Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery

Coupled subdaily and multiweek cycles during the lava dome erupt of Soufriere Hills Volcano, Montserrat

Observations of volcanoes extruding andesitic lava to produce lava domes often reveal cyclic behaviour. At Soufriere Hills Volcano, Montserrat, cycles with sub-daily and multi-week periods have been recognised on many occasions. These two types of cycle have been modelled separately as stick-slip magma flow at the junction between a dyke and an overlying cylindrical conduit (Costa et al. 2012), and as the filling and discharge of magma through the elastic-walled dyke (Costa et al., 2007a) respectively. Here, we couple these two models to simulate the behaviour over a period of well-observed multi-week cycles, with accompanying sub-daily cycles, from 13 May to 21 September 1997. The coupled model captures well the asymmetrical first-order behaviour: the first 40% of the multi-week cycle consists of high rates of lava extrusion during short period/high amplitude sub-daily cycles as the dyke reservoir discharges itself. The remainder of the cycle involves increasing pressurization as more magma is stored, and extrusion rate falls, followed by a gradual increase in the period of the sub-daily cycles

Understanding causality and uncertainty in volcanic observations: an example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes

Bayesian deep learning for large scale environmental data modelling

Deep learning – machine learning using deep neural networks – is an efficient way to discover patterns in data that may be more complex than we could manually hypothesise. Here we learn spatio-temporal models that harness information from gridded auxiliary datasets, such as digital terrain models and satellite imagery, by learning task-relevant derivatives of these with no requirement for manual feature engineering. By operating within the Bayesian probabilistic framework, we can learn well-calibrated deep models that quantify epistemic and aleatoric uncertainties and avoid overfitting despite the capacity of deep models to do so.Engineering and Physical Sciences Research Council (EPSRC

Bayesian deep learning for spatial interpolation in the presence of auxiliary information

This is the final version. Available on open access from Springer via the DOI in this record. Earth scientists increasingly deal with ‘big data’. For spatial interpolation tasks, variants of kriging have long been regarded as the established geostatistical methods. However, kriging and its variants (such as regression kriging, in which auxiliary variables or derivatives of these are included as covariates) are relatively restrictive models and lack capabilities provided by deep neural networks. Principal among these is feature learning: the ability to learn filters to recognise task-relevant patterns in gridded data such as images. Here, we demonstrate the power of feature learning in a geostatistical context by showing how deep neural networks can automatically learn the complex high-order patterns by which point-sampled target variables relate to gridded auxiliary variables (such as those provided by remote sensing) and in doing so produce detailed maps. In order to cater for the needs of decision makers who require well-calibrated probabilities, we also demonstrate how both aleatoric and epistemic uncertainty can be quantified in our deep learning approach via a Bayesian approximation known as Monte Carlo dropout. In our example, we produce a national-scale probabilistic geochemical map from point-sampled observations with auxiliary data provided by a terrain elevation grid. By combining location information with automatically learned terrain derivatives, our deep learning approach achieves an excellent coefficient of determination (R2=0.74) and near-perfect probabilistic calibration on held-out test data. Our results indicate the suitability of Bayesian deep learning and its feature-learning capabilities for large-scale geostatistical applications where uncertainty matters.Engineering and Physical Sciences Research Council (EPSRC

Periodic sulphur dioxide degassing from the Soufriere Hills Volcano related to deep magma supply

Soufrière Hills Volcano produced prodigious quantities of sulphur dioxide (SO2) gas throughout 1995–2013. An unprecedented, detailed record of SO2 flux shows that high SO2 fluxes were sustained through eruptive pauses and for two years after the end of lava extrusion and are decoupled from lava extrusion rates. Lava extrusion rates have exhibited strong 1- to 2-year cyclicity. Wavelet analysis demonstrates periodicities of c. 5 months and c. 2 years within the SO2 time series, as well as the shorter cycles identified previously. The latter period is similar to the wavelength of cycles in lava extrusion, albeit non-systematically offset. The periodicities are consistent with pressure changes accompanying deformation in a coupled magma reservoir system whereby double periodic behaviour may arise from limited connectivity between two reservoirs. During periods of lava extrusion SO2 is released together with the lava (yielding the c. 2 year period), albeit with some offset. In contrast, when magma cannot flow because of its yield strength, SO2 is released independently from lava (yielding the c. 5 month period). Our results have implications for eruption forecasting. It seems likely that, when deep supply of magma ceases, gas fluxes will cease to be periodic

Бокс как вид спорта, дающий студентам преимущества в будущей профессии.

Geodetic surveying is a core volcano monitoring technique. Measurements of how the crust deforms can give valuable insight into the mechanisms and processes that drive an eruption, and the way in which they change. Various geodetic observables, including ground deformation and gravity changes, have been recorded on Montserrat throughout the eruption. Instrumentation and surveying networks used to make such measurements have evolved significantly since 1995, providing increasingly accurate and robust observations. The detailed research that has been facilitated by these rich geodetic datasets has illuminated many aspects of the Soufrière Hills Volcano (SHV) and demonstrated eruptive mechanisms that are relevant to the study of other volcanoes. We have compiled a history of the geodetic study of the eruption on Montserrat, detailing the development of surveying techniques, network design and data processing since 1995. We then underline some of the key geodetic observations and review some of the most significant research that has contributed to our understanding of this volcanic system. Finally, we apply a series of typical deformation inversion models to deformation observations, and discuss the parameter sensitivity of such modelling approaches and how confidently they can be applied to identify the characteristics of the mechanisms feeding the eruption