency domain analysis. The analysis results show that NASA data only showan increase in thermal energy in 2016, which is related to the influence of El Niño, and therefore is climatological. In contrast, the LOK data shows a strong energy anomaly (dark red) in May–June 2018, which began to form in January, peaked before the earthquake, and then decreased after August 2018, coinciding with the post-earthquake period of Lombok.The differences in energy patterns between NASA and LOK indicate that pre-seismic thermal anomalies appear more prominent in the local data. Our findingsindicate a possible higher sensitivity of surface observation data to changes in subsurface heat fluxthat indicate leading up to a seismic event. Therefore, we utilized an exploratory approach for detecting and characterizing non-stationary thermal anomalies as anearly indicator of seismic activity

Bulkis Kanata

https://ijicom.respati.ac.id/index.php/ijicom/article/view/203/132

International Journal of Informatics and Computation (IJICOM

2025

Fajar bagus W

PDF

English

Text