What does this research mean for the field?

Automated recognition and localization systems, utilizing techniques ranging from classical signal processing to deep learning, are essential for processing massive volcanic seismic datasets, though scalable real-time solutions remain a critical challenge. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to systematically map the techniques used for automatic recognition and localization of volcanic seismic events.

June 1, 2026Open Access

Systematic mapping study: automatic recognition and localization of volcanic seismic events

Key Points

The study aims to systematically map the techniques used for automatic recognition and localization of volcanic seismic events.
Reviewed various types of volcanic microearthquakes and their identification
Analyzed computational techniques including classical signal processing and machine learning
Identified the most studied volcanoes for system testing and outlined future research directions
Automated systems effectively detect and classify different volcanic microearthquakes.
A variety of computational techniques, including deep learning, showed promise for real-time applications.
Identified challenges in scalability and the need for more robust solutions to enhance monitoring capabilities.

Abstract

Active volcanoes represent a significant hazard to surrounding communities, requiring continuous and reliable monitoring. While seismic signals constitute the main source of information, each volcano produces massive datasets that are difficult to process and interpret manually. Consequently, automated recognition and localization systems have become essential for the real-time detection and assessment of volcanic activity. This article presents a systematic mapping of the state of the art in automatic volcanic seismic events recognition and localization systems. It reviews the different types of volcanic microearthquakes (e.g., Long Period, Volcano Tectonic, Tremor, among others) that can be identified and analyzes the main computational techniques employed, ranging from classical signal processing and statistical methods to modern machine learning and deep learning approaches. In particular, this study describes how these techniques are applied to volcanic seismic events detection, classification, and localization. Additionally, we identify the most studied volcanoes worldwide to serve as reference points for testing these systems. Finally, we discuss current challenges, the need for scalable real-time solutions, and outline possible research directions to address them.

Bookmark

View Full Paper