A volcanic event forecasting model for multiple tephra records, demonstrated on Mt. Taranaki, New Zealand

Damaschke, Magret; Cronin, Shane J.; Bebbington, Mark

doi:10.1007/s00445-017-1184-y

Cited by 31 publications

(15 citation statements)

References 81 publications

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“…Note that equation 15does not depend explicitly on Δt, meaning that the eruption rate is considered to be constant throughout the history of the volcanic field, so the volcano record is represented by a simple Poisson process. This inference is questionable since eruption frequencies can vary strongly over time (e.g., Bebbington and Cronin 2011;Leonard et al 2017;Damaschke et al 2018), but a Poisson model is a sensible first-order approach in volcanic areas with long eruptive histories and/or patchy eruptive records (e.g., Connor et al 2013;El Difrawy et al 2013;Runge et al 2014;Bartolini et al 2015;Gallant et al 2018; Nieto-Torres and Martin Del Pozzo 2019).…”

Section: Methods and Resultsmentioning

confidence: 99%

“…This procedure was performed as an ideal case for comparing the results with those obtained assuming a long-term average recurrence rate (see next subsection), which only needs the total number of eruptions and the ages of the oldest and youngest eruptions. However, more sophisticated estimations can be conducted by including age uncertainties, and running some Monte Carlo simulations to obtain better recurrence rates and confidence intervals (e.g., Bebbington 2013;Connor et al 2013), or by building a very complete eruption age dataset (e.g., Damaschke et al 2018).…”

Section: Limitations Of the Applicationmentioning

confidence: 99%

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MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields

Bertin

Lindsay

Becerril³

et al. 2019

J Appl. Volcanol.

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This paper introduces an open source computer code to perform an integrated probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields. The program, named MatHaz, is a set of Matlab scripts that follows a sequential methodology. After the user has provided a set of input files, this tool first estimates the spatial probability of future volcanic vents, then the temporal probability of future volcanic events, and finally models up to five volcanic phenomena (pyroclastic density currents, ballistic projectiles, lava flows, lahars, and tephra fallout) following a probabilistic approach. These results can be combined and depicted as an integrated quantitative (and/or qualitative) volcanic hazard map, with weightings of hazard factors chosen by the user. We illustrate the use of this tool by applying it to the Carrán-Los Venados Volcanic Field in southern Chile. The opensource, replicable, and user-friendly nature of the code allows its application to any volcanic region of the world, regardless of its extent, type, and amount of volcano-structural data.

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Limitations Of the Applicationmentioning

confidence: 99%

MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields

Bertin

Lindsay

Becerril³

et al. 2019

J Appl. Volcanol.

Self Cite

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“…Although existing methods typically share the same workflow as that shown above, they vary in the way they define the conditional intensity function λ(t |X ). Traditional models typically utilize prescribed distributions such as the Poisson distribution [191], Gamma distribution [53], Hawks [69], Weibull process [56], and other distributions [219]. For example, Damaschke et al [56] utilized a Weibull distribution to model volcano eruption events, while Ertekin et al [72] instead proposed the use of a non-homogeneous Poisson process to fit the conditional intensity function for power system failure events.…”

Section: Continuous-time Predictionmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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“…Accurate, high-temporal resolution data on eruption ages are crucial to better constrain the geochemical and petrological evolution of volcanic systems (e.g. Kersting and Arculus, 1994;Hildenbrand, 2004;Cadoux et al, 2005), as well as to infer hazard parameters such as recurrence rates and repose periods (Marzocchi and Zaccarelli 2006;Damaschke et al 2018;Reyes-Guzman et al 2018). The more accurately the volcanic activity is known, the better its recurrence can be documented and its potential risk constrained (Turner et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…detailed variations of eruption rates through time and space (Hora et al 2007;Lahitte et al 2012;Germa et al 2015). Moreover, eruption ages help identify vent migration patterns (Tanaka et al 1986;Connor and Hill 1995;Condit and Connor 1996;Heizler et al 1999) in dispersed, monogenetic volcanic fields (Nemeth and Kereszturi 2015), and volcanic processes, such as magma crystallisation, vesiculation and fragmentation, that are crucial for eruption forecasting in both monogenetic (Kereszturi et al 2017) and polygenetic volcanic systems (Turner et al 2011;Damaschke et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Eruptive history of the Late Quaternary Ciomadul (Csomád) volcano, East Carpathians, part I: timing of lava dome activity

et al. 2019

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Located at the southern tip of the Intra-Carpathian Volcanic Range in Romania, and composed of a dozen dacitic lava domes, the Ciomadul (Csomád) volcanic complex is the youngest eruptive centre of the Carpatho-Pannonian Region. Whereas, in the last decade, the explosive history of Ciomadul since 50 ka has been well constrained by numerous studies, the chronology of the dome sequence still lacks robust chronological constraints and an extended analysis of all available data. Here, we apply a detailed K-Ar dating approach to refine the chronology of the lava dome eruptions, using the unspiked K-Ar Cassignol-Gillot technique. Our dating focused on the most voluminous central part of the lava dome complex. New eruption ages were determined following a strict separation (of 10 g) of groundmass from about 3 kg of unaltered sample rocks, thereby isolating material whose Manuscript Click here to access/download;Manuscript;Lahitte_BUVO-D-18-00067_R3.doc Click here to view linked References cooling was contemporaneous with the eruption. The newly applied methodology, mainly consisting of a double full preparation, first at larger grain size (~ 0.4 mm) and then at < 100 µm, provides an appropriate procedure to separate suitable material to obtain the K-Ar age of the eruption, i.e. the sample's groundmass, in which there is no risk of the presence of older, inherited crystals. Our new geochronological data set gives an improved insight into the temporal construction of the Ciomadul volcanic complex, where (due to the method applied here) all ages are younger than those from previous studies that used whole-rock K-Ar ages. Our new results show that Ciomadul's volcanic activity began with the construction of the southeastern, peripheral domes from ca. 850 ka to 440 ka. After a ca. 250 ky long repose period, the activity resumed in the northern part at around 200 ka, with subsequent domes emplaced between 200 and 130 ka, aligned roughly north-south in the westerncentral part of the complex. Following a 30 ky long quiescence period, the eastern-central domes formed between 100 and 60 ka. In addition to the chronological history of lava dome volcanism, we also investigated the sequence of crystallisation of mineral phases present in the lavas with respect to the modification of eruption ages. Ages obtained on pure minerals (plagioclase, amphibole and biotite) are systematically older than those obtained on groundmass, showing that most of them formed up to 1.85 Myr before eruption in a longlived, pre-Ciomadul magmatic system. Crystal size distributions (CSD) data support the age contrasts between juvenile groundmass and older inherited minerals. After injection of new magma and convective mixing with crystal clots, ascent of the resulting led to eruptions of material representing contrasting ages.

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A volcanic event forecasting model for multiple tephra records, demonstrated on Mt. Taranaki, New Zealand

Cited by 31 publications

References 81 publications

MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields

MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields

Event Prediction in Big Data Era: A Systematic Survey

Eruptive history of the Late Quaternary Ciomadul (Csomád) volcano, East Carpathians, part I: timing of lava dome activity

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