Development of a Volcanic Monitoring System with CNN

Our research, in collaboration with Modemat and the Geophysical Institute of Escuela Politécnica Nacional, presents a methodological framework for developing an advanced volcanic monitoring system that combines thermal imaging with artificial intelligence, specifically designed for volcanic surveillance needs in Ecuador. The system processes raw images captured by FLIR cameras (.fff format) through metadata extraction, thermal analysis and automated classification. The key element of the system lies in its three-dimensional tensor processing that captures both the spatial dimensions (x,y pixel coordinates) and the temporal evolution (z-dimension) of the thermal patterns. This approach simultaneously analyzes three fundamental aspects: thermal information, edge detection and thresholds at different temperature levels.

For analyzing these complex data, we implemented a Multi-Branch Convolutional Neural Network architecture. This architecture processes the three types of thermal information in parallel, later merging the extracted features to generate accurate classification of volcanic state. The model was trained with an extensive thermal dataset (approximately 7 GB), implementing regularization techniques to ensure its performance under variable conditions.