Search Results (132)

The expansion of offshore renewable energy development in Portugal necessitates accurate geological and geotechnical site characterization, especially in regions with limited baseline information. This study focuses on the S. Pedro de Moel area (NW central Portugal), which is characterized by sandy sediments, with the aim of supporting the preliminary design of anchoring and foundation systems for renewable energy structures. An integrated methodology was applied, combining multibeam bathymetry, acoustic backscatter data, high-resolution seismic reflection profiling, sediment sampling, and onshore laboratory testing. Seismic interpretation identified three subsurface units: (1) a deformed carbonated sandstone serving as the acoustic basement; (2) a well-graded sandy gravel layer, up to 8 m thick, interpreted as a marginal marine deposit; and (3) a modern sandy deposit, up to 7 m thick, with variable silt content. Geotechnical analyses yielded effective friction angles for sandy sediments ranging from 39 to 44°, and deformation moduli between 22 MPa and 54 MPa. The sedimentary succession exhibits marked lateral and vertical heterogeneity, which must be considered in engineering design. This cost-effective methodology offers a viable alternative to offshore in situ testing, enabling medium-scale site characterization and providing essential information to support the development of offshore renewable energy infrastructure. Full article

Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality assessment and increased waste. To address this, we developed a 4D-grading pipeline that fuses visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging with structured-light 3D scanning to non-destructively evaluate both internal defects and external form. Our contributions are (1) flagging the defects in fruits based on the reflectance information, (2) accurate shape and defect measurement based on the 3D data of fruits, and (3) an interpretable, decision-tree framework that assigns USDA-style quality (Premium, Grade 1/2, Reject) and size (Small–Extra Large) labels. We demonstrate this approach through preliminary results, suggesting that 4D hyperspectral imaging may offer advantages over single-modality methods by providing clear, interpretable decision rules and the potential for adaptation to other produce types. Full article

This study investigates the subsurface rooting of the Merija anticline in the Missour Basin, Morocco, with a focus on copper mineralization exploration. A sequential geophysical workflow was implemented, combining gravity surveys, electrical resistivity (ER), and induced polarization (IP) methods. The gravity data, acquired along spaced profiles extending from outcropping areas to Quaternary-covered zones, clearly delineated the structural continuity of the anticline beneath the cover. The application of trend filtering in covered areas allowed the removal of regional effects, successfully isolating residual anomalies associated with the buried continuation of the anticline. Interpolated Bouguer anomaly maps highlighted a major regional fault, interpreted as controlling the deep rooting of the anticline. A resistivity profile was then deployed perpendicular to this fault, providing detailed imaging of the anticline’s geometry and lithological contrasts. Complementary IP profiles conducted near the mine site targeted the detection of chargeability anomalies associated with copper mineralization dominated by malachite, confirming the electrical signature of copper mineralization, particularly within the sandstone and conglomerate formations of the Lower Cretaceous. To validate the geophysical interpretations, a drilling campaign was conducted, which confirmed the presence of the identified lithological units and the anticline rooting, as revealed by geophysical data. This approach provides a robust framework for copper exploration in the Merija area and can be adapted to similar geological contexts elsewhere. Full article

Our understanding of foreland basin subsurface structures relies heavily on seismic reflection data. The seismic profile across the eastern Rioni foreland basin in western Georgia is critical for characterizing its deformation structural style. We applied fault-related folding and thrust wedge theories to interpret the seismic profile and construction structural cross-section, which reveals that compressional structures are controlled by multiple detachment levels. Both thin-skinned and thick-skinned structures are identified. The seismic profile and structural cross-section reveal the presence of normal faults, reverse faults, thrust faults, duplexes, triangle zone, and crustal-scale duplexes. The deep-level detachment within the basement is responsible for the development of the crustal-scale duplexes. These structures appear to have formed through the reactivation of pre-existing normal faults during compressive deformation. Based on our interpretation, the imaged duplex system likely represents the western subsurface continuation of the Dzirula Massif. Full article

The mining of essential minerals is often made more difficult by subsurface geological structures such as dykes and contacts. The a priori knowledge of these features can greatly mitigate the problems that they would otherwise cause. For that reason, techniques such as geophysics and drilling are used to plan the mining in detail. This manuscript introduces a new technique which allows for the interpretation of aeromagnetic data without any knowledge of the source of the magnetic anomalies. In addition, the method is stable and does not rely on higher-order derivatives of the data, unlike many other approaches. Platinum mining is extremely important in South Africa, providing much-needed employment and bringing funds to the economy as a whole. The proposed method is demonstrated using data from the Eastern Bushveld complex, where platinum mining is widespread. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

This study was conducted in the northeast of the Tadla plain, within the Beni Mellal-Khenifra region of Morocco. The primary objective is to elucidate the geometric and hydrogeological characteristics of this aquifer by analyzing and interpreting data from deep boreholes as well as gravimetric and electrical measurements using GIS analysis. First, the regional gradient was established. Then, the initial data were extracted. Subsequently, based on the extracted data, a gravity map was created. The investigation of the Bouguer anomaly’s gravity map exposes the presence of a regional gradient, with values varying from −100 mGal in the South to −30 mGal in the North of the area. These Bouguer anomalies often correlate with exposed basement rock areas and variations in the thickness of sedimentary layers across the study area. The analysis of existing electrical survey and deep drilling data confirms the results of the gravimetry survey after applying different techniques such as horizontal gradient and upward extension on the gravimetric map. The findings enabled us to create a structural map highlighting the fault systems responsible for shaping the study area’s structure. The elaborated structural map serves as an indispensable geotectonic reference, facilitating the delineation of subsurface heterogeneities and providing a robust foundation for further hydrogeological assessments in the Tadla Plain. Full article

A deep interaction between groundwater and surface hydrology characterizes karst environments. These settings feature closed depressions, whose hydrological role varies depending on whether they have genetic and hydraulic relationships with overland–subsurface flow (epigenic) or deep groundwater circulation (hypogenic). Epigenic dolines and poljes are among the diagnostic landforms of karst terrains. In this study, we applied a hydrological criterion to map closed depressions—including dolines—across the Alburni karst massif, in southern Italy. A GIS-based, semi-automatic approach was employed, combining the sink-filling method (applied to a 5 m DEM) with the visual interpretation of various informative layers. This process produced a raster representing the location and depth of karst closed depressions. This raster was then used to automatically delineate endorheic areas using classic GIS tools. The resulting map reveals a thousand dolines and hundreds of adjacent endorheic areas. Endorheic areas form a complex mosaic across the massif, a feature that had been poorly emphasized in previous works. The main morphometric features of the dolines and endorheic areas were statistically analyzed and compared with the structural characteristics of the massif. The results of the proposed mapping approach provide valuable insights for groundwater management, karst area protection, recharge modeling, and tracer test planning. 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

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

In subsurface geological mapping, it is more than advisable to compare different solutions obtained with neural and other algorithms. Here, for such comparison, we used the previously published and well-prepared dataset of subsurface data collected from the Bjelovar Subdepression, a 2900 km² large regional macrounit in the Croatian part of the Pannonian Basin System. Data on depth were obtained for the youngest (the shallowest) Lonja Formation (Pliocene, Quaternary) and mapped using neural network (NN), inverse distance weighting (IDW), and ordinary kriging (OK) algorithms. The obtained maps were compared based on square error (using k-fold cross-validation) and the visual interpretation of isopaches. Two other algorithms were also tested, namely, random forest (RF) and extreme gradient boosting (XGB) algorithms, but they were rejected as inappropriate for this purpose solely based on the visuals of the obtained maps, which did not follow any interpretable geological structures. The results showed that NN is a highly adjustable method for interpolation, with adjustment for numerous hyperparameters. IDW showed its strength as one of the classical interpolators, and its results are always located close to the top if several methods are compared. OK is the relative winner, showing the flexibility of variogram analysis regarding the number of data points and possible clustering. The presented variogram model, even with a relatively high sill and occasional nugget effect, can be well fitted into OK, giving better results than other methods when applied to the presented area and datasets. This was not surprising because kriging is a well-established method used exclusively for interpolation. In contrast, NN and machine learning algorithms are used in many fields, and these algorithms, particularly the fitting of hyperparameters in NN, simply cannot be the best solution for all. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

Accurate characterization of subsurface geological structures, particularly those obscured by strong coal-seam reflections, is essential for hydrocarbon exploration in subtle reservoirs. Enhancing seismic resolution remains a pivotal technical challenge in addressing this demand. Here, we present a multitrace reflectivity inversion method guided by geological sparsity principles. This method establishes quantitative relationships between sparse inversion operators and the spatial positions of stratigraphic boundaries. Specifically, by integrating prior geological knowledge, such as stratigraphic boundaries and stable sedimentary structures, as constraint operators within the sparsity matrix, this method results in a geologically interpretable and robust inversion framework. Subsequently, we validated this method through synthetic data and field applications in a carbonate fracture–cavity reservoir in the Ordos Basin of western China. The enhanced seismic resolution demonstrates that our method effectively restores shielded reservoir reflections beneath coal seams. Clearer than conventional sparse inversion techniques, the coherence attribute of the enhanced seismic resolution reveals distinct fracture–cavity geometries. Moreover, integrated analyses of well logs, fracture–cavity characterization, and drilling production data further confirm the accuracy and reliability of the inversion results. In conclusion, this method effectively leverages accurate geological structural information to enhance localized seismic resolution, thereby providing robust support for the exploration of subtle hydrocarbon reservoirs. Full article

Geothermal resources represent one of the most vital renewable energy sources, offering substantial development potential within the energy sector. Wudalianchi, renowned as one of China’s prominent volcanic clusters, has undergone extensive underground volcanic activities, suggesting a promising capacity for geothermal resource accumulation. This paper is the first to apply the cross-gradient gravity-magnetic joint inversion method to study the shallow structures in the Laoheishan Volcano and surrounding areas of Wudalianchi, based on high-precision measured gravity and magnetic data. The inversion results indicate the presence of a rock body at a depth of approximately 2 km beneath the Laoheishan and Bijiashan regions, which simultaneously exhibits characteristics of low density, high magnetization, and low seismic velocity. Integrating previous research findings, the rock body is interpreted as basalt formed during magmatic activity, retaining remanent magnetism. Furthermore, the rock body contains fractures filled with fluids, thereby excluding the possibility of a shallow magma chamber or dry hot rocks beneath the Laoheishan area. These rock bodies are interconnected at depth and align with the NE and SE fault directions in the Wudalianchi area, confirming that these faults govern the region’s volcanic activities. The inversion results, from the perspectives of density and magnetic susceptibility, elucidate the material distribution in the shallow subsurface of the Laoheishan and surrounding areas, providing new evidence for further exploration of geothermal resources in the region. Full article

This study presents the results of the Magnetotelluric (MT) survey aimed at resolving the subsurface structures in the northern part of the Pingtung Plain. Data analysis was conducted using ten local observation stations and one remote reference station. Due to the significant noise of the urban environment, the process of obtaining high-quality results proved to be challenging. The impact of such noise on the transfer function estimation is demonstrated, emphasizing the need for careful data selection and processing to mitigate its effects. The results reveal a distinct low–high–low-resistivity trend in the subsurface, with the Quaternary–Neogene sediment boundary estimated to be up to 500 m deep. Additionally, this study maps depths of up to 4 km, where it indicates possible faulting structures below the study area, which may be related to the previously assumed structures south of the study area. Given the limited, available deep subsurface information of the study area, these findings offer a preliminary understanding of the subsurface characteristics of the northern Pingtung Plain, which may contribute to ongoing research on the geological characteristics of the region while taking into account the importance of addressing urban noise when interpreting MT data. Full article