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Abstract

The Santiaguito Volcanic Complex is located in Guatemala’s Western Volcanic Highlands, along the westernmost section of the Central American Volcanic Arc. Santiaguito presents multiple hazards (including pyroclastic density flows, lava flows, lahars, explosions and debris avalanches) to the local population, which numbers 1.6 million people within the 30 km2 around the volcano. Lahars are mixtures of water and pyroclastic debris that includes gases which can rapidly initiate and flow at speeds of tens of meters per second down wide barrancas (canyons) making them highly destructive. Lahar occurrence strongly correlates with rainfall at the volcano and they are commonplace in the long rainy season. They pose a great hazard to local inhabitants who regularly cross the channels as they live and work on farms on or near the flanks of the barrancas. INSIVUMEH, the national seismic and volcano monitoring agency, has recently built a network of 10 seismic stations that can monitor these flows, in collaboration with external agencies. Using the seismic data from the 2022 rainy season we have built a Lahar catalogue. We supplement the dataset by adding synthetic Lahar waveforms to it, informed by the existing recorded events. We then use the hybrid catalogue to develop a Convolutional Neural Network Lahar detector, always testing on real events. We test using different input lengths to find an ideal timeliness/accuracy ratio for Lahar early warning. We plan to extend the method for Lahar location tracking. We expect the method to be portable to other volcanic areas.