Search Results (226)

Spatial heterogeneity in soil deposition poses a significant challenge to accurate geotechnical characterization, which is essential for sustainable infrastructure development. This study presents an advanced geotechnical data-driven mapping framework, based on a monotonized and augmented formulation of Shepard’s inverse distance weighting (IDW) algorithm, implemented through the Google Earth Engine (GEE) platform. The approach is rigorously evaluated through a comparative analysis against the classical IDW and Kriging techniques using standard key performance indices (KPIs). Comprehensive field and laboratory data repositories were developed in accordance with international geotechnical standards (e.g., ASTM). Key geotechnical parameters, i.e., standard penetration test (SPT-N) values, shear wave velocity (Vs), soil classification, and plasticity index (PI), were used to generate high-resolution geospatial models for a previously unmapped region, thereby providing essential baseline data for building infrastructure design. The results indicate that the augmented IDW approach exhibits the best spatial gradient conservation and local anomaly detection performance, in alignment with Tobler’s First Law of Geography, and outperforms Kriging and classical IDW in terms of predictive accuracy and geologic plausibility. Compared to classical IDW and Kriging, the augmented IDW algorithm achieved up to a 44% average reduction in the RMSE and MAE, along with an approximately 30% improvement in NSE and PC. The difference in spatial areal coverage was found to be up to 20%, demonstrating an improved capacity to model spatial subsurface heterogeneity. Thematic design maps of the load intensity (LI), safe bearing capacity (SBC), and optimum foundation depth (OD) were constructed for ready application in practical design. This work not only establishes the inadequacy of conventional geostatistical methods in highly heterogeneous soil environments but also provides a scalable framework for geotechnical mapping with accuracy in data-poor environments. Full article

This paper investigates the combined application of seismic inversion and migration for processing seismic data in the depth domain. Seismic inversion serves as a widely used practical tool allowing the derivation of detailed subsurface models from seismic data. In this study, we implement a constrained total variation inversion algorithm. The inversion input data comprise true-amplitude depth imaging results along with the depth migration velocity model. Furthermore, we develop and examine an iterative algorithm that jointly performs acoustic seismic inversion and depth migration. This approach aims to refine high-frequency and smooth low-frequency components of the depth velocity model. We validate our methods through numerical experiments using both synthetic data and a realistic Marmousi model. Full article

Global climate changes impact the Arctic seas by decreasing the sea ice area and changing the inorganic and organic matter supply via rivers and coastal permafrost thawing. Therefore, climate change may affect biogeochemical processes in the Kara Sea (KS) and Laptev Sea (LS), which form the Arctic Transpolar Drift. This study explores the effect of the KS shelf water supply on seawater parameters in the LS in late summer and early fall 2007, 2008, 2018, 2019, and 2024 using ship-borne (temperature, salinity, dissolved oxygen, nutrients, and pH), satellite-derived (sea surface heights, geostrophic current velocities), and model (current velocities) data. The results demonstrate that an inflow of KS shelf water with salinity of 33.0–34.5, high Apparent Oxygen Utilization values (50–110 µM), and increased concentrations of the dissolved inorganic phosphorus (DIP~ 0.7–1.2 µM), dissolved inorganic nitrogen (DIN~ 4–12 µM) and silicic acid (DSi~ 10–18 µM) enriches the subsurface layer of the LS with nutrients. The distributions of Atlantic—derived water (ADW) and KS shelf water in the LS from August to October depend on water dynamics caused by wind and river discharge. High Lena River discharge and westerly (downwelling favorable) winds promoted the supply of the KS shelf water to the LS through Vilkitsky Strait. In the area of the central trough of the LS, the KS shelf water can be modified by mixing with ADW. Mixing ADW with high DIN/DIP ratios (DIN~ 10 µM at DIP of 0.80 µM) and KS shelf water with low DIN/DIP ratios (DIN~ 8 µM at DIP of 0.80 µM) leads to changes in the DIN vs. DIP ratio in the subsurface layer of the LS. Full article

In marine seismic surveys, towed streamers record only pressure data with limited offsets and insufficient low-frequency content, whereas Ocean Bottom Nodes (OBNs) acquire multi-component data with wider offset and sufficient low-frequency content, albeit with sparser spatial sampling. Elastic full waveform inversion (EFWI) is used to estimate subsurface elastic properties by matching observed and synthetic data. However, using only towed streamer data makes it impossible to reliably estimate shear-wave velocities due to the absence of direct S-wave recordings and limited illumination. Inversion using OBN data is prone to acquisition footprint artifacts. To overcome these challenges, we propose a hierarchical joint inversion method based on P- and S-wave separation (PS-JFWI). We first derive novel acoustic-elastic coupled equations based on wavefield separation. Then, we design a two-stage inversion framework. In Stage I, we use OBN data to jointly update the P- and S-wave velocity models. In Stage II, we apply a gradient decoupling algorithm: we construct the P-wave velocity gradient by combining the gradient using PP-waves from both towed streamer and OBN data and construct the S-wave velocity gradient using the gradient using PS-waves. Numerical experiments demonstrate that the proposed method enhances the inversion accuracy of both velocity models compared with single-source and conventional joint inversion methods. Full article

Assessment of contaminant dispersal in sandstones requires hydraulic characterization with a combination of datasets that span from the core plugs to wellbores and up to the field scale as the matrix and fractures are both hydraulically conductive. Characterizing the hydraulic properties of the matrix is fundamental because contaminants diffuse into the fractured porous blocks. Fractures are highly conductive, and the determination of the number of hydraulically active rock discontinuities makes discrete fracture network models of solute transport reliable. Recent advances (e.g., active line source temperature logs) in hydro-geophysics have allowed the detection of 40% of hydraulically active fractures in a lithified sandstone. Tracer testing has revealed high (~10⁻⁴–10⁻² ms⁻¹) flow velocities and low (~10⁻²–10⁻⁴) effective porosities. Contaminants can therefore move rapidly in the subsurface. The petrophysical characterization of the plugs extracted from the cores, in combination with borehole hydro-geophysics, allows the characterization of either matrix or fracture porosity, but the volume of sandstone characterized is low. Tracer tests cannot quantify matrix or fracture porosity, but the observation scale is larger and covers the minimum representative volume. Hence, the combination of petrophysics, borehole hydro-geophysics, and tracer testing is encouraged for the sustainable management of solute transport in dual porosity sandstones. Full article

Seismic data processing is essential in hydrocarbon exploration, with normal moveout (NMO) correction being a pivotal step in enhancing seismic signal quality. However, conventional NMO correction often suffers from the stretch effect, which distorts seismic reflections and degrades data quality, especially in long-offset data. This study addresses the issue by analyzing synthetic models and proposing a nonhyperbolic stretch-free NMO correction technique. The proposed method significantly improves seismic data quality by preserving up to 90% of the original amplitude, maintaining frequency content stability at 30 Hz, and achieving a high reduction of stretch-related distortions. Compared to conventional NMO, our technique results in clearer seismic gathers, enhanced temporal resolution, and more accurate velocity models. These improvements have substantial implications for high-resolution subsurface imaging and precise reservoir characterization.This work offers a robust and computationally efficient solution to a longstanding limitation in seismic processing, advancing the reliability of exploration in geologically complex environments. Full article

Grass-covered levees commonly protect river and estuarine areas against flooding. Climate-induced water level changes may increasingly expose these levees to overflow events. This study investigates whether grass-covered levees can withstand such events, and under what conditions failure may occur. Between 2020 and 2022, full-scale overflow tests were conducted at the Living Lab Hedwige-Prosperpolder along the Dutch–Belgian Scheldt Estuary to assess erosion resistance under varying hydraulic conditions and vegetation states. A custom-built overflow generator was used, with instrumentation capturing flow velocity, water levels, and erosion progression. The results show that well-maintained levees with intact grass cover endured overflow durations up to 30 h despite high terminal flow velocities (4.9–7.7 m/s), without structural damage. In contrast, levee sections with pre-existing surface anomalies, such as animal burrows, slope irregularities, surface damage, or reed-covered soft soils, failed rapidly, often within one to two hours. Animal burrows facilitated subsurface flow and internal erosion, initiating fast, retrograde failure. These findings highlight the importance of preventive maintenance, particularly the timely detection and repair of anomalies. Once slope failure begins, the process unfolds rapidly, leaving no practical window for intervention. Full article

Understanding tortuosity is essential for accurately modeling fluid flow in complex porous media, particularly in the sub-surface reservoir rock; therefore, tortuosity estimation was evaluated using three approaches: Streamline streamline simulations via the Lattice Boltzmann Method (LBM), geometric pathfinding using Dijkstra’s algorithm, and empirical modeling based on pore-structure parameters. The analysis encompassed 1963 micro-Computed Tomography (micro-CT) images of Brazilian pre-salt carbonate and sandstone samples, with the effective porosity extracted from LBM velocity fields, isolating flow-contributing pores, establishing streamline tortuosity as the reference standard. Sandstones exhibited relatively narrow tortuosity ranges (Dijkstra: 1.29–1.75; Streamline: 1.18–2.61; Empirical: 1.18–4.42), whereas carbonates display greater heterogeneity (Dijkstra: 1.00–3.18; Streamline: 1.00–3.68; Empirical: 1.59–4.93). Model performance assessed using the corrected Akaike Information Criterion (AICc) revealed that the best agreement with the data was achieved by the semi-empirical model incorporating coordination number and minimum throat length (AICc = −113.11), followed by the Dijkstra-based geometrical approach (−99.74) and the empirical porosity-based model (202.23). There was a nonlinear inverse correlation between tortuosity and effective porosity across lithologies. This comprehensive comparison underscores the importance of incorporating multiple pore-scale parameters for robust tortuosity prediction, improving the understanding of flow behavior in heterogeneous reservoir rocks. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

The electric submersible pump (ESP) is a critical component in subsurface resource extraction systems, yet the presence of gas in the working medium significantly affects its performance. To investigate the impact of impeller perforation on gas–liquid mixing and internal flow characteristics, unsteady numerical simulations were conducted based on the Euler–Euler multiphase flow model. The transient evolution of the gas phase distribution, flow behavior, and liquid phase turbulent entropy generation rate was analyzed under an inlet gas volume fraction of 5%. Results show that under part-load flow conditions, impeller perforation reduces the amplitude of dominant frequency fluctuations and enhances periodicity, thereby mitigating low-frequency disturbances. Under design flow conditions, it leads to stronger dominant frequencies and intensified low-frequency fluctuations. Gas phase distribution varies little under low and design flow rates, while at high flow rates, gas accumulations shift from the midsection to the outlet with rotor rotation. As the flow rate increases, liquid velocity rises, and flow streamlines become more uniform within the channels. Regions of high entropy generation coincide with high gas concentration zones: they are primarily located near the impeller inlet and suction side under low flow, concentrated at the inlet and mid-passage under design flow, and significantly reduced and shifted toward the impeller outlet under high flow conditions. The above results indicate that the perforation design of ESP impellers should be optimized according to operating conditions to improve gas dispersion paths and flow channel geometry. Under off-design conditions, perforations can enhance operational stability and transport performance, while under design conditions, the location and size of the perforations must be precisely controlled to balance efficiency and vibration suppression. Full article

Landslides pose significant risks to human life and infrastructure, driven by a complex interplay of geological and hydrological factors. This study investigates the ongoing slope instability affecting the village of Borrano, in Central Italy, where large-scale landslides are triggered or reactivated by extreme rainfall and seismic activity. A multidisciplinary approach was employed, integrating traditional geological surveys, direct investigations, and advanced geophysical techniques—including electrical resistivity tomography (ERT) and seismic refraction tomography (SRT)—to characterize subsurface structures. Additionally, Sentinel-1 interferometric synthetic aperture radar (InSAR) was employed to parametrize the deformation rates induced by the landslide. The results reveal a complex geological framework dominated by the Teramo Flysch, where weak clayey facies and structurally controlled dip-slopes predispose the area to gravitational instability. ERT and SRT identified resistivity and velocity contrasts associated with shallow and depth sliding surfaces. At the same time, satellite-based synthetic aperture radar (SAR) data confirmed persistent slow movements, with vertical displacement rates between −10 and −24 mm/year. These findings underscore the importance of lithological heterogeneity and structural settings in the evolution of landslides. The integrated geophysical and remote sensing approach enhances the understanding of slope dynamics. It can be used to cross-check interpretations, capture displacement trends, characterize the internal structure of unstable slopes, and resolve the limitations of each method. This synergy provides a more comprehensive assessment of complex slope instability, offering valuable insights for hazard mitigation strategies in landslide-prone areas. Full article

This paper introduces a novel methodology for subsurface characterization in mineral exploration, through the simultaneous joint inversion of seismic and geoelectrical data. By combining complementary information provided by multidisciplinary geophysical data, the joint inversion yields a more accurate and consistent representation of subsurface properties. Furthermore, the joint inversion algorithm is empowered by dynamic hyperparameter self-adjustment. Hyperparameters are settings or configuration values that control the behavior of the inversion algorithm but are not directly learned from the data. Examples include regularization weights, coupling parameters, learning rates (if using gradient-based methods), and number of iterations. In traditional approaches, these values must be manually selected or tuned, often through trial and error, which is time-consuming and may lead to suboptimal results. Instead, in the approach here introduced, a self-adaptive mechanism monitors the evolution of the cost function and optimization performance, automatically tuning hyperparameters to enhance convergence toward an optimal (global) solution. For the purposes of this preliminary study, the method is tested on synthetic 2D geophysical scenarios featuring resistivity and seismic velocity anomalies representative of potential mineral targets. Results show the effectiveness of the approach in accurately identifying these subsurface anomalies. Finally, we show that this joint inversion technique holds significant promise for mineral exploration, particularly in detecting geological features such as ore bodies and mineralized zones, which can manifest as contrasts in seismic velocity and resistivity. Full article

As a significant geological hazard in large–scale engineering construction, deep subsurface voids demand effective and precise detection methods. Cross–hole radar tomography overcomes depth limitations by transmitting/receiving electromagnetic (EM) waves between boreholes, enabling the accurate determination of the spatial distribution and EM properties of subsurface cavities. However, conventional inversion approaches, such as travel–time/attenuation tomography and full–waveform inversion, still face challenges in terms of their stability, accuracy, and computational efficiency. To address these limitations, this study proposes a deep learning–based imaging method that introduces the concept of travel–time fingerprints, which compress raw radar data into structured, low–dimensional inputs that retain key spatial features. A large synthetic dataset of irregular subsurface cavity models is used to pre–train a UNET model, enabling it to learn nonlinear mapping, from fingerprints to velocity structures. To enhance real–world applicability, transfer learning (TL) is employed to fine–tune the model using a small amount of field data. The refined model is then tested on cross–hole radar datasets collected from a highway construction site in Guizhou Province, China. The results demonstrate that the method can accurately recover the shape, location, and extent of underground cavities, outperforming traditional tomography in terms of clarity and interpretability. This approach offers a high–precision, computationally efficient solution for subsurface void detection, with strong engineering applicability in complex geological environments. Full article

This study aims to overcome the technical bottleneck of non-invasive differentiation between the rammed earth layer and pebble layer in complex shallow subsurface environments, particularly focusing on the challenge of detecting highly heterogeneous pebble layers with complex wavefield characteristics. Using the western city wall of the Baodun site (Xinjin, Sichuan, China) as a case study, we introduce a high-resolution seismic detection technique combined with controllable high-frequency seismic source excitation to investigate the response characteristics of high-frequency components and energy variations of seismic waves in different strata, thereby revealing differences in physical properties between the rammed earth layer and pebble layer. Through high-frequency data acquisition, specialized processing, and interpretative analysis of seismic data, we successfully distinguish the two strata and delineate pebble-related anomalous zones. The results also indicate that, due to complex geological conditions, the reflection and refraction patterns of seismic waves in the pebble layer are exceptionally intricate. Moreover, the interplay of abrupt seismic velocity variations, interference waves, and other contributing factors leads to pronounced heterogeneity and strong scattering characteristics in the seismic data across the time, frequency, and phase domains. This research overcomes the limitations of conventional geophysical methods and confirms the applicability of high-frequency seismic techniques to complex near-surface archaeological contexts. It provides robust scientific support for the archaeological study of the Baodun site and offers a methodological reference for subsurface mapping of pebble layer in prehistoric urban landscapes. Full article