Fault System Research Articles

Brittle Regime Slip Partitioned Damage and Deformation Mechanisms Along the Eastern Denali Fault Zone in Southwestern, Yukon

AbstractRare bedrock exposures of the eastern Denali fault zone in southwestern Yukon allow for the measurement, sampling, and analyses of brittle regime fault slip data and deformation mechanisms to explore relations to far field, oblique plate motions. Host rock lithologies and associated slip surfaces show episodic damage zone‐related deformation and calcite ± hematite ± chlorite related hydrothermal fluid flow. This regional scale network of asymmetric fault damage is spatially and kinematically linked to a discrete and narrow fault core. Fault network observations, orientations, slip data, and strain inversions document a slip partitioned strike‐slip fault system with locally and mutually overprinting strike‐, oblique‐, and dip‐slip components. Microstructural analyses reveal crystal plastic and co‐seismic brittle deformation mechanisms active in a narrow range of upper crustal temperature, pressure, fluid, and chemical conditions. The net damage related slip is not exclusively formed by a single kinematic system, but rather a fully partitioned, time integrated system likely operative for much of the fault's brittle regime evolution temporally constrained by previously published thermochronometric data. Although the fault slip data was collected from outcrop‐scale exposures at sites tens of kilometers apart, results show remarkable correlation between fault kinematics and plate motions along the ∼580 km long eastern Denali fault segment. End member, subhorizontal, northeast directed reverse and north directed dextral strike slip fault strain axes closely reflect relative plate motion interactions over at least the last 30 m.y. and act as a proxy for far‐field stresses compatible with the kinematics of the damage zone network.

Topographic ridges express late Quaternary faulting peripheral to the New Madrid seismic zone, intraplate USA: Their tectonic implications

Northeast-trending linear topographic ridges in Pliocene and Quaternary sediments adjacent to the New Madrid seismic zone (NMSZ), intraplate North America, have been long speculated to be neotectonic landforms related to reactivation of basement faults of the eastern Mississippi Valley Rift margin. Earthquake epicenters and paleoseismological studies show that eastern rift margin faults (ERMF) were active during late Quaternary south of a restraining bend in the Mississippi Valley Rift fault complex, but the ERMF zone north of the restraining bend (ERM-N) and underlying the linear ridges is seismically quiescent. A previous P-wave seismic reflection survey across the most prominent linear ridge (Rives lineament) revealed that it overlies a horst in Eocene sediment. To ascertain whether the Rives lineament could have been produced by surface faulting/folding, we collected electrical resistivity tomography (ERT) and ground penetrating radar (GPR) profiles to image the ridge's shallow subsurface features. We interpret shallow folding and faulting of late Pleistocene sediment in our ERT and GPR images. Additionally, convexity of multiple topographic profiles of scarps along margins of the Rives lineament was quantified and compared to the convexity of profiles of known neotectonic scarps and of fluvial terrace riser profiles within the region. Comparison of these profiles reveals a high degree of similarity between the scarps along the Rives lineament margins and the Holocene Reelfoot thrust scarp. When combined, our results strongly suggest late Pleistocene or Holocene folding and faulting created this ridge despite its current seismic quiescence, and thus they provide useful insight for assessing the seismic hazard potential of quiescent faults in strike-slip systems in general. We conclude that the late Quaternary series of movements documented on the Reelfoot thrust was initiated to accommodate crustal shortening after transpressional fault movements on the ERM-N became inactive in late Pleistocene or early Holocene and that movement along the ERM-N may resume if the Reelfoot thrust has an interval of inactivity in the future. Our results suggest how fault interactions within a strike-slip system can change, making faults with significant amounts of seismic potential appear as though they are inactive.

Evidence for an aseismic gap between the Mw6.8 Pütürge (Elazığ) and 7.8 Pazarcık (Kahramanmaraş) earthquakes in the East Anatolian Fault System, southeast Türkiye

Evidence for an aseismic gap between the Mw6.8 Pütürge (Elazığ) and 7.8 Pazarcık (Kahramanmaraş) earthquakes in the East Anatolian Fault System, southeast Türkiye

Dynamic responses of monopile offshore wind turbines under different wind-wave misalignment angles

Wind-wave misalignment is a common occurrence for monopile offshore wind turbines during extreme typhoon conditions. This phenomenon has a significant impact on the structural dynamic response of turbine structures and poses a threat to their structural integrity. In this study, finite element analysis is conducted on 75 sets of working conditions of monopile offshore wind turbines to investigate their dynamic response under different wind turbine operating states, wind-wave misalignment angles, and randomness of wind and wave load conditions. The results of the analysis indicate that the impact of wind-wave misalignment angles on the dynamic response of the monopile offshore wind turbine varies depending on the operational conditions. Furthermore, the study reveals that there are variations in the dynamic response of the monopile offshore wind turbine under different combinations of wind and wave loads. During the parked state and yaw system fault of the monopile offshore wind turbine, the most adverse working conditions need to consider wind-wave misalignment angles of 0° and 180°. In contrast, the most adverse working conditions for operational states emerge when the wind-wave misalignment angle is 90°. These findings highlight the importance of considering wind-wave misalignment angles and the randomness of wind and wave load conditions when assessing the dynamic response and structural integrity of monopile offshore wind turbines.

Characteristics and fire-inducing risk analyses of arc faults in low-voltage electrical systems

Arc fault is a prevalent phenomenon in low-voltage residential power systems and the main cause of electrical fires. This paper explores the fire risks associated with arc faults and employs standards from arc fault detection devices (AFDD) to create an arc fault experimental system complemented by an integrated multi-sensor system for ignition related data collection. The objective is to synthetically examine the dynamic characteristics and ignition mechanisms of arc faults. This paper investigates the generation mechanisms, waveforms, and energy characteristics of arc faults, including waveform distinctions across different load types and patterns of energy variation during arcing. Data from the multi-sensor system become the cornerstone of the correlation analysis between the current, energy, and ignition phenomena, facilitating the construction of an ignition probability model based on maximum likelihood estimation. The reliability of the model is verified across various datasets with a 95 % confidence interval, offering a quantitative fire-inducing risk assessment of arc faults. Furthermore, the paper assesses the AFDD standards’ break time limits and evaluates the performance of seven AFDD prototypes based on the proposed model, demonstrating the effectiveness of this technology in mitigating the risks of fires induced by arc faults. This study systematically analyzes the behaviors and ignition mechanisms of arc faults, thereby providing scientific evidence and data that support the enhancement of arc fault detection technologies and the development of fire prevention strategies.

Relocation of the 8 September 2023 High Atlas, Morocco, Earthquake Aftershock Sequence

ABSTRACT The earthquake that occurred on 8 September 2023, with a magnitude of 6.8, was the most destructive earthquake event in Morocco in the past decade. This earthquake took place in the Al Haouz region, located in the western part of the High Atlas Mountain range. To better understand what caused and triggered this earthquake, the earthquake catalogs including P and S arrival times were collected from the Moroccan seismic network and combined with regional data from the International Seismological Centre. The mainshock and aftershocks were relocated by using iLoc, a state-of-the-art single-event location algorithm, and then by the multiple event location double-difference algorithm, hypoDD. The improved earthquake relocations using iLoc and the double-difference methods provide sharper lineation of seismicity and agree well with tomographic images of the earthquake zone. The seismicity distribution and the focal mechanism of the mainshock indicate that the earthquake sequence has occurred along the South Atlas fault system.

Lineaments Mapping Using Shaded Relief Images Derived from Landsat-9 Imagery at Diyala Government, NE Iraq

The Landsat-9 satellite images with a 30 m resolution at Diyala government was evaluated using the shaded relief technique to extract and map the surface lineaments. The processing of the azimuth and latitude method results in four shaded images (315\45, 200\50, 50\90, and 110\60). The optimal shaded relief (110\60) was used to extract the lineaments using the manual process. The results indicated that the primary structural trend of the lineaments was in the NW-SE direction, with a minor presence of NE-SW trends. It can be concluded that these structures are closely associated with the Makhoul-Hamrin trend in the Low Folded Zone (LFZ) influenced by the prevailing tectonic regime. In contrast, the lineaments structural trend of NE-SW would result from the neotectonics regime that linked to the listric fault system. On the other hand, the southeastern part of the study area shows no surface features due to the stress of the tectonic regime that affected the Tertiary deposits.

Vulnerability Assessment for Naval Ships against Air-Explosive Impulses: Modified Damage-Extent Method Incorporating Structural Capacity

Abstract This study introduces an enhanced damage-extent method that incorporates the structural load-bearing capacity of the hull to assess the vulnerability of naval ships to explosive loads. This vulnerability assessment predicts the area of damage to the hull structure and calculates the probability of onboard equipment experiencing functional losses due to the explosive load, thus allowing various design alternatives to be evaluated. The proposed methodology improves upon traditional damage-volume-based approaches, such as damage-radius and ellipsoid methods, by considering the hull's structural stiffness and intrinsic damage resistance. It integrates the hull's structural resistance to the load, enhancing the damage assessment process for both the hull and equipment. This approach facilitates damage prediction for different hull designs by comparing the allowable impulse with the explosive pressure. In assessing the functionality loss and vulnerability of the equipment within the damaged hull, the network of equipment functions is considered. An anti-ship cruise missile with a sea-skimming trajectory is investigated as the explosive charge, with procedures established to simulate its trajectory and impact location on the hull. Hundreds of potential internal and external explosion points are generated, predicting the explosive pressure at each location. The shock wave, including incident overpressure, reflection pressure, and quasi-static gas pressure, is converted into impulses, taking into account the configuration of hull compartments to accurately predict these pressures and equivalent impulse. The resulting impulse is compared with the intrinsic damage capacity of each compartment's structure to assess potential damage. System network and fault tree analysis evaluate the loss of function and vulnerability of equipment within the damaged hull. Finally, the proposed capacity-based damage extent method demonstrates more accurate damage assessment compared to traditional methods, overcoming the limitations of damage-radius and ellipsoid approaches by considering hull strength

Distinguishing fault offset from climatically modulated channel deflections: Insights from the Pearblossom slip-rate site, Mojave section of the San Andreas fault, California, USA

Fault slip histories are essential for understanding seismic hazard and regional fault system development but fundamentally depend on identifying, dating, and reconstructing displaced markers. Here, we use a case study of the Pearblossom site along the Mojave section of the San Andreas fault in California (USA) to show how pulses of sediment aggradation during wet periods can complicate such reconstructions by producing “imposter offsets”—landforms that develop with an initial deflection that is easily misread as tectonic displacement caused by fault slip. Specifically, we document two channels on the downstream side of the fault: a subtle one that we interpret to have been beheaded and displaced 24–49 m from a source channel on the upstream side of the fault, and a second and more prominent one that we interpret as an imposter offset of 36–88 m. Using optically stimulated luminescence dating, we determine that the source channel incised between 1.44 ± 0.43 ka and 1.27 ± 0.18 ka with a subsequent phase of alluvial fan aggradation at ~0.6 ka, when the channel with the imposter offset formed. Because the pulse of fan deposition coincides temporally with a wet period in Southern California precipitation records, we attribute formation of the imposter offset and the alluvial fan into which it incised to climatically modulated deposition at the site. Comparing precipitation records with charcoal ages compiled from multiple Mojave Desert region locations suggests that other slip-rate sites may be similarly affected. Although climatic effects can complicate slip-rate studies, we show that the morphology and upstream position of the deflected channel can indicate whether a site likely records useful information about fault slip.

Geomorphological Insights to Analyze the Kinematics of a DSGSD in Western Sicily (Southern Italy)

Deep-Seated Gravitational Slope Deformations (DSGSDs) are common in many geological environments, and due to their common limited displacement rate, they can remain unrecognized for a long time. Among the most significant events in Sicily is the Mt. San Calogero DSGSD. To contribute to a better understanding of its characteristics, including the geologic setting promoting its development, ongoing kinematics, and mechanism, a specific analysis was completed. In this paper, the results of this analysis, based on a three-folded strategy, are provided and interpreted in the context of DSGSD predisposing conditions and controlling factors. Especially, field observations associated to visual interpretation of aerial imagery were used for the identification and mapping of main geological features and landforms, high-resolution X-Band DInSAR data enabled researchers to fully characterize the deformational behavior of the slope, while a reduced complexity slope stability analysis allowed them to reconstruct the deep geometry of the DSGSD. Results from the analysis indicate that the DSGSD of Mt. San Calogero is composed of three blocks corresponding to fault-bounded tectonic elements and characterized by a specific kinematics and sensitivity to external forcing (i.e., rainfall), multiple landslides are associated to the DSGSD in the area and the deep geometry of the DSGSD is concave upward and resemble the characteristics of a rotational slide. The interpretation of the results suggests that the formation and the deformation of the Mt. San Calogero DSGSD are linked with the local and regional fault systems related to the Sicilian orogen, while shallow landslides are triggered, in clayey terrains, mostly by rainfalls. In addition, the integrated approach reveals that active tectonics and rainfalls in the San Calogero massive relief are the main driving forces of its different deformation behavior.

Advanced Methods for Monitoring and Fault Diagnosis of Control Loops in Common Rail Systems

Common rail systems are a key component of modern diesel engines and highly increase their performance. During their working lifetime, there could be critical damages or failures related to aging, like backlash or friction, or out-of-spec operating conditions, like low-quality fuel with, e.g., the presence of water or particles or a high percentage of biodiesel. In this work, suitable data-driven methods are adopted to develop an automatic procedure to monitor, diagnose, and estimate some types of faults in common rail systems. In particular, the pressure control loop operating within the engine control unit is investigated; the system is described using a Hammerstein model composed of a nonlinear model for the control valve behavior and an extended linear model for the process dynamics, which also accounts for the presence of external disturbances. Three different sources of oscillations can be successfully detected and quantified: valve stiction, aggressive controller tuning, and external disturbance. Selected case studies are used to demonstrate the effectiveness of the developed methodology.

Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

Refrigerant charging discrepancies constitute the predominant malfunctions in air conditioning systems. Achieving the optimal charging level is crucial for system performance, underscoring the importance of precise refrigerant level prediction. This study introduces an algorithm designed for the quantitative detection of refrigerant charging errors by integrating the Markov Transition Field (MTF), Convolutional Neural Networks (CNN), and Multi-head Self-Attention (MSA) mechanisms. A high-precision enthalpy difference chamber was employed to establish a Variable Refrigerant Flow (VRF) refrigerant charging test bench. This setup facilitated the analysis of system parameter sensitivity to charging faults and aided in the creation of a training dataset for the algorithm. Comparative analysis was conducted against Support Vector Machines (SVM), Random Forests (RF), CNN with Self-Attention (AT), and MTF-CNN-MSA. The findings reveal that our method adeptly captures temporal dependencies and dynamic shifts in time series as visual representations, offering novel insights for discerning fault patterns within such data. Notably, the maximum pressure variations at high-pressure and low-pressure points were 0.25 MPa and 0.07 MPa, respectively, with temperature shifts of 12 °C and 3.5 °C at the high and low-temperature points. The high-pressure and high-temperature points are particularly sensitive to changes in refrigerant charging, and parameters from these sections were utilized to construct the dataset. The CNN-MSA algorithm demonstrates consistent performance across various fault types, effectively delineating fault characteristics. The accuracies achieved by SVM, RF, CNN-AT, and MTF-CNN-MSA were 84.38 %, 73.75 %, 88.13 %, and 93.75 %, respectively. In comparison, the CNN-MSA algorithm was able to more accurately detect refrigerant charge faults at different levels.

New Approach for Tracking, Monitoring, and Diagnosing Faults in a Photovoltaic System in Real Time: An Experimental and Case Study

Photovoltaic installations have emerged as a cornerstone of sustainable energy production, playing a pivotal role in the global transition towards renewable sources of electricity. As the demand for clean energy surges, so too does the need for robust monitoring and diagnostic strategies to ensure the efficient operation of these systems. This study presents a novel methodology for the real-time tracking, monitoring, and diagnosing of faults in photovoltaic systems (PVSs), emphasizing their crucial role in sustainable energy production amid the global shift to renewable energy. The study was conducted in Guelma, Algeria, during the spring and summer seasons. The investigation utilized WatchPower simulation software over a 24-hour performance analysis of the photovoltaic system. On June 19, 2024, with a high temperature of 32°C, the system achieved a peak output power of 600W under optimal conditions, validating its efficiency in energy generation. The study also analyzed the effects of shading on energy output by comparing data from a shaded day on May 14, 2024, at 25°C, to an unshaded day on June 21, 2024, at 32°C, during peak sunlight hours from 9:00 AM to 2:15 PM, the period when sunlight is at its strongest. The results showed a significant drop in output power from 600W to 450W due to shading, underscoring the importance of real-time monitoring to detect performance inefficiencies. This research not only enhances operational reliability and maintenance strategies for PV systems but also demonstrates the effectiveness of integrating real-time data analytics to support decision-making in similar environments.

Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.

Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.

Research on the Technology of Improving the Compatibility of DC Measurement Devices Based on the Operation Experience of Siemens Hardware Solutions

The aim of this study was to manufacture alternative Siemens DC measurement system equipment to solve the problems of increased failure rates of foreign DC measurement equipment and the stopped production of key parts. In this paper, the overall structure and common faults of the Siemens DC measurement system are introduced, the difference between TDM signal frame structure and FT3 frame structure is detailed, and the related functional structure, chip type, and function of the Siemens distal module and merging unit (including laser function) are studied. The remote module, merging unit (including laser function), and related hardware design and software design to replace the Siemens DC measurement system were developed, and the test environment and control protection, fault recording wave for communication, and other functional tests were built. The test results show that the domestic remote module, merging unit (laser function), and other technologies communicate normally with the existing Siemens and the domestic mainstream manufacturers, and the accuracy meets the requirements of the 0.2 level.

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.

Tectonic activation and the risk of Ilisu Dam collapse to Iraq through modelling and simulation using HEC-RAS

Floods caused by dam failures can cause huge losses of life and property, especially in estuarine areas and valleys. In spite of all the capabilities and great improvements reached by man in the construction of dams and their structures, they will remain helpless before the powerful forces of nature, especially those related to tectonic activation, and the occurrence of earthquakes of different intensities.The region extending from the Ilisu Dam in Turkey to the Mosul Dam in Iraq was chosen as an area for this study, and the HEC-RAS application was used to simulate the collapse of the Ilisu Dam due to a major earthquake, to know the magnitude of the risks and losses that could result from this. The Ilisu Dam was built very close to a highly tectonically active fault system, particularly the East Anatolian Fault (EAF), which is one of the largest tectonically active faults in the world with a length of 500 km. This region has witnessed past and present earthquakes of high magnitude (M > 7), especially in the EAF, so the construction of the Ilisu Dam near the EAF fault system is of great concern, as it was built in a basin with very complex seismic activity and geology.Using the HEC-RAS simulation application, the study found that the flood resulting from the collapse of the Ilisu Dam would reach the edges of the Mosul Dam Lake in just 13 h. With a flow of more than 100,000 m3/s, more than 10 billion m3 of water will flow into the Mosul Dam Lake within four days of the disaster. This will lead to the collapse of the Mosul Dam and direct the flood wave of the collapse of these dams towards Baghdad through Mosul, Tikrit, and Samarra. This could pose risks to all Iraqi cities located within the Iraqi sedimentary plain (Mesopotamia), from south of the Mosul Dam up to Basra, in a scenario similar to Noah’s Flood.

Large non-karst caves of the Altai-Sayan mountain region: morphology and genesis

In the Altai-Sayan mountain region, located in southern Siberia, there are a number of caves developed in rocks in which karst processes are impossible to occur. In East Sayan, these are the Big Oreshnaya and Dudinskaya caves, the total length of which is more than 50 km and 35 km, respectively. Within the Altai Mountains, these are the Altaiskaya and Kek-Tash caves with a total length of 4.7 and 3.2 km, respectively. Mapping of the listed caves revealed that the morphology of their passages and the general structure differ significantly from those of caves in carbonate rocks. The passages have a flattened shape in cross section. Their width is usually meters and tens of meters, vertical extension is tens and hundreds of meters, and lateral extension is hundreds of meters. In general, the pattern of the passages is arranged in a box-like structure, where the pasages fit into a system of disjoint extended subvertical planes with rare short subvertical planes connecting them. The structure of the passages of the studied caves resembles the structure of karst caves, rotated vertically by 90°. The main directions of the passages there are not aligned in subhorizontal planes, but in subvertical ones. The revealed structure corresponds to the geometry of the structural pattern of Late Cenozoic fault systems formed as a result of subhorizontal compression. Signs of abundant argillization (alteration of the initial substance by hydrothermal processes and its replacement by newly formed clay minerals) were found in the studied caves. The formation mechanism of the studied caves is offered: removal of the substrate argillized by hydrothermal processes along the fault zones, by groundwater without significant participation of karst processes.

Rupture process of the 2019 Mw 6.8 Davao Del Sur earthquake, Philippines from geodetic and seismic data

Because of its complex tectonic setting, the Philippines experiences vigorous seismic activity. On December 15, 2019, an Mw 6.8 earthquake struck Davao del Sur, the Philippines, severely damaging infrastructure and adversely affecting the population. In this study, we used Interferometry Synthetic Aperture Radar (InSAR) and teleseismic broadband data to explore the earthquake’s source parameters, rupture process, and tectonic characteristics through a Bayesian modeling approach and Coulomb stress analysis. An analysis of InSAR suggested that the mainshock occurred on an NW–SE oriented single plane within the Cotabato fault system, with the Makilala-Malungon fault being the causative fault. The Joint inversion revealed that the rupture initiated in the middle crust (depth: approximately 16.5 km), exhibiting a blind, left-lateral strike-slip fault rupture propagation with minimal reverse slip and no surface rupture. The slip was concentrated in a distinct asperity spanning a depth of 3–24 km, with the peak slip reaching approximately 1.8 m. The source time function of this event indicated a single pulse with a total source duration of approximately 17 s. The static stress drop value was low, which may be related to the brittle condition of the crust. Coulomb stress changes calculations suggested earthquake risk in the future.