Geoscience Applications Research Articles

Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network

Recent advancements in Artificial Intelligence (AI), particularly deep learning, have significantly improved lithology identification in reservoir exploration by leveraging micrographic rock imagery. Deep neural networks excel in feature extraction, enhancing classification accuracy. However, these models are prone to domain shifts, which often degrade their performance in real-world applications. This paper proposes an unsupervised domain adaptation framework that integrates Fisher linear discriminant analysis and Online Hard Example Mining (OHEM) to mitigate domain shifts and improve classification, particularly in datasets with imbalanced classes. The model employs a ω-balanced global–local domain discriminator to align feature distributions between different domains and introduces focal loss with class-wise weighted factors for better handling of imbalanced data. Additionally, an adapted version of OHEM identifies difficult samples during training, allowing the model to concentrate on challenging cases. The proposed method is validated on micrographic rock imagery from the Tibet, Qinghai, and Xinjiang regions, achieving an average accuracy of 83.2%, which is 13.8% higher than ResNet50 and at least 1% superior to other domain adaptation models. This research highlights the potential of AI-driven solutions in geoscientific applications and provides a robust framework for unsupervised lithology classification.

Iterative algorithm for computing magnetic field considering remanent magnetization and demagnetization

SUMMARY Efficient and high-precision methods for performing large-scale numerical calculations under high magnetic susceptibility conditions are essential for magnetic exploration applications. This paper proposes an algorithm for magnetic field modelling considering both demagnetization and remanent magnetization. The 3-D partial differential equation of magnetic potential is reduced to a 1-D ordinary differential equation in the space-wavenumber domain through a 2-D Fourier transform in the horizontal direction. Using the quadratic interpolation finite element method to obtain the pentagonal equation, the chasing method is used to solve the equation efficiently, and a contraction operator based on electromagnetic integral equation method is introduced to ensure stable iteration convergence. The validity of the algorithm was confirmed by comparing it with analytical solutions. Numerical examples demonstrate the importance of considering demagnetization under high magnetization conditions. Test results reveal that magnetic susceptibility is the sole factor affecting convergence among the factors examined. Further analysis showed that, for the same magnetic susceptibility, the algorithm converges with the same number of iterations and higher precision is achieved with an increasing number of nodes. Under the same grid configuration, higher magnetic susceptibility requires more iterations to converge and results in lower algorithm precision. The algorithm presented in this paper shows higher efficiency compared to those based on integral equations. Moreover, we demonstrate the algorithm's high accuracy when applied to models with anomalies near boundaries. Finally, the adaptability of the algorithm to complex geological models and undulating topography is verified by combining model tests with DEM data. This algorithm presents an efficient and high-precision method for magnetic field calculations under conditions of high susceptibility and strong remanent magnetization, offering promising prospects for geoscience applications.

Terrain analysis in forested areas: consequences of using DSM instead of DTM

Abstract. Relief mapping through dense tropical forest is a challenge, which can be met by processing radar images. Various global digital elevation models (DEMs) are available, either free or paid, the most widely used methods for producing them at global scale being photogrammetry and short wavelength radar interferometry. However, the resulting DEMs do not directly represent the ground surface but rather the canopy or something in between. Since most users need terrain models for a variety of applications in geoscience, water management etc. but use such canopy models for lack of anything better, it was relevant to assess the consequences of this inappropriate but unavoidable choice. This paper presents a study carried out in the Brazilian Amazon, where an airborne P-band radar interferometric DEM was used as a reference based on its accuracy assessed in previous studies. A well-established DEM (SRTM) and newer ones (Copernicus and Fabdem), as well as a DEM obtained from aerial X-bands radar data, were compared to the reference according to different criteria related to elevation (accuracy and a typical application, i.e., dam filling simulation) and to slope at different scales (accuracy and a typical application, i.e., terrain classification). The results highlighted the risks of using short wavelengths to represent the terrain and emphasized the importance of slope in different resolution scales.

Effect of Variable Gravity Field on Dual Component Convection in a Couple Stress Fluid Saturated Anisotropic Porous Layer With Temperature‐Dependent Heat Source

ABSTRACTThis study examines how gravity fluctuations, the couple stress parameter, anisotropic parameters, and heat source collectively influence dual‐component convection in the porous layer. The linear analysis is conducted utilizing the normal mode technique. The authors proposed three categories of gravity fluctuation, namely: (a) linear, (b) parabolic, and (c) exponential. Expressions for both stationary and oscillatory Rayleigh numbers are derived using the Galerkin approach. Neutral stability curves for both stationary and oscillatory modes are analyzed, with graphical representations to show the effects of various stability parameters, including gravity fluctuations, couple stress, anisotropy, and heat source. The results show that the mechanical anisotropy parameter and Vadasz number lead to system destabilization, while the couple stress parameter, Lewis number, gravity parameter, solute Rayleigh number, and thermal anisotropy parameter help to stabilize the system. Furthermore, the system is more stable with exponential gravity fluctuations and less stable with parabolic gravity fluctuations. This finding offers insights into thermal convective instability in porous media, impacting applications in geoscience, engineering, and environmental science.

Poroelastic Response of a Fractured Rock to Hydrostatic Pressure Oscillations

AbstractPoroelastic coupling between fractures and the surrounding rock is important to numerous applications in geosciences. We measure the in‐situ fluid pressure and local strain response of a fractured carbonate sample to hydrostatic pressure oscillations. A linear poroelastic model that represents the rock sample is parameterized using X‐ray imaging and ultrasonic wave transmission measurements. The numerical solution, based on Biot's quasistatic equations, is consistent with the measured frequency dependent dispersion of the apparent bulk modulus of the background matrix and the in‐situ pore pressure response, which is caused by fluid pressure diffusion from the compliant fractures into the stiffer matrix. The observed fluid pressure diffusion is causally related to the numerically quantified intrinsic attenuation at seismic frequencies, which is a major contributor to the dissipation of seismic waves. Our analysis supports the use of a simple approximation of fractures as compliant and planar inclusions in numerical simulations based on linear poroelasticity.

Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea

Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint statistics (MPS) to develop a high-resolution 3D crustal P-wave velocity structure model of the SCS. Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. The resulting 3D model provides a detailed and reliable representation of the SCS crust, offering critical insights for studies on tectonic evolution, resource exploration, and geodynamic processes. This work highlights the potential of combining deep learning with geostatistical methods for geological modeling, providing a robust framework for future applications in geosciences. The flexibility of our approach also suggests its applicability to other regions and geological attributes, paving the way for more comprehensive and data-driven investigations of Earth’s subsurface.

On-orbit Moving Target Detection Method Based on Dual Images from Taijing-IV 01 Satellite

Abstract. With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a novel method for detecting moving targets, offering a broader detection range compared to traditional satellite video techniques, facilitating orbital target recognition from dual panchromatic image strips. Our experimental setup on the Taijing-IV 01 satellite, launched on February 27, 2022, successfully acquired two image strips separated by one second. These strips contain speed and directional information of moving objects, extractable through the frame differencing technique. We propose combining frame differencing with lightweight deep learning for target detection, extracting regions of interest (ROIs) to focus on areas with potential moving targets. This approach reduces the workload of wholeimage target detection, decreasing data processing volume by 89%. By optimizing the YOLOv8 network and using techniques like feature map fusion of low-level and high-resolution features, we enhance sensitivity to small targets. Consequently, the model size is reduced by 79%, the mean Average Precision (mAP) increases by approximately 1.8% and 4.5%, and detection speed rises by 26%. This method introduces a new paradigm in remote sensing data services, facilitating rapid acquisition and real-time transmission of positions and image information of moving targets to the ground. This significantly reduces bandwidth requirements for transmitting remote sensing information, presenting a novel strategy for data acquisition and processing in large-scale Earth observation systems and geoscientific applications.

Enhancing forensic applications through the mineralogy of clays in surface soil horizon: A case study of baixada fluminense, Southeast Brazil

Clay minerals possess chemical, mineralogical, and crystallographic characteristics that make them potential forensic markers, as they can reflect geochemical and weathering processes on a detailed scale. Therefore, this study aims to use the mineralogy of the clay fraction to distinguish samples from the surface soil horizons in four municipalities in Brazil, to assess the possibility of geographical tracking for forensic applications. Studies characterizing clays in environments with a variety of soil types in forensic pedology are necessary but still in their early stages. Furthermore, due to the high incidence of homicides involving the displacement of bodies in the region and the low resolution of these crimes, this research is crucial for geoscientific applications, with the potential for global replication. The samples were separated by centrifugation and analyzed using X-ray diffractometry, X-ray fluorescence, and thermogravimetry. Minerals were determined through quantitative mineralogical analyses using Rietveld and Biscaye methods. Additionally, in conjunction with the crystallographic parameters of kaolinite (Full Width at Half Maximum – FWHM), it was possible to form 8 Clay Mineral Assemblages and Gibbsite (CMAG), whose geographical delimitation considered geological and hydrographic aspects. The determination of CMAGs is based on mineralogy and is defined as follows: CMAG 1: kaolinite+(illite); CMAG 2: kaolinite+(gibbsite); CMAG 3: kaolinite + illite + montmorillonite; CMAG 4: kaolinite + illite+(gibbsite); CMAG 5: ordered kaolinite+(illite)+(gibbsite); CMAG 6: kaolinite + gibbsite+(illite); CMAG 7: disordered kaolinite + illite + gibbsite; CMAG 8: kaolinite+(illite)+(chlorite). The results indicate that the quantification of clay minerals in samples from surface soil horizons, FWHM, and the % of Al in goethite can represent valuable tools for forensic pedology. Moreover, through the characterization of iron and titanium (hydr)oxides, it was possible to distinguish samples with a distance of 335 m between them, further expanding the capability to identify the geographical origin of samples, making them potential forensic markers.

Coupled Modelling of Brine Availability in Salt-Based Disposal Facilities - Learning from DECOVALEX 2023

Salt is a potential host rock or caprock for deep geological disposal facilities for radioactive waste and has several favourable properties compared to other candidate rock types. In particular, intact salt has an extremely small porosity and permeability, which limits water availability and transmissivity. This has tended to lead to disposal concepts in salt being considered as ‘mostly dry’ and hence ideally suited to limiting groundwater interactions that could otherwise lead to corrosion of waste packages and mobilisation of radionuclides over isolation timescales (10 5 -10 6 years). However, there is plentiful experimental evidence to suggest that excavations in bedded salt can exhibit non-trivial inflows. These inflows often increase on heating and are thought to arise due to complicated interactions between thermal, hydrogeological and mechanical processes in the salt. Task E in the international collaborative DECOVALEX 2023 programme was established to attempt to better understand how these coupled processes affect brine availability in bedded salt. A summary of the UK team's learning from the task is given in this paper that may be of relevance when constructing models to inform future safety cases for radioactive waste disposal in salt, and other energy geoscience applications where brine interactions are a potentially complicating factor.

Geodesic metrics for RBF approximation of some physical quantities measured on sphere

The radial basis function (RBF) approximation is a rapidly developing field of mathematics. In the paper, we are concerned with the measurement of scalar physical quantities at nodes on sphere in the 3D Euclidean space and the spherical RBF interpolation of the data acquired. We employ a multiquadric as the radial basis function and the corresponding trend is a polynomial of degree 2 considered in Cartesian coordinates. Attention is paid to geodesic metrics that define the distance of two points on a sphere. The choice of a particular geodesic metric function is an important part of the construction of interpolation formula.We show the existence of an interpolation formula of the type considered. The approximation formulas of this type can be useful in the interpretation of measurements of various physical quantities. We present an example concerned with the sampling of anisotropy of magnetic susceptibility having extensive applications in geosciences and demonstrate the advantages and drawbacks of the formulas chosen, in particular the strong dependence of interpolation results on condition number of the matrix of the system considered and on round-off errors in general.

Hydrothermal mineral replacement in the apatite-rhabdophane-monazite system: Experiments, reaction mechanisms and geological implications

Apatite, rhabdophane and monazite are actinide- and rare-earth element (REE)-bearing phosphate minerals with diverse geoscientific applications. However, their mineral-fluid reaction mechanisms below 200 °C are understudied. Insufficient understanding as to how these minerals behave during hydrothermal alteration impedes useful interpretations of REE-U-Th mobilisation in regolith profiles, diagenetic environments and low-temperature hydrothermal systems. This work experimentally investigates the replacement of apatite by rhabdophane in a Ce-doped acidic fluid at 30 °C, and of rhabdophane by monazite in REE-barren acidic fluids at 180 °C. Rhabdophane replaced apatite via the coupled dissolution-precipitation mechanism, forming nanoscale prisms clustered into spherulitic aggregates. Interstitial voids that formed between rhabdophane and apatite indicate how rhabdophane precipitation was the rate-limiting step. Void widths varied in response to apatite's anisotropic dissolution; rapid dissolution parallel to the c-axis formed the widest voids (50–125 μm) and thickest rhabdophane layers (20–50 μm), while slow dissolution perpendicular to the c-axis formed narrower voids (< 1 μm) and thinner layers (< 5 μm). At 180 °C, monazite also replaced rhabdophane via the dissolution-precipitation mechanism. Replacement rates increased with increasing phosphate concentrations, implying that monazite precipitation was the rate-limiting step. However, microscale rhabdophane aggregates were pseudomorphically preserved. This is attributed to the shared orientation of rhabdophane prisms (not pseudomorphically preserved) and their intergranular boundaries which, as microreactors, guided monazite precipitation. Both replacement reactions were accompanied by a loss of U to solution, which is attributed to the stabilisation of uranyl-phosphate complexes under acidic conditions. Results indicate that: 1) anisotropic dissolution can govern the extent of rate coupling between dissolution and precipitation during mineral replacement; 2) the pseudomorphic replacement of mineral aggregates can occur when precipitation is the rate-limiting step; 3) rhabdophane is a feasible (and perhaps necessary) metastable precursor to authigenic monazite precipitation in low temperature hydrothermal systems; 4) highly porous aggregates of both rhabdophane and monazite generate insufficient EPMA totals, such that they cannot be differentiated by EPMA alone, and; 5) phosphate complexes can mobilise U under certain hydrothermal conditions, with implications for geochronology, nuclear waste management and the formation of REE deposits.

Predicting multiphase flow behavior of methane in shallow unconfined aquifers using conditional deep convolutional generative adversarial network

Numerical modeling is an essential tool for geoscience applications involving multiphase flow behavior. Performing numerical simulation is, however, computationally intensive due to multi-scale, non-linear, and multi-physics nature of mechanisms controlling multiphase flow dynamics. Additionally, the computational challenges associated with traditional numerical simulations limit their applicability for repetitive simulations required in inverse modeling, uncertainty analysis, and optimization tasks. To overcome these limitations, surrogate modeling has recently emerged as a viable solution. A surrogate model, which functions as a regression model, is trained using numerical simulation data. It approximates the input–output relationships within a dynamic fluid environment. We design surrogate models, based on conditional deep convolutional generative adversarial network (cDC-GAN), to map the cross-domain between input and output pairs in a multiphase multicomponent system, focusing on methane (CH4) migration in shallow unconfined aquifers. The cDC-GAN technique is selected due to its ability to generate high-fidelity surrogate models that effectively capture complex spatial distributions and heterogeneities. This technique excels in handling high-dimensional data, learning the intricate relationships between inputs and outputs, and producing realistic representations of subsurface phenomena. The trained cDC-GANs consider capillary entry pressure heterogeneity as input because this geologic attribute places first-order control on CH4 migration in subsurface. The outputs include CH4 saturation, water saturation, residual saturation of CH4, CH4 mole fraction, and CH4 molality. For each output, a separate cDC-GAN model is trained. Once trained, cDC-GANs can at any given time determine CH4 distribution in a shallow unconfined aquifer, both qualitatively and quantitatively. The cDC-GAN approach can be considered as a valuable complement to numerical simulations for predicting multiphase flow behavior in other geoscience-, energy-, and environmental applications such as contaminant transport, hydrocarbon production, and geological carbon dioxide and hydrogen storage.

Accuracy and hydrographic realism of an under-forest DEM derived from airborne P-band radar interferometry over a wide area in the Brazilian amazon

ABSTRACT P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton’s law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments.

Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds

Abstract. Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector.

A stable deep adversarial learning approach for geological facies generation

The simulation of geological facies in an unobservable volume is essential in various geoscience applications. Given the complexity of the problem, deep generative learning is a promising approach to overcome the limitations of traditional geostatistical simulation models, in particular their lack of physical realism. This research aims to investigate the application of generative adversarial networks and deep variational inference for conditionally simulating channelized reservoir in underground volumes. In this paper, we review the generative deep learning approaches, in particular the adversarial ones and the stabilization techniques that aim to facilitate their training. We also study the problem of conditioning deep learning models to observations through a variational Bayes approach, comparing a conditional neural network model to a Gaussian mixture model. The proposed approach is tested on 2D and 3D simulations generated by the stochastic process-based model Flumy. Morphological metrics are utilized to compare our proposed method with earlier iterations of generative adversarial networks. The results indicate that by utilizing recent stabilization techniques, generative adversarial networks can efficiently sample complex target data distributions.

Analysis and diagnosis of abnormal SLR validation results for BeiDou-3 SECM-B MEO C225 and C226 satellite orbits

Satellite laser ranging (SLR) is the only stand-alone and external tool for verifying the accuracy of microwave-based GNSS orbits. On February 2, 2023, the International Laser Ranging Service initiated the SLR global tracking campaign for the complete BeiDou-3 medium Earth orbit (MEO) constellation. This initiative provides an opportunity to unveil the potential issue for BeiDou-3 MEO orbit modeling. An examination of the SLR validation results of precise BeiDou-3 MEO orbits across different IGS/Multi-GNSS Experiment (MGEX) analysis centres (ACs) reveals that the SLR residual standard deviation for BeiDou-3 SECM-B MEOs C225 and C226 orbits reaches 18 cm. This figure is approximately four to nine times those for other BeiDou-3 MEOs. This contradicts the fact that the orbit quality of C225 and C226 is comparable to other satellites. By analyzing the correlation between SLR residuals and de-trend clock residuals, detector-specific bias performance, and the distribution characteristics of SLR residuals relative to satellite azimuth and nadir angles, we diagnose that abnormal SLR validation results for C225 and C226 originate from a large deviation in the official horizontal offsets for the laser retroreflector arrays (LRAs) in the satellite reference frame. LRA horizontal offsets for C225 and C226 are estimated based on SLR residuals. With our adjusted LRA offset estimates, the SLR residual metrics for C225 and C226 orbits (across various IGS/MGEX ACs) are comparable to the results obtained for other BeiDou-3 MEOs. Additionally, our adjustments enable the identification of their orbit modeling errors across different IGS/MGEX ACs according to the pattern of SLR residual variation. From the study, spacecraft manufacturers should be encouraged to conduct a comprehensive review of ground calibrations for BeiDou LRA offsets. This is crucial for optimizing BeiDou orbit model and enhancing its applications in high-precision geosciences.

Generalized Yang Chizhong filtering and interpolation method without stationarity assumption

The stationarity assumption of geostatistical methods is difficult to satisfy in practice. To overcome this limitation, this study proposed a geometric and statistical coupling strategy for modeling spatial dependence structures and developed a generalized Yang Chizhong filtering and interpolation (GYangCZ) method without the assumption of stationarity. In this work, we theoretically prove the effectiveness of Yang Chizhong filtering in fitting spatial dependence structures from a geometric perspective, and develop an orientation-constrained Yang Chizhong filtering to fit the local and discontinuous spatial dependence structures. To measure nonstationary spatial dependence structure, we define a local statistical indicator (i.e., fundamental variation function) by comparing the variance of the original data and the fitted geometric surfaces obtained under different filtering radii. The fundamental variation function is used as the kernel function to obtain the approximate best linear unbiased estimators at unobserved locations. We theoretically demonstrate that when only a linear drift exists in local areas, GYangCZ does not require the stationarity assumption. GYangCZ was used to estimate the gold grade of the Xiadian gold deposit in China. The results show that GYangCZ outperformed ordinary kriging, moving window kriging, and kriging convolution networks. GYangCZ is easy to implement with wide applications in geoscience.

Quantifying the spatial associations among terrain parameters from digital elevation models

AbstractTerrain parameters describe the morphological characteristics of a surface and are an important part of the application of geoscience. At present, there is still a lack of scientific and systematic research on the associations among terrain parameters. In this article, 14 representative terrain parameters are selected to explore their spatial association relationships by using the quantification methods of linear correlation degree, distribution similarity and influence degree. Then, we discuss the effects of the digital elevation model (DEM) cell size and landform region on the associations among terrain parameters. Furthermore, the mechanism of the association relationships among terrain parameters is also analyzed to achieve a better understanding of terrain analysis. This study revealed that the similarity in calculation methods, the derivation of terrain parameters from other parameters, the geographic significance of representations, and the characteristics and appearances of landforms are significant factors contributing to the associations of terrain parameters. The terrain parameters with strong associations remained stable with changes in cell size and landform region. However, terrain parameters with weak associations significantly respond to this variety of factors. We quantify the associations among terrain parameters, provide a new perspective for examining the associations among terrain parameters, and provide guidance for the selection of terrain parameters in geoscience research.

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations.

An Experimental Investigation on the Energy Signature Associated With Multiphase Flow in Porous Media Displacement Regimes

AbstractThis study investigates the energy signature associated with multiphase flow in porous media displacement regimes. We proposed that net efficiency, as the conversion of external work applied to surface energy generated, provides new insights into the transition of flow regimes. The combined effects of wettability and flow rates on immiscible fluid‐fluid displacement is experimentally investigated using high‐resolution imaging in microfluidic flow cells, which allows for tracking the evolution of interfacial area under applied external work. Our study focuses on the morphology of invasion patterns and the efficiency of conversion of external work to surface energy. The results show that the morphology of invasion patterns under unfavorable displacement condition is sharper than that under favorable displacement condition with larger ratios of length and width for fingers. The efficiency of conversion of external work to surface energy decreases with the increase of contact angles and reduces greatly with the increase of Capillary numbers. With favorable displacement conditions, the efficiency of conversion is consistently higher than that for unfavorable displacement. The trends of external work and surface energy serve as a signature of the transition between different flow regimes, which exhibits alteration especially at low Capillary numbers. The findings highlight the importance of wettability and flow rates in determining the efficiency of fluid‐fluid displacement in porous media. Understanding the energy signature associated with multiphase flow can have implications for various geoscience applications, such as oil/gas recovery, groundwater remediation, and underground energy storage.