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Global efforts to tame CO2 emissions include the use of renewable energy sources, such as geo-energy harnessing. However, injecting pressurised fluids into the deep underground can induce earthquakes, hence converting CO2-related risk into seismic risk. Induced seismicity hazard is characterised by the overall seismic activity afb that is normalised by the injected fluid volume V and the parameter b of the Gutenberg–Richter law. The (afb,b) set has so far been estimated for a dozen of reservoir stimulations, while at least 53 geothermal fluid stimulations are known to exist, based on our survey. Here, we mined the induced seismicity literature and were able to increase the number of estimates to 39 after calculating afb from related published parameters and by imputing b with its expectation where this parameter was missing (0.65 ≤ b ≤ 2.9, with mean 1.16). Our approach was a two-step procedure: we first reviewed the entire literature to identify seismic hazard information gaps and then did a meta-analysis to fill those gaps. We find that the mean and median afb estimates slightly decrease from afb ≈ −2.2 to afb = −2.9 and −2.4, respectively, and that the range of observations expands from −4.2 ≤ afb ≤ 0.4 to −8.9 ≤ afb ≤ 0.4, based on a comprehensive review unbiased towards high-seismicity experiments. Correcting for potential ambiguities in published parameters could further expand the range of possibilities but keep the mean and the median relatively close to original estimates, with afb ≈ −2.3 and −2.4, respectively. In terms of the number of earthquakes induced (function of 10afb), our meta-analysis suggests that it is about half the number that could previously be inferred from published afb estimates (i.e., half the seismic hazard). These results are hampered by high uncertainties, demonstrating the need to re-analyse past earthquake catalogues to remove any ambiguity and to systematically compute afb in future geothermal projects to reduce uncertainty in induced seismicity hazard assessment. Such uncertainties are so far detrimental to the further development of the technology.

Induced seismicity is one of the main factors that reduces societal acceptance of deep geothermal energy exploitation activities, and felt earthquakes are the main reason for closure of geothermal projects. Implementing innovative tools for real-time monitoring and forecasting of induced seismicity was one of the aims of the recently completed COSEISMIQ project. Within this project, a temporary seismic network was deployed in the Hengill geothermal region in Iceland, the location of the nation’s two largest geothermal power plants. In this paper, we release raw continuous seismic waveforms and seismicity catalogues collected and prepared during this project. This dataset is particularly valuable since a very dense network was deployed in a seismically active region where thousand of earthquakes occur every year. For this reason, the collected dataset can be used across a broad range of research topics in seismology ranging from the development and testing of new data analysis methods to induced seismicity and seismotectonics studies.Measurement(s)Seismic waveforms (seismograms) • Seismicity (Origin time, location and magnitude of earthquakes)Technology Type(s)Seismic stations (velocity sensors) • SeisComP data acquisition and processing system

[...]we reformulate the 2017 results2 using two-parameter logistic regression (that is, one neuron) and obtain the same performance as that of the 13,451-parameter DNN. [...]we strongly believe that deep learning is revolutionizing data analytics in many domains12,13, including statistical seismology14. [...]the objective of our study is not to restrain its use in this field, but to stimulate a further research effort15. Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-019-1582-8.

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.

This paper presents the seismic mitigation of typical storage tanks where extreme loading conditions are considered by safe shutdown earthquakes. To reproduce the main dynamic properties of the superstructure, a standard structural model was considered, where both the presence of the impulsive mode and of the convective mode were considered. Thus, to protect the tank from strong earthquakes, finite locally resonant multiple-degrees-of-freedom (MDoFs) metafoundations were designed and developed; and resonator parameters together with bistable columns were optimized by means of an improved time domain multiobjective optimization procedure. Also, the stochastic nature of the seismic input was taken into account. Therefore, it is proposed: (i) a linear metafoundation endowed with one/two layers and multiple cells, linear springs, and linear viscous dampers; and (ii) a relevant foundation equipped with columns operating in an elastic buckled state. With this arrangement, additional flexibility and dissipation against horizontal seismic loadings are activated. It was shown in both cases, how each metafoundation can be successfully optimized via a sensitivity-based parameter technique. Thus, the performance of the optimized metafoundations was assessed by means of time history analyses; and results were compared with a storage tank endowed with both rigid foundation solutions. Finally, single cells were analysed in the frequency domain while finite lattices and periodic metafoundations in the linear and bistable regime were characterized by means of dispersion relationships.

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.