Detection and Location of Tremor signals: A case study from East Java, Indonesia.

Abstract

AbstractMt. Bromo is a strombolian-type volcano in East Java, Indonesia, that entered into one of its main eruption periods; between November 2015 and January 2016. The activity was registered by several seismic stations, including a 2-years temporary seismic network deployed to study the dynamics of a near sediment-hosted geothermal system called LUSI. Therefore, the following work reports the achieved detections of the events recorded by using all 3 components, and their locations in latitude, longitude and depth, all from a proposed automatic processing procedure. The detection was done using a coincidence trigger method in the available components to adjust the start and end of each signal, and the first-arrival picks were validated through a polarization analysis. The location process was achieved using the Oct-Tree importance sampling method throughout a probabilistic density function. A total of 32’787 events were detected with higher occurrence during a number of periods, where some of them can be correlated to the eruption period of Mt. Bromo. The events detected are mainly tremor signals of 2 to 4 minutes duration, with weak first-arrival impulses. The locations with low errors (RMS≤1.0; 3’965 events) revealed multiple superficial sources, concentrated between 0and 5 Km depth, originating from Mt. Bromo and 4 other main volcanic structures nearby. Other sources are found deeper, between 10 to 50 Km depth, and are attributed mainly to interactions between the magmatic chambers of the volcanoes, and movements in pre-existing sutures zones(faults) from overpressure of magmatic activity. Chronologically, a peak preceding the main eruption was found, characterized by an increase in Volcano-Tectonic-type (VT) signals beneath Mt. Bromo. This is consistent with cases observed at other similar strombolian-type volcanoes prior to eruptions. After an assessment of the automatic processing procedure used, we suggest promising improvements for future works by: 1) applying an association method based on the same principle as the coincidence trigger used in the detection step, and 2) using the polarization analysis in a sliding window along the event signal to re-pick the first-arrivals.

AbstractMt. Bromo is a strombolian-type volcano in East Java, Indonesia, that entered into one of its main eruption periods; between November 2015 and January 2016. The activity was registered by several seismic stations, including a 2-years temporary seismic network deployed to study the dynamics of a near sediment-hosted geothermal system called LUSI. Therefore, the following work reports the achieved detections of the events recorded by using all 3 components, and their locations in latitude, longitude and depth, all from a proposed automatic processing procedure. The detection was done using a coincidence trigger method in the available components to adjust the start and end of each signal, and the first-arrival picks were validated through a polarization analysis. The location process was achieved using the Oct-Tree importance sampling method throughout a probabilistic density function. A total of 32’787 events were detected with higher occurrence during a number of periods, where some of them can be correlated to the eruption period of Mt. Bromo. The events detected are mainly tremor signals of 2 to 4 minutes duration, with weak first-arrival impulses. The locations with low errors (RMS≤1.0; 3’965 events) revealed multiple superficial sources, concentrated between 0and 5 Km depth, originating from Mt. Bromo and 4 other main volcanic structures nearby. Other sources are found deeper, between 10 to 50 Km depth, and are attributed mainly to interactions between the magmatic chambers of the volcanoes, and movements in pre-existing sutures zones(faults) from overpressure of magmatic activity. Chronologically, a peak preceding the main eruption was found, characterized by an increase in Volcano-Tectonic-type (VT) signals beneath Mt. Bromo. This is consistent with cases observed at other similar strombolian-type volcanoes prior to eruptions. After an assessment of the automatic processing procedure used, we suggest promising improvements for future works by: 1) applying an association method based on the same principle as the coincidence trigger used in the detection step, and 2) using the polarization analysis in a sliding window along the event signal to re-pick the first-arrivals.