This session focusses on the computational and methodological developments necessary for progress towards more sustainable energy. We welcome submissions that address a diverse range of topics -- including simulation e.g. of themo-chemical flow processes, subsurface imaging, data fusion and AI -- with their application to critical resources as a unifying theme.
Orals: Fri, 8 May, 08:30–10:10 | Room -2.93
Geothermal energy is a crucial component of the global transition to sustainable and green energy systems due to its renewable and long-term availability. In order to study potential resources, we need to describe the subsurface by solving inverse problems. The complexity and uncertainty of these problems require the use of probabilistic inversion approaches that repeatedly solve partial differential equations over a grid of parameters describing the subsurface domain. Frequently, the high dimensionality of the parameter space to be inferred implies prohibitive computational times and reduces the sensitivity of each parameter as the grid is refined. In this work, we implement and discuss adaptive parametrization strategies in Bayesian inversions. We model the thermal conductivity structure of 2D sections of the Earth's upper mantle and perform Markov chain Monte Carlo (MCMC) inversions to recover the thermal conductivity as a probability distribution based on the likelihood of the temperature measurements. To verify the solution, we first parametrize the physical properties of the subsurface domain equal to the high-dimensional finite element grid. In order to determine the optimal metaparameters on the run we rely on adaptive MCMC techniques that accelerate the convergence and reduce the risk of getting trapped in local minima. We then use a new parametrization based on the physical structure of the geological faults of the mantle that reduces the dimensionality of the problem. By relying on transdimensional sampling through reversible-jump MCMC, we consider the number of parameters as an unknown of the inversion. In these methods, the algorithm is allowed to increase the number of parameters to invert when the solutions found are not accurate enough and to decrease it when the accuracy of the solution is not significantly affected. Our results show that we recover the thermal conductivity structure both with and without adaptive parametrization, and the performance is improved when using transdimensionality. Moreover, the proposed transdimensional inversion decreases or increases the number of parameters locally, thereby providing an efficient and robust method for addressing the often challenging lack of information on subsurface heterogeneity.
Keywords: geothermal energy; Markov chain Monte Carlo; reversible jump MCMC; transdimensional inversion; adaptive parametrization; finite elements; Poisson equation.
How to cite: Dols, A., Amaya, M., Zlotnik, S., and Díez, P.: Adaptive parameterization in Bayesian inversions using transdimensional methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12436, https://doi.org/10.5194/egusphere-egu26-12436, 2026.
Magma migration is a complex natural process that controls volcanism, the formation of many types of ore deposits, the development of geothermal reservoirs and the thermal structure, and long-term evolution of the lithosphere [1-3]. Because the dynamics of magma migration are difficult to observe directly, numerical simulations provide a powerful tool to investigate magmatic systems, the coupled physiochemical processes involved, and the range of spatial and temporal scales over which these processes operate.
In this study, we present a new multi-phase numerical framework to study magma migration within the Earth, with a particular emphasis on the mechanical interactions between melt and solid. The framework is based on multiphase flow in porous media and it incorporates realistic rheological descriptions of lithospheric rocks, including visco-elasto-viscoplastic behavior, damage, strain weakening and the generation of porosity due to plastic deformation. Interaction between the fluid (magma) and solid (host rock) phases are described via a set of equations derived from a formal phase-averaging framework. An arbitrary Eulerian-Lagrangian solver is used to discretize the equations and solve the fully-coupled system. The validity of the model, and its potential to study multi-scale magmatic systems, are demonstrated using well-known benchmark tests and targeted numerical experiments.
Keywords: Dynamics of lithosphere and mantle, Mechanics, Numerical modeling, Physics of magma, Plasticity
REFERENCES
- [1] Keller, D. A. May, and B. J. Kaus, “Numerical modelling of magma dynamics coupled to tectonic deformation of lithosphere and crust,” Geophys. J. Int., Vol. 195, pp. 1406-1442, (2013).
- [2] Li, A. E. Pusok, T. Davis, D. A. May, and R. F. Katz, “Continuum approximation of dyking with a theory for poro-viscoelastic-viscoplastic deformation,” Geophys. J. Int., Vol. 234, pp. 2007-2031, (2023).
- [3] Oliveira, J. C. Afonso, S. Zlotnik, and P. Diez, “Numerical modelling of multiphase multicomponent reactive transport in the Earth’s interior,” Geophys. J. Int., Vol. 212, pp. 345-388, (2018).
Acknowledgment
EarthSafe Doctoral Network has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101120556.
How to cite: Hosseinian, N., Afonso, J. C., García-González, A., and Zlotnik, S.: Numerical modeling of magma migration in lithospheric rocks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5509, https://doi.org/10.5194/egusphere-egu26-5509, 2026.
A key challenge in the 21st century is the successful implementation of the energy transition, which hinges on the development of sustainable (energy) resources. In this context, hydrogen gas (H2) generated by natural processes is a promising source of clean energy. However, we urgently need to develop the concepts and exploration strategies for this promise of natural H2 energy to become a reality.
The most likely mechanism of large-scale natural H2 generation in nature is the serpentinization of ultramafic mantle rocks during their chemical reaction with water. In order to predict the bulk serpentinization and natural H2 generation that may lead to the development of exploitable H2 deposits, we consider the following “recipe” for efficient serpentinization, which involves three main ingredients: (1) (fresh) mantle rocks that need to be at (2) optimal temperatures between ca. 200-350˚C (the serpentinization window), and (3) in contact with ample water for the reaction to take place. The serpentinization window can be expected at 8-12 kilometers below the Earth’s surface. However, mantle rocks are normally found at much greater depth; thus these rocks must be brought closer to the surface (exhumed) through geodynamic processes. Moreover, water needs to reach such depths along large faults or other structures that cut into the exhumed mantle. The challenge we are faced with is to understand where (and when) these ingredients may come together in nature, and how much natural H2 may be generated.
Numerical geodynamic modelling is an ideal means to tackle this issue since it allows us not only to test how mantle rocks can be exhumed, but also to trace the temperature conditions and potential water availabilitiy (Zwaan et al. 2025). By combining this information, we assess favorable settings and timing of bulk natural H2 generation in different geodynamic systems. Subsequently, we consider where the natural H2 could be exploited. The serpentinizing mantle source rocks at 8-12 km depth cannot be directly targeted. Ideally, the natural H2 would instead migrate and accumulate in sedimentary reservoir rocks at depths of only a couple of kilometers that are connected with the mantle source rocks via migration pathways (e.g., faults). Importantly, all key elements need to be in place for the system to work.
Our first-order modelling work and the development of natural H2 system concepts greatly helps to direct natural H2 resource exploration efforts, for example in the Alps and Pyrenees. Moreover, substantial opportunity lies in refining both the geodynamic modelling and natural H2 system analysis, in field- and laboratory testing of our H2 system concepts, and in extending such a “mineral system” modelling approach to other types of natural resources that are crucial to the energy transition.
Reference:
Zwaan, F., Brune, S., Glerum, A.C., Vasey, D.A., Naliboff, J.B., Manatschal, G., & Gaucher, E.C. 2025: Rift-inversion orogens are potential hot spots for natural H2 generation, Science Advances, 11, eadr3418. https://doi.org/10.1126/sciadv.adr3418
How to cite: Zwaan, F., Glerum, A. C., Brune, S., Vasey, D. A., Naliboff, J. B., Manatschal, G., and Gaucher, E. C.: Numerical geodynamic modelling for natural H2 resource exploration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3352, https://doi.org/10.5194/egusphere-egu26-3352, 2026.
The prevalence of viscous-dominated regimes within the Earth’s interior gives rise to Stokes-like flow systems in numerous geodynamical applications. A prominent example is sublithospheric mantle convection, which constitutes the primary driving mechanism behind the evolution of dynamic topography. In this context, numerical simulations provide more physically consistent estimates of the Lithosphere–Asthenosphere Boundary (LAB) depth than those derived from first-order isostatic approximations [1].
However, the associated computational overburden is exceptionally high, particularly when accounting for material nonlinearities. The challenge is further complicated when attempting to incorporate them within a Markov Chain Monte Carlo (MCMC) framework that requires an exceptionally large number of evaluations [2], limiting their applicability to large-scale studies and underscores the need for novel and computationally efficient Reduced-Order Modeling (ROM) methodologies [3].
Results from linear Model Order Reduction (MOR) techniques indicate that the complexity of the problem surpasses the capabilities of projection-based ROMs designed to produce globally accurate solutions. This work introduces a localized, goal-oriented criterion to enhance linear reducibility and employs Neural Network (NN) surrogates to replace high-fidelity solver evaluations. These methodological advances jointly underpin the development of a hybrid offline–online reduction framework that efficiently reduces computational complexity while preserving the required levels of accuracy, enabling seamless model updates during parameter-space exploration.
REFERENCES
[1] Afonso, J. C., Rawlinson, N., Yang, Y., Schutt, D. L., Jones, A. G., Fullea, J., & Griffin, W. L. (2016). 3-D multiobservable probabilistic inversion for the compositional and thermal structure of the lithosphere and upper mantle: III. Thermochemical tomography in the Western-Central U.S. Journal of Geophysical Research: Solid Earth, 121(10), 7337–7370. https://doi.org/10. 1002/2016jb013049
[2] Ortega-Gelabert, O., Zlotnik, S., Afonso, J. C., & Diez, P. (2020). Fast Stokes Flow Simulations for Geophysical-Geodynamic Inverse Problems and Sensitivity Analyses Based on Reduced Order Modeling. Journal of Geophysical Research: Solid Earth, 125(3). https://doi.org/10.1029/ 2019jb018314
[3] Hesthaven, J.S., Rozza, G., Stamm, B. (2015). Certified Reduced Basis Methods for Parametrized Partial Differential Equations. SpringerBriefs in Mathematics. Springer International Publishing AG, Cham. https://doi.org/10.1007/978-3-319-22470-1
How to cite: Ramadan, M., Pichi, F., and Rozza, G.: Toward Efficient Stokes Flow Simulations in Multi-Observable Thermo-Chemical Tomography Using Model Order Reduction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6024, https://doi.org/10.5194/egusphere-egu26-6024, 2026.
Multi-Observable Thermochemical Tomography (MTT) is a simulation-driven, joint probabilistic inversion framework designed to estimate the thermochemical state of the Earth’s lithosphere by integrating geophysical datasets with complementary sensitivities. By jointly inverting observables such as gravity and geoid anomalies, surface heat flow, seismic dispersion, body-wave data, and magnetotelluric responses, MTT directly estimates primary thermodynamic variables, including temperature, pressure, and bulk composition, from which all secondary physical properties are derived through internally consistent thermodynamic models. This bottom-up approach provides physically-consistent constraints on lithospheric structure across regional to prospect scales.
Within this framework, MTT offers a powerful basis for characterizing lithospheric architecture and compositional domains that are commonly examined in mineral systems studies. In particular, MTT can help delineate major crustal- and lithospheric-scale structures, identify metasomatized/altered domains, and map thermochemical contrasts that serve as lithospheric-scale proxies commonly associated with specific classes of magmatic and hydrothermal mineral systems.
Despite recent advances incorporating ray-based seismic tomography solvers (Fomin, I., Afonso, J. C., Gorbatov, A., Salajegheh, F., Dave, R., Darbyshire, F. A., et al. (2026). Multi-observable thermochemical tomography: New advances and applications to the superior and North Australian cratons. Journal of Geophysical Research: Solid Earth, 131, e2025JB031939. https://doi.org/10.1029/2025JB031939 ), the integration of full-waveform seismic solvers within the MTT framework has not yet been achieved. Full-waveform inversion (FWI) offers enhanced sensitivity to both seismic velocity and density and the potential for improved spatial resolution relative to traditional tomography approaches. However, the computational cost of FWI remains prohibitive, particularly in probabilistic or ensemble-based inversion settings required for uncertainty quantification.
This contribution presents a computational strategy aimed at reducing the cost of full wavefield simulations to enable probabilistic seismic FWI within the MTT framework. We extend reduced-order modeling (ROM) techniques to the spectral element method (SEM), which is widely used for accurate time-domain seismic wave propagation in complex geological settings. Specifically, we consider projection (Galerkin)–based ROMs in which the SEM wavefield is approximated in a low-dimensional reduced basis constructed from representative high-fidelity solutions. While ROM approaches are well established for simpler formulations, their application to SEM-based elastic wave simulations remains challenging due to the method’s high dimensionality and complex operator structure. Beyond MTT, such reductions are also relevant to SEM-based workflows that require large numbers of forward simulations, including ground motion studies and FWI with many sources at regional-to-global scales.
We develop and test a reduced-order SEM formulation using synthetic benchmark models relevant to lithospheric-scale imaging. Results demonstrate computational speed-ups of up to two orders of magnitude relative to full SEM simulations, while retaining sufficient accuracy in simulated wavefields for inversion purposes. These results represent a first proof of concept toward incorporating probabilistic FWI into multi-observable thermochemical tomography and reducing a key computational barrier to uncertainty-aware, physics-based lithospheric imaging.
How to cite: Jamasb, A., Afonso, J.-C., Garcia Gonzalez, A., Rozza, G., Pichi, F., Zlotnik, S., Meijde, M. V. D., and Fadel, I.: Enabling Probabilistic Full Waveform Inversion in Multi-Observable Thermochemical Tomography through Reduced-Order Spectral Element Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13298, https://doi.org/10.5194/egusphere-egu26-13298, 2026.
Mantle convection plays a fundamental role in governing the thermal and dynamical
evolution of terrestrial planets, yet its numerical simulation remains computationally ex-
pensive due to strong nonlinearities, high Rayleigh numbers, and the presence of thin
thermal boundary layers. In this work, we present a non-intrusive reduced-order modeling
(ROM) framework for two-dimensional mantle convection based on Proper Orthogonal
Decomposition combined with Radial Basis Function interpolation (POD–RBF).
High-fidelity full-order model (FOM) simulations are first performed using a finite-
volume discretization of the incompressible Boussinesq equations under the infinite-Prandtl-
number approximation. The FOM is carefully validated across a wide range of Rayleigh
numbers. Particular attention is devoted to high-Rayleigh-number regimes, where mesh
refinement studies are conducted to improve accuracy and ensure reliable reference solu-
tions.
The ROM is constructed from snapshot data of velocity and temperature fields. POD
analysis reveals a rapid decay of singular values, indicating a low-dimensional structure
of the solution manifold. The parametric dependence of the reduced coefficients is recon-
structed using RBF interpolation, yielding a fully data-driven and non-intrusive ROM.
To rigorously assess predictive capability, the ROM is validated using test points ex-
cluded from the training dataset. Leave-One-Out cross-validation demonstrates that the
ROM accurately predicts unseen solutions across the parameter space, with low relative
L2 errors for both velocity and temperature fields. Field-level comparisons confirm that
the dominant flow structures and thermal patterns are faithfully reproduced.
The framework is further extended to transient simulations, where both time and
Rayleigh number are treated as parameters. This two-dimensional parametric unsteady
ROM successfully captures time-dependent dynamics while providing significant compu-
tational speed-up. The proposed approach offers a robust and efficient tool for parametric
mantle convection modeling and provides a solid basis for future extensions toward three-
dimensional configurations and uncertainty quantification.
How to cite: Haider, Q., Tonicello, N., Girfoglio, M., and Rozza, G.: Non-Intrusive POD–RBF Reduced OrderModeling for Parametric and Transient MantleConvection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13256, https://doi.org/10.5194/egusphere-egu26-13256, 2026.
Geophysical inverse problems are inherently ill-posed due to sparse, noisy, and indirect observations, making Uncertainty Quantification (UQ) a fundamental requirement for reliable subsurface characterization. Bayesian inversion provides a comprehensive probabilistic framework for inferring subsurface parameters by coherently combining prior knowledge with observational data through the likelihood function. However, the practical deployment of Bayesian methods in large-scale geophysical settings is often hampered by the prohibitive computational cost of repeated forward model evaluations. In this context, uncertainty is often not solely driven by observational noise; a substantial and sometimes dominant contribution arises from model error, resulting from simplified physical descriptions, numerical discretization, and uncertain boundary conditions. When these sources of uncertainty are neglected or inadequately represented, Bayesian inversions may yield biased posterior estimates and unrealistically narrow uncertainty bounds. These limitations are particularly acute in deep Earth applications, where complex rheologies, poorly constrained geometries, and computationally intensive forward models coexist.
A key challenge is the accurate delineation of the Lithosphere–Asthenosphere Boundary (LAB), which plays a central role in controlling mantle dynamics, lithospheric deformation, and deep geothermal processes. Despite the necessity of relying on Bayesian approaches to estimate the LAB and its associated uncertainties, the high computational cost of repeated evaluations of the forward solver makes this unfeasible within realistic time frames [1]. To address these limitations, this work investigates Reduced-Order Modeling (ROM) techniques to enable efficient Bayesian inversion of LAB geometry in geodynamical Stokes flow models. ROMs construct low-dimensional surrogates of high-fidelity solvers, allowing rapid forward simulations while preserving the dominant physical behavior of mantle flow. By integrating ROMs with Bayesian inference, the proposed framework enables effective and reliable UQ for LAB characterization.
Keywords: Geophysical inverse problems; Bayesian inversion; Uncertainty Quantification; Reduced-Order Modeling; Lithosphere–Asthenosphere Boundary
Acknowledgement This research was conducted within the EarthSafe Doctoral Network and has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101120556.
References [1] Olga Ortega-Gelabert, Sergio Zlotnik, Juan Carlos Afonso, and Pedro D´ıez. Fast stokes flow simulations for geophysical-geodynamic inverse problems and sensitivity analyses based on reduced order modeling. Journal of Geophysical Research: Solid Earth, 125(3):e2019JB018314, 2020.
How to cite: Shahzaib, M., Díez, P., Zlotnik, S., Muixí, A., and Amaya, M.: Coupling Bayesian Inversion and Reduced-Order Modeling: Application to Lithosphere–Asthenosphere Boundary Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8341, https://doi.org/10.5194/egusphere-egu26-8341, 2026.
Three-dimensional (3D) Magnetotelluric (MT) probabilistic inversion remains rare in real-world applications because it requires solving the forward problem thousands to millions of times, often making the computational cost prohibitive. Since the total duration of an inversion is directly controlled by the performance of the forward solver, the high computational overhead of 3D MT modeling remains a significant challenge, particularly for large-scale problems requiring high mesh resolutions. To address the poor scaling of existing strategies, we introduce DD–POD, a hybrid framework that integrates Domain Decomposition (DD) with Proper Orthogonal Decomposition (POD). The DD formulation partitions the global problem into subdomains, bypassing the memory limitations of traditional direct solvers and enabling simulations with substantially finer discretizations. Implementing this distributed architecture alone yields simulations that are at least 50% faster than global full-order approaches. Building on this foundation, the integration of POD eliminates the need for repeated large-scale linear system solves within the iterative DD process, delivering total forward-solver speed-ups exceeding 90%. Benchmark experiments and a real-world case study demonstrate that DD–POD consistently outperforms standard global POD strategies in computational efficiency with an acceptable trade-off in numerical accuracy.
(This work was supported by the Marie Sklodowska-Curie Actions (Doctoral Network with Grant agreement No. 101120556))
How to cite: Tao, L., Zlotnik, S., Muixí, A., Zyserman, F. I., Afonso, J. C., and Diez, P.: Reducing Computational Costs in 3D Magnetotelluric Simulations via Domain Decomposition and Reduced-Order Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10570, https://doi.org/10.5194/egusphere-egu26-10570, 2026.
Posters on site: Thu, 7 May, 16:15–18:00 | Hall X2
The conventional hydrocarbon accumulation model in the Xihu Depression is predominantly characterized by “late-stage accumulation.” However, with advancing exploration, the potential occurrence of commercially significant early hydrocarbon charging events in the Paleogene Pinghu Formation has become a subject of considerable debate. This study examines the accumulation mechanisms of natural gas in the Pinghu Formation through an integrated approach incorporating scanning electron microscopy (SEM), systematic fluid inclusion analysis, and natural gas carbon isotope geochemistry, with a particular focus on the evolutionary patterns of authigenic illite within the reservoir.
SEM observations reveal three distinct morphological types of authigenic illite in the Pinghu Formation reservoirs: honeycomb, bridge-like, and fibrous. The crystallization of these illite types is primarily governed by diagenetic temperature and pore fluid pH: honeycomb illite forms at low temperatures (60 to 110°C) via smectite transformation; bridge-like illite develops at 120 to 140°C in association with acidic dissolution of K-feldspar; and fibrous illite requires temperatures above 140°C and alkaline conditions for the illitization of kaolinite. A key anomaly contradicting conventional diagenetic sequences was identified: in the shallower and cooler Huagang Formation reservoirs, fibrous illite constitutes up to 76% of the illite assemblage, whereas in the deeper and presumably hotter Pinghu Formation reservoirs, honeycomb and bridge-like types dominate (collectively 65%), with markedly reduced overall abundance. This inverse distribution with depth is interpreted as evidence of early hydrocarbon charging during deep burial of the Pinghu Formation. The introduction of acidic hydrocarbons inhibited the transformation of kaolinite to fibrous illite, thereby preserving the earlier illite morphologies and providing direct mineralogical evidence for an early accumulation event during the Huagang Movement.
Geological analysis further supports the coupling of key elements conducive to early accumulation: during the Huagang Movement, source rocks had reached burial depths sufficient for hydrocarbon generation (Ro ≥ 0.5%), providing a material basis for large-scale expulsion. Concurrently, the superposition of the Yuquan and Huagang movements facilitated the development of structural–lithologic traps. At this stage, the average porosity of the Pinghu Formation reservoirs was approximately 21%, not yet entering the tightening phase, providing high-quality reservoir space for early hydrocarbon filling and accumulation.
Fluid geochemical data provide additional robust evidence: hydrocarbon inclusions exhibiting yellow fluorescence with homogenization temperatures peaking between 105 and 135°C record an early hydrocarbon charging event. Furthermore, the methane δ¹³C values of Pinghu Formation natural gas (–38‰ to -34‰) are significantly lighter than those of the overlying Huagang Formation (–34‰ to 29‰), consistent with an early-generated, low-maturity gas source, effectively distinguishing fluid origins between early and late accumulation phases.
Based on the above research, an early accumulation model governed by the combined effects of “paleo-highs and high-quality reservoirs” is established for the Pinghu Formation. This provides a key predictive model for early-stage reservoir exploration in basins with similar geological conditions worldwide, thereby further expanding new exploration frontiers.
How to cite: Li, L. and chen, Z.: Evidence from Illite Crystal Evolution: Exposing the Early Phases and Patterns of Hydrocarbon Accumulation in the Pinghu Formation of the Xihu Depression in the East China Sea., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-58, https://doi.org/10.5194/egusphere-egu26-58, 2026.
Eduardo Monsalvea, Claudia Pavez-Orregob, Ángela Floresa, Nicolás Barbosab, Eckner Chaljuba, Rodrigo Palma-Behnkea, Nikolai H. Gaukåsd, Didrik R. Småbråtend, Diana Comtec*.
- a) Department of Electrical Engineering / Energy Center, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, Chile
- b) Department of Applied Geosciences, Geophysics, SINTEF Industry, Trondheim, Norway
- c) Advanced Mining Technology Center, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, Chile
- d) Department of Sustainable Energy Technology, SINTEF Industry, Oslo, Norway
In a global context marked by increasing energy demand and growing constraints on the large-scale deployment of conventional renewable sources, the exploration of alternative energy pathways has become increasingly relevant. Within this framework, vibrational energy harvesting (VEH) has garnered attention due to its potential to exploit ambient energy sources that are typically overlooked, such as mechanical vibrations. In particular, seismic vibrations, both natural and anthropogenic, represent a persistent and spatially distributed energy resource in regions characterized by intense industrial activity and significant seismicity.
This study presents a systematic and replicable methodology for assessing the energy harvesting potential from real seismic vibrations, with a specific focus on high-vibration environments, such as mining areas and urban settings. The proposed framework aims to quantify both the theoretical potential of the vibrational resource, understood as the maximum energy available in the environment, and the technical potential, defined by the current capability of electromagnetic energy harvesters (EMEHs) to capture and convert this energy into usable electrical power.
The developed methodology consists of six main stages: (i) seismic data acquisition, (ii) signal preprocessing, (iii) event identification, (iv) event characterization and classification, (v) device selection, and (vi) dynamic simulation for harvested power estimation. Continuous seismic records are analyzed to detect and isolate energetically relevant events of both natural and anthropogenic origin, including earthquakes, microseisms, blasting activities, and vehicular traffic. These events are characterized in terms of amplitude, frequency content, and duration, providing objective criteria to evaluate their relevance for energy harvesting applications. Representative seismic excitations are subsequently used as non-stationary inputs to a dynamic model of an EMH, enabling the estimation of the harvested power associated with each event type without parameter optimization. This approach allows for a direct comparison between different vibrational sources under realistic operating conditions and highlights the influence of site-specific factors such as local geology, proximity to vibration sources, and spectral characteristics of ground motion.
The application of the proposed framework to a mining environment in northern Chile reveals distinct, yet partially overlapping, ranges of harvestable power across different classes of seismic events. The results demonstrate a strong spatial dependence on the vibrational energy resource and emphasize the necessity of localized assessments when evaluating the feasibility and robustness of vibrational energy harvesting systems. This work contributes a methodological foundation for resource-oriented evaluation, providing quantitative insight into whether seismic vibrations can realistically support low-power applications such as autonomous sensors and monitoring systems.
How to cite: Monsalve, E.: Evaluating Seismic Vibrations as an Energy Resource in Mining and Urban Environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5931, https://doi.org/10.5194/egusphere-egu26-5931, 2026.
The concentration of critical minerals and metals occurs within 200 km of the transition between thick and thin lithosphere(Hoggard et al., 2020). Understanding the mechanisms behind this distribution requires characterizing a variety of deep Earth processes of different scales and nature. Among these processes, mantle melting is a critical initial step, controlling compositions of early melts and to the stability of cratonic lithosphere. These melting processes are governed by complex phase equilibria which determines proportions and compositions of mineral assemblages, depending on pressure, temperature and bulk composition.
We investigate computational strategies for coupling mantle convection codes such as ASPECT with thermodynamic equilibrium calculations tools like MAGEMin. While a direct coupling would provide accurate phase equilibria predictions, it comes at a significant computational cost for large-scale geodynamic models. Our research explores developing surrogate models using machine learning and neural network techniques to approximate these thermodynamic calculations more efficiently.
We present our preliminary research involving methodological approaches and discuss the computational trade-offs involved in different coupling strategies. A simplified geodynamic model demonstrates potential workflows for this approach. This research is a step towards a more integrated computational framework for a thermo-chemical geodynamic model, which will have important implications for modelling critical mineral formation in complex geodynamic settings.
References:
- Hoggard, Mark J., Karol Czarnota, Fred D. Richards, David L. Huston, A. Lynton Jaques, and Sia Ghelichkhan. “Global Distribution of Sediment-Hosted Metals Controlled by Craton Edge Stability.” Nature Geoscience 13, no. 7 (July 2020):504–10.https://doi.org/10.1038/s41561-020-0593-2
How to cite: Chakraborty, A., van Hunen, J., Valentine, A., Zlotnik, S., and García González, A.: Toward integrated geodynamic-petrological modelling: coupling ASPECT with thermodynamic calculations , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9853, https://doi.org/10.5194/egusphere-egu26-9853, 2026.
The identification of hydro-mechanical parameters governing earthfill dam behaviour under transient loading conditions is essential for reliable interpretation of monitoring data and predictive analysis. Although coupled flow–deformation models can represent these processes in detail, their direct use in inverse analyses is often prohibitive due to the large number of forward simulations required. This work addresses the efficient estimation of material parameters in earthfill dams by integrating a reduced-order formulation of the problem into an inverse strategy.
A transient, nonlinear hydro-mechanical model for unsaturated soils is considered in the context of a sensor-driven inverse problem, where piezometric measurements are used to constrain model parameters. Reduced-order models based on proper orthogonal decomposition (POD) are introduced to enable repeated model evaluations within the inversion procedure while retaining the key features of the hydro-mechanical response. The framework targets the estimation of relevant soil properties, such as hydraulic conductivity, water retention characteristics, and mechanical stiffness, and is illustrated using both synthetic observations and field piezometer data from the Glen Shira dam during rapid drawdown events.
REFERENCES
[1] Pinyol, N. M., Alonso, E. E., Olivella, S. (2008). Rapid drawdown in slopes and embankments. Water Resources Resarch, 44(5). doi: 10.1029/2007WR006525
[2] Nasika, C., Díez, P., Gerard, P., Massart, T.J., Zlotnik, S. (2022). Towards real time assessment of earthfill dams via Model Order Reduction. Finite Elements in Analysis & Design, 199: 103666. doi: 10.1016/j.finel.2021.103666
How to cite: Zlotnik, S., Schrader, J., Beatrice, J., García González, A., and Muixí, A.: Hydro-mechanical parameter estimation in earthfill dams using reduced-order models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13600, https://doi.org/10.5194/egusphere-egu26-13600, 2026.
The reliable assessment of tailings dam response under transient hydro-mechanical loading is a key challenge for mining infrastructure safety and risk management. High-fidelity numerical models capable of representing coupled groundwater flow and deformation in partially saturated soils provide valuable insight into internal states of the dam, but their computational demands often limit their use in operational settings, such as scenario analysis or near–real-time monitoring.
We consider a transient, nonlinear hydro-mechanical finite element model describing groundwater flow in unsaturated soils and apply a proper orthogonal decomposition (POD)–based reduced-basis formulation to accelerate simulations. While POD effectively reduces the number of unknowns, the computational cost of assembling nonlinear operators remains tied to the full-order mesh dimension, limiting efficiency gains. To address this bottleneck, hyper-reduction techniques are investigated that construct reduced approximation spaces for the nonlinear terms themselves, with the goal of alleviating computational cost relative to standard full-order finite element simulations.
How to cite: Muixí, A., Monforte, L., García-González, A., and Zlotnik, S.: Hyper-reduced POD formulation for the hydro-mechanical assessment of tailings dams, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13610, https://doi.org/10.5194/egusphere-egu26-13610, 2026.
Earth imaging is central to our ability to understand our planet and is important for exploration of critical minerals, geothermal energy resources detection, and mitigation of natural hazards such as earthquakes and the study of plate tectonics. As a result, there is a need for more precise images of the earth’s interior. However, as this imaging process is ill-posed and lossy, the images obtained are inevitably a blurry version of the truth. This makes it challenging to robustly interpret results and draw inferences about geophysical systems.
The full waveform inversion (FWI) has been the state-of -the-art for high-fidelity and physically consistent subsurface imaging, however, its computational expense has driven exploration into machine learning (ML) techniques. These data-driven ML techniques can perform seismic inversion, directly mapping seismic data to subsurface properties without executing the iterative physics modelling loop of FWI. While their success is highly dependent on the availability of comprehensive, high-quality training data, they have proven capable of delivering subsurface predictions orders of magnitude faster than traditional methods.
In our attempt to obtain physically consistent subsurface images while ensuring cheap inferences, we will explore opportunities for ‘seismic super-resolution’: generation of higher-resolution images by combining observed data with prior knowledge about likely structures and the physics of wave propagation. Our approach involves the combination of machine learning techniques for numerical upscaling and physics – informed neural networks ensuring that the underlying laws of physics are embedded within results.
In this presentation, we will highlight some of the challenges and opportunities in this approach
and present some early results from numerical experiments.
How to cite: Mahmud, M. O., Valentine, A. P., Reinarz, A. K., and Hunen, J. V.: Seismic Super-resolution Leveraging Machine Learning Techniques , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15494, https://doi.org/10.5194/egusphere-egu26-15494, 2026.
In mineral exploration, on-site analytical techniques provide tools for real-time data acquisition, supporting informed decision-making. Portable instruments such as handheld X-ray fluorescence (pXRF) and short-wave infrared (SWIR) hyperspectral spectrometers enable rapid, non-destructive collection of geochemical and mineralogical information directly from drill cores. When effectively integrated and interpreted, these datasets offer powerful tools for advancing geological understanding and refining 3D models, ultimately improving vectoring toward mineralization and supporting more efficient, sustainable exploration
Where traditional interpretation methods are often subjective and time-consuming, data-driven approaches, particularly machine learning, can identify patterns and correlations within large datasets, accelerating analysis. In this study, we propose a machine learning framework for fusing drill-core hyperspectral and geochemical point data to enhance geological modeling.
Methodologies were applied and tested in two gold target sites hosted by an Archean Ilomantsi Greenstone Belt in eastern Finland. The geology at the selected sites is dominated by visually homogeneous schistose metasediments exhibiting intense sericite–chlorite alteration. Hence, these target areas provide an ideal natural environment for evaluating machine-learning approaches aimed at refining lithological and lithogeochemical discrimination and alteration mineralogy interpretations. The data-fusion and predictive modeling approach has the potential to significantly extend the data-driven geological models in 3D to enhance geological understanding and controls of the Au mineralization.
Lithogeochemical data were first partitioned into distinct compositional groups using the K-means clustering algorithm. The resulting cluster assignments served as training labels for a supervised learning framework aimed at linking geochemical classes to hyperspectral signatures. Selected SWIR spectral parameters corresponding to geochemical sampling points, together with their assigned labels, were used to train a Random Forest (RT) classifier. The trained model was applied to unclassified spectral data to infer lithogeochemical classes to produce a predictive model.
Despite the generally noisy nature of both pXRF and spectral point data and overall, rather poor probability measures of the RT model (< 50% for most classes), in 3D, a clear and spatially reasonable model is produced. Along-strike continuation of lithogeochemical stratigraphy provides a validation argument supporting the success of the predictive model beyond areas with both lithogeochemical and hyperspectral data.
This approach leverages existing drill holes in a fast and cost-efficient manner by utilizing portable data-acquisition technologies. Machine-learning-based integration of multi-sourced datasets is demonstrated to improve lithological/lithogeochemical discrimination and predict subsurface geological features. This aids in the delineation of drilling targets more accurately, supporting dynamic, data-driven decision-making in mineral exploration.
How to cite: Luolavirta, K. and Ojala, J.: Machine learning framework for the integration of drill-core hyperspectral and geochemical point data to enhance geological modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19052, https://doi.org/10.5194/egusphere-egu26-19052, 2026.
Rare-earth elements (REE) form indispensable components of daily life, as they are essential constituents of the modern high-technology applications, including clean energy, high-tech electronics, and ultimately to achieve the sustainable development goals of the United Nations. With a growth rate of approximately 10–15% per year, the demand for REE has been increased significantly. However, production and supply chains of REEs are very limited, especially due to the rare occurrences and/or discoveries of REE-enriched deposits. It also invokes an alarming situation, since the REE industry is largely controlled by a small number of countries across the globe, with one holding the dominant position in both mining and processing. Consequently, there is an increasing interest in the REE exploration studies across the globe for finding out new potential sources.
Granitic pegmatites are considered as important sources of rare metals, such as REEs, and other high-field strength elements (HSFE) such as U, Th, Y, Zr, Hf, Nb, Ta and large-ion lithophile element (LILE) such as Li, Rb, and Cs. Here, we report the occurrence of rare-metal granitic pegmatites associated with alkaline granite complex of Munnar in the southern Indian shield. The mineralized pegmatites are intruded along and across the shear planes of granites. The pegmatites are composed of quartz, K-feldspar, plagioclase, biotite and muscovite. Several veins also contain magnetite, pyrite and pyrrhotite. They are characterized by high ΣREEs contents ranging from 1318 ppm to 7682 (avge. 3992 ppm). The chondrite-normalized REE patterns of the pegmatites are characterized by a strong enrichment of LREE over HREE, with a (La/Yb)N ratio between 42 and 1000, with characteristic negative Eu anomalies. The ΣREE of host granites ranges between118 and 6502 ppm. The REE patterns of the pegmatites suggest that the pegmatites are formed from LREE enriched melt, generated possibly during the shearing of host granitic rock. During this process the incompatible REEs are concentrated in the melt causing LREE enrichment, which eventually intruded into the lower curst as granitic pegmatites. This indicates enhanced mobility of REE during alteration of host granites. Thus, the study imposes important insights into the sources and enrichment mechanisms of REEs in the parent rocks as well as their remobilization during alteration processes forming ion-adsorption REE deposits in their weathered crusts.
How to cite: Chettootty, S., Sivankutty, R., and Vasundharan, K.: Rare earth element (REE) enriched granitic pegmatites associated with alkaline granite complex of southern India: Source characteristics, enrichment mechanisms, and insights into potential ion-adsorption REE deposits, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-790, https://doi.org/10.5194/egusphere-egu26-790, 2026.
