Probability Of Eruption Research Articles

Large Eruptive and Confined Flares in Relation to the Solar Active Region Evolution

Solar active regions (ARs) provide the required magnetic energy and the topology configuration for flares. Apart from conventional static magnetic parameters, the evolution of AR magnetic flux systems should have nonnegligible effects on magnetic energy store and the trigger mechanism of eruptions, which would promote the prediction for the flare using photospheric observations conveniently. Here we investigate 322 large (M- and X-class) flares from 2010 to 2019, almost the whole solar cycle 24. The flare occurrence rate is obviously higher in the developing phase, which should be due to the stronger shearing and complex configurations caused by affluent magnetic emergences. However, the probability of flare eruptions in decaying phases of ARs is obviously higher than that in the developing phase. The confined flares were in nearly equal counts to eruptive flares in developing phases, whereas the eruptive flares were half over confined flares in decaying phases. Yearly looking at flare eruption rates demonstrates the same conclusion. The relationship between sunspot group areas and confined/erupted flares also suggested that the strong field make constraints on the mass ejection, though it can contribute to flare productions. The flare indexes also show a similar trend. It is worth mentioning that all the X-class flares in the decaying phase were erupted, without the strong field constraint. The decaying of magnetic flux systems had facilitation effects on flare eruptions, which may be consequent on the splitting of magnetic flux systems.

How a non harmonic-like tremor at a volcanic lake could be caused by sustained wind: A case of study of Taupō volcano

Taupō volcano lies underneath the largest lake in New Zealand. It undergoes unrest roughly every ten years, has minor eruptions every few hundred years, and has supereruptions. Understanding volcanic seismic signals provides insights into the volcano dynamics and helps to anticipate eruptions. Harmonic and non-harmonic volcanic tremor are common signals that indicate fluid mobilisation in volcanic systems. We observed a signal that resembled non-harmonic tremor during 2019 at Taupō volcano. Careful time-frequency analysis of seismic data and weather data from around the lake revealed that the signals were not magmatic in origin, but were most likely from lake microseisms caused by special conditions of the wind, which generates wind-driven waves in the lake. The frequency range of lake microseisms at Taupō starts and ends with a dominant frequency of 0.7–1.0 Hz, and during the maximum energy peak, the dominant frequency is of 0.50–0.56 Hz. Such signals may be common in caldera lakes, which need to be distinguished from tremor caused by volcanic activity, so as not to exaggerate the probability of eruptions.

The spatiotemporal evolution of monogenetic scoria cones in the Paricutin-Tancítaro region, Mexico: Results from a Morpho-chronological analysis and its consequences on the distributed volcanic hazard

The Paricutin-Tancítaro region (PTR) within the Michoacán-Guanajuato monogenetic Volcanic Field (MGVF) is characterized by a large stratovolcano, Tancítaro enclosed in a dense distribution of monogenetic volcanoes, mainly scoria cones, that includes the well-known Paricutin. The succession of seismic swarms beginning 54 years after the birth of Paricutin in 1943 represents a challenge for apprising the region's volcanic and seismic hazards. In this work, we introduce a novel methodology to assess the spatiotemporal evolution of the monogenetic scoria cones and the distributed volcanic hazards. We first performed a morpho-chronometric analysis of 171 scoria cones to estimate their relative ages, which revealed an increasing trend in the rate of monogenetic eruptions over the last 120 kyr, especially in the last 20 kyr, with a current mean waiting interval between monogenetic eruptions of only 120 yr. In a second step, we estimate the spatiotemporal evolution of monogenetic volcanic activity in PTR using a Voronoi tessellation to represent the spatial density distribution of scoria cone emplacement through time to detect dynamic shifts in volcanic activity locations in the region. This approach thus reveals spatial dynamic patterns in the rates of monogenetic eruptions over time. Subsequently, a Poisson process is assumed to estimate the spatially distributed cone-forming eruption probabilities based on the morpho-chronometrically analyzed cones. Remarkably, a high spatial correlation was found between the areas with the highest probabilities and the location of the recent seismic swarms recorded in the PTR.

Influence of the geometrical shape of a prominence and the structure of the coronal magnetic field on the probability of eruption, flare and coronal mass ejection development

The equilibrium conditions of the magnetic flux rope containing the prominence depend on the properties of the surrounding magnetic field in the corona and the geometry of the flux rope itself. The eruption of a prominence is usually associated with a loss of stability in the external magnetic field upon reaching a height above which the decay index of the field exceeds the critical value for the development of eruptive instability. For flux ropes with an axis in the form of a straight line or a circle, the critical value of the decay index of the field lies in the range of 1.0—1.5. Based on extrapolation of the magnetic field into the corona from field measurements in the photosphere, it would be possible to predict the probability of eruption of a particular prominence. However, taking into account the fact that the ends of the magnetic flux rope are rooted in the photosphere and remain fixed due to being frozen into the photospheric plasma significantly affects the critical value of the index and complicates the forecast problem. If the magnetic flux rope retains the shape a segment of a torus in the process of evolution, then the critical value of the decay index for its apex depends on what part of the torus it constitutes, being minimal for approximately half of the torus and having a value significantly less than unity. How the eruption of the flux rope will develop after the loss of equilibrium also depends on what part of the complete torus it constitutes at the moment of the onset of the eruption. Shorter flux ropes accelerate very vigorously, but only for a short time, generating stronger electric induction fields that initiate flare processes. However, the final speed that a short flux rope can achieve during acceleration is less than that of longer flux ropes that accelerate less intensely but for a longer time. The induction effects of the latter are less pronounced, so that they are capable of producing only weak flare-like manifestations. Thus, the eruption of a short prominence, which has gained a relatively low speed, can be stopped at a certain height in the corona without generating a coronal mass ejection. But such a “failed eruption” contributes to the development of flare phenomena. On the contrary, eruptions of longer prominences more often lead to the formation of coronal mass ejections and weak flare manifestations.

Influence of the Geometrical Shape of a Prominence and Structure of the Coronal Magnetic Field on the Probability of Eruption, Flare Development, and Coronal Mass Ejection

Influence of the Geometrical Shape of a Prominence and Structure of the Coronal Magnetic Field on the Probability of Eruption, Flare Development, and Coronal Mass Ejection

Volcanic Geohazard Analysis - Case Study from the Ujung Lemah Abang Nuclear Power Plant Area, Central West Java

The paper discusses the proposed development plan of a nuclear power plant in the Ujung Lemah Abang (ULA) region, located in the northern part of Mount Muria in Central Java, Indonesia. The study evaluates the potential volcanic disasters at the proposed site of the Ujung Lemah Abang nuclear power plant (NPP-ULA) near Mount Muria using the Bayesian method, which is based on a probabilistic model of the spatial distribution of volcanic events in the past and the geological processes model.Mount Mulia is a polygenetic volcano that undergoes periodic eruptions alternating between lateral and central eruption-dominated phases. Semivariogram analysis shows a range of 150,000 years. This corresponds to the average duration of volcanic activity and the time between eruptions. Mount Muria volcanic complex can be classified as an extinct volcano with limited future eruption potential based on its last activity 320,000 years ago. The probability of eruption occurrences in Muria within the next 100 years is 1.21856 x 10−5. With this probability of eruptions, it appears that the Muria Mtn is not going to be erupted anytime soon. However, when a new magma system emerges, resulting in epicenter concentrations beneath Mount Muria, micro-earthquake monitoring is required to detect magmatic activities beneath the Muria Volcanic Complex. To estimate the likelihood of phreatomagmatic eruptions, geothermometer monitoring in deep wells is required as an active monitoring of hydrothermal activity in the Muria Volcanic Complex.

Aso volcano, Japan: assessing the 100-year probability of a new caldera-forming eruption based on expert judgements with Bayes Net and Importance Sampling uncertainty analysis

The Aso-4 explosive eruption on Kyushu, Japan, 89,500 years ago was one of the biggest eruptions in the last one hundred millennia, with a magnitude of approximately M8. Modern society requires the likelihood of natural events with potentially disastrous consequences to be evaluated, even if probabilities of occurrence are diminishingly small. For some situations, it is not satisfactory to assert an event scenario probability is “negligible” or can be “ignored”. Judicial hearings or litigation may require risk levels to be quantified, in which case, statements of scientific confidence could be decisive. Internationally, e.g., for nuclear site safety evaluations, event likelihoods on order of 10–7/year are often considered for quantitative assessment. At such hazard levels, this might include evaluating the proposition that a particular volcano can deliver a future super-eruption, a supposition that could be attached to Aso volcano. But, simplistically taking the average recurrence interval between past caldera-forming eruptions at a given volcano is an unreliable guide to the likelihood of a future repeat: each past event represented a unique set of tectonic and magmatic conditions within a continually evolving volcanic system. Such processes are not temporally stationary nor statistically uniform. To evaluate the probability of a new M8 event at Aso, within the next 100 years, we performed a comprehensive stochastic probability uncertainty analysis using a model implemented with advanced computational Bayes Net (BN) software. Our eruption process model is informed by multiple strands of evidence from volcanology, petrology, geochemistry and geophysics, together with estimates of epistemic (knowledge) uncertainty, adduced from reviews of published data, modelling and from expert judgement elicitation. Several lines of evidence characterise the likely structure, magmatic composition and eruptive state of the present-day Aso volcano, which has had numerous smaller eruptions since Aso-4. To calculate the probability of another M8 eruption of Aso, we implemented probabilistic ‘Importance Sampling’ in our model. With this approach, we find the chance of an Aso-4 scale eruption (characterised by mean volume 500 km3 DRE and approximate 90% credible interval [210 ‥ 1200] km3 DRE) is less than 1–in–1 billion in the next 100 years (i.e., < 10–9 probability). Based on current volcanological understanding and evidence, we believe this probability estimate is robust to within an order of magnitude.

Assessing long-term tephra fallout hazard in southern Italy from Neapolitan volcanoes

Abstract. Nowadays, modeling of tephra fallout hazard is coupled with probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position, and meteorological conditions. In this framework, we present a prototypal methodology to carry out the long-term tephra fallout hazard assessment in southern Italy from the active Neapolitan volcanoes: Somma–Vesuvius, Campi Flegrei, and Ischia. The FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high-resolution volcanic hazard assessment, relying on a Bayesian procedure, in order to provide the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-year exposure time. Our results are expressed in terms of absolute mean hazard maps considering different levels of aggregation, from the impact of each volcanic source and eruption size class to the quantification of the total hazard. This work provides, for the first time, a multi-volcano probabilistic hazard assessment posed by tephra fallout, comparable with those used for seismic phenomena and other natural disasters. This methodology can be applied to any other volcanic areas or over different exposure times, allowing researchers to account for the eruptive history of the target volcanoes that, when available, could include the occurrence of less frequent large eruptions, representing critical elements for risk evaluations.

Magma reservoir growth and ground deformation preceding the 79 CE Plinian eruption of Vesuvius

The 79 CE eruption of Vesuvius is the first documented Plinian eruption, also famous for the archaeological ruins of Pompeii and Herculaneum. Although much is known regarding the eruption dynamics and magma reservoir, little is known about the reservoir shape and growth, and related ground deformation. Numerical modelling by Finite Element Method was carried out, aimed at simulating the reservoir growth and ground deformation with respect to the reservoir shape (prolate, spherical, oblate) and magma overpressure. The modelling was tuned with volcanological, petrological and paleoenvironmental ground deformation constraints. Results indicate that the highest magma overpressure is achieved considering a prolate reservoir, making it as the most likely shape that led to eruption. Similar deformations but lower overpressures are obtained considering spherical and oblate reservoirs. These results demonstrate that ground deformation may not be indicative of eruption probability, style/size, and this has direct implications on surveillance at active explosive volcanoes.

Eruptive history of Mt. Fuji over the past 8000 years based on integrated records of lacustrine and terrestrial tephra sequences and radiocarbon dating

Reliable records of past volcanic activities are essential for assessing future eruption probabilities and establishing effective volcanic disaster mitigation plans. We integrated the lacustrine and terrestrial sequences of tephra deposits at the northeastern foot of Mt. Fuji based on high-resolution radiocarbon (14C) dating to elucidate its eruptive history over the past 8000 years. Accurate chronologies of 29 tephra layers in the Lake Yamanaka sediments (core YA-1) were determined using the age-depth model of the 14C of bulk organic matter after variability correction of lake reservoir ages. The following ages were compared to those of major tephra deposits in the proximal trench section (MF20-01) and in the literature. From ∼7300 to ∼3200 cal yr BP, four tephra layers in core YA-1 (i.e., YA-T1, 2, 7, and 10) were likely correlated with the scoria fall deposits S-3, 5, 6, and 10 respectively, as reported in the outcrops near the lake. The core also features six undetected eruptions from ∼5050 to ∼3900 cal yr BP, demonstrating the advantages of lake sediments proximal to the volcano in identifying previously undetected eruptions. After ∼3200 cal yr BP, five tephra layers in core YA-1, ranging from ∼2900 to ∼1600 cal yr BP (YA-T11, 12, 14, 20, and 22), showed similar age ranges as the major tephra layers in MF20-01 (IY5, 8, 11, 25, and 26), with identical lithological characteristics and stratigraphic orders. This suggests that the tephra sequence in the trench section positively reflects the major eruptive events over the past 2900 years, providing a basis for the comprehensive reconstruction of volcanic eruption histories in the region.

PSORIASIFORM DRUG ERUPTION INDUCED BY ANTI-TUBERCULOSIS MEDICATION: A CASE REPORT

Objective: Psoriasiform drug eruptions can be induced by several drugs. Psoriasis is a chronic inflammatory disease characterized by T-cell-mediated cytokine production that drives the hyperproliferation and abnormal differentiation of keratinocytes. Drugs can cause new lesions when there is no prior history or family history of psoriasis. Based on the psoriatic drug eruption probability score, β‐blockers, synthetic anti‐malarial drugs, non‐steroidal anti‐inflammatory drugs (NSAIDs), lithium, digoxin and tetracycline antibiotics are relevant in psoriasis.&#x0D; Methods: A 58-year-old male was admitted to the Department of Respiratory Medicine at B. P. S G. M. C, Khanpur Kalan, Sonepat as a case of pulmonary tuberculosis and was put on anti-tubercular drugs CAT-I (according to RNTCP guidelines). The patient had a history of diabetes mellitus and hypertension for the past six years. On the third day of initiation of ATT, the patient started developing a psoriasiform rash. The psoriasiform rash began to improve within a few days after discontinuing the ATT. A causality assessment was done as per the WHO-UMC scale, which showed that the adverse events were likely caused due to the ATT.&#x0D; Results: Psoriasiform rash is a severe adverse drug reaction characterized by widespread lesions. Among all the various adverse drug reactions, lichenoid drug eruption is commonly associated with anti-tuberculosis medication and needs to be differentiated from psoriasiform eruption. The underlying pathomechanism of drug-induced psoriasiform eruptions remains uncertain, although several immunological interactions have been hypothesized.&#x0D; Conclusion: ATT has been reported to cause psoriasiform rash, and its drug component needs to be reconsidered in view of safer alternatives available.

A simple two-state model interprets temporal modulations in eruptive activity and enhances multivolcano hazard quantification.

Volcanic activity typically switches between high-activity states with many eruptions and low-activity states with few or no eruptions. We present a simple two-regime physics-informed statistical model that allows interpreting temporal modulations in eruptive activity. The model enhances comprehension and comparison of different volcanic systems and enables homogeneous integration into multivolcano hazard assessments that account for potential changes in volcanic regimes. The model satisfactorily fits the eruptive history of the three active volcanoes in the Neapolitan area, Italy (Mt. Vesuvius, Campi Flegrei, and Ischia) which encompass a wide range of volcanic behaviors. We find that these volcanoes have appreciably different processes for triggering and ending high-activity periods connected to different dominant volcanic processes controlling their eruptive activity, with different characteristic times and activity rates (expressed as number of eruptions per time interval). Presently, all three volcanoes are judged to be in a low-activity state, with decreasing probability of eruptions for Mt. Vesuvius, Ischia, and Campi Flegrei, respectively.

What is the probability of unexpected eruptions from potentially active volcanoes or regions?

Since the start of the twentieth century, 101 potentially active volcanoes have produced their first Holocene eruption, as recorded in the volcanoes of the world (VOTW) database. The reactivation of potentially active volcanoes is often a surprise, since they tend to be less well-studied and unmonitored. The first step towards preparing for these unexpected eruptions is to establish how often potentially active volcanoes have erupted in the past. Here, we use our previously developed FRESH (First Recorded EruptionS in the Holocene) database to estimate the past regional Average Recurrence Interval (ARI) of these unexpected events. Within the most complete portions of the FRESH database, a FRESH (i.e., the first recorded eruption from a potentially active volcano) has occurred as frequently as every ~ 7 years in the Pacific Ocean region (~ 50 years of relatively complete record) and ~ 8 years in Izu, Volcano, and the Mariana Islands region (~ 150 years of relatively complete record). We use the regional frequency to estimate the annual probability of a FRESH at individual potentially active volcanoes in selected regions of Asia–Pacific, which ranged from 0.003 for Izu, Volcano, and Mariana Islands to 1.35 × 10−5 for Luzon. Population exposure around potentially active volcanoes showed that at volcanoes such as Kendeng (Indonesia) and Laguna Caldera (Philippines), more than 30 million people reside within 100 km of the summit. With this work, we hope to establish how often potentially active volcanoes erupt, while identifying which regions and which potentially active volcanoes may require more attention.

Development of a Bayesian event tree for short-term eruption onset forecasting at Taupō volcano

Taupō volcano, located within the Taupō Volcanic Zone (TVZ) in the central North Island of Aotearoa-New Zealand, is one of the world’s most active silicic caldera systems. Silicic calderas such as Taupō are capable of a broad and complex range of volcanological activity, ranging from minor unrest episodes to large destructive supereruptions. A critical tool for volcanic risk management is eruption forecasting. The Bayesian Event Tree for Eruption Forecasting (BET_EF) is one probabilistic eruption forecasting tool that can be used to produce short-term eruption forecasts for any volcano worldwide. A BET_EF model is developed for Taupō volcano, informed by geologic and historic data. Monitoring parameters for the model were obtained through a structured expert elicitation workshop with 30 of Aotearoa-New Zealand’s volcanologists and volcano monitoring scientists. The eruption probabilities output by the BET_EF model for Taupō volcano’s 17 recorded unrest episodes (between 1877 and 2019) were examined. We found time-inhomogeneity in the probabilities stemming from both the changes over time in the monitoring network around Taupō volcano and increasing level of past data (number of non-eruptive unrest episodes). We examine the former issue through the lens of the latest episodes, and the latter by re-running the episodes assuming knowledge of all 16 other episodes (calibration to 2021 data). The time variable monitoring network around Taupō volcano and parameter weights had a substantial impact on the estimated probabilities of magmatic unrest and eruption. We also note the need for improved monitoring and data processing at Taupō volcano, the existence of which would prompt updates and therefore refinements in the BET_EF model.

On the feasibility and usefulness of high-performance computing in probabilistic volcanic hazard assessment: An application to tephra hazard from Campi Flegrei

For active volcanoes, knowledge about probabilities of eruption and impacted areas becomes valuable information for decision-makers to develop short- and long-term emergency plans, for which probabilistic volcanic hazard assessment (PVHA) is needed. High-resolution or spatially extended PVHA requires extreme-scale high-performance computing systems. Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth; www.cheese-coe.eu), an effort was made to generate exascale-suitable codes and workflows to collect and process in some hours the large amount of data that a quality PVHA requires. To this end, we created an optimized HPC-based workflow coined PVHA_HPC-WF to develop PVHA for a volcano. This tool uses the Bayesian event tree methodology to calculate eruption probabilities, vent-opening location(s), and eruptive source parameters (ESPs) based on volcano history, monitoring system data, and meteorological conditions. Then, the tool interacts with the chosen hazard model, performing a simulation for each ESP set or volcanic scenario (VS). Finally, the resulting information is processed by proof-of-concept-subjected high-performance data analytics (HPDA) scripts, producing the hazard maps which describe the probability over time of exceeding critical thresholds at each location in the investigated geographical domain. Although PVHA_HPC-WF can be adapted to other hazards, we focus here on tephra (i.e., lapilli and ash) transport and deposition. As an application, we performed PVHA for Campi Flegrei (CF), Italy, an active volcano located in one of the most densely inhabited areas in Europe and under busy air traffic routes. CF is currently in unrest, classified as being in an attention level by the Italian Civil Protection. We consider an approximate 2,000 × 2,000 × 40 km computational domain with 2 km grid resolution in the horizontal and 40 vertical levels, centered in CF. To explore the natural variability and uncertainty of the eruptive conditions, we consider a large number of VSs allowing us to include those of low probability but high impact, and simulations of tephra dispersal are performed for each of them using the FALL3D model. Results show the potential of HPC to timely execute a vast range of simulations of complex numerical models in large high-resolution computational domains and analyze great volumes of data to obtain quality hazard maps.

Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand

Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption likelihood is essential for risk calculations that underpin volcano access decisions and disaster response. But estimating eruption probabilities is notoriously difficult for sudden onset eruptions. Here, we describe two retrospectively developed models for short-term (48 h) probabilistic forecasting of phreatic eruptions at Whakaari. The models are based on a pseudo-prospective analysis of seven Whakaari eruptions whose precursors were identified by time series feature engineering of continuous seismic data. The first model, an optimized warning system, could anticipate six out of seven eruptions at the cost of 14 warning days each year. While a warning is in effect, the probability of eruption is about 8% in 48 h, which is about 126 times higher than outside the warning. The second model used isotonic calibration to translate the output of the forecast model onto a probability scale. When applied pseudo-prospectively in the 48 h prior to the December 2019 eruption, it indicated an eruption probability up to 400 times higher than the background. Finally, we quantified the accuracy of these seismic data-driven forecasts, alongside an observatory expert elicitation that used multiple data sources. To do this, we used a forecast skill score that was benchmarked against the average rate of eruptions at Whakaari between 2011 and 2019. This exercise highlights the conditions under which the three different forecasting approaches perform well and where potential improvements could be made.

Probabilistic volcanic hazard assessment at an active but under-monitored volcano: Ceboruco, Mexico

A probabilistic volcanic hazard assessment (PVHA) for Ceboruco volcano (Mexico) is reported using PyBetVH, an e-tool based on the Bayesian Event Tree (BET) methodology. Like many volcanoes, Ceboruco is under-monitored. Despite several eruptions in the late Holocene and efforts by several university and government groups to create and sustain a monitoring network, this active volcano is monitored intermittently rather than continuously by dedicated groups. With no consistent monitoring data available, we look at the geology and the eruptive history to inform prior models used in the PVHA. We estimate the probability of a magmatic eruption within the next time window (1 year) of ~ 0.002. We show how the BET creates higher probabilities in the absence of monitoring data, which if available would better inform the prior distribution. That is, there is a cost in terms of higher probabilities and higher uncertainties for having not yet developed a sustained volcano monitoring network. Next, three scenarios are developed for magmatic eruptions: i) small magnitude (effusive/explosive), ii) medium magnitude (Vulcanian/sub-Plinian) and iii) large magnitude (Plinian). These scenarios are inferred from the Holocene history of the volcano, with their related hazardous phenomena: ballistics, tephra fallout, pyroclastic density currents, lahars and lava flows. We present absolute probability maps (unconditional in terms of eruption size and vent location) for a magmatic eruption at Ceboruco volcano. With PyBetVH we estimate and visualize the uncertainties associated with each probability map. Our intent is that probability maps and uncertainties will be useful to local authorities who need to understand the hazard when considering the development of long-term urban and land-use planning and short-term crisis management strategies, and to the scientific community in their efforts to sustain monitoring of this active volcano.

A new perspective on eruption data completeness: insights from the First Recorded EruptionS in the Holocene (FRESH) database

Identifying the most complete (best recorded) portion of an eruption record is essential before estimating eruption recurrence and probability. This is typically achieved by plotting cumulative eruptions through time. Here, we evaluate eruption data completeness from a new perspective, by compiling the first dated Holocene eruption from each volcano in the Volcanoes of the World (VOTW) database (i.e., First Recorded EruptionS in the Holocene (FRESH)). In our first analysis, we compared the subregional distribution of FRESH with time using Kolmogorov-Smirnov (KS) test. We found that the eruption record was best categorised into 31 regions containing subregions with similar degrees of completeness. This opened the way to define new Relative Completeness Date(s) (RCD) as a function of eruption size, volcanic characteristics, and region, by identifying multiple points in the record where the root-mean-square (RMS) level changes abruptly, corresponding to a gap, a decrease or increase in the FRESH rate. Regional RCDs in the Common Era (CE) range from as recently as 1964 CE in the Indian Ocean (southern) to 200 CE in Middle East and Western Indian Ocean. In contrast, some regions like Kamchatka and Mainland Asia have near-constant rates of FRESH over the last 12,000 years, making RCDs impossible to assign. We present and make available our FRESH database, and describe and implement an automatic approach to detect RCDs across our newly defined volcanic regions. We suggest that the different degrees of completeness observed at a regional scale can be explained by: socio-historical events, access to geological studies, submarine volcanism, and/or remoteness. The FRESH database, together with the new regions and proposed RCDs can be used in future studies to estimate eruption probabilities at volcanoes without Holocene records and identify which subregions are most likely to produce a FRESH in the future.

Automated Eruption Forecasting at Frequently Active Volcanoes Using Bayesian Networks Learned From Monitoring Data and Expert Elicitation: Application to Mt Ruapehu, Aotearoa, New Zealand

Volcano observatory best practice recommends using probabilistic methods to forecast eruptions to account for the complex natural processes leading up to an eruption and communicating the inherent uncertainties in appropriate ways. Bayesian networks (BNs) are an artificial intelligence technology to model complex systems with uncertainties. BNs consist of a graphical presentation of the system that is being modelled and robust statistics to describe the joint probability distribution of all variables. They have been applied successfully in many domains including risk assessment to support decision-making and modelling multiple data streams for eruption forecasting and volcanic hazard and risk assessment. However, they are not routinely or widely employed in volcano observatories yet. BNs provide a flexible framework to incorporate conceptual understanding of a volcano, learn from data when available and incorporate expert elicitation in the absence of data. Here we describe a method to build a BN model to support decision-making. The method is built on the process flow of risk management by the International Organization for Standardization. We have applied the method to develop a BN model to forecast the probability of eruption for Mt Ruapehu, Aotearoa New Zealand in collaboration with the New Zealand volcano monitoring group (VMG). Since 2014, the VMG has regularly estimated the probability of volcanic eruptions at Mt Ruapehu that impact beyond the crater rim. The BN model structure was built with expert elicitation based on the conceptual understanding of Mt Ruapehu and with a focus on making use of the long eruption catalogue and the long-term monitoring data. The model parameterisation was partly done by data learning, complemented by expert elicitation. The retrospective BN model forecasts agree well with the VMG elicitations. The BN model is now implemented as a software tool to automatically calculate daily forecast updates.

Factors associated with the eruption of the impacted maxillary third molars after second molar extraction

In orthodontic treatment, the space left after extracting the maxillary second molar (MxM2) may be filled by the eruption of the impacted third molar (MxM3). However, little is known about the factors associated with the eruption of the impacted MxM3. We aimed to characterize the clinical factors associated with the time taken for MxM3 eruption after MxM2 extraction. We analyzed factors associated with late MxM3 eruption (>500 days after MxM2 extraction) in 84 molars. Prespecified risk factors were entered into logistic regression models to estimate odds ratios (ORs). The median duration between MxM2 extraction and MxM3 eruption was 302 days (interquartile range, 140-424). Significant factors associated with late MxM3 eruption included the proximity of the MxM3 root to the maxillary sinus floor (OR, 51.72), the distance between the occlusal plane of the MxM3 and the apical third of the MxM2 roots (OR, 16.56), MxM3 angulation and depth of ≥20° (OR, 5.58), ANB angle of<2° (OR, 9.05), and ≥1.5 mm distal movement of the maxillary first molar (MxM1) from its original position at the time of MxM2 extraction and MxM3 eruption (OR, 12.9). The probability of late MxM3 eruption was 0% (0 out of 30) with no risk, 6.9% (2 out of 29) with 1 risk factor, and 52% (13 out of 25) with ≥2 risk factors. We identified 5 clinical factors associated with late MxM3 eruption after MxM2 extraction. The probability of late MxM3 eruption increased as the number of present risk factors increased. These findings can be used for risk stratification during orthodontic treatment.