Magmatic or Not Magmatic? The 2015–2016 Seismic Swarm at the Long-Dormant Jailolo Volcano, West Halmahera, Indonesia

Passarelli, Luigi; Heryandoko, Nova; Cesca, Simone; Rivalta, Eleonora; Rasmid,; Rohadi, Supriyanto; Dahm, Torsten; Milkereit, Claus

doi:10.3389/feart.2018.00079

Cited by 33 publications

(19 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can be considered an evolution of the concept of template matching in which similar events are detected on the basis of the similarity with given examples (templates). Template matching is often used to detect aftershocks (Peng and Zhao, 2009), but also in volcano seismology to detect VT swarms (Passarelli et al, 2018), to characterize pre-eruptive sequences (Lengliné et al, 2016) or Long Period subevent multiplets (Matoza et al, 2015). The unsupervized approach on the other hand is usually more useful for tackling the analysis of continuous data (Seydoux et al, 2020).…”

Section: Machine Learning Approachesmentioning

confidence: 99%

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

et al. 2021

View full text Add to dashboard Cite

Seismic activity in volcanic settings could be the signature of processes that include magma dynamics, hydrothermal activity and geodynamics. The main goal of this study is to analyze the seismicity of Lipari Island (Southern Tyrrhenian Sea) to characterize the dynamic processes such as the interaction between pre-existing structures and hydrothermal processes affecting the Aeolian Islands. We deployed a dense seismic array of 48 autonomous 3-component nodes. For the first time, Lipari and its hydrothermal field are investigated by a seismic array recording continuously for about a month in late 2018 with a 0.1–1.5 km station spacing. We investigate the distribution and evolution of the seismicity over the full time of the experiment using self-organized maps and automatic algorithms. We show that the sea wave motion strongly influences the background seismic noise. Using an automatic template matching approach, we detect and locate a seismic swarm offshore the western coast of Lipari. This swarm, made of transient-like signals also recognized by array and polarization analyses in the time and frequency domains, is possibly associated with the activation of a NE-SW fault. We also found the occurrence of hybrid events close to the onshore Lipari hydrothermal system. These events suggest the involvement of hot hydrothermal fluids moving along pre-existing fractures. Seismological analyses of one month of data detect signals related to the regional tectonics, hydrothermal system and sea dynamics in Lipari Island.

show abstract

Section: Machine Learning Approachesmentioning

confidence: 99%

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Jailolo with shallow depths< 10 km and caused by tectonic activities for earthquakes on the islands of Ternate and Tidore. Passarelli et al (2018) in their study have stated that the swarm were caused by micro-seismic earthquake activities beneath Mt. Jailolo, the results of the study were more towards magmatic activities despite no indication of magma intrusion was found.…”

Section: Introductionmentioning

confidence: 90%

Study of P and S Wave Quality Factor (Qα and Qβ) Around Mt. Jailolo

Ulfiana

Wandono

Sianipar

et al. 2020

JGF

Self Cite

View full text Add to dashboard Cite

Mt. Jailolo is a B type volcano that has never erupted after 1600. Seismic activities around Mt. Jailolo have never been recorded until the swarm in November 2015. Several studies have been done to determine thecause of the swarm, but it is not certain whether the cause of the swarm is tectonic or volcanic activities. The study of attenuation characteristics has never been carried out in the area around Mt. Jailolo. Attenuation characteristics are important to provide the medium information which seismic waves pass through and it can also be applied to the volcanic areas as preliminary disaster mitigation. The main objective of this study is to analyze attenuation characteristics often expressed by Quality factor (Q-factor) of P and S seismic wave (Qα and Qβ), which are inversely proportional to attenuation factor (1/Q). Calculations of Qα and Qβ are obtained using coda normalization method. The study area location is around Mt. Jailolo at 127.3◦ - 127.6◦E and 0.9◦ - 1.2◦ N. Data have been collected with 12 Short Period temporary 7G sensors network belongs to GFZ and BMKG. This study uses 147 swarm events from the sensors with a threshold magnitude of Mw< 5.0, during April 2017. The study obtains Qα(f) = 9.61814f 1.12981 and Qβ(f) = 19.10690f 1.22843. The current analysis concludes that the attenuation beneath Mt. Jailolo corresponds to the volcanic swarms which may have been triggered by its deeper layer’s magmatic activity.

show abstract

“…Additional characteristics of the seismicity can help resolve the ambiguity. For example, a recent paper by Passarelli et al [81] determined that lateral magmatic dike intrusion was the most probable cause of a swarm at Jailolo volcano. This was based on careful assessment of hypocentre migration, focal mechanisms, non-double-couple components, surface fracture orientation and tectonic considerations.…”

Section: (D) Seismicitymentioning

confidence: 99%

Thoughts on the criteria to determine the origin of volcanic unrest as magmatic or non-magmatic

Pritchard

Mather

McNutt

et al. 2019

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

As our ability to detect volcanic unrest improves, we are increasingly confronted with the question of whether the unrest has a magmatic origin (magma on the move) or a non-magmatic origin from a change in the hydrothermal system (fluids that are not magma on the move) or tectonic processes. The cause of unrest has critical implications for the potential eruptive hazard (e.g. used in constructing Bayesian Event Trees), but is frequently the subject of debate, even at well-studied systems. Here, we propose a set of multi-disciplinary observations and numerical models that could be used to evaluate conceptual models about the cause of unrest. These include measurements of gas fluxes and compositions and the isotopic signature of some components (e.g. H 2 , He, C, SO 2 , H 2 O , CH 4 and CO 2 ), the spatial and temporal characteristics of ground deformation, thermal output, seismicity, changes in gravity, and whether there is topographic uplift or subsidence spanning hundreds to thousands of years. In several volcanic systems, both magmatic and non-magmatic unrest is occurring at the same time. While none of these observations or models is diagnostic on its own, we illustrate several examples where they have been used together to make a plausible conceptual model of one or more episodes of unrest and whether eruptions did or did not follow the unrest. This article is part of the Theo Murphy meeting issue ‘Magma reservoir architecture and dynamics’.

show abstract

Magmatic or Not Magmatic? The 2015–2016 Seismic Swarm at the Long-Dormant Jailolo Volcano, West Halmahera, Indonesia

Cited by 33 publications

References 89 publications

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

Study of P and S Wave Quality Factor (Qα and Qβ) Around Mt. Jailolo

Thoughts on the criteria to determine the origin of volcanic unrest as magmatic or non-magmatic

Contact Info

Product

Resources

About