CyberSec Research

We present a direct inverse modeling method named SURGIN, a SURrogate-guided Generative INversion framework tailed for subsurface multiphase flow data assimilation. Unlike existing inversion methods that require adaptation for each new observational configuration, SURGIN features a zero-shot conditional generation capability, enabling real-time assimilation of unseen monitoring data without task-specific retraining. Specifically, SURGIN synergistically integrates a U-Net enhanced Fourier Neural Operator (U-FNO) surrogate with a score-based generative model (SGM), framing the conditional generation as a surrogate prediction-guidance process in a Bayesian perspective. Instead of directly learning the conditional generation of geological parameters, an unconditional SGM is first pretrained in a self-supervised manner to capture the geological prior, after which posterior sampling is performed by leveraging a differentiable U-FNO surrogate to enable efficient forward evaluations conditioned on unseen observations. Extensive numerical experiments demonstrate SURGIN's capability to decently infer heterogeneous geological fields and predict spatiotemporal flow dynamics with quantified uncertainty across diverse measurement settings. By unifying generative learning with surrogate-guided Bayesian inference, SURGIN establishes a new paradigm for inverse modeling and uncertainty quantification in parametric functional spaces.

Multidimensional up-down deconvolution effectively eliminates surface-related multiples from ocean-bottom seismic data. Recently, several down-down deconvolution methods have been introduced as attractive alternatives. Whereas multidimensional up-down deconvolution fully accounts for lateral variations of the medium parameters, the underlying theory of some of the down-down deconvolution methods is essentially based on the assumption that the medium is horizontally layered. We derive representations for multidimensional down-down deconvolution for laterally varying media in a uniform manner and discuss the pros and cons of the various methods.

Poroelasticity -- coupled fluid flow and elastic deformation in porous media -- often involves spatially variable permeability, especially in subsurface systems. In such cases, simulations with random permeability fields are widely used for probabilistic analysis, uncertainty quantification, and inverse problems. These simulations require repeated forward solves that are often prohibitively expensive, motivating the development of efficient surrogate models. However, efficient surrogate modeling techniques for poroelasticity with random permeability fields remain scarce. In this study, we propose a surrogate modeling framework based on the deep operator network (DeepONet), a neural architecture designed to learn mappings between infinite-dimensional function spaces. The proposed surrogate model approximates the solution operator that maps random permeability fields to transient poroelastic responses. To enhance predictive accuracy and stability, we integrate three strategies: nondimensionalization of the governing equations, input dimensionality reduction via Karhunen--Lo\'eve expansion, and a two-step training procedure that decouples the optimization of branch and trunk networks. The methodology is evaluated on two benchmark problems in poroelasticity: soil consolidation and ground subsidence induced by groundwater extraction. In both cases, the DeepONet achieves substantial speedup in inference while maintaining high predictive accuracy across a wide range of permeability statistics. These results highlight the potential of the proposed approach as a scalable and efficient surrogate modeling technique for poroelastic systems with random permeability fields.

Sun-induced fluorescence (SIF) as a close remote sensing based proxy for photosynthesis is accepted as a useful measure to remotely monitor vegetation health and gross primary productivity. In this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence. By comparing absolute absorption line depths by means of the corresponding wavelet coefficients, a relative reflectance is retrieved independently of the fluorescence, i.e. without introducing a coupling between reflectance and fluorescence. The fluorescence can then be derived as the remaining offset. This method can be applied to arbitrary chosen wavelength windows in the whole spectral range, such that all the spectral data available is exploited, including the separation into several frequency (i.e. width of absorption lines) levels and without the need of extensive training datasets. At the same time, the assumptions about the reflectance shape are minimal and no spectral shape assumptions are imposed on the fluorescence, which not only avoids biases arising from wrong or differing fluorescence models across different spatial scales and retrieval methods but also allows for the exploration of this spectral shape for different measurement setups. WAFER is tested on a synthetic dataset as well as several diurnal datasets acquired with a field spectrometer (FloX) over an agricultural site. We compare the WAFER method to two established retrieval methods, namely the improved Fraunhofer line discrimination (iFLD) method and spectral fitting method (SFM) and find a good agreement with the added possibility of exploring the true spectral shape of the offset signal and free choice of the retrieval window. (abbreviated)

Submarine cables play a critical role in global internet connectivity, energy transmission, and communication but remain vulnerable to accidental damage and sabotage. Recent incidents in the Baltic Sea highlighted the need for enhanced monitoring to protect this vital infrastructure. Traditional vessel detection methods, such as synthetic aperture radar, video surveillance, and multispectral satellite imagery, face limitations in real-time processing, adverse weather conditions, and coverage range. This paper explores Distributed Acoustic Sensing (DAS) as an alternative by repurposing submarine telecommunication cables as large-scale acoustic sensor arrays. DAS offers continuous real-time monitoring, operates independently of cooperative systems like the "Automatic Identification System" (AIS), being largely unaffected by lighting or weather conditions. However, existing research on DAS for vessel tracking is limited in scale and lacks validation under real-world conditions. To address these gaps, a general and systematic methodology is presented for vessel detection and distance estimation using DAS. Advanced machine learning models are applied to improve detection and localization accuracy in dynamic maritime environments. The approach is evaluated over a continuous ten-day period, covering diverse ship and operational conditions, representing one of the largest-scale DAS-based vessel monitoring studies to date, and for which we release the full evaluation dataset. Results demonstrate DAS as a practical tool for maritime surveillance, with an overall F1-score of over 90% in vessel detection, and a mean average error of 141 m for vessel distance estimation, bridging the gap between experimental research and real-world deployment.

Saltwater Intrusion (SWI) threatens freshwater availability, agriculture, and ecosystem resilience in coastal regions. While sea-level rise (SLR) is a known driver of long-term salinization, the counteracting role of freshwater discharge remains underexamined. Here, we combine long-term observations with numerical modeling and machine learning reconstruction to quantify the buffering capacity of freshwater outflows across the U.S. coastline. In systems such as Delaware Bay and parts of the Gulf and South Atlantic coasts, the salt front has shifted seaward in recent decades, linked to increased discharge, despite SLR over that time period. We show that a 10 - 35% increase in freshwater flow can offset the salinity impact of 0.5 m of SLR, though regional variation is significant. With future discharge trends diverging spatially, SWI responses will be highly uneven. These results highlight the critical role of freshwater management in mitigating salinity risks under climate change, with implications for water resource resilience, coastal planning, and long-term adaptation strategies.

The oceanic mantle lithosphere has considerable potential to store chemically bound water, thereby being an important factor for the deep water cycle. However, the actual extent of hydrous alteration in such mantle rocks is debated. Geodynamic modeling has the potential to directly predict the extent of fluid flow through oceanic lithosphere, and, in turn, the extent of serpentinization. By comparing theory and numerical simulations, we demonstrate that conventional geodynamic models are inherently inconsistent with the physics of brittle deformation, and, as a result, they overestimate the extent of fluid flow during extension. In contrast to the extensive serpentinization often inferred with bending-related processes during subduction, limited serpentinization is consistent with theoretical predictions and geophysical observations.

Rock geophysical properties are widely reported to exhibit non-linear behaviours under low-stress conditions (below 10-20 MPa) before transitioning to the linear elastic stage, primarily due to the closure of microcracks and grain interfaces. Image-based modelling of rock deformation struggles to effectively characterise the microcrack closure effect because of the partial-volume effect, where image voxels are larger than microcracks and contain both pore and solid phases. This study presents a novel method to simulate non-linear rock deformation under elevated stress conditions. The method reconstructs digital rock models by treating partial-volume voxels as transitional phases that incorporate microcracks. By assigning intermediate elastic moduli and assuming that the pore portion within each partial-volume voxel deforms before the remaining solid content, the method employs the finite element method to simulate rock deformation and calculate the porosity of the deformed model. The method is tested on two Bentheimer sandstone models, and the results demonstrate its ability to predict the non-linear changes in porosity and elastic properties as the effective stress increases. This work provides a new pathway for image-based modelling of non-linear rock deformation considering the microcrack closure effect, offering valuable insights into the complex mechanical behaviour of rocks under confinement.

Farmed landscapes provide a natural laboratory to test how management reshapes near-surface hydrodynamics. Combining distributed acoustic sensing with physics-based hydromechanical modeling, we tracked minute-resolution, meter-scale changes across experimental fields with controlled tillage and compaction histories. We find that dynamic capillary effects, rate-dependent suction stresses during wetting and drying, govern transient stiffness and moisture redistribution in disturbed soils, producing sharp post-rain velocity drops from near-surface saturation and large hysteretic velocity rebounds driven by evapotranspiration. By pairing a seismic rainfall proxy with a drainage closure, we invert velocity changes to estimate evapotranspiration, revealing how disturbance alters flux partitioning and storage. These results establish agroseismology as a non-invasive, extendable tool to uncover soil hydromechanics, explain why conventional farming intensifies variability, and provide new constraints for Earth system models, land management, and hazard resilience.

Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approach as a novel paradigm to overcome these limitations, not by creating a new forecaster, but by optimally combining the outputs of existing models. The MoWE model is trained with significantly lower computational resources than the individual experts. Our model employs a Vision Transformer-based gating network that dynamically learns to weight the contributions of multiple "expert" models at each grid point, conditioned on forecast lead time. This approach creates a synthesized deterministic forecast that is more accurate than any individual component in terms of Root Mean Squared Error (RMSE). Our results demonstrate the effectiveness of this method, achieving up to a 10% lower RMSE than the best-performing AI weather model on a 2-day forecast horizon, significantly outperforming individual experts as well as a simple average across experts. This work presents a computationally efficient and scalable strategy to push the state of the art in data-driven weather prediction by making the most out of leading high-quality forecast models.

Full waveform inversion (FWI) is crucial for reconstructing high-resolution subsurface models, but it is often hindered, considering the limited data, by its null space resulting in low-resolution models, and more importantly, by its computational cost, especially if needed for real-time applications. Recent attempts to accelerate FWI using learned wavefield neural operators have shown promise in efficiency and differentiability, but typically suffer from noisy and unstable inversion performance. To address these limitations, we introduce a novel physics-informed FWI framework to enhance the inversion in accuracy while maintaining the efficiency of neural operator-based FWI. Instead of relying only on the L2 norm objective function via automatic differentiation, resulting in noisy model reconstruction, we integrate a physics constraint term in the loss function of FWI, improving the quality of the inverted velocity models. Specifically, starting with an initial model to simulate wavefields and then evaluating the loss over how much the resulting wavefield obeys the physical laws (wave equation) and matches the recorded data, we achieve a reduction in noise and artifacts. Numerical experiments using the OpenFWI and Overthrust models demonstrate our method's superior performance, offering cleaner and more accurate subsurface velocity than vanilla approaches. Considering the efficiency of the approach compared to FWI, this advancement represents a significant step forward in the practical application of FWI for real-time subsurface monitoring.

Inertial waves in fluid regions of planets and stars play an important role in their dynamics and evolution, through energy, heat and angular momentum transport and mixing of chemicals. While inertial wave propagation in flows prescribed by solid-body rotation is well-understood, natural environments are often characterized by convection or zonal flows. In these more realistic configurations, we do not yet understand the propagation of inertial waves or their transport properties. In this work, we focus on the interaction between inertial waves and geostrophic currents, which has thus far only been investigated using ray theory, where the wave length is assumed to be small relative to the length scale of the current, or averaging/statistical approaches. We develop a quasi-two-dimensional analytical model to investigate the reflection and transmission of inertial waves in the presence of a localized geostrophic shear layer of arbitrary width and compare our theoretical findings to a set of numerical simulations. We demonstrate that, in contrast to ray theory predictions, partial reflections occur even in subcritical shear layers and tunnelling with almost total transmission is possible in supercritical shear layers, if the layer is thin compared to the wavelength. That is, supercritical shear layers act as low-pass filters for inertial wave beams allowing the low-wavenumber waves to travel through. Thus, our analytical model allows us to predict interactions between inertial waves and geostrophic shear layers not addressed by ray-based or statistical theories and conceptually understand the behaviour of the full wavefield around and inside such layers.

Groundwater flow in an unconfined aquifer resting on a horizontal impermeable layer with a boundary condition of a rapid increase in the source water level is considered in this work. The newly introduced condition, referred to as the backward power-law head condition, represents a situation where the water level in the adjacent water body increases more rapidly than do conventional problems, which can only represent a situation akin to a traveling wave under rising water level conditions, given its consideration of infinite time. This problem admits the similarity transformation which allows the nonlinear partial differential equation to be converted into a nonlinear ordinary differential equation via the Boltzmann transformation. The reduced boundary value problem is interpreted as the initial value problem for a system of ordinary differential equations (ODE), which can be numerically solved via Shampine's method. The numerical solutions are in good agreement with Kalashinikov's special solution, which is also introduced into the Boussinesq equation. We find that the solution is consistent with the limit of the forward power-law head condition. The new approximate analytical solution and the associated wetting front position are derived by assuming that the solution has the form of quadratic polynomials, which enables the description of the time progression of a real front position. The obtained approximation is compared to Shampine's solution to check the accuracy. Furthermore, the finite element method is applied to the original partial differential equation (PDE), which validates Shampine's solution for use as a benchmark.

Solar energy adoption is critical to achieving net-zero emissions. However, it remains difficult for many industrial and commercial actors to decide on whether they should adopt distributed solar-battery systems, which is largely due to the unavailability of fast, low-cost, and high-resolution irradiance forecasts. Here, we present SunCastNet, a lightweight data-driven forecasting system that provides 0.05$^\circ$, 10-minute resolution predictions of surface solar radiation downwards (SSRD) up to 7 days ahead. SunCastNet, coupled with reinforcement learning (RL) for battery scheduling, reduces operational regret by 76--93\% compared to robust decision making (RDM). In 25-year investment backtests, it enables up to five of ten high-emitting industrial sectors per region to cross the commercial viability threshold of 12\% Internal Rate of Return (IRR). These results show that high-resolution, long-horizon solar forecasts can directly translate into measurable economic gains, supporting near-optimal energy operations and accelerating renewable deployment.

This paper presents a level-set based structural approach for the joint inversion of full-waveform and gravity data. The joint inversion aims to integrate the strengths of full-waveform inversion for high resolution imaging and gravity inversion for detecting density contrasts over extensive regions. Although common studies typically only observe full-waveform inversion assisting gravity inversion, we propose three key points that enable gravity data to complement full-waveform data in the joint inversion. (i) Based on the well-posedness theorem, we consider a volume mass distribution where the density-contrast value is imposed as a priori information, ensuring that the gravity data provide meaningful information. (ii) We utilize a level-set formulation to characterize the shared interface of wave velocity and density functions, connecting multi-physics datasets via the structural similarity of their inversion parameters. (iii) We develop a balanced and decaying weight to regulate the influence of multi-physics datasets during joint inversion. This weight comprises a balanced part that accounts for the differing scales of full-waveform and gravity data, and a decaying part designed to effectively utilize the features and advantages of each dataset.

We investigate the linear onset of thermal convection in rotating spherical shells with a focus on the influence of mechanical boundary conditions and thermal driving modes. Using a spectral method, we determine critical Rayleigh numbers, azimuthal wavenumbers, and oscillation frequencies over a wide range of Prandtl numbers and shell aspect ratios at moderate Ekman numbers. We show that the preferred boundary condition for convective onset depends systematically on both aspect ratio and Prandtl number: for sufficiently thick shells or for large $\text{Pr}$, the Ekman boundary layer at the outer boundary becomes destabilising, so that no-slip boundaries yield a lower $\text{Ra}_c$ than stress-free boundaries. Comparing differential and internal heating, we find that internal heating generally raises $\text{Ra}_c$, shifts the onset to larger wavenumbers and frequencies, and relocates the critical column away from the tangent cylinder. Mixed boundary conditions with no-slip on the inner boundary behave similarly to purely stress-free boundaries, confirming the dominant influence of the outer surface. These results demonstrate that boundary conditions and heating mechanisms play a central role in controlling the onset of convection and should be carefully considered in models of planetary and stellar interiors.

Fault-slip rockbursts, triggered by seismic rupture of nearby or remote faults, constitute a significant geohazard during deep underground excavations. Although these events occur frequently in underground projects, their underlying mechanisms are not yet fully understood. Most studies tacitly assume dynamic stress waves as the main triggering factor, often disregarding the role of coseismic static stress changes associated with fault slip. This paper introduces a novel analytical framework to diagnose both static and dynamic coseismic stress perturbations and quantify their contributions to fault-slip rockburst around a circular tunnel. Building on linear elastic fracture mechanics, seismic source theory, and the Kirsch solution, the model assesses whether coseismically elevated maximum tangential stress on the tunnel boundary under static and dynamic triggering effects is sufficient to induce failure around the tunnel. We extensively test our framework using synthetic case studies that represent typical fault-slip rockburst scenarios. Our results yield a rockburst hazard map that delineates regions of elevated triggering potential in the near-field and far-field of the seismogenic fault, and classify the triggering types as static, dynamic, or dual. We perform a comprehensive parametric sensitivity analysis to investigate how key factors, including seismic source characteristics, rock mass properties, and in-situ stress conditions, influence the spatial distribution of rockburst susceptibility. The model is further applied to a historical fault-slip rockburst event at the Gotthard Base Tunnel, effectively capturing the triggering mechanism of the observed failure. Our research provides a physically grounded and computationally efficient analytical framework with the results carrying significant implications for rockburst hazard assessments during deep underground excavations.

Geomorphometric modeling and mapping of ice-free Antarctic areas can be applied for obtaining new quantitative knowledge about the topography of these unique landscapes and for the further use of morphometric information in Antarctic research. Within the framework of a project of creating a physical geographical thematic scientific reference geomorphometric atlas of ice-free areas of Antarctica, we performed geomorphometric modeling and mapping of five key coastal oases of Enderby Land, East Antarctica. These include, from west to east, the Konovalov Oasis, Thala Hills (Molodezhny and Vecherny Oases), Fyfe Hills, and Howard Hills. As input data, we used five fragments of the Reference Elevation Model of Antarctica (REMA). For the coastal oases and adjacent ice sheet and glaciers, we derived models and maps of eleven, most scientifically important morphometric variables (i.e., slope, aspect, horizontal curvature, vertical curvature, minimal curvature, maximal curvature, catchment area, topographic wetness index, stream power index, total insolation, and wind exposition index). In total, we derived 60 maps in 1:50,000 and 1:75,000 scales. The obtained models and maps describe the coastal oases of Enderby Land in a rigorous, quantitative, and reproducible manner. New morphometric data can be useful for further geological, geomorphological, glaciological, ecological, and hydrological studies of this region.