Search Results (176)

The Hongche Fault Zone in the Junggar Basin exhibits significant spatiotemporal variations in the relationship between fault systems and hydrocarbon accumulation across different structural belts. Two key factors contribute to this phenomenon: frequent tectonic activities and well-developed Paleozoic fault systems. To date, no detailed studies have been conducted on the fault systems in the Paleozoic strata of the Hongche Fault Zone. In this study, the fault systems in the Paleozoic strata of the Hongche Fault Zone were systematically sorted out for the first time. Furthermore, the controlling effects of active faults in different geological periods on hydrocarbon charging were clarified. Firstly, basing on the 3D seismic and well-log data, the structural framework and fault activity, fault systems, source-contacting faults were characterized. Vertically, the Hongche Fault Zone experienced three major thrusting episodes followed by one weak extensional subsidence Stage, forming four principal tectonic layers: Permian (Thrusting Episode I), Triassic (Thrusting Episode II), Jurassic (Thrusting Episode III), and Cretaceous–Quaternary (Post-Thrusting Subsidence). Laterally, six fault systems are identified: Middle Permian (Stage I), Late Triassic (Stage II), Jurassic (Stage III), post-Cretaceous (Stage IV), as well as composite systems from Middle Permian–Jurassic (Stages I–III) and Late Triassic–Jurassic (Stages II–III). These reveal multi-stage, multi-directional composite structural characteristics in the study area. According to the oil–source correlation, the Carboniferous reservoir is primarily sourced by Permian Fengcheng Formation source rocks in the Shawan Sag. Hydrocarbon migration tracing shows that oil migrates along faults, progressively charging from depression zones to thrust belts and uplifted areas. In this process, fault systems exert hierarchical controls on accumulation: Stage I faults dominate trap formation, Stages II and III faults regulate hydrocarbon migration, accumulation, and adjustment, while Stage IV faults influence hydrocarbon conduction in Mesozoic–Cenozoic reservoirs. By clarifying the fault-controlled hydrocarbon accumulation mechanisms in the Hongche Fault Zone, this study provides theoretical guidance for two key aspects of the Carboniferous reservoirs in the study area: the optimization of favorable exploration zones and the development of reserves. Full article

The online diagnosis technology used to determine the internal thermal status and defects of GIS equipment is important. In the existing GIS bus thermal defect fault diagnosis methods, sensors are usually installed on the highest and lowest temperature areas of the enclosure surface, and then an artificial neural network is established to obtain the highest temperature inside the GIS. These methods only consider the temperature under steady-state conditions, and the temperature signals collected by sensors are different, which leads to low accuracy and weak generality. This paper investigated the transient temperature evolution characteristics defined as a sequence of temperature values over time, and adopted them as new features. The steady and transient enclosure and environment temperature data were used to train the Generalized Regression Neural Network (GRNN) for the inside busbar temperature inversion. Experimental tests proved that the proposed method has a higher accuracy compared to traditional characteristic parameters, especially for the less significant temperature rise. This article provides a technical means for determining the internal temperature rise status of GIS equipment through external temperature monitoring in practical applications. Full article

The increasing burial depth of deep shale formations in the southern Sichuan leads to more complex in situ stresses and geological structures, which in turn raises the challenges of hydraulic fracturing. Although enlarging the treatment scale and injection rate can enhance reservoir stimulation, the intensive development of faults and fractures in deep shale formations aggravates stress instability, inducing casing deformation, fracture communication, and other engineering risks that constrain efficient shale gas production. In this study, a cross-scale geomechanical model linking the regional to near-wellbore domains was constructed. A dynamic evolution equation was established based on flow–stress coupling, and a numerical conversion from the geological model to the finite element model was implemented through self-programming, thereby developing a simulation method for dynamic geomechanical evolution during hydraulic fracturing. Results indicate that dynamic variations in pore pressure dominate stress redistribution, while near-wellbore heterogeneity and mechanical property distribution significantly affect prediction accuracy. The injection of fracturing fluid generates a high-pressure gradient that drives pore pressure diffusion along fracture networks and faults, exhibiting a strong near-wellbore but weak far-field non-steady spatial attenuation. As the pore pressure increases, the peak value reaches 1.4 times the original pressure. The triaxial stress shows a negative correlation and decreases. The horizontal minimum principal stress shows the most significant drop, with a reduction of 15.79% to 20.68%, while the vertical stress changes the least, with a reduction of 5.7% to 7.14%. Compared with the initial stress state, the horizontal stress difference increases during fracturing. Rapid pore-pressure surges and fault distributions further trigger stress reorientation, with the magnitude of rotation positively correlated with the intensity of pore-pressure variation. The high porosity and permeability characteristics of the initial fracture network lead to a rapid attenuation of the stress around the wellbore. In the middle and later stages, as the pressure balance is achieved through fracture filling, the pore pressure rises and the stress decline tends to stabilize. The findings provide significant insights into the dynamic stress evolution of deep shale reservoirs during fracturing and offer theoretical support for optimizing fracturing design and mitigating engineering risks. Full article

Mechanical fault samples of disconnectors are scarce, the fault types vary, and the self-evidence is weak, which leads to a lack of perfect fault diagnosis methods, and hidden defects cannot be found in time. To solve this problem, a mechanical fault diagnosis method for disconnectors based on a parallel dual-channel feature fusion model is proposed. Firstly, the optimal parameters for variational mode decomposition (VMD) are obtained using the black-winged kite algorithm (BKA). After the signal decomposition, the kurtosis values of each intrinsic mode function (IMF) are calculated, screened, and reconstructed. The reconstructed signal is input into the gated recurrent unit (GRU) to capture its time-series characteristics. Then, the vibration signal is generated by the recurrence plot (RP) to generate the atlas set and input into the vision Transformer (ViT) to extract its spatial characteristics. Finally, the time-series and spatial characteristics are fused, the multi-head self-attention mechanism is used for training, and softmax is used for fault classification. The measured data results show that the diagnostic accuracy of the model for mechanical fault types reaches 97.9%, which is 3.2%, 4.3%, 1.0%, 2.4%, 2.9%, 1.8%, 2.1%, 9%, and 7.5% higher than the other nine models numbered #2–#10, respectively, verifying its effectiveness and adaptability. Full article

Large-scale blackouts in power systems are often triggered by weak links susceptible to cascading failures. As the concentrated reflection of the system’s weak links, identifying key transmission sections and further implementing safety control measures are of great significance for ensuring the stable operation of the system. This paper proposes a multi-criteria-based method for identifying key transmission sections and an optimal strategy for the prevention–emergency coordinated control of key transmission sections. Firstly, a line criticality index based on three characteristics—topology, power flow, and voltage—has been established to identify critical lines. Furthermore, search for all initial transmission sections that include the critical line, and form the initial transmission section set for each critical line, then, based on the analysis of the Theil index of power flow impact rate distribution after the failure of critical lines, a key transmission section identification method integrating multiple criteria is proposed. Then, based on the anticipated faults of key transmission sections, an optimization model for the prevention–emergency coordinated control of key transmission sections is established. A constraint relaxation factor is introduced to divide the above model into two independent sub-problems, then the golden section method is applied to update the value of constraint relaxation factors, so as to iteratively search for the optimal solution of the model. Finally, the feasibility and correctness of the proposed method are verified through the simulation and analysis of the IEEE 39-bus system. The results demonstrate that the proposed method can effectively identify the key transmission sections of the system and improve the operational safety of the system through the prevention–emergency coordinated optimal control strategy. Full article

In coal mine non-solidly grounded systems, high-impedance faults generate minimal zero-sequence currents with obscured characteristics and strong interference, complicating faulted line identification. Existing methods rarely address three-phase imbalance and variable cable parameters, causing selection errors. To this end, a method for identifying the non-effective ground fault routing of mining cables based on Steady-State Impedance Analysis (SSIA) and Holmes–Duffing oscillator small-signal detection is proposed. Firstly, based on SSIA, the mapping relationship that the phase of the zero-sequence current variation in the faulted line is the same as the phase of its voltage relative to the faulted ground is derived before and after the occurrence of the fault. Meanwhile, identifiable differences exist in both phase and amplitude of the zero-sequence current change in faulty lines compared to non-faulty lines before and after fault occurrence. This is used as the criterion for high-impedance ground fault line selection. In the mining environment, zero-sequence current variations are characterized as weak signals, which poses significant challenges for detection. Thus, a Holmes–Duffing oscillator weak signal detection method is proposed. Based on chaotic principles, accurate line selection is achieved by diagnosing chaotic states in oscillator-generated phase trajectories. A specific mine grid simulation via MATLAB/Simulink 2023b validates the method’s efficacy and applicability. Full article

Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This study proposes an effective method for extracting urbanization intensity by integrating Sentinel-1, Sentinel-2, and its derived synthetic aperture radar and spectral indices features, combined with texture features. The small baseline subset interferometric synthetic aperture radar technique was employed to monitor land subsidence in Chongqing between 2018 and 2024. Furthermore, the relationships among urbanization intensity, metro construction, groundwater dynamics, and land subsidence were systematically analyzed. Finally, geographical detector and multiscale geographically weighted regression models were employed to explore the interactive effects of anthropogenic, topographic, geological-tectonic, climatic, and land surface characteristic factors contributing to land subsidence. The findings reveal that (1) the method proposed in this paper can effectively extract urbanization intensity and provide an important approach to analyze the influence of urbanization on land subsidence. (2) Land subsidence along newly opened metro lines was more pronounced than along existing lines. The shorter the interval between metro construction completion and the start of operation, the greater the subsidence observed within the first 3 months of operation, which indicates that this interval influences land subsidence. (3) Overall, groundwater dynamics and land subsidence showed a clear correlation from June 2022 to June 2023, a phenomenon largely caused by the extreme summer high temperatures of 2022, triggering reduced precipitation and a notable groundwater decline. Beyond this period, however, only a weak correlation was observed between groundwater fluctuations and land subsidence trends, indicating that other factors likely dominated subsidence dynamics. (4) The anthropogenic factors have a higher relative influence on land subsidence than other drivers. In terms of q-value, the top six factors are road network density > precipitation > elevation > enhanced normalized difference impervious surface index > population density > nighttime light, while distance to fault exhibits the least explanatory power. Given Chongqing’s exemplary status as a mountainous city, this study offers a foundational reference for subsequent quantitative analyses of land subsidence and its drivers in other mountainous cities worldwide. Full article

This study aims to investigate the sedimentary facies characteristics of the coal seam roof in the Qinglong and Longfeng coal mines and their control over water abundance. By collecting core samples and well logging data from both mining areas, multiple methods were employed, including core observation, thin-section analysis, sedimentary microfacies distribution mapping, nitrogen adsorption tests, and nuclear magnetic resonance analysis, to systematically analyze the depositional environments, types of sedimentary microfacies, and their distribution patterns. Results indicate that the roof of Qinglong Coal Mine is predominantly composed of sandy microfacies with well-developed faults, which not only increase fracture porosity but also provide water-conducting pathways between surface water and aquifers, significantly enhancing water abundance. In contrast, Longfeng Coal Mine is characterized mainly by muddy microfacies, with small-scale faults exhibiting weak water-conducting capacity and relatively low water abundance. Hydrochemical analysis indicates that consistent water quality between Qinglong’s working face, karst water, and goaf water confirms fault-induced aquifer–surface water connectivity, whereas Longfeng’s water quality suggests weak aquifer–coal seam hydraulic connectivity. The difference in water hazard threats between the two mining areas primarily stems from variations in sedimentary microfacies and fault structures. Full article

Effective detection of acoustic signals plays a crucial role in numerous fields, including industrial equipment fault prediction and environmental monitoring. Acoustic sensing technology, owing to its substantial information carrying capacity and non-contact measurement advantages, has garnered widespread attention in relevant applications. However, the effective detection of weak target acoustic signals amidst strong noise interference remains a critical challenge in this field. The core bottleneck lies in the difficulty of traditional detection methods to simultaneously achieve both high sensitivity and high directionality. To address this limitation, this work proposes a line-defect phononic crystal (PnC) structure that enables directional enhancement and detection of weak target signals under intense spatial noise interference by coupling defect state localization characteristics with anisotropy mechanisms. Through theoretical derivation and finite element numerical simulation, the directional enhancement properties of this structure were systematically validated. Furthermore, numerical simulations were conducted to validate the detection of weak harmonic signals and weak bearing fault signals under strong spatial noise interference. The results demonstrate that this line-defect phononic crystal (PnC) structure exhibits high feasibility and outstanding performance in detecting weak acoustic signals. This work provides novel insights for developing new acoustic detection methods combining high sensitivity with high directivity, showcasing unique advantages and broad application prospects in acoustic signal sensing, enhancement, and localization. Full article

Predicting favorable reservoirs controlled by source-to-sink systems in rift basins is a current research focus. Using seismic, core, drilling, logging, and thin-section data, this paper systematically identifies fan types and their reservoir characteristics controlled by two boundary faults in the southern steep-slope zone of Wushi Sag, Beibuwan Basin, South China Sea. The analysis compares differences in (1) source–channel–margin–sink systems and (2) diagenetic facies, dividing the sink area into migratory and progradational fans. Results show that migratory fans are associated with denudation. Sediments migrate through wide, deep “V”-shaped valleys, forming fan deltas that are large in area but short in progradation. Lithology is dominated by fine sandstone with siltstone interbeds, reservoirs’ diagenetic evolution is weak, pores are mainly primary, and Type I–II reservoirs are developed. In contrast, progradational fans reflect weaker source area denudation, with sediments prograding through narrow, shallow “U”-shaped valleys. These form broom-shaped fan deltas that are small in area but long in progradation, with lithology dominated by fine sandstone interbedded with mudstone. Reservoirs show strong diagenetic evolution, well-developed secondary porosity, and Type II–III reservoirs. Reservoir prediction models indicate that high-quality migratory reservoirs are large, with excellent physical properties and oil-bearing capacity, mainly in fan stacking zones. High-quality progradational reservoirs are concentrated in the fan midsections, with strong cementation and secondary porosity. These findings provide a theoretical basis for reservoir prediction and oil and gas exploration in the southern steep-slope zone of Wushi Sag. Full article

This paper proposes a novel method for locating single-phase grounding faults in generator stator windings with high resistance, which are typically challenging to locate due to weak fault characteristics. The method utilizes an active voltage injection technique combined with traveling wave reflection analysis, singular value decomposition (SVD) denoising, and discrete wavelet transform (DWT). A DC voltage signal is then injected into the stator winding, and the voltage and current signals at both terminals are collected. These signals undergo denoising using SVD, followed by DWT, to identify the arrival time of the traveling waves. Fault location is determined based on the reflection and refraction of these waves within the winding. Simulation results demonstrate that this method achieves high accuracy in fault location, even with fault resistances up to 5000 Ω. The method offers a reliable and effective solution for locating high-resistance faults in generator stator windings without requiring winding parameters, demonstrating strong potential for practical applications. Full article

From the perspectives of theoretical design and practical application, the existing fault diagnosis methods with the complex identification process owing to manual feature extraction and the insufficient feature extraction for time series data and weak fault signal is not suitable for AC/DC microgrids. Thus, this paper proposes a fault diagnosis method that integrates a convolutional neural network (CNN) with a long short-term memory (LSTM) network and attention mechanisms. The method employs a multi-scale convolution-based weight layer (Weight Layer 1) to extract features of faults from different dimensions, performing feature fusion to enrich the fault characteristics of the AC/DC microgrid. Additionally, a hybrid attention block-based weight layer (Weight Layer 2) is designed to enable the model to adaptively focus on the most significant features, thereby improving the extraction and utilization of critical information, which enhances both classification accuracy and model generalization. By cascading LSTM layers, the model effectively captures temporal dependencies within the features, allowing the model to extract critical information from the temporal evolution of electrical signals, thus enhancing both classification accuracy and robustness. Simulation results indicate that the proposed method achieves a classification accuracy of up to 99.5%, with fault identification accuracy for noisy signals under 10 dB noise interference reaching 92.5%, demonstrating strong noise immunity. Full article

As low-voltage distribution networks incorporate increasingly diverse loads, series arc faults exhibit weak characteristics that are easily masked by load currents, leading to high misjudgment rates in traditional detection methods. This paper proposes a series arc fault detection method based on an improved Light Gradient Boosting Machine (LightGBM) model. First, a test platform containing 12 household loads was built to collect arc data from both individual and composite loads. Composite loads refer to composite load conditions where multiple devices are running simultaneously and arcing occurs on some loads. To address the challenge of feature extraction, Variational Mode Decomposition (VMD) is employed to isolate the fundamental frequency component. To enhance high-frequency arc characteristics, singular value decomposition (SVD) is then applied. A multidimensional statistical feature set—comprising peak-to-peak value, kurtosis, and other indicators—is constructed. Finally, the LightGBM algorithm is used to identify arc faults based on these features. To overcome the LightGBM model’s limited ability to focus on hard-to-classify samples, a dynamic weighted hybrid loss function is developed. Experiments demonstrate that the proposed method achieves 98.9% accuracy across 223,615 sample groups. When deployed on STM32H723VGT6 hardware, the average fault alarm time is 83.8 ms, meeting requirements. Full article

Geothermal resources in arid inland basins are important for clean energy development, yet their circulation and geochemical mechanisms remain insufficiently understood. This study investigates the hydrochemical characteristics and formation mechanisms of geothermal water in the Zhangye–Minle Basin, an arid inland region in northwestern China. A total of nine geothermal water samples were analyzed using major ion chemistry, stable isotopes (δ²H, δ¹⁸O), tritium (³H), and radiocarbon (¹⁴C) to determine recharge sources, flow paths, and geochemical evolution. The waters were predominantly of the Cl–Na and Cl·SO₄–Na types, with total dissolved solids ranging from 3432.00 to 5810.00 mg/L. Isotopic data indicated that recharge originated from atmospheric precipitation and snowmelt in the Qilian Mountains, with recharge altitudes between 2497 and 5799 m. Tritium and ¹⁴C results suggested that most samples were recharged before 1953, with maximum ages exceeding 40,000 years. Gibbs diagrams and ion ratio plots demonstrated that water–rock interaction was the primary geochemical process, while cation exchange was weak. Na⁺ was mainly derived from halite, albite, and mirabilite, while SO₄²⁻ originated largely from gypsum. The calculated reservoir temperatures using cation geothermometers ranged from 57 °C to 148 °C. The deep circulation of geothermal water was closely related to NNW-trending fault zones that facilitated infiltration and heat accumulation. These findings provide new insights into the recharge sources, circulation patterns, and geochemical processes of geothermal systems in fault-controlled basins, offering a scientific basis for their sustainable exploration and development. Full article

With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. However, traditional CNN models still have deficiencies in the extraction of early weak fault features and the suppression of high noise. In response to these problems, this paper proposes a convolutional neural network (SAWCA-net) that integrates spectrum-guided dynamic variable-width convolutional kernels and dynamic interactive time-domain–channel attention mechanisms. In this model, the spectrum-adaptive wide convolution is introduced. Combined with the time-domain and frequency-domain statistical characteristics of the input signal, the receptive field of the convolution kernel is adaptively adjusted, and the sampling position is dynamically adjusted, thereby enhancing the model’s modeling ability for periodic weak faults in complex non-stationary vibration signals and improving its anti-noise performance. Meanwhile, the dynamic time–channel attention module was designed to achieve the collaborative modeling of the time-domain periodic structure and the feature dependency between channels, improve the feature utilization efficiency, and suppress redundant interference. The experimental results show that the fault diagnosis accuracy rates of SAWCA-Net on the bearing datasets of Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) reach 99.15% and 99.64%, respectively, which are superior to the comparison models and have strong generalization and robustness. The visualization results of t-distributed random neighbor embedding (t-SNE) further verified its good feature separability and classification ability. Full article