PICO
At the same time, rapid advances in imaging, microscopy, and monitoring techniques are producing large, high-dimensional datasets across a wide range of scales and modalities. Visualisation and correlation methods are therefore becoming central to the modelling workflow, enabling meaningful comparisons between simulations, laboratory experiments, and natural observations, and facilitating the identification of patterns, structures, and emergent behaviour in complex magmatic systems.
This session brings together computational modelling, visualisation, and data correlation approaches applied to volcanic and magmatic processes across the GMPV domain. We invite contributions that develop, apply, or validate forward and inverse models, machine learning techniques, and other computational methods. We also welcome work demonstrating advanced 2D, 3D, and 4D visualisation, multiscale data integration, and cross-technique correlation, particularly where these approaches bridge scales and connect models with observations and experiments.
The session aims to provide a platform for in-depth technical exchange between researchers working on modelling, data analysis, and visualisation, strengthening links between computational, experimental, and observational communities within GMPV.
PICO: Mon, 4 May, 16:15–18:00 | PICO spot 2
Understanding how pre-eruptive processes manifest in geophysical observables remains a central challenge in volcanology and volcanic hazard assessment. Among these observables, seismic tremor, a persistent ground vibration commonly recorded at active volcanoes, holds strong potential for eruption forecasting, yet its temporal evolution is notoriously difficult to interpret. Bridging tremor observations with eruption forecasting therefore requires computational frameworks that explicitly link tremor characteristics to the degree of volcanic unrest and the likelihood of eruption. Here, we present two complementary computational frameworks for eruption forecasting from continuous seismic tremor data that integrate physics-based forward modeling, inverse methods, and machine learning. Both approaches are tested using the 13 paroxysms of Shishaldin Volcano (Alaska) that occurred between July and November 2023. The first framework is physics-informed and relies on data assimilation to invert tremor observations and retrieve subsurface pressure evolution. It couples a physical model of tremor generation, rooted in multiphase gas accumulation and porous-media flow within the upper conduit, with genetic algorithm optimization and Monte Carlo simulations. This approach captures the effects of magma ascent, volatile exsolution, partial conduit sealing, and gas transport on transient tremor signals, revealing pressure increases of several MPa and a systematic rise in eruption probability hours before each paroxysm. The second framework is data-driven and applies pattern-recognition techniques to extract physically motivated seismic features (e.g., dominant frequency, amplitude, kurtosis, entropy), which are combined with a supervised machine-learning classifier (random forest) to estimate eruption probabilities. Despite their differing philosophies, both frameworks consistently relate pre-eruptive tremor evolution to probabilistic eruption forecasts. Together, these results demonstrate how computational approaches can enhance the interpretation of seismic tremor, provide quantitative insight into magma–volatile interactions, and advance eruption forecasting and volcanic hazard assessment strategies.
How to cite: Girona, T., Fee, D., Burgos Delgado, V., Haney, M., Power, J., and Lopez, T.: Physics-informed and data-driven eruption forecasting from seismic tremor, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16947, https://doi.org/10.5194/egusphere-egu26-16947, 2026.
Surface deformation measured from satellites has provided useful information about the magma plumbing system at active volcanoes. Observed deformation results from complex interactions and coupling between the magma and the host rock. Fracturing of the crust during its deformation can make the pattern of surface displacement even more complex. Models taking into account both the fluid and solid phases of natural systems and linking them are a crucial next step for a better understanding of natural systems and observed deformation. We use the software MFiX (Multiphase Flow with Interphase eXchanges) which considers two phases: a fluid phase computed with a Computational Fluid Dynamics (CFD) method, and a solid phase discretized as spherical particles computed using Discrete Element Methods (DEM) method. Spherical particles are bonded together. Bonds can break at any time step, such that actual fractures can develop through the simulations. We present here the modified drag force between fluid and particles that allows us to model a bonded packing of particles impermeable to a fluid phase. Reproducing a set of analogue experiments, we simulate the injection of fluid in a spherical cavity. Rock tests implemented in MFiX allow us the precise calibration of the packing to the gelatine mechanical properties. The injected volume, the cavity dilatation, the fluid pressure evolution and the surface deformation are measured in the numerical modelling and compared to analogue experiment for benchmarking. We show that this new model has the potential to model the magmatic phase and coupling it to the elastic and brittle deformation of the surrounding rock.
How to cite: Morand, A., Burgisser, A., Rust, A., Zmajkovic, G., and Biggs, J.: Coupling a fluid phase with a discretised solid phase: Benchmarking a Computational Fluid Dynamics-Discrete Element Methods (CFD-DEM) model with analogue experiments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10820, https://doi.org/10.5194/egusphere-egu26-10820, 2026.
Magma bodies play a critical role in Earth's geological evolution across a wide range of scales from local-scale volcanic activity to crustal-scale petrogenesis, and planetary-scale magma ocean solidification. The internal flow dynamics of melt-dominated magma bodies are dominated by crystal-driven convection where flow is driven by the significant density contrast between crystalline solid phases and their carrier melt. The same density difference can also cause crystals to settle/float and sediment into cumulate/flotation layers with important implications for the compositional and structural evolution of magma bodies and resulting igneous rocks.
As magma bodies range in size from metre-scale crustal chambers to thousand kilometre-scale planetary magma oceans, the resulting dynamics cover a wide range of flow regimes. Here we present the mathematical derivation, scaling analysis, and two-dimensional numerical implementation of a model for crystal settling and crystal-driven convection with a focus on two characteristic length-scales: the crystal size governing crystal settling relative to the magma, and the layer depth governing the convective vigour of the magma as a particulate suspension.
We adapt standard approaches from particle sedimentation and turbulent flow theories to produce a model framework which treats the magmatic suspension as a continuum mixture fluid applicable across the entire range of relevant crystal sizes and layer depths. As mixture continuum models resolve dynamics at the system scale, some critical aspects of local scale dynamics remain unresolved. Here, we focus on two: the fluctuating motion of particles during sedimentation, and the development of eddies cascading down to small scales in turbulent convection. Our continuum model represents both processes by an effective diffusivity, i.e., the settling and eddy diffusivities, which enhance mixing. Two random noise flux fields are then added proportional to these diffusivities to reintroduce some stochasticity which is lost by not resolving the underlying fluctuating processes. Whereas this type of treatment based on statistical mechanics has long been adopted in general fluid mechanics, it has not received much attention in geodynamic modelling.
We find that crystal size matters most in 1–10 m crustal magma bodies where the crystal settling speed comes to within one to two orders of magnitude of the convective speed and the settling diffusivity is dominant. For moderately sized (>10–100 m) crustal magma bodies up to planetary-sized magma oceans laminar to turbulent convection regimes dominate where the flow behaviour converges towards that of a single fluid with crystallinity behaving as a buoyancy-carrying scalar field like temperature or chemical concentration with eddy diffusivity dominating over settling diffusivity. Whereas our model does not consider thermo-chemical evolution and phase change we expect similar behaviours to pertain to fully coupled thermo-chemical-mechanical magma flow problems.
How to cite: Keller, T. and Aellig, P.: Modelling crystal settling and crystal-driven convection from crustal to planetary scales , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10859, https://doi.org/10.5194/egusphere-egu26-10859, 2026.
Understanding melt generation, transport, and crust formation within a mid-ocean ridge context is a compelling challenge in geoscience. These systems are indirectly observable, both spatially and temporally, and our current understanding therefore relies on poorly resolved geophysical imaging and geochemical signatures preserved in erupted products. Previous numerical studies incorporating two-phase melt transport have greatly improved our understanding of melt migration and focusing beneath mid-ocean ridges [1,2,3]. However, these models typically simplify the treatment of crustal formation and have a limited ability to make a direct comparison between model predictions and observed mid-ocean ridge basalt (MORB) compositions.
We present a new two-dimensional staggered-grid finite-difference model based on the framework of [3,4]. Implemented in MATLAB, the model is designed to simulate magmatic systems at mid-ocean ridges. The model solves fully compressible solid-state mantle flow coupled to two-phase melt transport and includes a novel multi-component model of mantle melting and crust formation.
A key advance of this framework is an in-situ melt extraction and crust formation algorithm that conserves mass and enables the development of a crustal layer along the seafloor rather than artificially removing melt from the ridge axis as most previous models do. The model further includes a multi-component model of major, trace, and isotopic composition to understand petrogenesis and geochemical evolution through melt production, focusing, and extraction. This allows for a more detailed comparison with real-world geochemical datasets.
The petrogenesis component of the model is calibrated to allow for the prediction of MORB compositions based on the underlying physical dynamics. This enables us to test the sensitivity of crustal production and composition to variations in physical parameters such as spreading rate, mantle potential temperature, mantle composition, and mantle rheology. Additionally, it allows us to assess whether different melt focusing end members from active to passive flow regimes result in a detectable geochemical signature.
The primary aim of this work is to develop a flexible modelling framework that can be used to explore the parameter space governing passive and active melt focusing and understand how mantle and melt dynamic regimes are expressed in petrological and geochemical observables.
[1] Katz, 2008: https://doi.org/10.1093/petrology/egn058
[2] Katz, 2010: https://doi.org/10.1029/2010GC003282
[3] Keller et al., 2017: https://doi.org/10.1016/j.epsl.2017.02.006
[4] Keller and Suckale, 2019: https://doi.org/10.1093/gji/ggz287
How to cite: Swan, S., Keller, T., Keir, D., and Gernon, T.: A Two-Phase, Multi-Component Geochemical Model of Mid-Ocean Ridge Magmatism, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13045, https://doi.org/10.5194/egusphere-egu26-13045, 2026.
Partial melting occurs in the upwelling mantle due to adiabatic decompression, and melt is thought to be transported through a channelized network formed by reaction-infiltration instability. Earlier studies of melt channelization primarily focused on melt transport while neglecting the melt production process, whereas recent models that incorporate decompression melting argue that adiabatic melting stabilizes reactive flow and suppresses channel formation. Therefore, how reactive flow interacts with decompression melting remains poorly understood for the mantle melt transport problem.
To better understand this problem, we present a two-phase flow model in an upwelling, compacting, and chemically reactive medium, based on conservation of mass, momentum, and composition for a solid-melt system. The mass transfer rate from solid to melt includes contributions from both chemical reaction and adiabatic decompression melting. Using this framework, we first derive a vertical, one-dimensional steady-state melting model. We then introduce small perturbations to this base state and perform two-dimensional, time-dependent simulations. The results demonstrate that significant melt channelization can occur in the presence of melting driven by adiabatic decompression.
We further explore the evolution of magmatic channels across parameter space and identify the key controls on this behaviour. In particular, we find that the porosity-dependent bulk viscosity, which controls the solid compaction, is a key stabilizing mechanism in the system. We analyse the balance between reactive melting and compaction associated with decompression melting, and explore the parameter regime under which melt channelization may occur in the mid-ocean ridge system dominated by decompression melting.
How to cite: Huang, M., Rudge, J., and Rees Jones, D.: Reactive melt channelization in an upwelling mantle, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15118, https://doi.org/10.5194/egusphere-egu26-15118, 2026.
Numerical modelling is an essential approach for investigating the rheological, thermal, and dynamical processes that control lava flow behaviour. In this study, we present a numerical analysis of lava flows emplaced during the 6–8 December 2015 eruption of Mount Etna, employing a shallow-water-approximation model solved using a finite-volume method. We assess the influence of temperature-dependent, as opposed to isothermal, Newtonian, Bingham, and Herschel–Bulkley rheologies on lava flow morphology, together with the effects of discharge-rate variability, vent location, and the post-eruption phase of flow propagation. The results demonstrate that temperature plays a dominant role in governing lava flow advancement. Thermal Newtonian and Bingham models successfully reproduce the observed flow dynamics and runout distances, whereas the nonlinear Herschel–Bulkley model, with a temperature-dependent power-law index, underestimates the flow extent. Simulated thickness distributions closely agree with field observations, accurately capturing lava accumulation near the vent and at the flow front. By contrast, isothermal models significantly overestimate lateral spreading and fail to replicate the observed emplacement patterns. Post-eruption simulations indicate that cooling controls lava flow evolution following the cessation of effusion, resulting in increased viscosity, flow starvation, and eventual arrest. Sensitivity analyses further reveal that small variations in vent position and discharge-rate distribution can substantially alter lava flow pathways.
How to cite: Ismail-Zadeh, A., Zeinalova, N., and Tsepelev, I.: Numerical modeling of lava flows at Mount Etna: Influence of lava rheology on flow morphology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5134, https://doi.org/10.5194/egusphere-egu26-5134, 2026.
Mafic magmatic enclaves are common in silicic magmatic systems and often signal recharge of shallowly stored magma with basaltic magma from depth. They are associated with volcanic eruption triggers and help sustain shallow magma systems. After formation, enclaves may settle, erupt, or remain mobile, but their fate is mostly unknown. Textures like glassy rims and high crystallinity reflect their response to mixing and flow. Convective motion can disrupt boundaries between magmas, and over time, the magma body can hybridize through diffusion and mechanical breakdown.
This study investigates how mechanical disintegration affects the survival of mafic enclaves during mixing. The enclave interface can erode as crystals are plucked away by fluid-solid interactions, gradually shrinking the enclave. We use a new numerical model (LBM-DEM) to simulate the mechanical response of crystals at the enclave boundary and explore how these interactions influence the rate of enclave breakup.
Our simulations show that at high viscosities, the breakup process becomes independent of viscosity. Instead, fluid influx and the initial position of crystals mainly control the rate of enclave disintegration.
How to cite: Scheel, J., Gardner, M., and Ruprecht, P.: Plucked Apart: Grain-Scale Mechanics of Mafic Enclave Disintegration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6173, https://doi.org/10.5194/egusphere-egu26-6173, 2026.
Volcanic eruptions are complex processes involving multiple interacting phases, such as ascending magma, exsolved gases, deformation of the host rock and atmospheric dynamics. Typically, numerical models treat the sub-aerial eruptive column and the subsurface rock deformation as distinct domains due to the different timescales and material properties involved. This study presents a 2D numerical framework that couples the propagation of atmospheric waves with the elastic deformation of the host rock via a unified formulation. Using a finite volume method to solve the conservative form of the mass and momentum equations on a staggered grid, we demonstrate that this formulation can correctly predict the localisation of shock waves in the atmosphere, as well as the propagation of elastic waves in the host rock. Furthermore, we show that a single discretisation can capture both the conversion of acoustic waves into elastic waves from the atmosphere to the host rock, and the reverse process. This provides a foundation for fully coupled models of explosive volcanic events to potentially offers new insights into the interaction between the subsurface and the atmosphere during these processes.
How to cite: Dominguez, H., Kaus, B., Ranocha, H., Moulas, E., and Utkin, I.: Modelling volcanic eruptions from the volcano to the atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19176, https://doi.org/10.5194/egusphere-egu26-19176, 2026.
A broad range of microscopy tools and imaging techniques is available for studying geoscientific samples. Often, multiple imaging datasets are correlated to connect chemical and/or physical information to investigate complex systems. However, combining datasets obtained from different imaging techniques remains challenging. They often cannot be directly matched due to differences in resolution, scale, or instrument calibration.
One solution is the application of markers on samples. Several techniques exist for applying markers on the surface of polished geoscientific samples, such as thin sections. These markers can be used during sample handling to identify the area of interest and ensure reproducible sample placement. After data acquisition, they enable accurate scaling and co-registration of different imaging datasets during data processing. Marker application techniques range from accessible, simple, and cost-effective approaches to more complex, specialized, and expensive methods, depending on the intended purpose.
I will provide a brief overview of the available techniques and highlight the use of microlithography on thin sections, a technique that enables writing nano- to microsized symbols on sample surfaces. These markers provide a practical solution for simplifying the correlation of multiple datasets and support a deeper understanding in geoscientific research.
How to cite: de Boer, R., Wielens, D., and de Groot, L.: The challenge of correlating imaging datasets in geoscience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19574, https://doi.org/10.5194/egusphere-egu26-19574, 2026.
The EXCITE² Network
Seléne van der Poel, Geertje W. ter Maat, Oliver Plümper, Richard J.F. Wessels & the EXCITE team
The EXCITE² Network is transforming Earth and environmental material science with transnational access to 40 worldclass European imaging facilities in 22 research institutes across 14 European and partner countries. Researchers anywhere can now explore complex processes in Earth materials across scales ranging from nanometers to decimeters. This yields unprecedented insights into critical areas such as environmental toxicity and human health, sustainable extraction of critical metals for renewable energy, and safe long-term storage of climate-relevant gases.
EXCITE² also brings together expertise and pioneers innovative services, tools, and training, to enhance the ability of users to address complex scientific challenges. The ‘EXCITE Academy’ offers an open community and collaborative platform for sharing knowledge, tools, experiences and expertise though monthly EXCITE Academy Webinars, live events and the online searchable database ‘Academy Hub’. Innovative services and tools include AI-driven data analysis and next-generation imaging technologies.
By fostering interdisciplinary collaboration between academia, industry, and diverse scientific fields, EXCITE² accelerates innovation and strengthens Europe's position in global sustainability efforts. The initiative actively supports capacity building through tailored training programs for early-career researchers, fully embedded within the principles of European open science.
Through its commitment to scientific excellence, sustainability, and societal impact, EXCITE² is shaping the future of Earth and environmental research. Interested in joining the network? Apply for transnational access via our open call! Visit the EXCITE² website (https://excite-network.eu) for more information.
How to cite: van der Poel, S.: The EXCITE² Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21832, https://doi.org/10.5194/egusphere-egu26-21832, 2026.
Fluid-induced mineral replacement reactions play a key role in controlling porosity generation and permeability evolution in geologic systems. However, the dynamic feedback between pore structure development and fluid transport remains poorly quantified. This study investigates the spatiotemporal evolution of reaction-induced pore space in the fluid-driven KBr–KCl system using time-resolved synchrotron X-ray tomography. Due to its high solubility and rapid reaction kinetics, the KBr–KCl system serves as an effective analogue for fluid–rock interactions in natural settings. We performed two operando experiments at the TOMCAT beamline (Swiss Light Source): one with direct KCl solution flow over a KBr crystal, and another using a pressurized X-ray-transparent cell. Machine-learning-based segmentation enabled quantitative analysis of porosity evolution through spatiotemporal correlation functions and transport property estimation. We identified a three-stage pore evolution process: (1) rapid pore channel formation along crystallographic axes with high reaction rates and a rough interface; (2) a transitional stage characterised by smoother interfaces and enhanced lateral connectivity; and (3) a steady-state regime where permeability continues to increase due to pore coarsening and reduced tortuosity. These results advance our quantitative understanding of how reaction-induced porosity governs dynamic fluid–rock interactions.
How to cite: Amiri, H., Dialeismas, V., Freitas, D., Rizzo, R., Fusseis, F., and Pleumper, O.: Quantifying Reaction-Induced Porosity During KBr–KCl Replacement: 4D Synchrotron Tomography and Statistical Microstructure Descriptors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5065, https://doi.org/10.5194/egusphere-egu26-5065, 2026.
The 3.46- 3.1 Ga Dwalile Supracrustal Suite (DSS) of the Ancient Gneiss Complex in Eswatini constitutes one of the world’s oldest greenstone belts, recording a prolonged crustal evolution from the Palaeoarchaean to Mesoarchaean. Archaean metasediments are commonly poorly preserved, with matrix minerals frequently altered or no longer in equilibrium with garnet porphyroblasts due to superimposed metamorphic events. Consequently, garnet textures, when integrated with petrological observations and both major- and trace-element geochemistry, may provide valuable insights into the entire metamorphic history.
Garnet-staurolite schists of the DSS mainly differ in their garnet and staurolite modes and their unusual garnet microstructures. In some samples, the almandine garnets are distributed as thin boudinaged layers consisting of elongated ribbons, with local resorption textures and peninsular features surrounded by coarse recrystallised quartz. The euhedral garnet cores are only visible in compositional maps. Other schists consist of staurolite-mica rich layers intertwined with garnetite layers containing almandine garnet.
The complexity of these garnet grains cannot be adequately captured by spot analyses using techniques such as electron probe microanalysis (EMPA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Instead, the polyphase nature of the microstructures is investigated by a multi-scale, multi-modal imaging approach that integrates complementary techniques, including X-ray micro–computed tomography for three-dimensional structural information and electron backscattered diffraction, EMPA major element, and LA-ICP-MS trace element mapping.
The EBSD maps show distinct microsctructural differences between the samples. Many of the garnetite porphyroblasts are consisting of polycrystals with distinct crystal orientations, evidence for aggregation due to pervasive fluid influx which has accelerated garnet nucleation. Whereas, the garnet banding surrounding older euhedral cores often show the same preferred orientation as the cores themselves, but distinct differences in orientation occur between individual cores and between sections of the garnet banding. This may be the result of accelerated garnet growth due to channelled fluid flow during metamorphism.
The garnet growth is mainly associated with amphibolite-facies metamorphism recorded by monazite at ca. 3.16 Ga, at maximum pressures of ~4 kbar and temperatures of 510–540 °C. However, to better resolve the complexity of the microstructures, additional geochronology targeting distinct garnet generations and other mineral phases associated with fluid activity may be necessary.
How to cite: van Schijndel, V., Stevens, G., Hoffmann, E. J., Günter, C., Plümper, O., and Amiri, H.: Visualising Garnets: Linking complex microstructures through a multi-modal approach to reveal metamorphic history, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10604, https://doi.org/10.5194/egusphere-egu26-10604, 2026.
Computational approaches in geochemistry are increasingly central to advancing our understanding of magmatic and volcanic systems as well as general Earth System processes. These methods rely on the integration of heterogeneous geochemical datasets spanning multiple spatial and temporal scales, analytical techniques, and material types. However, the effective reuse of such data remains limited by inconsistent metadata, ambiguous terminology, and insufficient interoperability between major geochemical data resources.
The Digital Geochemical Data Infrastructure (DIGIS) addresses these challenges as part of the "OneGeochemistry Initiative" by modernizing and integrating two foundational geochemical databases: GEOROC (Geochemistry of Rocks of the Oceans and Continents) and GeoReM (Geological and Environmental Reference Materials). GEOROC and other databases provided to the community through the EarthChem Portal provide open access to millions of geochemical analyses of igneous and metamorphic rocks, minerals, and glasses, while GeoReM curates critically evaluated data on reference materials used for calibration, quality control, and uncertainty assessment in geoanalytical laboratories worldwide. Re-establishing and strengthening interoperability between these complementary resources is essential for computational studies that require traceable, reproducible, and quantitatively robust input data.
This effort requires development and implementation of shared, machine-readable controlled vocabularies covering sample descriptions, lithology, mineralogy, geological setting, analytes, material matrices, methods, and reference materials. These vocabularies harmonize legacy data in GEOROC and GeoReM, while remaining compatible with international data standards developed by the OneGeochemistry Initiative. By linking observational data and rich metadata, the integrated system enables more flexible data filtering, uncertainty-aware model input, and reproducible benchmarking of computational results.
Recent computational studies illustrate the scientific value of such harmonized geochemical data infrastructures. Machine-learning approaches have successfully leveraged large global GEOROC data compilations to quantitatively discriminate tectono-magmatic settings and extract compositional features related to magma generation and evolution. Combining volcanic eruption histories with interoperable GEOROC and PetDB datasets from the EarthChem portal has further enabled data-driven exploration of magma compositional variability across tectonic environments. In parallel, emerging machine-learning-based petrological models, such as thermobarometers trained on large, standardized compositional melt and mineral datasets, demonstrate how consistent geochemical input data are critical for inferring magma storage conditions and differentiation.
This contribution highlights how sustained investment in FAIR-aligned geochemical data infrastructures directly support advances in computational magmatic studies. By improving interoperability of international geochemical databases, such as GEOROC and GeoReM, through controlled vocabularies, we provide a foundation for computational volcanic and magmatic studies, uncertainty-aware analysis, and quantitative modelling.
How to cite: Kallas, L., Traun, M. K., Renno, A. D., Garbe-Schönberg, D., Sarbas, B., Sturm, A., Möller-McNett, S., Kurzawe, D., Willbold, M., Lehnert, K., and Wörner, G.: Interoperable Geochemical Data Infrastructures for Computational Magmatic Studies through Controlled Vocabularies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15083, https://doi.org/10.5194/egusphere-egu26-15083, 2026.
Mineral phase information derived from scanning electron microscopy (SEM) combined with energy-dispersive spectroscopy (EDS) is commonly restricted to selected imaged areas, while large parts of a sample remain unmapped. The main challenge is to predict mineral phase information from the locally measured EDS regions to the full sample surface, relying on BSE imaging that can cover the entire sample because of its short acquisition times. In this study, we analyze three distinct lithologies - granite, marl (Muschelkalk), and sandstone (Bundsandstein) - using the MaPro software (Jiang et al., 2022). MaPro applies a physics-informed decision tree to analyze EDS data in conjunction with high-resolution backscattered electron (BSE) data for each lithology. After thresholding, mineral phases are segmented from the EDS maps, generating pixel-based phase maps that are used as ground truth for subsequent predictions. In comparison with the original EDS data, the ground truth allows pixel-wise phase analysis, which is essential for subsequent data processing. A random forest–based machine learning (ML) model was trained using MaPro phase analyses to predict phases across broader sample areas. The predicted phase distributions show very good agreement with the MaPro ground truth. Prediction accuracy is higher for relatively homogeneous lithologies such as sandstone and granite, and decreases for a more heterogeneous sample such as the marl. The fine-grained domains produce the largest errors in the MaPro analysis and, consequently, in the ML predictions. In these areas, mineral phases with similar compositions are more difficult for the ML classifier to distinguish and therefore require more ground-truth data than compositionally distinct phases. The results enable a reliable assessment of mineral phases across the entire sandstone sample and across large areas of the granite and marl samples, achieving extensive coverage with short analytical times.
How to cite: Schmitz, J., Schmatz, J., Jiang, M., Wellmann, E., Weiler, M., Hawemann, F., and Toy, V.: Filling the Gaps: Machine Learning Prediction of Sparse Mineral Phase Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17364, https://doi.org/10.5194/egusphere-egu26-17364, 2026.
X-ray micro-computed tomography (micro-CT) has become a widely used non-destructive technique in geosciences for three-dimensional visualization and quantitative analysis of geomaterials. However, in laboratory-based systems, spatial resolution is constrained by a trade-off between sample size, X-ray flux, and focal spot size, with the highest achievable resolutions typically in the micrometer range. In addition, near-surface regions are often affected by imaging artifacts such as beam hardening, cone-beam artifacts, and partial volume effects, which complicate accurate surface characterization. This constraint is particularly significant because many key physical and chemical processes are highly sensitive to the details of surface geometry. Surface properties—including roughness and pore morphology—play a critical role in governing fluid flow, chemical reactions, and mechanical behavior in rocks, making precise measurement essential for understanding geomaterials at multiple scales.
High-resolution techniques such as FIB-SEM can provide detailed three-dimensional information, but they are destructive and time-consuming. Synchrotron-based X-ray CT offers a non-destructive alternative with higher spatial resolution, although access to synchrotron facilities is limited. Surface profilometry, particularly when combining confocal microscopy and focus variation microscopy, provides an additional non-destructive and time-efficient approach for acquiring high-resolution three-dimensional surface topography.
This study presents a correlative imaging workflow that integrates laboratory X-ray micro-CT with surface profilometry measurements on Bentheimer sandstone. The micro-CT dataset was acquired at the Ghent University's Center for X-ray Tomography (UGCT) using the CoreTOM (Tescan) with a voxel size of 6.5 μm, while the surface profilometer S neox (Sensofar) achieved a lateral spatial resolution of up to 0.34 μm. The workflow includes data acquisition, registration, and combined multiscale visualization.
The applicability of this approach is demonstrated by comparing surface modifications before and after nano-silica treatment of Bentheimer sandstone. The correlative dataset reveals morphological changes that cannot be resolved by micro-CT alone, including reduced surface roughness and partial infilling of surface-connected pores. At the same time, micro-CT captures complementary information on the penetration depth and spatial distribution of the treatment products. Together, these observations highlight the added value of integrating surface profilometry with micro-CT for quantitative near-surface characterization of geomaterials.
Acknowledgment: This abstract is part of project Fluidcontrol (with project number G065224N) which is financed by Research Foundation–Flanders (FWO). Ghent University's Center for X-ray Tomography (BOF.COR.2022.008) and IOF (project FaCT F2021/IOF-Equip/021) are also acknowledged.
How to cite: Zhang, Z., Ellman, S., Schröer, L., and Cnudde, V.: Correlative X-ray Micro-CT and Surface Profilometry for Multiscale 3D Characterization of Sandstone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1392, https://doi.org/10.5194/egusphere-egu26-1392, 2026.
Abstract: The second member of the Kongdian Formation (Ek2) in the Cangdong Sag, Bohai Bay Basin, China, develops thick organic-rich shale sequences with significant resource exploration potential. However, a systematic understanding of the coupling relationship between shale lithofacies and pore structure remains unclear, hindering in-depth analysis of shale oil enrichment mechanisms.
To clarify the microscopic pore structure characteristics of different shale lithofacies, this study takes the Ek2 shales in the Cangdong Sag as the research subject, the samples were collected from wells GX, G, G1, GD, and GY in the Cangdong Sag. Multiple techniques, including X-ray diffraction (XRD), total organic carbon (TOC) analysis, field emission-scanning electron microscopy (FE-SEM), gas adsorption (N2 and CO2), advanced mineral identification and characterization system (AMICS) mineral quantitative analysis, and focused ion beam-scanning electron microscopy (FIB-SEM) 3-D reconstruction, were employed for multi-scale characterization of the microscopic pore structure.
The results indicate: (1) Five shale lithofacies types are developed in the study area: laminated felsic shale, laminated mixed shale, massive mixed shale, laminated carbonate shale, and massive carbonate shale. (2) Different lithofacies exhibit various reservoir space types, including inorganic pores, organic matter pores, and micro-fractures, with significant differences in pore structure. The dominant pore size range for all shale lithofacies is 2–200 nm, indicating that nanoscale pores serve as the primary contributors to storage capacity. Among them, the laminated felsic shale and laminated mixed shale lithofacies possess larger pore volumes due to the presence of macropores and micro-fractures. The connectivity of organic-rich laminated shale facies is superior to other shale lithofacies. (3) Syngenetic organic matter, interstitial organic matter, and organic matter-clay composites exhibit different morphologies and contact relationships with minerals, leading to differential contributions to pore volume, connectivity, and development. Syngenetic organic matter in high-frequency laminated shales can enhance pore structure. (4) The deposition and evolution of organic matter and mineral components control the modification of the reservoir pore system: the pressure resistance of the felsic mineral framework favors pore preservation; dissolution pores are widely developed in laminated carbonate shale and massive carbonate shale lithofacies, but mineral cementation restricts their porosity and pore connectivity; moderate TOC content and corrosive fluids generated during thermal evolution migrating along lamina interfaces and micro-fracture channels are significant factors causing differences in reservoir properties among different lithofacies.
Keywords: Shale lithofacies; Pore structure; Controlling factors; Second member of Kongdian Formation; Cangdong Sag
How to cite: Feng, G., Chen, S., Yan, J., Zhang, L., and Pu, X.: Lithofacies-Based Analysis of Pore Structure Characteristics and Controlling Factors of Shale Reservoirs: A Case Study of the Second Member of the Kongdian Formation in the Cangdong Sag, Bohai Bay Basin, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1974, https://doi.org/10.5194/egusphere-egu26-1974, 2026.
