Search Results (57)

Historical masonry minarets are highly vulnerable to seismic actions due to their slender geometry, limited tensile capacity, and material heterogeneity. However, their response to near-fault ground motions characterized by velocity pulses remains insufficiently explored. This study investigates the seismic response of the historical Tavanlı Mosque Minaret (1894, Trabzon, Türkiye) subjected to pulse-like (PL) and non-pulse-like (NPL) near-fault ground motions. A three-dimensional finite element model (FEM) was developed in ANSYS Workbench and systematically calibrated using empirical formulations to represent the current dynamic condition of the structure. Seismic performance was evaluated through linear dynamic analyses in terms of displacement demands, principal stress distribution, and drift-ratio-based performance levels. The results indicate that model calibration significantly modifies the dynamic characteristics, increasing the fundamental frequency from 0.734 Hz to 1.126 Hz and reducing displacement demands by approximately 35–76% across the considered records. Despite this improvement, PL ground motions consistently generate more critical deformation demands than NPL motions, frequently exceeding Collapse Prevention (CP) limits even when Peak Ground Acceleration (PGA) values are relatively low. A key finding is that seismic demand cannot be reliably predicted by peak intensity measures or pulse-period ratios ($T_p/T_1$) alone; rather, velocity-related parameters and pulse coherence govern the structural response. These results demonstrate that integrating empirical model calibration with pulse-sensitive seismic analysis is essential for reliable seismic assessment and conservation planning of slender historical masonry structures located in near-fault regions. The study offers a systematic framework that integrates model calibration and pulse-sensitive seismic analysis for evaluating the drift-controlled response of slender historical masonry minarets in near-fault regions. Full article

The constituent elements of source-to-sink systems and their coupling relationships are key controls on the development of sedimentary systems and the spatial distribution of sand bodies. Taking the Paleogene strata in the southern Laizhouwan Sag of the Bohai Bay Basin as a case study, we integrate drilling, logging, core, thin-section, and high-resolution 3D seismic data to quantitatively characterize basement lithology and effective provenance area, drainage-unit subdivision, types and scales of sediment transport pathways, and geometric parameters of depositional fans, within a source-to-sink analytical framework. The results show that: (1) Two distinct provenance types are developed in the southern Laizhouwan Sag, including Proterozoic granitic–gneissic basement and Mesozoic volcanic–clastic basement. These provenance types exhibit pronounced differences in effective source area, vertical relief, and drainage-network configuration across different sequence stages. (2) Two main categories of sediment transport pathways are identified, namely paleo-valleys and fault-controlled troughs. V-shaped, U-shaped, and W-shaped paleo-valleys show systematic morphological transitions along topographic gradients. The width-to-depth ratio of transport channels exerts a significant control on depositional fan scale, with U-shaped valleys exhibiting the highest sediment transport efficiency. Finally, (3) the depositional domain is dominated by near-source fan-delta systems, whose scale shows a strong positive correlation with effective provenance area and transport-channel morphology. Overall, the southern Laizhouwan Sag is characterized by a typical fault-controlled, steep-slope source-to-sink system, in which sedimentary system distribution is jointly governed by effective provenance area, sediment transport pathway geometry, and fault-related slope-break zones. This study provides a quantitative example and methodological reference for source-to-sink system characterization and prediction of favorable sand body distribution in continental rift basins. Full article

With engineering architecture being shifted to meet the requirements of sustainable development, the need for optimized design solutions places precise engineering methods at the core of the contemporary industrial transition toward data-driven strategies. A timely conversion to lightweight components in drivetrain systems has led to the prominent use of high-strength polymer gears, establishing them as a critical point of interest in the field of power transmission. However, as the conversion to polymer gears relies on expensive and time-consuming laboratory testing, there is a standstill in evaluating the structural properties specific to polymer gear design. In addition, one of the major concerns in the development of polymer-based gear drives is linked with their operational performance and dynamic response under fault conditions influenced by surface wear. To address these difficulties, a framework for surface wear prediction is developed, enabling precise design optimization for specific drivetrain requirements. Computations of wear progression over multiple duty cycles are built upon the mathematical background of Archard’s wear theory, while internal changes in gear contact pressure distribution are constructed on Winkler’s surface model. The framework provides an innovative support for polymer gear systems, as it imports the three-dimensional (3D) scanning data of gear geometry, therefore enabling the analysis of actual flank surfaces with designated surface modifications and manufacturing errors. The framework’s effectiveness, confirmed by experimental validation, demonstrates a superior estimation of contact parameters and overall performance compared to traditional design methods, highlighting scalable solutions that contribute to ongoing industrial engineering objectives. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase association and event location, which underscores the importance of identifying S waves. Given that individual RTS cases may consist of only hundreds of events, it is crucial for algorithms to be trained on small datasets or to detect effectively using external, global training data. To evaluate this, we compared the efficiency of a deep learning global detection model, transfer learning to the RTS database, a specialized neural network designed for RTS, and manual detection of seismic signals. Transfer learning efficiency was database dependent. Additional interpretation and parametrization of detection results are assumed. Therefore, the emphasis is on phase detection, rather than phase picking accuracy, and detection sensitivity is more important than its specificity. Phase association plays a vital role in detecting seismic signals, facilitating the elimination of most false picks. As a result, the comparisons of detections were based on parameters related to the location of seismic events. The findings indicate that neither the automatic signal detection methods nor the manual methods alone are sufficient. However, their combination significantly enhances detectability. The final catalogs cover up to 30% more events compared to the previous manual. It fulfills the main aim of applying a neural network detector, which is to increase the number of seismic events in the catalog. It may also be further utilized in the research of the triggering process, such as identifying fluid paths and determining fault geometry. Full article

A central challenge in tight fault-depression reservoirs is understanding how three-dimensional fracture structures control gas storage and flow. This study introduces a data-driven, geologically informed framework that integrates structural-mechanical coupling to decipher fracture networks within the Shahezi Formation. Our model, based on rock failure criteria, achieves quantitative fracture prediction across one-dimensional to three-dimensional scales. This capability overcomes the limitations inherent in single-method approaches for tight, fracture-dominated reservoirs. By synthesizing sedimentary facies-controlled reservoir modeling, sweet-spot inversion, and geo-engineering integration, we establish a predictive system for accurate reservoir assessment. The continental clastic Shahezi Formation is typified by secondary fractures. This study utilizes leverage small-scale data (core, thin section, log) to quantify key parameters (fracture density, aperture), enabling a systematic analysis of fracture typology, heterogeneity, and controls. Building on this foundation, and spatially constrained by large-scale datasets (seismic interpretation, stress-field simulations), we developed a robust fracture development model for deep tight reservoirs. Stress-field modeling delineated fracture-prone zones, where a discrete fracture network (DFN) model was built to characterize 3D fracture geometry and connectivity. Integrating simulated fracture size and aperture-derived permeability allowed us to quantify fracture contribution to total permeability, ultimately mapping favorable targets. The results identify favorable zones primarily in the western sector of the study area, forming an NS-trending, belt-like distribution. They are mainly concentrated around the wells Changshen-4, Changshen-40, and Changshen-41. This distribution is clearly controlled by the Qianshenzijing Fault. Full article

The Ordovician No. 6 fault zone reservoir in the Shunbei Oilfield exhibits ultra-deep-burial, high-pressure, and high-temperature conditions. Its pronounced tectonic control and significant heterogeneity render traditional in situ stress prediction methods—based on linear elasticity and anisotropy assumptions—inadequate for accurately characterizing the evolution and uncertainty of carbonate reservoir stiffness. Therefore, quantitatively predicting the development patterns and distribution characteristics of the Shunbei No. 6 structural fault zone is crucial for the exploration and development of Ordovician carbonate reservoirs in the Shunbei region. This study integrates wave impedance inversion with high-confining-pressure PFC particle flow biaxial test results to establish a constitutive calibration system consistent with seismic and experimental data. It introduces a nonlinear weakening function incorporating higher-order derivative constraints to fuse structural fracture and effective stress weakening effects, enabling dynamic correction of elastic parameters. This approach establishes a novel in situ stress prediction model. Simulation results indicate a predicted range for maximum horizontal principal stress between 201 and 261 MPa, with minimum horizontal principal stress ranging from 124 to 173 MPa. Predicted stress values for three key wells exhibit measurement errors within 6.92% compared to actual logging data, displaying a zoned spatial distribution consistent with regional tectonic stress evolution patterns. Simultaneously, sensitivity analysis reveals that the Young’s modulus fitting accuracy improved from 0.89 to 0.95, with a 43% reduction in mean square error, with the proportion of outliers reduced to below 1%. This significantly enhances response continuity and numerical stability in high-gradient disturbance zones and stiffness drop regions. The new model explicitly incorporates the nonlinear coupling between fracture geometry and pore pressure disturbance into the parameter field, eliminating systematic bias along fracture zones. Higher-order derivative constraints suppress numerical oscillations in high-gradient areas, stabilizing variance and preventing anomaly propagation. Residual distributions exhibit enhanced symmetry and reduced spatial autocorrelation, effectively suppressing numerical oscillations and divergence in complex fracture zones while significantly improving stress prediction accuracy for the study area. Overall, this research provides novel methodologies for predicting in situ stresses in ultra-deep carbonate reservoirs, offering engineering guidance and parameterization references for scheme deployment in complex fractured karst systems. Full article

Fault slips induced by hydraulic fracturing are the primary mechanism of casing de-formation during deep shale gas development in Sichuan’s Luzhou Block, where de-formation rates reach 51% and severely compromise productivity. To address a critical gap in existing research on quantitative risk assessment systems, we developed a probabilistic model integrating pore pressure evolution dynamics with Monte Carlo simulations to quantify slip risks. The model incorporates key operational parameters (pumping pressure, rate, and duration) and geological factors (fault friction coefficient, strike/dip angles, and horizontal stress difference) validated through field data, showing >90% slip probability in 60% of deformed well intervals. The results demonstrate that prolonged high-intensity fracturing increases slip probability by 32% under 80–100 MPa pressure surges. Meanwhile, an increase in the friction coefficient from 0.40 to 0.80 reduces slip probability by 6.4% through elevated critical pore pressure. Fault geometry exhibits coupling effects: the risk of low-dip faults reaches its peak when strike parallels the maximum horizontal stress, whereas high-dip faults show a bimodal high-risk distribution at strike angles of 60–120°; here, the horizontal stress difference is directly proportional to the slip probability. We propose optimizing fracturing parameters, controlling operation duration, and avoiding high-risk fault geometries as mitigation strategies, providing a scientific foundation for enhancing the safety and efficiency of shale gas development. Full article

Over the last three decades, composite structures have become increasingly more common in everyday life, such as in wind turbines as part of the solution to produce clean energy, and their use in the aerospace industry due to their advantages over conventional materials. Most of these advantages are dependent upon the reliability and quality of the manufacturing process to ensure that there are no defects/faults or imperfections during manufacturing. Thus, it is critical to monitor the enclosed environment of moulds during fabrication in real time. This need has caused many researchers—past and present—to create or apply many sensing technologies to achieve real-time monitoring of the manufacturing processes of composite structures to ensure that the structures can meet their requirements. A consequence of these research activities is the myriad of sensing schemes, (for example, optical, electrical, piezo, and nanomaterial schemes and the use of digital twins) available to consider, and the investigations all of them have both strengths and weaknesses for a given application, with no apparent option having a distinct advantage. This review reveals that the best possible sensing solution depends upon a large set of parameters, the geometry of the composite structure, the required specification, and budget limits, to name a few. Furthermore, challenges remain for researchers trying to find solutions, such as a sensing scheme that can directly detect wrinkles/waviness during the laying-up procedure, real-time detection of the resin flow front throughout the mould, and the monitoring of the resin curing spatially, all at a spatial resolution of ~1 cm with the required sensitivity along with the need to obtain the true interpretation of the real-time data. This review offers signposts through the variety of sensing options, with their advantages and failings, to readers from the composite and sensing community to aid in making an informed decision on the possible sensing approaches to help them meet their composite structure’s desired function and tolerances, and the challenges that remain. Full article

We present a method for 3D gravity inversion using ellipsoidal parametrization and Particle Swarm Optimization (PSO), aimed at estimating the geometry, density contrast, and orientation of subsurface bodies from gravity anomaly data. The subsurface is modeled as a set of prolate ellipsoids whose parameters are optimized to minimize the misfit between observed and predicted anomalies. This approach enables efficient forward modeling with closed-form solutions and allows the incorporation of geometric and physical constraints. The algorithm is first validated on synthetic models with Gaussian noise, successfully recovering complex multi-body configurations with acceptable uncertainty. A statistical analysis based on multiple PSO runs provides interquartile ranges (IQRs) to quantify inversion stability. The method is then applied to a real microgravity dataset from the Nirano Salse mud volcanoes (northern Italy) using a field acquisition strategy previously described in the literature. Unlike earlier studies based on commercial software, our inversion uses the ellipsoidal–PSO framework. The best-fitting model includes four ellipsoids (two low- and two high-density), reproducing the main features of the observed Bouguer anomaly with a prediction error of 20–25%. The inferred geometry suggests that fluid migration is controlled by fault-related damage zones rather than shallow reservoirs. This method is robust, interpretable, and applicable to both synthetic and real cases, with potential uses in geotechnical, volcanic, and hydrogeological studies. Full article

Sudden coal and gas outbursts pose a significant hazard in deep-seated coal seam extraction, necessitating reliable risk assessment methods. Traditionally, assessments focus on gas-dynamic parameters, but experience shows they must be supplemented with tectonic factors such as fault-related disturbances, weak interlayers, and increased fracturing. Even minor faults in the Karaganda Basin can weaken the coal massif and trigger outbursts. The integration of 3D modeling enhances risk evaluation by incorporating both dynamic (gas-related) and static (tectonic) parameters. Based on exploratory drilling and geophysical studies, these models map coal seam geometry, fault positioning, and high-risk structural zones. In weakened coal areas, stress distribution changes can lead to avalanche-like gas releases, even under normal gas-dynamic conditions. An expert scoring system was used to convert geological and gas-dynamic data into a comprehensive risk index guiding preventive measures. An analysis of Karaganda Basin incidents (1959–2021) shows all outbursts occurred in geological disturbance zones, with 43% linked to fault proximity, 30% to minor tectonic shifts, and 21% to sudden coal seam changes. Advancing 3D modeling, geomechanical analysis, and microseismic monitoring will improve predictive accuracy, ensuring safer coal mining operations. Full article

The spatial variation characteristics of near-fault velocity pulses lack in-depth understanding, and it is difficult to consider this feature in probabilistic seismic hazard analysis and the ground motion input for structural seismic analysis. Based on ground motion simulation, this study performs spatial characteristic analysis of velocity pulses. The Mw 6.58 strike-slip Imperial Valley and the Mw 6.8 dip-slip Northridge earthquakes are adopted as the cases, and the simulation method is validated by comparing synthetics with observations. The multi-component broadband ground motion fields are simulated, and the pulse parameters and the pulse area are extracted using the multi-component pulse identification method. The spatial characteristics of various pulse parameters are analyzed. The results show that for a single earthquake, the pulse period is a spatial variable related to source-to-site geometry, the pulse amplification factor has great spatial variation, and the orientation of the maximum pulse component is controlled by the radiation pattern. Finally, the influence of slip distribution on pulse is explored based on two earthquakes, in which the uniform slip, the random slip, and the hybrid slip are combined with different rupture directions. This study contributes to a more reasonable consideration of pulse-like ground motion in seismic risk assessment and earthquake response analysis. Full article

This study focuses on the complex stress distribution in coal seams influenced by normal fault using the fault development zone of the LF-M1 oilfield in southern China as a case study. Based on 3D seismic and drilling data, a key research area was delineated, and strata were reclassified considering rock parameter similarity. An FLAC3D model encompassing hanging wall, normal fault, and footwall strata was developed to systematically analyze geostress near the fault under various conditions. The results indicate that the normal fault induces non-uniform and discontinuous stress patterns in the coal seam’s transverse plane. Stress weakening occurs near the fault, with a pronounced concentration on its flanks, approaching in situ stress levels in the far field. Coal’s Poisson’s ratio, elastic modulus, and fault dip negatively correlate with horizontal in situ stress, whereas other parameters show positive correlations. The maximum horizontal stress is more sensitive to parameter variations than the minimum. Stress weakening is most influenced by coal’s Poisson’s ratio, followed by coal’s elastic modulus, fault elastic modulus, fault Poisson’s ratio, fault dip, and fault thickness and the coal seam thickness. Notably, a 20% decrease in coal’s Poisson’s ratio leads to a 23.32% stress reduction at measuring point 1. Conversely, the coal seam thickness has a minimal impact on stress across the fault. When the coal seam thickness increases by 20%, the maximum horizontal stress at measuring point 2 only decreases by 0.06%. In summary, fault geometry, rock mechanics parameters, and external loads collectively complicate stress distributions near faults, posing risks of drilling accidents such as wellbore instability, leakage, and reservoir damage, necessitating careful consideration. Full article

This paper presents three 3D reconstructions of different analogue models used to reproduce, interpret, and describe the geological setting of a seismogenic area in Southern Italy—the Messina Strait. Three-dimensional analysis is a technique that allows for less sparse and more congruent and coherent information about a study zone whose complete understanding reduces uncertainties and risks. A thorough structural and geodynamic description of the effects of low-angle normal faulting in the same region through analogue models has been widely investigated in the scientific literature. Sandbox models for fault behaviour during deformation and the effects of a Low Angle Normal Fault (LANF) on the seismotectonic setting are also studied. The deformational patterns associated with seismogenic faults, rotational behaviour of faults, and other related problems have not yet been thoroughly analysed. Most problems, like the evolution of normal faults, fault geometry, and others, have been cited and briefly outlined in earlier published works, but a three-dimensional approach is still significant. Here, we carried out a three-dimensional digital model for a complete and continuous structural model of a debated, studied area. The aim of this study is to highlight the importance of fully representing faults in complex and/or non-cylindrical structures, mainly when the shape and dimensions of the fault(s) are key parameters, like in seismogenic contexts. Full article

This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models. Full article