Dynamics between earthquakes, volcanic eruptions, and geothermal energy exploitation in Japan

Intruding magma brings high temperatures close to the surface, thus offering possibilities for harnessing large amounts of heat for geothermal exploitation. Mount Aso in southern Japan showed frequent volcanic activity during 2016, accompanied by significant earthquake activities with tens of thousands of aftershocks (Kumamoto sequence). Here we investigate the influence of earthquake/volcanic activity on the future productivity of nearby geothermal power plants to determine whether the activity is detrimental or beneficial to energy exploitation. Model results show an increase in [Formula: see text] pressure and temperature with a spatio-temporal correlation between modeled earthquake locations and aftershock decay rates along the entire sequence, showing that seismic activity opened pre-existing vertical cracks providing pathways for the ascending magma. Interestingly, the minor but still significant eruption of Mount Aso in October 2021 may have enhanced future geothermal power generation, indicating a vigorous and active system, possibly increasing the future geothermal power production.ISSN:2045-232

Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19

There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter (α), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios

Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19

There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter (α), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios.ISSN:1660-4601ISSN:1661-782

Combining Site Characterization, Monitoring and Hydromechanical Modeling for Assessing Slope Stability

Rainfall-induced landslides are a disastrous natural hazard causing loss of life and significant damage to infrastructure, farmland and housing. Hydromechanical models are one way to assess the slope stability and to predict critical combinations of groundwater levels, soil water content and precipitation. However, hydromechanical models for slope stability evaluation require knowledge about mechanical and hydraulic parameters of the soils, lithostratigraphy and morphology. In this work, we present a multi-method approach of site characterization and investigation in combination with a hydromechanical model for a landslide-prone hillslope near Bonn, Germany. The field investigation was used to construct a three-dimensional slope model with major geological units derived from drilling and refraction seismic surveys. Mechanical and hydraulic soil parameters were obtained from previously published values for the study site based on laboratory analysis. Water dynamics were monitored through geoelectrical monitoring, a soil water content sensor network and groundwater stations. Historical data were used for calibration and validation of the hydromechanical model. The well-constrained model was then used to calculate potentially hazardous precipitation events to derive critical thresholds for monitored variables, such as soil water content and precipitation. This work introduces a potential workflow to improve numerical slope stability analysis through multiple data sources from field investigations and outlines the usage of such a system with respect to a site-specific early-warning system