A Reappraisal of Seismicity Recorded During the 1996 Gjálp Eruption, Iceland, in Light of the 2014–2015 Bárðarbunga–Holuhraun Lateral Dike Intrusion

Konstantinou, Konstantinos I.; Utami, Ika Wahyu; Giannopoulos, Dimitrios; Sokos, Efthimios

doi:10.1007/s00024-019-02387-x

Cited by 3 publications

(7 citation statements)

References 48 publications

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“…Furthermore, they showed an increase of δv{v in the caldera of Bárðarbunga from 8 to 28 September, evidence of the pressurisation of the magma chamber. Then, on 28 September, harmonic tremor was observed, suggesting the small subglacial eruption mentioned before [Konstantinou et al 2020].…”

Section: Figurementioning

confidence: 86%

“…4. 2.135147 etry has become more important for monitoring Iceland [Donaldson et al 2019;Konstantinou et al 2020;Li and Gudmundsson 2020;Sgattoni et al 2016;Woods et al 2018] and other seismically active areas around the globe [Ballmer et al 2013;Burtin et al 2010;Droznin et al 2015]. Seismic interferometry has been applied in several cases.…”

Section: Figurementioning

confidence: 99%

“…Other studies have used seismic interferometry to locate volcanic tremor sources, for example in Hawai'i [Ballmer et al 2013] and in Kamchatka [Droznin et al 2015]. Konstantinou et al [2020] used ambient noise interferometry to study the Gjàlp eruption in 1996, which occurred at the so-called fissure located between the Bárðarbunga and Grimsvötn volcanoes on 30 September. Ambient noise interferometry provided evidence for the scenario of a small subglacial eruption prior to the main event.…”

Section: Figurementioning

confidence: 99%

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Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland from the analysis of correlograms

Nowé¹,

Lecocq

Caudron

et al. 2021

Volcanica

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In this study, we locate and characterise the main seismic noise sources in the region of the Vatnajökull icecap (Iceland). Vatnajökull is the largest Icelandic icecap, covering several active volcanoes. The seismic context is very complex, with glacial and volcanic events occurring simultaneously and the classification between the two can become cumbersome. Using seismic interferometry on continuous seismic data (2011–2019), we calculate the propagation velocities and locate the main seismic sources by using hyperbolic geometry and a grid-search method. We identify and characterise permanent oceanic sources, seasonal glacial-related sources, and episodic volcanic sources. These results give a better understanding of the background seismic noise sources in this region and could allow the identification of seismic sources associated with potentially threatening events in real-time.

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Section: Figurementioning

confidence: 86%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland from the analysis of correlograms

Nowé¹,

Lecocq

Caudron

et al. 2021

Volcanica

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“…This PE increase was preceded by 8 days of PE decrease, which they associated with the lack of frequencies higher than 1 Hz. After the 5.6 Mw earthquake, earthquake swarms migrated to the Gjálp fissures featuring broader frequencies in the range of 0.1-9 Hz at station HOT23, located at 8 km distance (Konstantinou et al, 2020). Glynn and Konstantinou (2016) suggested that these higher frequencies increase the complexity, hence causing the PE to increase.…”

Section: Pe Extracting the Dynamical Information From Seismic Wavementioning

confidence: 99%

“…This drop is thought to be caused by intrinsic attenuation when hot magma ascended to the upper crust. If the depth of the magma chamber feeding the eruption is estimated to be between 8 and 12 km (Konstantinou et al, 2020), then the seismic sources are located at depths between mid to upper crust. The attenuation at this depth is much lower compared to the uppermost 4 km of crust (Menke et al, 1995).…”

Section: The Influence Of Source Strength and Path Effects Toward The...mentioning

confidence: 99%

Eruption Forecasting of Strokkur Geyser, Iceland, Using Permutation Entropy

Sudibyo

Eibl

Hainzl

et al. 2022

Preprint

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When a volcano becomes restless, it is challenging to assess whether it will lead to an actual eruption and determine the timing of the eruption onset. A magmatic intrusion starting at depth can (a) remain at depth, (b) stall just before reaching the surface, (c) erupt in sluggish and viscous extrusion, or (d) erupt rapidly or explosively (Moran et al., 2011). The process of magma migration involves interactions with the surrounding country rock, cooling magma bodies from previous eruptions, and (or) hydrothermal system (Moran et al., 2008). These interactions generate natural phenomena such as earthquakes, deformation, temperature changes, and gas emissions. These phenomena can be observed by geophysical and geochemical measurements (Moran et al., 2008) and integrated with the history of past eruptions in a framework of eruption forecasting (Whitehead & Bebbington, 2021).From a seismic point of view, eruptions can show precursors such as accelerating or decelerating earthquake rates. To assess this, monitoring institutes conventionally use methods to tabulate daily event counts (McNutt, 1996) and calculate the average amplitude for a certain window length (Endo & Murray, 1991). The Failure Forecast Method estimates the onset time of eruption by using the rate and the acceleration of seismic precursors associated with the rock failure caused by magma propagation (Boué et al., 2015). However, this method cannot deal with complex precursory signals, for example, that exhibits fluctuations or deceleration (Boué et al., 2015). Furthermore, due to the uncertainty of the eruption forecast and numerous false alarms (Bell et al., 2013), this method is not recommended to be stand-alone (Whitehead & Bebbington, 2021). Dempsey et al. (2020) tested a real-time Machine Learning framework to detect eruption precursors of five major eruptions at Whakaari

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Eruption Forecasting of Strokkur Geyser, Iceland, Using Permutation Entropy

et al. 2022

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A volcanic eruption is usually preceded by seismic precursors, but their interpretation and use for forecasting the eruption onset time remain a challenge. A part of the eruptive processes in open conduits of volcanoes may be similar to those encountered in geysers. Since geysers erupt more often, they are useful sites for testing new forecasting methods. We tested the application of Permutation Entropy (PE) as a robust method to assess the complexity in seismic recordings of the Strokkur geyser, Iceland. Strokkur features several minute‐long eruptive cycles, enabling us to verify in 63 recorded cycles whether PE behaves consistently from one eruption to the next one. We performed synthetic tests to understand the effect of different parameter settings in the PE calculation. Our application to Strokkur shows a distinct, repeating PE pattern consistent with previously identified phases in the eruptive cycle. We find a systematic increase in PE within the last 15 s before the eruption, indicating that an eruption will occur. We quantified the predictive power of PE, showing that PE performs better than seismic signal strength or quiescence when it comes to forecasting eruptions.

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A Reappraisal of Seismicity Recorded During the 1996 Gjálp Eruption, Iceland, in Light of the 2014–2015 Bárðarbunga–Holuhraun Lateral Dike Intrusion

Cited by 3 publications

References 48 publications

Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland from the analysis of correlograms

Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland from the analysis of correlograms

Eruption Forecasting of Strokkur Geyser, Iceland, Using Permutation Entropy

Eruption Forecasting of Strokkur Geyser, Iceland, Using Permutation Entropy

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