How does fault aperture evolution affect induced seismicity?

In this work, we compare three aperture models at Belgium’s VITO geothermal site: constant, stress-dependent, and stress- and slip-dependent.

Our results show that including both stress and slip effects gives a more conservative estimate of maximum earthquake magnitude, with computational costs comparable to the constant-aperture model.

Read the article: https://link.springer.com/article/10.1186/s40517-026-00412-6

#INJECT #GeothermalEnergy #InducedSeismicity #FaultMechanics #Geomechanics

How can we model complex fractures without building a complex mesh around them?

In this work, we extend the shifted interface method to coupled fluid flow and deformation in fractured porous media, allowing cracks and fractures to cut through the computational mesh freely.

The key advantage: complex fracture geometries, without complex remeshing, while retaining accurate hydro-mechanical simulations.

Read the article: https://www.sciencedirect.com/science/article/abs/pii/S0266352X26006993

#INJECT #Geomechanics #ShiftedInterfaceMethod #FEM #FracturedMedia #Poromechanics #Hydromechanics #NumericalMethods #FiniteElements #InducedSeismicity @David Michael Riley @Guglielmo Scovazzi @European Research Council (ERC) @ENSTA Paris

Using tools from control theory, we study how to estimate the parameters and hidden state variables of the rate-and-state friction model from noisy fault-slip measurements.

A key result is our exploration of identifiability: we show when fault-slip observations contain enough information to uniquely recover the hidden state variables and friction parameters of the rate-and-state friction model.

Read the article: https://onlinelibrary.wiley.com/doi/10.1002/nag.70391

#Geomechanics #FaultMechanics #RateAndStateFriction #InducedSeismicity #ControlTheory #StateEstimation #Identifiability @DavidRiley @DiegoGutierrezOribio @European Research Council (ERC), @ENSTA

In this work, we investigate how grain crushing and pore collapse drive the formation of localized shear zones in granular rocks. Using a Breakage Mechanics-based model—offering an efficient alternative to time-consuming or unfeasible Discrete Element simulations—enriched with a Cosserat continuum and calibrated on Bentheim sandstone, we show that fault cores evolve through coupled compaction, dilation, and permeability changes. Our results highlight the critical role of grain size evolution, grain breakage down to ultrafine particles, and solid fraction in controlling (hydro-)mechanical behavior during seismic slip.
For more details: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JB033062

#Geomechanics #FaultMechanics #GranularMedia #InducedSeismicity @NickCollinsCraft @JeanSulem @ItaiEinav @European Research Council (ERC), @ENSTA

We introduce solve_nivp, a Python library for robust time integration of nonsmooth ODE/DAE systems with impacts, switching, and inequality constraints. Such systems are widespread: frictional contact in mechanics, earthquakes, piecewise and switching behaviour in circuits, sliding–mode control, discontinuous rules in finance and energy markets…
By embedding nonsmooth rules directly into an implicit time-stepping scheme, the approach avoids regularisation and restrictive step sizes required by classical solvers. The package is supported by documentation, examples, and tests for reproducible use.
For more details: https://joss.theoj.org/papers/10.21105/joss.09775 & https://github.com/ERC-INJECT/solve_nivp & https://pypi.org/project/solve-nivp/

#Python #ScientificComputing #NumericalMethods #ComputationalMechanics #Earthquakes #ContactMechanics #Circuits #SlidingModeControl #Markets #ODE/DAE @DavidRiley @European Research Council (ERC), @Hi! PARIS Center – AI for Science, Business & Society, @ENSTA

This work presents an AI-driven approach for the sustainable use of subsurface renewable energy resources. By combining reinforcement learning with robust control theory, the framework adapts fluid injection strategies to mitigate human-induced seismicity while satisfying energy production objectives.
Numerical simulations show that this hybrid control–AI strategy can operate reliably under uncertain subsurface conditions and variable energy demand, offering a pathway toward safer and more intelligent geothermal and subsurface energy systems. More coming soon!
Read the article here: https://onlinelibrary.wiley.com/doi/10.1002/nag.3923

#ArtificialIntelligence #ReinforcementLearning #EnergyGeomechanics #InducedSeismicity #GeothermalEnergy #SubsurfaceEngineering #SustainableEnergy, @diego gutiérrez oribio @alexandros stathas, @European Research Council (ERC), @Hi! PARIS Center – AI for Science, Business & Society, @ENSTA

We recently published a study proposing a control-theory–based approach to minimize induced seismicity during fluid injection in underground reservoirs used for geothermal energy and subsurface storage. The method relies on a physics-based seismicity model combined with robust tracking control to regulate seismicity rates and fluid pressure while maintaining the fluid circulation required for energy production.
Numerical simulations demonstrate that this approach can safely manage seismic risk under uncertain reservoir conditions and variable energy demand, offering a mathematically grounded alternative to empirical mitigation strategies.
For more details: https://www.sciencedirect.com/science/article/pii/S2352380824000376?via%3Dihub

#InducedSeismicity #GeothermalEnergy #ControlTheory #EnergyGeotechnics #GeoEnergy #SubsurfaceEngineering, @diego gutièrrez oribio @European Research Council (ERC) @ENSTA

We recently published a study exploring how control theory can be used to optimize hydraulic stimulation while mitigating induced seismicity in geothermal systems using a single-well. Using a robust control we show how injection schedules can be adapted in real-time to increase reservoir stimulation efficiency while keeping seismic risk within regulatory limits.
The framework is demonstrated on the 2018 Otaniemi geothermal project, highlighting how control can outperform traditional traffic-light approaches in both safety and performance.
For more details: https://www.sciencedirect.com/science/article/pii/S0375650525001476?via%3Dihub

#GeothermalEnergy #InducedSeismicity #ControlTheory #EnergyTransition #Geomechanics #SustainableEnergy, @taeho kim @diego gutiérrez oribio, @mateo acosta, @jean philippe avouac, @European Research Council (ERC), @ENSTA

We recently published a study introducing a POD–TANN framework for multiscale modeling in geomechanics. By combining Proper Orthogonal Decomposition with thermodynamics-based neural networks, the approach enables accurate, energetically consistent macroscopic models directly derived from high-fidelity simulations, with a strong reduction in computational cost.
For more details: https://onlinelibrary.wiley.com/doi/epdf/10.1002/nag.3891

@giovanni piunno, @cristina jomni, @European Research Council (ERC), @ENSTA, @CentraleNantes
#ComputationalMechanics, #TANN, #ReducedOrderModeling, #Geomechanics, #Homogenization, #Macroelements

Traditional time integrators struggle with nonsmooth systems, often slowing down or losing accuracy. Our new approach uses Reinforcement Learning to automatically choose optimal time steps, adapting even when the dynamics are nonsmooth. Tested on three challenging problems, a sliding-mode control system, a diode circuit, and a seismic fault model, our RL-based integrator significant speed-ups, reaching up to 10× faster in the hardest cases. A promising new direction for efficient time integration across complex dynamical systems.
For more details: https://link.springer.com/article/10.1007/s44379-025-00048-6