Search Results (1,592)

The Holy Cross Mountains represent an isolated outcrop of Palaeozoic rocks located in the Trans-European Suture Zone, which is the boundary between the Precambrian East European Craton and Phanerozoic mobile belts of South-Western Europe. Despite extensive structural history studies, high-resolution seismic profiling has not been applied to this region until now. This research introduces near-surface seismic imaging of the Holy Cross Fault, separating two tectonic units of different stratigraphic and deformation history. In our study, we utilize a carefully designed weight drop source survey with 5 m shot and receiver spacing and 4.5 Hz geophones. The imaging technique, combining seismic reflection profiling and travel time tomography, reveals detailed fault geometries down to 400 m. Precise data processing, including static corrections and noise attenuation, significantly enhanced signal-to-noise ratio and seismic resolution. Furthermore, the paper discusses various fault imaging techniques with their shortcomings. The data reveal a complex network of intersecting fault strands, confirming general thrust fault geometry of the fault system, that align with the region’s tectonic evolution. These findings enhance understanding of the Holy Cross Mountains’ structural framework and provide valuable reference data for future studies of similar tectonic environments. Full article

In this paper, we propose a method for locating discontinuities in grounding grids using a multistatic radar. The objective is to determine the fault position in the structure by injecting an ultra-wideband pulse (Gaussian monocycle) at one of the corners of the grid and analyzing the transient signals obtained at two sensors and at the transceiver. To perform the analysis and validation of the developed method, simulations based on the finite-difference time-domain (FDTD) technique were carried out to numerically solve Maxwell’s equations. The voltage signals obtained in an intact grounding grid are used as a reference. Differences between these reference voltages and the voltages obtained with the faulty grid are calculated. With these difference signals, the parameters of the radar ellipses and circle are obtained, which delimit the area where the fault can be found. These parameters depend on the wave propagation speed and the arrival times of the signals at the sensors and the transceiver. The results show that the proposed method is able to reduce the estimated fault location area to a range of 2% to 19% of the total grid area. In addition, the average distance between the actual fault and the center of the estimated region varies between 3.0 and 4.0 m. Full article

This paper presents a new method for fault location in transmission lines with series compensation, using data from voltage and current measurements at both terminals, applied to artificial neural networks. To determine the fault location, we present the proposal of using current phasors, obtained from the oscillography recorded during the short circuit, as the input to the neural network for training. However, the method does not rely on the internal voltage values of the sources or their respective equivalent Thevenin impedances to generate training files for the neural network in a transient simulator. The source data are not known exactly at the time of the short circuit in the transmission line, leading to greater errors when neural networks are applied to real electrical systems of utility companies, which reduces the dependency on electrical network parameters. To present the new method, a conventional fault location algorithm based on neural networks is initially described, highlighting how the dependency on source parameters can hinder the application of the artificial neural network in real cases encountered in utility electrical systems. Subsequently, the new algorithm is described and applied to simulated and real fault cases. Low errors are obtained in both situations, demonstrating its effectiveness and practical applicability. It is noted that the neural networks used for real cases are trained using simulated faults but without any data from the terminal sources. Although we expect the findings of this paper to have relevance in transmission lines with series compensation, the new method can also be applied to conventional transmission lines, i.e., without series compensation, as evidenced by the results presented. Full article

Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. However, existing fault diagnosis methods often trade off between high accuracy and localization. To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning. By irradiating different frequency-modulated light, this method separates photocurrent and directly measures the photoelectric conversion efficiency of each panel, achieving both high accuracy and localization. Through machine learning classification algorithms, the current amplitude and frequency of each photovoltaic panel are identified to achieve fault identification and localization. Compared to other methods, the strengths of this method lie in its ability to achieve high-speed and high-accuracy fault identification and localization by measuring only the short-circuit current. Additionally, the equipment cost is low. The feasibility of the proposed method is demonstrated through practical experimentation. It is determined that when utilizing a neural network algorithm, the fault identification speed meets measurement requirements (5800 obs/s), and the fault diagnosis accuracy is optimal (97.8%). Full article

On 5 September 2022, the moment magnitude (Mw) 6.7 Luding earthquake struck in the Xianshuihe Fault system on the eastern edge of the Tibet Plateau, illuminating the seismic gap in the Moxi segment. The fault system geometry and rupture process of this earthquake are relatively complex. To better understand the underlying driving mechanisms, this study first uses the Interferometric Synthetic Aperture Radar (InSAR) technique to obtain static surface displacements, which are then combined with Global Positioning System (GPS) data to invert the coseismic slip distribution. A machine learning approach is applied to extract a high-quality aftershock catalog from the original seismic waveform data, enabling the analysis of the spatiotemporal characteristics of aftershock activity. The catalog is subsequently used for fault fitting to determine a reliable fault geometry. The coseismic slip is dominated by left-lateral strike-slip motion, distributed within a depth range of 0–15 km, with a maximum fault slip > 2 m. The relocated catalog contains 15,571 events. Aftershock activity is divided into four main seismic clusters, with two smaller clusters located to the north and south and four interval zones in between. The geometry of the five faults is fitted, revealing the complexity of the Xianshuihe Fault system. Additionally, the Luding earthquake did not fully rupture the Moxi segment. The unruptured areas to the north of the mainshock, as well as regions to the south near the Anninghe Fault, pose a potential seismic hazard. Full article

GNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few millimeters per year. This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. GNSS data from four CORS stations collected over 1-, 2-, and 3-year intervals with observation durations of 2, 4, 6, 8, and 12 h, were analyzed to generate velocity estimates. Position accuracies, observation intervals, and corresponding velocity accuracies formed two datasets for the East and North components. ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. The findings reveal that the Random Forest, which makes more accurate and reliable predictions by evaluating many decision trees together, achieved over 90% accuracy for both components. Velocity accuracies of ±1.3 mm/year were obtained for 1-year interval data, while accuracies of ±0.6 mm/year were achieved for the 2- and 3-year intervals. Three campaigns were deemed sufficient for Holocene faults with higher slip rates. However, for Quaternary faults with lower slip rates, longer observation periods or additional campaigns are necessary to ensure reliable velocity estimates. This highlights the need for GNSS observation planning based on fault activity. Full article

The Xiong’ershan district is situated on the southern margin of the North China Craton (NCC) and located within the Qinling–Dabieshan Orogen’s orogenic zone. It is adjacent to the XiaoQinling mining district and exhibits very favorable geological conditions for mineralization, as the district contains numerous gold deposits, positioning it as one of the key gold-producing areas of China. The Miaoling gold deposit is a hydrothermal deposit and is controlled by the Mesozoic nearly NS-trending fault. The ore bodies are hosted in the Mesoproterozoic Xiong’er Group of the Changcheng System of volcanic rocks, with reserves reaching large-scale levels. Pyrite is the main gold-bearing mineral and can be classified into four generations: early-stage fine- to medium-grained euhedral to subhedral cubic pyrite (Py1); medium- to coarse-grained euhedral to subhedral cubic granular pyrite in quartz veins (Py2a); fine-grained subhedral to anhedral disseminated pyrite in altered rocks (Py2b); and late-stage anhedral granular and fine-veinlet pyrite in later quartz veins (Py3). Through in situ trace element analysis of the pyrite using LA-ICP-MS, a positive correlation between Au and As was observed during the main mineralization stage; gold mainly exists as a solid solution within the pyrite lattice, and the ablation signal curve reflecting the intensity of trace element signals showed that gold also occurs as micron-scale mineral inclusions. The trace element content suggested a gradual increase in oxygen fugacity from Stage 1 to Stage 2, followed by a decrease from Stage 2 to Stage 3. The Co/Ni values in the pyrite (0.56 to 62.02, with an average of 12.34) exhibited characteristics of magmatic hydrothermal pyrite. The in situ sulfur isotope analysis of the pyrite using LA-MC-ICP-MS showed δ³⁴S values of 4.24‰ for Stage 1, −6.63‰ to −13.79‰ for Stage 2, and −4.31‰ to −5.15‰ for Stage 3. Considering sulfur isotope fractionation, the δ³⁴S value of the hydrothermal fluid during the main mineralization stage was calculated to be between 0.31‰ and 2.68‰. Full article

The Jinshajiang fault zone is the western boundary fault of the Sichuan–Yunnan block, located east of the Qinghai–Tibetan Plateau. It is a complex tectonic suture belt with multi-phase activity and is characterized by multiple sets of parallel or intersecting faults. Using high-resolution image interpretation, seismic geological surveys, and trench studies, we examined the Holocene activity and obtained the paleoseismic sequences on the middle segment of the fault zone. Thus, we could analyze the kinematic characteristics of the fault and its potential risk of strong earthquakes. Our results indicated that the predominant movement of the fault zone was strike-slip motion. In the Jinshajiang fault zone, the Late Quaternary horizontal slip rates of the north-northeast-trending Yarigong fault and the northeast-trending Ciwu fault were 3.6 ± 0.6 mm/a and 2.5 ± 0.5 mm/a, respectively. Three paleoseismic events were identified on the Yarigong fault, dated 6745–3848, 3742–1899, and 1494–1112 cal BP, and on the Ciwu fault, constrained to 32,566–29,430, 24,056–22,990, and 2875–2723 cal BP. The last major earthquake on the Ciwu fault occurred approximately 2800 years ago; therefore, its future seismic hazard deserves attention. Full article

The Pamir is located on the northwestern margin of the Tibetan Plateau, which is an area of intense continental deformation and part of the famous India–Himalaya collision zone. The dominant structural deformation in the eastern Pamir is characterized by a 250 km long east–west extensional fault system, known as the Kongur Shan extensional system (KSES), which has developed a series of faults with different orientations and characteristics, resulting in highly complex structural deformation and lacking sufficient geodetic constraints. We collected Sentinel-1 SAR data from December 2016 to March 2023, obtained high-resolution ascending and descending LOS velocities and 3D deformation fields, and combined them with GPS data to constrain the current motion characteristics of the northeastern Pamirs for the first time. Based on the two-dimensional screw dislocation model and using the Bayesian Markov chain Monte Carlo (MCMC) inversion method, the kinematic parameters of the fault were calculated, revealing the fault kinematic characteristics in this region. Our results demonstrate that the present-day deformation of the KSES is dominated by nearly E–W extension, with maximum extensional motion concentrated in its central segment, reaching peak extension rates of ~7.59 mm/yr corresponding to the Kongur Shan. The right-lateral Muji fault at the northern end exhibits equivalent rates of extensional motion with a relatively shallow locking depth. The strike-slip rate along the Muji fault gradually increases from west to east, ranging approximately between 4 and 6 mm/yr, significantly influenced by the eastern normal fault. The Tahman fault (TKF) at the southernmost end of the KSES shows an extension rate of ~1.5 mm/yr accompanied by minor strike-slip motion. The Kashi anticline is approaching stability, while the Mushi anticline along the eastern Pamir frontal thrust (PFT) remains active with continuous uplift at ~2 mm/yr, indicating that deformation along the Tarim Basin–Tian Shan boundary has propagated southward from the South Tian Shan thrust (STST). Overall, this study demonstrates the effectiveness of integrated InSAR and GPS data in constraining contemporary deformation patterns along the northeastern Pamir margin, contributing to our understanding of the region’s tectonic characteristics. Full article

The Middle Permian Maokou Formation carbonate rocks in the southern Sichuan Basin are import targets for hydrocarbon exploration, with numerous gas fields discovered in structural traps. However, as exploration extends into slope and syncline zones, the limestone reservoirs become denser, and fluid distribution becomes increasingly complex, limiting efficient exploration and development. Identifying the key factors controlling natural gas accumulation is therefore critical. This study is the first to apply deep learning techniques to fault detection in the southern Sichuan Basin, identifying previously undetected WE-trending subtle strike-slip faults (vertical displacement < 20 m). By integrating well logging, seismic, and production data, we highlight the primary factors influencing natural gas accumulation in the Maokou Formation. The results demonstrate that 80% of production comes from less than 30% of the well, and that high-yield wells are strongly associated with faults, particularly in slope and syncline zones where such wells are located within 200 m of fault zones. The faults can increase the drilling leakage of the Maokou wells by (7–10) times, raise the reservoir thickness to 30 m, and more than double the production. Furthermore, 73% of high-yield wells are concentrated in areas of fault intersection with high vertical continuity. Based on these insights, we propose four hydrocarbon enrichment models for anticline and syncline zones. Key factors controlling gas accumulation and high production include fault intersections, high vertical fault continuity, and local structural highs. This research demonstrates the effectiveness of deep learning for fault detection in complex geological settings and enhances our understanding of fault systems and carbonate gas reservoir exploration. Full article

Direct-drive permanent magnet synchronous machines (DDPMSMs) have recently become an ideal candidate for applications such as military, robotics, electric vehicles, etc. These machines eliminate the need for a transmission mechanism and excitation coil circuits, which enhances the system’s overall efficiency and decreases the likelihood of failures. However, it may incur demagnetization faults. Due to the characteristic of having a large number of pole pairs, this type of machine exhibits numerous demagnetization fault modes, which poses a challenge in locating demagnetization faults. This paper proposed a probabilistic neural network (PNN)-based diagnostic system to detect and locate demagnetization faults in DDPMSMs, using information obtained through three toroidal-yoke-type search coils arranged at the bottom of the stator slot. A rotor partition method is proposed to solve the problem of demagnetization fault location difficulty caused by various fault modes. Demagnetization fault location is achieved by sequentially diagnosing the condition of each partition of permanent magnets. Three demagnetization fault identified signals (DFISs) are constructed by the voltage of the three toroidal-yoke coils, which are used as inputs of PNNs. Three PNNs have been designed to map the extracted features and their corresponding types of demagnetization faults. The database for training and testing the PNNs is generated from a DDPMSM with different demagnetization conditions and different operating conditions, which are established through an experimentally validated mathematical model, an FEM model, and experiments. The simulation and experimental test results showed that the accuracy in diagnosing the location of the demagnetization fault is 99.2% when the demagnetization severity is 10%, which demonstrates the effectiveness of the proposed three PNN-based diagnostic approach. Full article

This study examines the hydrogeological response to the 12 September 2016 Gyeong-Ju earthquake (ML 5.8) in the southeastern region of the Korean Peninsula. Using 2D hydro-mechanical coupled bonded particle modeling, we simulated the dynamic fault rupture process to analyze stress redistribution and its impact on pore pressure and groundwater levels (GWLs). The results indicated that compressional areas correlated strongly with pore pressure increases and GWL rises, while extensional areas showed decreases in both. Observations from the groundwater monitoring Well 5 at Gyeong-Ju San-Nae and Well 8 at Gyeong-Ju Cheon-Buk, located approximately 15 km from the earthquake’s epicenter, aligned well with the model’s predictions and interpretation, providing validation for the simulation. These findings highlight the capability of hydro-mechanical models to capture fault-induced hydrological responses and offer valuable insights into the interplay between seismic activity and groundwater systems. Full article

Carbon capture and storage (CCS) technology is a crucial and effective tool to achieve China’s dual carbon goals. The primary locations suitable for underground CO₂ storage include depleted oil and gas reservoirs, deep saline aquifers, and deep unmineable coal seams. Among these, deep saline aquifers are widely distributed in most of the world’s sedimentary basins, and they offer significant advantages—such as substantial storage capacity, well-established technology, high safety standards, and cost effectiveness—making them crucial geological reservoirs for carbon dioxide storage. In comparison to foreign countries’ projects on CO₂ capture, utilization, and storage (CCUS) technology, China’s initiatives have been implemented more recently, and no research has been conducted on the geological storage of CO₂ in the deep saline aquifers within the study area. In this study, we systematically analyzed the key factors for the geological storage of CO₂ in saline reservoirs within the northwest plain of Shandong Province: the Paleogene Shahejie Formation saline aquifer, and the lower reservoir of the Minghuazhen Formation saline aquifer located east of the Zhanhua–Lijin–Dongying line. The CO₂ geological storage potential of these aquifers was assessed using the evaluation methodology of the United States Department of Energy, yielding a result of 30.355 billion tons. An evaluation index system of CO₂ geological storage suitability was established. Evaluation indices for regions in the study area were assigned according to this evaluation index, and the score and grade of each unit were obtained. The results indicated that the Huimin latent fault depression, Dongying latent fault depression, Dezhou latent fault depression, and Dongming–Shenxian latent fault depression are suitable prospective areas for CO₂ geological storage in the saline aquifers of Shandong Province’s northwest plain. Full article

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG. Our method leverages large language models (LLMs) to conduct causal analysis on textual data related to equipment faults derived from sensor networks, thereby significantly enhancing the accuracy and efficiency of fault diagnosis. This paper details a series of experiments that validate the effectiveness of the KG-ICL method in accurately diagnosing fault causes and locations of industrial equipment systems. By leveraging LLMs and structured knowledge, our approach offers a robust tool for condition monitoring and fault management, thereby improving the reliability and efficiency of operations in industrial sectors. Full article

This article is about the detection of a partial inter-turn short-circuit fault in a brushless motor with permanent magnets (BLPMM). The detection of a single inter-turn short circuit is a difficult issue. The authors of this article tested the sensitivity of the tested powertrain damage detection method. The diagnostic method developed for BLPMM allows for any configuration of the motor winding. A number of analysed configurations have been applied for the combined star–delta connection (YΔ). For the combined star–delta connection Y/Δ, the problem of partial short circuit at two locations was analysed. In the first case, this was the short-circuit winding phenomena in the star part (SC1). In the second case, the short circuit was connected in part to the delta (SC2). A mathematical model has been developed that takes into account both the type of connection and the chance of a partial short circuit of the coil. Based on numerical calculations, the sensitivity of diagnostic methods is designated for both cases. Furthermore, the impact of partial short circuits on motor performance was also examined. The effect of a partial inter-turn short-circuit fault on motor parameters was also determined. Laboratory verification was carried out. Full article