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Enhancing Volcanic Seismic Monitoring of Semeru Volcano Using Data Integration and Automated Workflows

Martanto1, 2 , Corentin Caudron1, Thomas Lecocq1, Devy Kamil Syahbana2, Andri Dian Nugraha3, Alexander Yates2

  • Affiliations:  1G-Time, Université Libre de Bruxelles, Brussels, Belgium; 2Center for Volcanology and Geological Hazard and Mitigation, Bandung, Indonesia; 3Bandung Institute Technology, Bandung, Indonesia 

  • Presentation type: Poster

  • Presentation time: Monday 16:30 - 18:30, Room Poster Hall

  • Poster Board Number: 160

  • Programme No: 3.1.52

  • Theme 3 > Session 1

Abstract

On December 4, 2021, Semeru Volcano in Indonesia erupted, producing pyroclastic density currents (PDCs), resulted in 51 fatalities, 169 injuries, and 22 missing people. Semeru is primarily monitored using seismic as primary instrumentation, utilizing various seismic methods to perform analyzes, assess, and evaluate volcanic activity. At the time of the eruption, data acquisition, processing, and analysis were conducted separately and lacking integration. This study aims to enhance seismic volcano monitoring methods by integrating seismic data acquisition, governance, and processing into a unified and streamlined system. Improvements in data governance were achieved through standardizing data formats, establishing storage, and implementing a database for data indexing. Specifically, the seismic methods were used are RSAM (Real-time Seismic Amplitude Measurement), MSNoise (Monitoring Seismic Velocity Changes using Ambient Seismic Noise), Seismic Event Clustering, and REDPy (Repeating Earthquake Detector). While MSNoise inherently features a built-in database structure, custom database schema designs were developed for RSAM and REDPy. These three methods were integrated into a single workflow using Apache Airflow. This integration enables real-time monitoring of data availability and completeness, allowing for more effective data quality control. The results of this study demonstrate that integrating seismic data acquisition, storage, governance, and processing significantly improves data quality, analysis, and processing efficiency. This approach has the potential to be adapted for monitoring other volcanoes in Indonesia, improving early warning systems and volcanic hazard assessments.