Characterization of volcanic regimes and identification of significant transitions using geophysical data: a review

Carniel, Roberto

doi:10.1007/s00445-014-0848-0

Cited by 34 publications

(24 citation statements)

References 150 publications

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“…This makes the methodology presented here applicable to volcanoes surrounded by a sparse monitoring seismic network (similar perhaps to HOTSPOT) in a real-time fashion. Finally, we should mention that our results complement earlier attempts to detect dynamical changes in noise or tremor signals prior to volcanic activity313233.…”

Section: Discussionsupporting

confidence: 84%

Reduction of randomness in seismic noise as a short-term precursor to a volcanic eruption

Glynn

Konstantinou

2016

Sci Rep

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Ambient seismic noise is characterized by randomness incurred by the random position and strength of the noise sources as well as the heterogeneous properties of the medium through which it propagates. Here we use ambient noise data recorded prior to the 1996 Gjálp eruption in Iceland in order to show that a reduction of noise randomness can be a clear short-term precursor to volcanic activity. The eruption was preceded on 29 September 1996 by a Mw ~5.6 earthquake that occurred in the caldera rim of the Bárdarbunga volcano. A significant reduction of randomness started occurring 8 days before the earthquake and 10 days before the onset of the eruption. This reduction was observed even at stations more than 100 km away from the eruption site. Randomness increased to its previous levels 160 minutes after the Bárdarbunga earthquake, during which time aftershocks migrated from the Bárdarbunga caldera to a site near the Gjálp eruption fissure. We attribute this precursory reduction of randomness to the lack of higher frequencies (>1 Hz) in the noise wavefield caused by high absorption losses as hot magma ascended in the upper crust.

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Section: Discussionsupporting

confidence: 84%

Reduction of randomness in seismic noise as a short-term precursor to a volcanic eruption

Glynn

Konstantinou

2016

Sci Rep

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“…We normalised each spectrum to its maximum value, independently of each time window. We therefore lose the information regarding the amplitude time variations but highlight the time evolution of the relative importance of each frequency bands (Carniel, 2014).…”

Section: Volcanic Tremor Analysismentioning

confidence: 99%

“…To interpret the source of the seismic signal, spectral analyses are commonly performed (Konstantinou and Schlindwein, 2002;Carniel, 2014). Having only one seismic station for our period of interest, we can't discriminate between source, path and site effects.…”

Section: Volcanic Tremor Analysismentioning

confidence: 99%

“…It is a continuous seismic signal of volcanic origin (Carniel, 2010). Changes in tremor amplitude and frequency content can characterise several regimes (e.g., Ripepe et al, 2002;Harris et al, 2005;Carniel, 2014), and their detectability depends on the seismic network. Volcanic tremor often precedes and / or accompanies volcanic eruptions (Konstantinou and Schlindwein, 2002 and references therein), with the size of the eruption usually related to the tremor amplitude (McNutt, 1992).…”

Section: Introductionmentioning

confidence: 99%

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Using volcanic tremor for eruption forecasting at White Island volcano (Whakaari), New Zealand

Chardot

Jolly

Kennedy

et al. 2015

Journal of Volcanology and Geothermal Research

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“…With continuously growing monitoring networks and data fluxes, this way of analyzing data becomes more and more problematic. Therefore, the seismovolcanic monitoring (similarly to many other areas) must rely more on data‐intensive automatic methods for analysis and classification of signals leading to the idea of applying methods from the area of machine learning (e.g., Carniel, , ; Orozco‐Alzate et al., ). Machine learning approaches related to seismovolcanic data are most of the time applied on single time series from individual stations to perform blind source separation with dimension reduction methods such as Independant Component Analysis (Acernese et al, ; Cabras et al, , ; Ciaramella et al, ; Capuano et al, ), Nonnegative Matrix Factorization (Cabras et al, , ), or Degenerate Unmixing Estimation Technique (Moni et al, ).…”

Section: Introductionmentioning

confidence: 99%

Network‐Based Detection and Classification of Seismovolcanic Tremors: Example From the Klyuchevskoy Volcanic Group in Kamchatka

et al. 2018

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We develop a network‐based method for detecting and classifying seismovolcanic tremors. The proposed approach exploits the coherence of tremor signals across the network that is estimated from the array covariance matrix. The method is applied to four and a half years of continuous seismic data recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. We compute and analyze daily covariance matrices together with their eigenvalues and eigenvectors. As a first step, most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. Thus, volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the daily array covariance matrix's first eigenvector. Our main hypothesis is that these eigenvectors represent the principal components of the daily seismic wavefield and, for days with tremor activity, characterize dominant tremor sources. Those daily first eigenvectors, which can be used as network‐based fingerprints of tremor sources, are then grouped into clusters using correlation coefficient as a measure of the vector similarity. As a result, we identify seven clusters associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge and is fully automatic; and the database of the network‐based tremor fingerprints can be continuously enriched with newly available data.

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Characterization of volcanic regimes and identification of significant transitions using geophysical data: a review

Cited by 34 publications

References 150 publications

Reduction of randomness in seismic noise as a short-term precursor to a volcanic eruption

Reduction of randomness in seismic noise as a short-term precursor to a volcanic eruption

Using volcanic tremor for eruption forecasting at White Island volcano (Whakaari), New Zealand

Network‐Based Detection and Classification of Seismovolcanic Tremors: Example From the Klyuchevskoy Volcanic Group in Kamchatka

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