VEVCC program for concatenation of volcanic events based on cross-correlation analysis

Abstract

Volcanic eruptions pose a significant risk to communities located near active volcanoes. Disaster mitigation and risk reduction efforts rely on detecting and monitoring volcanic activity as early as possible. This article introduces VEVCC, a MATLAB-based application designed to precisely identify and extract volcanic seismic events from continuous data streams. VEVCC's primary objective is to facilitate the creation of an Excel file containing the arrival times of detected events, which can then be used for various purposes, such as early warning disaster mitigation and automated event identification via machine learning techniques. VEVCC utilizes cross-correlation algorithms to identify volcanic seismic events. It separates these events from background noise and other sources of seismicity, allowing for the construction of a clean and informative dataset. The extracted data is a valuable resource for estimating the frequency of volcanic events and evaluating patterns of volcanic activity. VEVCC's time-stamped event data is indispensable for improving early warning systems, real-time surveillance, and automated event identification. We tested the program on the Merapi volcano datasets during a 1998 campaign for a broadband experiment with the capability to extract the events automatically. Further machine-learning models and algorithms enhance the automatic recognition of volcanic events.