The MW5.5 earthquake on August 6,2023,in Pingyuan,Shandong,China:A rupture on a buried fault

On August 6,2023,a magnitude MW5.5 earthquake struck Pingyuan County,Dezhou City,Shandong Province,China.This event was significant as no large earthquakes had been recorded in the region for over a century,and no active fault had been previously identified.This study collects 1309 P-wave arrival times and 866 S-wave arrival times from 74 seismic stations less than 200 km to the epicenter to constrain the spatial distribution of the mainshock and its 125 early aftershocks by the double difference earthquake relocation method,and selects 864 P-waveforms from 288 stations located within 800 km of the epicenter to constrain the focal mechanism solution of the mainshock through centroid moment tensor inversion.The relocation and the inversion indicate,the Pingyuan MW5.5 earthquake was caused by a rupture on a buried fault,likely an extensive segment of the Gaotang fault.This buried fault exhibited a dip of approximately 75°to the northwest,with a strike of 222°,similar to the Gaotang fault.The rupture initiated at the depth of 18.6 km and propagated upward and northeastward.However,the ground surface was not broken.The total duration of the rupture was~6.0 s,releasing the scalar moment of 2.5895×1017 N·m,equivalent to MW5.54.The moment rate reached the maximum only 1.4 seconds after the rupture initiation,and the 90%scalar moment was released in the first 4.6 s.In the first 1.4 seconds of the rupture process,the rupture velocity was estimated to be 2.6 km/s,slower than the local S-wave velocity.As the rupture neared its end,the rupture velocity decreased significantly.This study provides valuable insights into the seismic characteristics of the Pingyuan MW5.5 earthquake,shedding light on the previously unidentified buried fault responsible for the seismic activity in the region.Understanding the behavior of such faults is crucial for assessing seismic hazards and enhancing earthquake preparedness in the future.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Formation mechanism of fault accommodation zones under combined stress in graben basin:Implications from geomechanical modeling

A fault accommodation zone is a type of structure that is defined as regulating displacement and strain between faults structure.Increasing numbers of fault accommodation zones are being identified in graben basins,indicating the potential exploration target and petroleum accumulation areas.This study aims to analyze the formation mechanism and development of fault accommodation zones under combined stress by a numerical simulation method considering geomechanical modeling.Using three-dimensional(3-D)seismic interpretation and fractal dimension method,exampled with the Dongxin fault zone,the fault activity and fault combination pattern were conducted to quantitatively characterize the activity difference in fault accommodation zones.Combined with mechanical experiment test,a geomehcanical model was established for fault accommodation zones in a graben basin.Integrating the paleostress numerical simulations and structural physical simulation experiment,the developmental characteristics and genetic mechanism of fault accommodation zones were summarized.Influenced by multi movements and combined stresses,three significant tectonic evolution stages of the Dongxing Fault Zone(DXFZ)were distinguished:During the E_(s)^(3)sedimentary period,the large difference in the stress,strain,and rupture distribution in various faults were significant,and this stage was the key generation period for the prototype of the DXFZ,including the FAZ between large-scale faults.During the E_(s)^(2)sedimentary period,the EW-trending symmetric with opposite dipping normal faults and the NE-SW trending faults with large scale were furtherly developed.The junction area of two secondary normal faults were prone to be ruptured,performing significant period for inheriting and developing characteristics of fault accommodation zones.During the Es1 sedimentary period,the high-order faults in the DXFZ exhibited the obvious fault depressions and strike-slip activity,and the fault accommodation zones were furtherly inherited and developed.This stage was the molded and formative period of the FAZ,the low-order faults,and the depression in the DXFZ.

Advances in seismological methods for characterizing fault zone structure

Large earthquakes frequently occur along complex fault systems.Understanding seismic rupture and long-term fault evolution requires constraining the geometric and material properties of fault zone structures.We provide a comprehensive overview of recent advancements in seismological methods used to study fault zone structures,including seismic tomography,fault zone seismic wave analysis,and seismicity analysis.Observational conditions limit our current ability to fully characterize fault zones,for example,insufficient imaging resolution to discern small-scale anomalies,incomplete capture of crucial fault zone seismic waves,and limited precision in event location accuracy.Dense seismic arrays can overcome these limitations and enable more detailed investigations of fault zone structures.Moreover,we present new insights into the structure of the Anninghe-Xiaojiang fault zone in the southeastern margin of the Qinghai-Xizang Plateau based on data collected from a dense seismic array.We found that utilizing a dense seismic array can identify small-scale features within fault zones,aiding in the interpretation of fault zone geometry and material properties.

Characterization and spatial analysis of coseismic landslides triggered by the Luding Ms 6.8 earthquake in the Xianshuihe fault zone, Southwest China

On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

Dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic computing.The event-based camera is adopted to capture the machine vibration states in the perspective of vision.

Estimation of Displacement and Extension due to Reverse Drag of Normal Faults: Forward Method

In the case of reverse drag of normal faulting, the displacement and horizontal extension are determined based on the established equations for the three mechanisms: rigid body, vertical shear and inclined shear. There are three sub-cases of basal detachment for the rigid body model: horizontal detachment, antithetic detachment and synthetic detachment. For the rigid body model, the established equations indicate that the total displacement on the synthetic base (Dt2) is the largest, that on the horizontal base (Dt1) is moderate, and that on the antithetic base (Dt3) is the smallest. On the other hand, the value of (Dt1) is larger than the displacement for the vertical shear (Dt4). The value of (Dt1) is larger than or less than the displacement for the inclined shear (Dt5) depending on the original fault dip δ0, bedding angle θ, and the angle of shear direction β. For all original parameters, the value of Dt5 is less than the value of Dt4. Also, by comparing three rotation mechanisms, we find that the inclined shear produces largest extension, the rigid body model with horizontal detachment produces the smallest extension, and the vertical shear model produces moderate extension.

Experimental investigation on the origin of carbonaceous materials in the fault zone of the Wenchuan earthquake

Carbonaceous materials in seismic fault zones may considerably influence seismic fault slip;however,the formation mechanism of carbonaceous materials remains unclear.In this study,we proposed a novel hypothesis for the formation of carbonaceous materials in fault gouge.Thus,we conducted a CO2 hydrogenation experiment in a high-temperature reactor at a co-seismic temperature,with fault gouge formed during the Wenchuan earthquake as the catalyst.Our experimental results demonstrate that carbonaceous materials in fault zones are formed on the fault gouge during the chemical reaction process,suggesting that the carbonaceous materials are possibly generated from the catalytic hydrogenation of CO2,followed by thermal cracking of its products.The results of this study provide a theoretical basis for understanding fault behavior and earthquake physics.

Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

Decadal Forecasts of Large Earthquakes along the Northern San Andreas Fault System, California: Increased Activity on Regional Creeping Faults Prior to Major and Great Events

The three largest earthquakes in northern California since 1849 were preceded by increased decadal activity for moderate-size shocks along surrounding nearby faults. Increased seismicity, double-difference precise locations of earthquakes since 1968, geodetic data and fault offsets for the 1906 great shock are used to re-examine the timing and locations of possible future large earthquakes. The physical mechanisms of regional faults like the Calaveras, Hayward and Sargent, which exhibit creep, differ from those of the northern San Andreas, which is currently locked and is not creeping. Much decadal forerunning activity occurred on creeping faults. Moderate-size earthquakes along those faults became more frequent as stresses in the region increased in the latter part of the cycle of stress restoration for major and great earthquakes along the San Andreas. They may be useful for decadal forecasts. Yearly to decadal forecasts, however, are based on only a few major to great events. Activity along closer faults like that in the two years prior to the 1989 Loma Prieta shock needs to be examined for possible yearly forerunning changes to large plate boundary earthquakes. Geodetic observations are needed to focus on identifying creeping faults close to the San Andreas. The distribution of moderate-size earthquakes increased significantly since 1990 along the Hayward fault but not adjacent to the San Andreas fault to the south of San Francisco compared to what took place in the decades prior to the three major historic earthquakes in the region. It is now clear from a re-examination of the 1989 mainshock that the increased level of moderate-size shocks in the one to two preceding decades occurred on nearby East Bay faults. Double-difference locations of small earthquakes provide structural information about faults in the region, especially their depths. The northern San Andreas fault is divided into several strongly coupled segments based on differences in seismicity.

Gravity-Driven Listric Growth Fault and Sedimentation in the Lagoa do Peixe, Rio Grande do Sul Coastal Plain, Brazil

High frequency, high resolution GPR surveys are successfully applied to investigate near-surface stratification architecture of sedimentary units in coastal plains and to define their depositional conditions. However, low frequency GPR surveys to investigate fault-related depositional systems at greater depths are scarce. This survey was designed investigate a > 100 km long linear escarpment that controls the northwest margin of the Lagoa do Peixe, an important lagoon in Rio Grande do Sul Coastal Plain (RGSCP, Brazil). The traditional approach points that RGSCP was developed by juxtaposition of four lagoons/barrier systems as consequence of sea level changes;no deformational structure is admitted to exist before. The low frequency GPR (50 MHz, RTA antenna) and geological surveys carried out in the RGSCP showed the existence of a large, gravity-driven listric growth fault controlling the Lagoa do Peixe escarpment and hangingwall sedimentation. The radargrams in four subareas along the Lagoa do Peixe Growth Fault could be interpreted following the seismic expression of rift-related depositional systems. The radargrams enabled to distinguish three main lagoonal deposition radarfacies. The lower lagoonal radarfacies is a convex upward unit, thicker close to growth fault;the radarfacies geometry indicates that fault displacement rate surpasses the sedimentation rate, and its upper stratum is aged ~3500 l4C years BP. The second lagoonal radarfacies is a triangular wedge restricted to the lagoon depocenter, whose geometry indicates that fault displacement and the sedimentation rates kept pace. The upper lagoonal radarfacies is being deposited since 1060 ± 70 l4C years BP, under sedimentation rate higher than fault displacement rate. The results indicate that low frequency GPR surveys can help in investigating fault-related depositional systems in coastal zones. They also point to a new approach in dealing with RGSCP stratigraphy.

Gravity Fault Subsidence and Beach Ridges Progradation in Quinta-Cassino (RS) Coastal Plain, Brazil

Ground penetrating radar (GPR) surveys have being applied to investigate very near-surface stratification of sedimentary units in coastal plains and to define their depositional conditions. This paper presents, however, low-frequency GPR survey to investigate fault-related depositional systems at greater depths. The Quinta-Cassino area in the Rio Grande do Sul Coastal Plain (RGSCP, Brazil) shows a wide strandplain that is made off by very long, continuous, and linear geomorphic features (beach ridges). This strandplain extends for ~70 km southward. The beach ridges show low-angle truncations against the Quinta escarpment, and also truncations in the strandplain. The traditional approach points that RGSCP was developed by juxtaposition of four lagoons/barrier systems as consequence of sea level changes;previous model assumes that no deformational episode occurred in RGSCP. The geophysical and geological surveys carried out in this area showed the existence of listric fault controlling the beach ridges in the escarpments and hanging-wall blocks. The radargrams could distinguish Pleistocene basement unit anticlockwise rotation, thickening of beach ridges radarfacies close to listric normal faults, and horst structures. These deformational features indicate that the extensional zone of a large-scale gravity-driven structure controlled the mechanical subsidence, the Holocene sedimentation and its stratigraphic and geomorphic features in the Quinta-Cassino area to build up an asymmetric delta. The results point to a new approach in dealing with RGSCP Holocene evolution.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed andWeakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

Machine learning solution for regional landslide susceptibility based on fault zone division strategy

Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.

Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of faults.The existingliterature witnessed various partial or inadequate solutions.On the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and prevention.Unlike traditional fault managementsystems,this research has three-fold contributions.First,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC connector.Secondly,this paper assesses the classificationdelay of each classification algorithm.Finally,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults accordingly.This work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various classifiers.In addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid classifier.The findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 s.On the other hand,EL techniques improved the accuracy in detecting fiberoptic faults.Thus,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).

The accessible seismological dataset of a high-density 2D seismic array along Anninghe fault

The scientific goal of the Anninghe seismic array is to investigate the detailed geometry of the Anninghe fault and the velocity structure of the fault zone.This 2D seismic array is composed of 161 stations forming sub-rectangular geometry along the Anninghe fault,which covers 50 km and 150 km in the fault normal and strike directions,respectively,with~5 km intervals.The data were collected between June 2020 and June 2021,with some level of temporal gaps.Two types of instruments,i.e.QS-05A and SmartSolo,are used in this array.Data quality and examples of seismograms are provided in this paper.After the data protection period ends(expected in June 2024),researchers can request a dataset from the National Earthquake Science Data Center.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.