ALBERT

Description: Geosciences, Vol. 7, Pages 129: Mainstreaming Multi-Risk Approaches into Policy Geosciences doi: 10.3390/geosciences7040129 Authors: Scolobig Anna Komendantova Nadejda Mignan Arnaud Multi-risk environments are characterized by domino effects that often amplify the overall risk. Those include chains of hazardous events and increasing vulnerability, among other types of correlations within the risk process. The recently developed methods for multi-hazard and risk assessment integrate interactions between different risks by using harmonized procedures based on common metrics. While the products of these assessments, such as multi-hazard and -risk indexes, maps, cascade scenarios, or warning systems provide innovative and effective information, they also pose specific challenges to policy makers and practitioners due to their novel cross-disciplinary aspects. In this paper we discuss the institutional barriers to the adoption of multi-risk approaches, summarizing the results of the fieldwork conducted in Italy and Guadeloupe and of workshops with disaster risk reduction practitioners from eleven European countries. Results show the need for a clear identification of responsibilities for the implementation of multi-risk approaches, as institutional frameworks for risk reduction remain to this day primarily single-risk centered. Authorities are rarely officially responsible for the management of domino effects between e.g., tsunamis and industrial accidents, earthquake and landslides, floods and electricity network failures. Other barriers for the implementation of multi-risk approaches include the limited measures to reduce exposure at the household level, inadequate financial capacities at the local level and limited public-private partnerships, especially in case of interactions between natural and industrial risks. Adapting the scale of institutions to that of multi-risk environments remains a major challenge to better mainstream multi-risk approaches into policy. To address it, we propose a multi-risk governance framework, which includes the phases of observation, social and institutional context analysis, generation of multi-risk knowledge and stakeholder engagement processes. Yet, more research is needed in order to test the framework and to identify the hallmark characteristics of effective multi-risk governance.

Description: We performed a series of 12 hydraulic stimulation experiments in a 20m×20m×20m foliated, crystalline rock volume intersected by two distinct fault sets at the Grimsel Test Site, Switzerland. The goal of these experiments was to improve our understanding of stimulation processes associated with high-pressure fluid injection used for reservoir creation in enhanced or engineered geothermal systems. In the first six experiments, pre-existing fractures were stimulated to induce shear dilation and enhance permeability. Two types of shear zones were targeted for these hydroshearing experiments: (i) ductile ones with intense foliation and (ii) brittle–ductile ones associated with a fractured zone. The second series of six stimulations were performed in borehole intervals without natural fractures to initiate and propagate hydraulic fractures that connect the wellbore to the existing fracture network. The same injection protocol was used for all experiments within each stimulation series so that the differences observed will give insights into the effect of geology on the seismo-hydromechanical response rather than differences due to the injection protocols. Deformations and fluid pressure were monitored using a dense sensor network in boreholes surrounding the injection locations. Seismicity was recorded with sensitive in situ acoustic emission sensors both in boreholes and at the tunnel walls. We observed high variability in the seismic response in terms of seismogenic indices, b values, and spatial and temporal evolution during both hydroshearing and hydrofracturing experiments, which we attribute to local geological heterogeneities. Seismicity was most pronounced for injections into the highly conductive brittle–ductile shear zones, while the injectivity increase on these structures was only marginal. No significant differences between the seismic response of hydroshearing and hydrofracturing was identified, possibly because the hydrofractures interact with the same pre-existing fracture network that is reactivated during the hydroshearing experiments. Fault slip during the hydroshearing experiments was predominantly aseismic. The results of our hydraulic stimulations indicate that stimulation of short borehole intervals with limited fluid volumes (i.e., the concept of zonal insulation) may be an effective approach to limit induced seismic hazard if highly seismogenic structures can be avoided.

Description: In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. We reviewed the literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994–2019 period) and found two emerging trends: an increasing interest in this domain over time and a complexification of ANN models toward deep learning. Despite the relatively positive results claimed in those studies, we verified that far simpler (and traditional) models seem to offer similar predictive powers, if not better ones. Those include an exponential law for magnitude prediction and a power law (approximated by a logistic regression or one artificial neuron) for aftershock prediction in space. Because of the structured, tabulated nature of earthquake catalogs, and the limited number of features so far considered, simpler and more transparent machine-learning models than ANNs seem preferable at the present stage of research. Those baseline models follow first physical principles and are consistent with the known empirical laws of statistical seismology (e.g., the Gutenberg–Richter law), which are already known to have minimal abilities to predict large earthquakes.

Description: The rapid increase in energy demand in the city of Reykjavik has posed the need for an additional supply of deep geothermal energy. The deep-hydraulic (re-)stimulation of well RV-43 on the peninsula of Geldinganes (north of Reykjavik) is an essential component of the plan implemented by Reykjavik Energy to meet this energy target. Hydraulic stimulation is often associated with fluid-induced seismicity, most of which is not felt on the surface but which, in rare cases, can be a nuisance to the population and even damage the nearby building stock. This study presents a first-of-its-kind pre-drilling probabilistic induced seismic hazard and risk analysis for the site of interest. Specifically, we provide probabilistic estimates of peak ground acceleration, European microseismicity intensity, probability of light damage (damage risk), and individual risk. The results of the risk assessment indicate that the individual risk within a radius of 2 km around the injection point is below 0.1 micromorts, and damage risk is below 10−2, for the total duration of the project. However, these results are affected by several orders of magnitude of variability due to the deep uncertainties present at all levels of the analysis, indicating a critical need in updating this risk assessment with in situ data collected during the stimulation. Therefore, it is important to stress that this a priori study represents a baseline model and starting point to be updated and refined after the start of the project.

Description: With the unfolding of the COVID-19 pandemic, mathematical modelling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long-term predictions were extremely challenging to address. In addition, it is still unclear which metric shall be used for a global description of the evolution of the outbreaks. Yet a robust modelling of pandemic dynamics and a consistent choice of the transmission metric is crucial for an in-depth understanding of the macroscopic phenomenology and better-informed mitigation strategies. In this study, we propose a Markovian stochastic framework designed for describing the evolution of entropy during the COVID-19 pandemic together with the instantaneous reproductive ratio. Then, we introduce and use entropy-based metrics of global transmission to measure the impact and the temporal evolution of a pandemic event. In the formulation of the model, the temporal evolution of the outbreak is modelled by an equation governing the probability distribution that describes a nonlinear Markov process of a statistically averaged individual, leading to a clear physical interpretation. The time-dependent parameters are formulated by adaptive basis functions, leading to a parsimonious representation. In addition, we provide a full Bayesian inversion scheme for calibration together with a coherent strategy to address data unreliability. The time evolution of the entropy rate, the absolute change in the system entropy, and the instantaneous reproductive ratio are natural and transparent outputs of this framework. The framework has the appealing property of being applicable to any compartmental epidemic model. As an illustration, we apply the proposed approach to a simple modification of the susceptible–exposed–infected–removed model. Applying the model to the Hubei region, South Korean, Italian, Spanish, German, and French COVID-19 datasets, we discover significant difference in the absolute change of entropy but highly regular trends for both the entropy evolution and the instantaneous reproductive ratio.

Description: The aftershock productivity law is an exponential function of the form K∝exp(αM), with K being the number of aftershocks triggered by a given mainshock of magnitude M and α≈ln (10) being the productivity parameter. This law remains empirical in nature although it has also been retrieved in static stress simulations. Here, we parameterize this law using the solid seismicity postulate (SSP), the basis of a geometrical theory of seismicity where seismicity patterns are described by mathematical expressions obtained from geometric operations on a permanent static stress field. We first test the SSP that relates seismicity density to a static stress step function. We show that it yields a power exponent q = 1.96 ± 0.01 for the power-law spatial linear density distribution of aftershocks, once uniform noise is added to the static stress field, in agreement with observations. We then recover the exponential function of the productivity law with a break in scaling obtained between small and large M, with α=1.5ln (10) and ln (10), respectively, in agreement with results from previous static stress simulations. Possible biases of aftershock selection, proven to exist in epidemic-type aftershock sequence (ETAS) simulations, may explain the lack of break in scaling observed in seismicity catalogues. The existence of the theoretical kink, however, remains to be proven. Finally, we describe how to estimate the solid seismicity parameters (activation density δ+, aftershock solid envelope r∗ and background stress amplitude range Δo∗) for large M values.

Description: It is still debated whether earthquake occurrence can be described as a single process or whether foreshocks are different phenomena. If foreshocks behaved differently, this would suggest a change of physical processes in the mainshock preparation phase that would boost hopes of forecasting large earthquakes. Most research on foreshocks focuses on case studies or uses global datasets in which recordings of small earthquakes are incomplete and are thus neglected. We do comprehensive foreshock statistics on all mainshocks in a regional earthquake catalog that is complete above M 2.5. To detect possible differences between foreshocks and seismicity that follows a uniform triggering model (the epidemic‐type aftershock sequence [ETAS] model), we perform a null‐hypothesis test. We also estimate the size of the differences between observed and ETAS‐simulated foreshocks.We define different sets of foreshocks using two different methods, because there is no unique definition: a nearest‐neighbor declustering technique (Zaliapin et al., 2008) and a variety of space–time windows (e.g., Agnew and Jones, 1991). We use data from southern California, northern California, and Italy. For each region, we first search an appropriate null model: an ETAS model that describes aftershock numbers well. In southern California, we find an appropriate spatiotemporal model that is characterized by a large productivity parameter α. After performing a null‐hypothesis test for different mainshock and foreshock magnitudes, we find foreshock signals (p〈0.05) for all mainshocks sizes and independent of the foreshock’s lower magnitude threshold. Observed mainshocks have more foreshocks than the ETAS model predicts.