Two types of volcanic tremor changed with eruption style during 1986 Izu‐Oshima eruption

Kurokawa, Aika; Takeo, Minoru; Kurita, Kei

doi:10.1002/2015jb012500

Cited by 24 publications

(21 citation statements)

References 25 publications

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“…Equation (1) is used in a limited frequency band, and the root-mean-square (RMS) is frequently used to evaluate the amplitude of continuous tremor (Battaglia & Aki, 2003;Battaglia et al, 2005;Kurokawa et al, 2016;Ogiso & Yomogida, 2012). The power spectral density (PSD) is calculated by summing those of the three components of the seismometer data in a moving time window with a length of 10 s (Figures 2c and 2d).…”

Section: Source Location Methods Using Amplitude Distributionmentioning

confidence: 99%

“…Source location of volcanic tremor is an efficient tool for monitoring volcanic activity. In determining the source location of tremor, spatial distribution of tremor amplitude is used in many studies (Battaglia and Aki, ; Battaglia et al, ; Cannata et al, ; Kumagai et al, , ; Kurokawa et al, ; Ogiso & Yomogida, ). The method assumes the seismic amplitude distribution by

A (D_{i}) = \frac{A_{0} S_{i}}{D_{i}^{α}} \exp ((), - B D_{i}), B = \frac{πf}{Qc},

where A 0 is the source amplitude, D i is the distance from the source, and S i is the site amplification factor.…”

Section: Introductionmentioning

confidence: 99%

“…Source location of volcanic tremor is an efficient tool for monitoring volcanic activity. In determining the source location of tremor, spatial distribution of tremor amplitude is used in many studies (Battaglia and Aki, 2003;Battaglia et al, 2005;Cannata et al, 2013;Kumagai et al, 2009Kumagai et al, , 2010Kurokawa et al, 2016;Ogiso & Yomogida, 2012). The method assumes the seismic amplitude distribution by…”

Section: Introductionmentioning

confidence: 99%

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Relative Source Locations of Continuous Tremor Before and After the Subplinian Events at Shinmoe‐dake, in 2011

Ichihara

Matsumoto

2017

Geophysical Research Letters

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Volcano monitoring systems are not always ready to resolve signals at the onset of eruptive activity. This study makes use of stations installed later to calibrate the performance of the stations that had been operated before the eruption. Seven stations recorded continuous volcanic tremor before and during the subplinian eruptions of Shinmoe-dake, Japan, in 2011. We estimated the source locations of the tremor using the amplitude distribution. The stability of the analysis was obtained by careful selection of time windows in which signals from a single source are dominated. The site effects and the regional attenuation factor were evaluated using tremor recorded after the major eruptions by a dense seismic array and a good number of stations. A tremor source changed its depth beneath the crater for 1 week before the major eruption, rising from a depth of a few kilometer to the water layer 3 times, each of which occurred following shallow inflation and minor eruptions. It is interpreted as migration of gas probably with magma, which further transported heat to the water layer and triggered the subplinian eruptions. Plain Language Summary Shinmoe-dake, Japan had a relatively large eruption in 2011 for the first time in 300 years. Although continuous seismic signal, which is called tremor, indicating magma or gas movement had been recorded before the main eruptive event, we did not have sufficient seismic stations to capture the magma movement. After the main eruption, more seismic stations were installed and recorded tremor that continued with less energetic eruptions. In this study, we make use of the data after the main eruption to calibrate the performance of the stations that had been operated before the eruption. We successfully revealed the movement of the tremor source before the eruption in correlation with shallow inflation and precursory eruptions. The result was interpreted as movement of magma and gas that supplied heat to the water layer in the ground and triggered the eruption. It is demonstrated that seismic stations even deployed after an eruption are useful in assessing processes preceding the eruption.

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Section: Source Location Methods Using Amplitude Distributionmentioning

confidence: 99%

A (D_{i}) = \frac{A_{0} S_{i}}{D_{i}^{α}} \exp ((), - B D_{i}), B = \frac{πf}{Qc},

where A 0 is the source amplitude, D i is the distance from the source, and S i is the site amplification factor.…”

Section: Introductionmentioning

confidence: 99%

“…Source location of volcanic tremor is an efficient tool for monitoring volcanic activity. In determining the source location of tremor, spatial distribution of tremor amplitude is used in many studies (Battaglia and Aki, 2003;Battaglia et al, 2005;Cannata et al, 2013;Kumagai et al, 2009Kumagai et al, , 2010Kurokawa et al, 2016;Ogiso & Yomogida, 2012). The method assumes the seismic amplitude distribution by…”

Section: Introductionmentioning

confidence: 99%

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Relative Source Locations of Continuous Tremor Before and After the Subplinian Events at Shinmoe‐dake, in 2011

Ichihara

Matsumoto

2017

Geophysical Research Letters

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“…Battaglia and Aki (2003) demonstrated that the eruptive tremor sources at Piton de la Fournaise volcano were good indicators of the locations of eruptive fissures; in a subsequent study demonstrated that the cumulative amplitude of tremor recorded throughout an eruption could be used to estimate the volume of lava erupted. Several studies have also shown that analyzing the spectral content and location of volcanic tremor can help track the evolution of magma migration (Di Lieto et al, 2007;Jellinek & Bercovici, 2011;Kurokawa et al, 2016) The application of machine learning (ML) techniques to the analysis of geophysical signals has become widespread in diverse settings such as the analysis of laboratory experiments Rouet-Leduc et al, 2017;Rouet-Leduc, Hulbert, Bolton, et al, 2018), tracking slow-slip in real Earth (Rouet-Leduc, , and phase association for the development of earthquake catalogs to © 2020. The Authors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

Ren

Peltier

Ferrazzini

et al. 2020

Geophysical Research Letters

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Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August–October 2015 eruption as well as the closing of the eruptive vent during the September–November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.

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“…These methods are useful for locating VT earthquakes with impulsive onsets, but LP events and tremor have emergent onsets, and location of their hypocenters by those methods is difficult or impossible. To overcome the limitations of the traditional methods, the amplitude source location (ASL) method proposed by Battaglia and Aki [] has been used to locate various volcano‐seismic signals including LP events and tremor [ Battaglia et al ., ; Battaglia et al ., , ; Kumagai et al ., , , , , ; Ogiso and Yomogida , , ; Ogiso et al ., ; Kurokawa et al ., ]. The ASL method uses high‐frequency seismic amplitudes and assumes isotropic radiation of S waves.…”

Section: Introductionmentioning

confidence: 99%

Theoretical basis of the amplitude source location method for volcano‐seismic signals

2017

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The source location method using high‐frequency seismic amplitudes based on the assumption of isotropic radiation of S waves has been used successfully to locate seismic events and tremor at volcanoes. This amplitude source location (ASL) method overcomes the limitations of traditional hypocenter determination methods that use onset arrival times and has great potential to improve volcano‐seismic monitoring and the investigation of source processes of volcano‐seismic signals. However, theoretical justification of the basic assumption used in the ASL method has not been provided in previous studies. In this study, we tested the ASL method by using seismic waveforms simulated with a finite difference method in realistic heterogeneous volcanic structures with intrinsic attenuation and topography. Our results showed that ASL determinations were successful when using waveforms characterized by multiple scattering that can be approximated by the diffusion model. We found that the energy solution of the diffusion model provided an amplitude‐distance relation that is similar to that used in the ASL method. Our results suggest that the ASL method is applicable to high‐frequency seismograms in highly heterogeneous media with transport mean free paths of 103 m or smaller and strong intrinsic attenuation. These medium parameters are consistent with those estimated at various volcanoes. Our study validates the basic assumption of the ASL method and justifies its use to locate the sources of high‐frequency seismic signals observed at volcanoes.

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Two types of volcanic tremor changed with eruption style during 1986 Izu‐Oshima eruption

Cited by 24 publications

References 25 publications

Relative Source Locations of Continuous Tremor Before and After the Subplinian Events at Shinmoe‐dake, in 2011

Relative Source Locations of Continuous Tremor Before and After the Subplinian Events at Shinmoe‐dake, in 2011

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

Theoretical basis of the amplitude source location method for volcano‐seismic signals

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