Fault System Research Articles

BIEM_CH: An Efficient Algorithm for Dynamic Rupture Simulation of Complex Fault Systems with Unstructured Meshes and Half-Space Green’s Function

Abstract In this study, a fast 3D dynamic rupture simulation algorithm, named BIEM_CH (Boundary Integral Equation Method for Complex fault systems in Half-space), is presented. This algorithm, based on exact half-space Green’s functions, supports both structured and unstructured discretization schemes, allowing for the effective handling of a wide range of fault geometries, from simple to complex. Because of the semianalytical nature of the boundary integral equation method, the integral kernel (surface integral of the spatial derivatives of Green’s functions) and rupture processes can be computed separately, making BIEM_CH particularly suitable for applications requiring numerous forward simulations, such as dynamic source inversion, for which only the rupture process needs to be recalculated once the integral kernel is obtained. The performance of the algorithm has been significantly enhanced, achieving up to a hundredfold speed increase through the use of exact closed-form solutions for the time-domain half-space Green’s function and matrix operations leveraging graphical processing unit acceleration, resulting in dynamic rupture simulations that can be completed in a matter of seconds. Moreover, BIEM_CH maintains excellent stability when the mesh dimension does not exceed 375 m, irrespective of whether structured or unstructured discretization schemes are used. In addition, this algorithm demonstrates good agreement with other methods in benchmark exercises conducted by the Southern California Earthquake Center and the U.S. Geological Survey’s dynamic rupture code verification project.

Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network

The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability.

The Chuquibamba Landslide Western Cordillera, Peru revisited: New Evidence of a Dry Debris Avalanche

Landslides represent a serious mountain hazard to lives and infrastructure, especially when geological factors such as highly fractured rocks, faulting, steep topography, and weathering combine with seismic triggering factors. Considering the potential of producing outcomes, we study the Chuquibamba rotational landslide that runs along the NW-SE Incapuquio-Challaviento fault system in southernmost Peru. Its elongated U-shaped and polylobate crown scarp is typical of fault-related landslides, and it is carved into the ignimbrites of the Chuquibamba Formation. The geomorphology of the failure and its associated deposit define seventeen coalescing rotational slides and a widespread debris avalanche. This deposit, the main topic of this study, dated at ca. 102 Å} 5 ka using 10Be (from previous works), is confined to the lower parts of the Grande River valley. It is exposed for about 22.5 km from 3,900 to 1,167 masl with its main front located at ~ 10 km upstream of the Majes River. It covers an area of 33.64 km2 with a minimum volume of 0.72 km3. The resulting deposit has an H/L = 0.12, which is typical of dry debris avalanches elsewhere. It consists of block and matrix facies that have different textural and granulometric features. At the time of its emplacement, the moving avalanche overpassed 20 and 12 m-high obstacles, attaining minimum speeds of 20 and 15 m/s at distances of 15 and 20 km from the source, respectively. After the landslide emplacement, the debris avalanche was re-mobilized by intense rains that produced debris flows, as attested by outcrops along the extension of the debris avalanche and beyond its front. All the features of the Chuquibamba dry avalanche, along with modern seismicity and the intersection of active faults in the region, suggest that the failure had a tectonic origin (uplift and movement along faults) instead of deglaciation, extraordinary rain, or extreme rock weathering. Therefore, landslide generation is a potential hazard in this area of Peru.

Volcanic Process Ground Deformation Using DInSAR Observations: The Case Study of the Sabancaya Volcano

Abstract. Remote sensing is one of the most useful tools when it comes to studying earth phenomena along large terrain extensions. Within this broad field, Differential Interferometric Synthetic Aperture Radar (DInSAR) has been evolving, and at this point is able to provide terrain deformation in millimetre-level precision. This analysis was conducted over the Sabancaya Volcano, in Perú, which is part of the Ampato-Sabancaya Volcanic Complex. This volcano has been active and continuously erupting since 2016, with various fault systems surrounding the volcanic complex. The main objective of this work was to map deformation caused by the volcanic system, processing Sentinel-1 products from 2016 and an interval that started in 2014 to 2017 to be able to study the effects of temporal decorrelation on interferometric products, being Sentinel-1 the first of the Copernicus Program satellite constellations conducted by the European Space Agency. The analysis showed deformation in the interval in which the eruption started and a deformation centre in the northern region of the volcanic complex, although the last one was found when studying a longer temporal baseline. Overall, the experiment shows that DInSAR is an effective tool to study volcanic systems.

A review of the Neogene formations and beds in Slovenia, Western Central Paratethys

Neogene sedimentary successions are found in eastern and northeastern Slovenia. Their formation was closely related to the evolution of the western part of the Pannonian Basin System and the Central Paratethys; it was influenced by global transgressive and regressive cycles as well as by global, regional and local tectonics. Several formations and beds were defined within the Neogene sedimentary successions which can be found in three separate areas. The first one includes the formations north of two major fault systems, the Periadriatic Fault System and the Mid-Hungarian Zone, which were associated with the Mura-Zala and the Styrian Basins, respectively. Successions south of these major faults are not formally connected to any of the major basins and developed in two distinct parts: a strip between Tunjice and Kozjansko to the north and the Krško area to the south. From the Egerian to the early Badenian, different areas exhibit different types of sedimentation, whereas from the middle Badenian onwards, sedimentation in different zones is partly comparable, depending on the type of depositional environment. Egerian and Eggenburgian sediments are only found south of the above-mentioned major fault zones, whereas Karpatian sedimentation only occurred north of them. Sediments in both areas are characterized by alternation of shallow marine, brackish and terrestrial sedimentation. In contrast, sedimentary environment in the Badenian evolved from a terrestrial, mainly fluvial environment with alluvial fans, through a transitional stage (delta and lagoon) to a shallow and deep marine environment. The Sarmatian sequences reflect a brackish environment with decreasing salinity and continue into extensive Pannonian delta systems that gradually fill the basins from west to east. The formations that correspond to the Pannonian in the Mura-Zala Basin can be correlated with the formations in the Krško area. The Pliocene is characterized by terrestrial sedimentation with the formation of rivers and lakes in the newly established intramontane basins.

Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach

Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective was further divided into sub-objectives, which included determining types of faults in power transmission systems, examining fault detection machine-learning models in power transmission systems, and assessing levels of accuracy of fault detection techniques in power transmission systems. The study used a qualitative research design, which entailed a systematic literature review that involved identifying scholarly articles from respectable international journal websites that aligned with examining how the deep-learning model enhanced fault detection capabilities in a three-phase transmission line simulated dataset. The inclusion criteria were that the study used journals published between 2023 and 2024. The study also included journals that tested Artificial Intelligence systems' accuracy in fault detection in three-phase transmission lines and distribution transformers. The study excluded sources that were older than 2023. The sources of information were journals written by professionals and scholars in the field of Artificial intelligence and engineering. The sourced journals used simulated and real databases to test fault detection accuracy in three-phase transmission lines and distribution transformers. The source of data for review was obtained from the internet via Google search with various credible academic journal websites being searched by the researcher. A total of 12 sources were searched. To assess the risk of bias in the included studies, the researcher used the Robvis methods that visualized risk-of-bias during the inclusion criteria for sources used in the systematic review. The techniques used to present the findings were narratives, graphs, and tables. The method used to synthesize results was comparative data analysis. The study included two studies, the sample size from the three possible journals the researcher identified. The characteristics of the studies were that they both informed the study's general and specific sub-objectives. The two studies also used the same methods to arrive at their findings, which made them ideal for comparative analysis. Finally, both studies compared older systems to new systems regarding Artificial Intelligence accuracy in fault detection. The study's findings were as follows: The study ranked the Novel glass box-based model as the best artificial intelligence machine-learning approach for accurately detecting electric power transmission lines and systems faults. The Novel glass box-based approach's accuracy was 99%, followed by the Convolution Neural Network model's accuracy of 98%, the Gated Recurrent Unit model's accuracy of 92%, the Random Forest model's accuracy of 90%, the Logistic Regression model's accuracy of 74%, and the Support Vector Classifier model's accuracy of 63%. The review was adequate to conclude that the Novel glass-box-based proposed approach was the most advanced and suitable model for detecting faults in electric power transmission lines. The study had one limitation: it reported the results based on two sources, which made the exclusion criteria a point of bias. The survey results implied that builders of new Artificial intelligence systems for fault detection in electric line transmitters should aim to produce 99% accurate systems to meet the recommendations and fill the gap that this study has reported. This study was funded by the researcher using family contributions. The study's author is Nijat Taghizad (ORCID ID: 0009-0005-0505-8767).

Earthquake Fault Rupture Modeling and Ground-Motion Simulations for the Southwest Iceland Transform Zone Using CyberShake

ABSTRACT CyberShake is a high-performance computing workflow for kinematic fault-rupture and earthquake ground-motion simulation developed by the Statewide California Earthquake Center to facilitate physics-based probabilistic seismic hazard assessment (PSHA). CyberShake exploits seismic reciprocity for wave propagation by computing strain green tensors along fault planes, which in turn are convolved with rupture models to generate surface seismograms. Combined with a faultwide hypocentral variation of each simulated rupture, this procedure allows for generating ground-motion synthetics that account for realistic source variability. This study validates the platform’s kinematic modeling of physics-based seismic wave propagation simulations in Southwest Iceland as the first step toward migrating CyberShake from its original study region in California. Specifically, we have implemented CyberShake workflows to model 2103 fault ruptures and simulate the corresponding two horizontal components of ground-motion velocity on a 5 km grid of 625 stations in Southwest Iceland. A 500-yr-long earthquake rupture forecast consisting of 223 hypothetical finite-fault sources of Mw 5–7 was generated using a physics-based model of the bookshelf fault system of the Southwest Iceland transform zone. For each station, every reciprocal simulation uses 0–1 Hz Gaussian point sources polarized along two horizontal grid directions. Comparison of the results in the form of rotation-invariant synthetic pseudoacceleration spectral response values at 3, 4, and 5 s periods are in good agreement with the Icelandic strong motion data set and a suite of empirical Bayesian ground-motion prediction equations (GMPEs). The vast majority of the physics-based simulations fall within one standard deviation of the mean GMPE predictions, previously estimated for the area. At large magnitudes for which no data exist in Iceland, the synthetic data set may play an important role in constraining GMPEs for future applications. Our results comprise the first step toward comprehensive and physics-based PSHA for Southwest Iceland.

Geochemical Characteristics and Origin of Crude Oils from Sunda and Asri Basins, Indonesia

The Sunda and Asri Basins are back-arc basins located SE of Sundaland at the present day. These basins contain Cenozoic sediments deposited in a Paleogene half-graben system. This rifing was followed by sagging during the Neogene associated with a north-south major fault system in the eastern part. The Sunda and Asri Basins constitute significant petroliferous basins in Indonesia which have produced more than a billion barrels of oil equivalent. Some studies conducted focus on geochemistry only in Sunda or Asri Basins, but a comprehensive geochemical study integrating data from both basins have never been conducted. This is the first paper to integrate the geochemical characteristics and the origin of crude oils from Sunda and Asri Basins. A total of eighteen crude oils were investigated to delineate their characteristics. The results provide an explanation about the origin of the organic matter and the genetic relationship between crude oils from Sunda and Asri Basins, and their probable source rocks. This study presents an in-depth geochemical characterization. The crude oils were classified as aliphatic oils, as indicated by their high saturated hydrocarbon fractions (>50 %). The API gravity values of the crude oils range from 25.3 to 39, and their sulfur content was low (0.01 %). Geochemistry analysis reveals a novel interpretation of crude oils originating from shale source rock deposited under oxic-suboxic conditions. Sunda and Asri crude oils exhibited relatively similar stable carbon compositions. The modest concentrations of biomarker 18α(H)-oleanane (OL) indicate that the source age is younger than Late Cretaceous. The crude oil in the Sunda and Asri Basins were charged from source rocks that reached early to peak maturity. The results of the current study strongly indicate the differentiation in lacustrine organofacies of crude oils from the Sunda and Asri Basins. The source rock for these crude oils were shales from the Paleogene syn-rift Banuwati Formation.

The Unusual Seismic Activity from 2021 to 2024 in Eastern Austria: Insights from Seismic Sequences Relocation and Moment Tensor Inversion

ABSTRACT From 2021 to 2024, unusually strong seismic activity, including multiple sequences with five 3.8≤ML≤4.6 shocks, struck the Vienna basin and Mur-Mürz fault systems in Eastern Austria. Three earthquakes (ML 4.6, 4.5, and 3.8) occurred between March and May 2021. These earthquakes were followed by an increase in seismic activity in March 2023, with an ML 4.3 earthquake to the southwest of the 2021 events. In February 2024, an ML 4.5 earthquake occurred 10 km southwest of the ML 4.3 in 2023. We investigate activated faults relying on nonlinear relocations of the seismic sequences and probabilistic moment tensor inversion. The seismic sequences’ temporal evolution indicates a migration of seismic activity and a decrease of focal depths from northeast to southwest, suggesting a potential reactivation of the major fault systems in the region. The joint interpretation of the results shows distinct clusters along fault segments and focal mechanisms that match the region’s complex tectonic activity.

Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear

Nowadays, induction motors and gearboxes play an important role in the industry due to the fact that they are indispensable tools that allow a large number of machines to operate. In this research, a diagnosis method is proposed for the detection of different faults in an electromechanical system through infrared thermography and a convolutional neural network (CNN). During the experiment, we tested different conditions in the motor and the gearbox. The induction motor was operated in four conditions, in a healthy state, with one broken bar, a damaged bearing, and misalignment, while the gearbox was operated in three conditions with healthy gears, 50% wear, and 75% wear. The motor failures and gear wear were induced by different machining operations. Data augmentation was then performed using basic transformations such as mirror image and brightness variation. Ablation tests were also carried out, and a convolutional neural network with a basic architecture was proposed; the performance indicators show a precision of 98.53%, accuracy of 98.54%, recall of 98.65%, and F1-Score of 98.55%. The system obtained confirms that through the use of infrared thermography and deep learning, it is possible to identify faults at different points of an electromechanical system.

Seismic Activity Along the Periadriatic and Sava Faults in the Past Two Millennia—An Archaeoseismological Assessment

Most of the Periadriatic Fault System has been active during the Oligocene and Miocene times. Its western part seems to be almost inactive ever since, while the eastern segments show limited seismic activity. We conducted a systematic archaeoseismological survey along the Periadriatic-Sava fault system, assessing buildings and archaeological sites for earthquake damage. Eleven sites, four Roman and seven Medieval, bear evidence of destructive earthquakes which occurred during the past 2000 years. These are (from east to west): Roman Siscia (Sisak) near the Sava fault in Croatia, Roman Celeia (Celje) at the Savinja/Sava faults in Slovenia, Magdalensberg (Roman) just north of the Karavanka fault, Medieval Villach, the Dobratsch landslide and Medieval Arnoldstein at the junction of Mölltal and Gailtal faults, Medieval Millstatt, Sachsenburg. and Roman Teurnia on the Mölltal Fault, Medieval Lienz (all in Austria) and San Candido on the Pustertal fault, as well as Medieval Merano and Tirol (in Italy) adjacent to the North Giudicarie fault zone. Damaged upright walls of Medieval buildings and deformed floors of Roman settlements testify to local intensity up to IX. Ongoing studies of archaeological stratigraphy and construction history allow the dating of one or more seismic events at each site, ranging from the 1st century AD to the 17th century. It is remarkable that the sites, 20 to 70 km apart, along a <400 km long segment of the Periadriatic Fault system, carry evidence for so many high-intensity destructive events, suggesting that the region is tectonically active.

Interpretation of a 3D Magnetotellurics Model of the Aceh and Seulimeum Segments of the Sumatran Fault Zone

The Sumatran Fault runs from the southeast (SE) to the northwest (NW) of Sumatra Island, with the highest slip rates reaching about 3.0 cm per year in the northwestern part. There is a seismic gap along this fault, including the northern Aceh domain, which consists of the Aceh and Seulimeum fault segments. Previous studies have used various methods to investigate the Sumatran Fault system, including seismic, geoelectric, gravity anomaly, and magnetotellurics (MT). The MT method has proven advantageous as it can non-destructively image a wide range of depths. However, previous studies using the two-dimensional (2D) MT inversion did not represent realistic information of the subsurface conditions. Therefore, a three-dimensional (3D) MT data inversion was used in this study to obtain more realistic information about the resistivity structure of the Aceh and Seulimeum segments. The results confirmed that the Sumatran Fault is a strike-slip fault, with a relatively northwest (NW)–southeast (SE) direction of conductivity strike with an angle of S 71.61° E from Groom–Bailey decomposition of MT data. The 3D resistivity distribution model from 33 stations showed that the Aceh Fault Segment is 20–30 km away, while the Seulimeum Fault Segment is 55–60 km away based on the MT data. The results also indicated a creeping zone at a depth of 2 km beneath the Aceh Fault Segment. Different rock formations were identified beneath the fault system, with the western part of the Aceh Segment dominated by high-resistivity metamorphic rocks (150–1000 Ωm) from the Triassic–Cretaceous age. The zone between the Aceh and Seulimeum fault segments exhibited low resistivity, characterized by volcanic rocks (1–15 Ωm) from the Lam Teuba Volcanic Formation and the Indrapuri Formation. Beneath the eastern part of the Seulimeum Fault Segment was found to consist of low-resistivity quaternary volcanic rocks (1–15 Ωm) and high-resistivity andesite rocks (4.5 × 104–1.7 × 105 Ωm). These findings correlated well with the geological map.

Fault-tolerant observer-based leader-following consensus control for LPV multi-agent systems using virtual actuators

This paper introduces a fault-tolerant consensus control approach for a continuous multi-agent system with actuator faults. The faults in the multi-agent system are modelled as additive and multiplicative actuator faults. The objective is to design a leader-following control for consensus-based Linear Parameter Varying (LPV) multi-agent systems. The proposed control approach employs a leader-following strategy, where the dynamics of the LPV virtual leader are defined. The synchronisation error between the followers and the virtual leader is considered, and a consensus control law is introduced. The fault-tolerant mechanism involves the design of a distributed LPV observer and an LPV virtual actuator. Fault-tolerance is achieved through the redistribution of control inputs based on the effectiveness of actuators and dynamic virtual actuators. The primary focus is on establishing Linear Matrix Inequalities (LMIs) conditions ensuring the existence of LPV consensus control, LPV observers, and LPV virtual actuator gains. To demonstrate the effectiveness of the proposed fault-tolerant control, simulation results considering a team of Unmanned Aerial Vehicles (UAVs) in formation under actuator faults are presented.

Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

The Limits of Applicability of the Gutenberg–Richter Law in the Problems of Seismic Hazard and Risk Assessment

The Gutenberg–Richter law establishes a log-linear relationship between the number of earthquakes that have occurred within some spatiotemporal volume and their magnitude. This similarity property presumably reflects fractal structure of the fault system in which earthquake sources are formed. The Gutenberg–Richter law plays a key role in the problems of seismic hazard and risk assessment. Using the Gutenberg–Richter relationship, we can estimate the average recurrence period of strong earthquakes from the recurrence rate of weaker earthquakes. Since the strongest earthquakes occur infrequently, with intervals of a few hundred years or more, it is not possible to directly assess their recurrence. From indirect geologic and paleoseismic estimates it often seems that strong earthquakes on individual faults occur more frequently than expected in accordance with the Gutenberg–Richter law. Such estimates underlie the hypothesis of the so called characteristic earthquakes. This hypothesis is in many cases additionally supported by the form of the magnitude–frequency distributions for individual faults, constructed from the data of modern earthquake catalogs. At the same time, an important factor affecting the form of the magnitude–frequency distribution is the choice of the spatial domain in which the distribution is constructed. This paper investigates the influence of this factor and determines the conditions under which the Gutenberg–Richter law is applicable for estimating the recurrence of strong earthquakes.

Digital Voting with Blockchain using Interplanetary File System and Practical Byzantine Fault Tolerance

Traditional voting schemes are often overwhelmed by problems such as deception, influence, and incompetence, which can be resolved by applying blockchain technology with transparency, decentralization, and immutability. This study proposes a safe and indisputable digital voting system with blockchain technology to maintain the integrity of the voting procedure. The reliability and privacy of the voting procedure are upheld with distributed ledger technology and cryptographic techniques. The essence of the proposed method is the immutability of the blockchain ledger, which ensures a tamper-proof record of each cast vote, promoting transparency and offering a way of audit for free verification. The proposed method employs cryptographic protocols to protect individual votes while preserving complete transparency and verifiability of the voting procedure. The InterPlanetary Filesystem (IPFS) is applied to ensure data integrity. Moreover, the practical Byzantine Fault Tolerance (pBFT) consensus algorithm is utilized to remove glitches in distributed settings. The proposed approach provides a decentralized platform where voters can cast their votes from anywhere without difficulty using an internet connection, eliminating the need for physical ballot papers and polling stations. Using immutable ledger and cryptographic security aspects in blockchain, the reliability of the voting procedure can be protected while maintaining voter anonymity and confidentiality. Finally, it is shown that the proposed scheme outweighs other existing approaches.

Strain Partitioning and Strike‐Slip Faulting in the Nanjieshan, North Tibetan Foreland: Progressive Widening of the Altyn Tagh Fault System With Implications for Regional Earthquake Hazards

AbstractLarge continental strike‐slip fault systems typically comprise a principal displacement zone and subordinate splay faults, exhibiting complex geometries, connectivity, and kinematics. Understanding the three‐dimensional arrangement of major and minor faults is important for determining strain distribution, landform development, and potential earthquake behavior. This study examines the Quaternary deformation, fault kinematics, and late Holocene ruptures along the Nanjieshan Fault system (NJSF), a major branch of the Altyn Tagh Fault (ATF) system in the North Tibetan Foreland. The E‐W‐trending NJSF comprises a principal strike‐slip fault flanked by two thrust faults, forming a regional, sinistral positive flower structure. Cosmogenic exposure dating, field data and remote sensing observations indicate that the strike‐slip rate of the main NJSF is ∼0.5 mm/a since ∼45 ka, consistent with the low slip rates observed on other faults northwest of the ATF. Paleoseismological trenches reveal three surface‐rupturing events on the NJSF in the last 2,500 years that may coincide temporally with the last two major earthquakes documented on the principal ATF, suggesting quasi‐simultaneous rupture behavior. Coulomb stress change modeling indicates that a future ATF earthquake is unlikely to trigger failure along the NJSF. Conversely, rupture of the NJSF could induce stress loading on the ATF, potentially bringing it to failure. A synchronous rupture on the ATF and its major branch faults could be facilitated by mechanically weakened fault rocks and supra‐hydrostatic fluid pressures within the deep southward‐dipping branch faults of the North Tibetan foreland. We conclude that improved prediction of future slip behavior along major strike‐slip fault systems requires detailed geochronological analysis of paleo‐earthquake events along subordinate branching faults.

LogOW: A semi-supervised log anomaly detection model in open-world setting

Log anomaly detection is a method for finding abnormal behavior and faults in systems. However, existing methods face two main challenges: the open-world problem and the cold-start problem. The open-world problem means that the test set may contain new classes that are not in the training set, while the cold-start problem means that the initial training data are scarce, both for normal and abnormal log sequences. Most existing methods assume a closed-world setting and rely on sufficient normal data, which limits their adaptability to new log environments.We propose LogOW, a novel log anomaly detection model that can learn from a few normal log sequences. The model finds emerging normal log sequences in the open-world setting through the open-world sample retrieval module. Through the incremental pre-training module, these log sequences are fine-tuned in an online mode for model parameters.First, we train a basic model from normal log sequences using Masked-Language Modeling(MLM). During the testing phase, we then combine the anomaly score and the uncertainty score obtained through a novel dynamic multi-mask to distinguish closed-world normal log sequences from the test set. Next, we cluster the open-world log sequences based on fused sequence and count features, and identify the abnormal ones and the new normal ones. Finally, we update our model with the new normal sequences in the next time period. Experiments on three log datasets and real-world airport logs show that our model outperforms traditional models in the open-world and lack of training data setting.

Long-Lived Seismic Instability of Large Intraplate Brittle Shear Zones Revealed by Distributed Slip Zones and Paleoseismic Frictional Melt, Eastern Botswana

In cratonic interiors, long-lived brittle shear zones host records of polyphase deformation, representing inherited structures that can host damaging earthquakes. Here, we explore the internal structure of the Kgomodikae Shear Zone (KSZ), signifying the western continuation of the ∼800-km long Precambrian Kgomodikae-Thabazimbi-Murchinson Fault System which extend along a region of widespread seismicity in southern Africa. At satellite-scale, the KSZ exhibits ENE-to-NE-striking subparallel zones of alternating high/low lineament clustering intensities, with peak-intensity zones that represent hydrologically-permeable principal brittle shear bands. In outcrops, we find pervasive occurrence of slip surfaces with dominant strike-slip paleo-slip vectors, and silica-cemented fault rocks hosting collocated quartz and pseudotachylyte vein clusters. Ground-based scanline fracture mapping reveals peak damage intensity in proximity of the satellite-mapped lineaments (localized high strain zones?), but with the pseudotachylytes occurring in both the peak- and their flanking lower-intensity damage zones. The results suggest that the KSZ hosted paleoseismic ruptures that were not confined to its principal slip zones but may have nucleated along or ruptured into the off-fault splays; and that the NW-striking splays have higher present-day shear reactivation tendency. In general, our findings highlight the nature of preexisting off-fault damage networks that potentially facilitate earthquake rupture and propagation patterns in intraplate regions.

Analysis of the Correlation between the Strike Direction of Oil-Gas Accumulations and Fault Systems in the Western South China Sea

Analysis of the Correlation between the Strike Direction of Oil-Gas Accumulations and Fault Systems in the Western South China Sea