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Geomorphic parameters, which reflect the migration of drainage divide responses, are widely used to assess tectonic activity. There have been several large earthquakes in the history of the Hetao Basin, within which the Sertengshan Piedmont Fault is important. This study highlights certain references for regional seismic risk assessment. Few studies have been conducted on the Sertengshan area from the perspective of geomorphic parameters. In this paper, ksn, HI, and Vf were obtained to indicate the tectonic activity in the Sertengshan area, and χ and Gilbert metrics were extracted to explore the state of the drainage basin. The results show that the tectonic activity varies spatially and is strong in the western part of the southern Sertengshan region and the northern part corresponding to the turning point of the fault. Most of the Sertengshan area is in the prime and old stages of geomorphological evolution, whereas some areas are in the juvenile stage. The old stage was mainly concentrated in the northern region, and the southern part was younger than the northern region. Overall, the Sertengshan area is tectonically active and affected by the activity of the Sertengshan Piedmont Fault. The western part of the divide migrated northwest, while the central and eastern parts tended to move southward. We suggest that the divide migration is influenced by tectonic activity and tends to move towards the direction of lower tectonic activity. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system. Design/methodology/approach: This model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model. Findings: Study results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper. Originality/value: The proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations. [ABSTRACT FROM AUTHOR]

The Sertengshan Range-front Fault (SRF) and Langshan Range-front Fault (LRF) constitute a normal fault system on the northern boundary of the Linhe Depression in its northwest, and an investigation of the LRF–SRF is significant for understanding the seismic activities of normal faults. We set four trenches at three study sites to reveal paleo-earthquakes in the western segment of the SRF (W-SRF) in this system, and established 5 surface rupture events along W-SRF since the Late Pleistocene. By integrating paleoseismic data from 27 trenches on multiple fault segments, we reconstructed the paleoseismic sequence for the LRF–SRF region since 15 ka, and identified 10 paleoseismic events with corresponding rupture segments and magnitudes. The slip rates at the three study sites decreased gradually from the west to east on the W-SRF, by comparing with previous studies, further evidencing that the tectonic activities of the two faults have gradually synchronized. Besides, based on the timing of the latest paleoseismic event (1.88−2.03 ka BP), earthquakes of > M 8 surface ruptures of >200 km can occur in the northwestern Ordos Block and its seismic behavior revealed the seismogenic possibility of normal faults triggering an M > 8 earthquake. • Identified at least 5 surface rupture events since the late Pleistocene on the W-SRF. • Reconstructed paleoseismic sequence of the SRF-LRF by integrating data of trenches. • The vertical slip rates decrease gradually from west to east on the W-SRF. • Confirmed the normal fault system can occur > M8 earthquake and cause surface ruptures >250 km. [ABSTRACT FROM AUTHOR]

Active buried faults are a type of potential seismic source that are typically very difficult to explore and study in detail due to their undetectability using traditional geological survey methods. A ring of active basins has currently formed around the stable Ordos Block. However, few studies have been conducted to determine the activity of buried faults in these basins. This study utilized the Xinding Basin of the northern Shanxi Rift System as the target area. Based on comprehensive evidence from shallow seismic exploration, joint geological multi-drilling, and Quaternary dating methods, a new buried active fault named the Xinzhou Fault (XF) was identified in the Dingxiang Graben, a subbasin of the Xinding Basin. The youngest faulted layer was dated to earlier than 144.84 ± 16.92ka and is likely at approximately 118.94 ± 15.2ka. The slip rate of the newly observed Xinzhou Fault could be limited to 0.08–0.12 mm/a since the Late Pleistocene. This fault that was the first NW-striking active fault in the Shanxi Rift System is likely a secondary tension fracture of the main NE-striking Holocene basin-controlling faults. Currently, a strong earthquake at M7.2 can be considered as the maximum seismic potential of the XF, thus posing a significant seismic hazard to the area of the Xinding Basin. The discovery of this NW-striking active fault likely presents a low seismic risk. However, the occurrence of a strong earthquake should not be ignored due to its high seismic hazard. • A NW-striking late Pleistocene active fault, named as Xinzhou Fault(XF) was identified. • The seismic potential of the newfound XF was posed at M7.2. • A enriched geodynamic model was further proposed. [ABSTRACT FROM AUTHOR]

The Langshan range-front fault (LRF) is an active Holocene normal fault that borders Langshan Mountain and the Hetao Basin, northwest of the Ordos Plateau, China. In this study, paleoseismic trenching was undertaken at three sites (North–South): Dongshen village (TC1), Qingshan (TC2), and Wulanhashao (TC3). The paleoevents ED1, ED2, ED3 from TC1 were constrained to 6.0 ± 1.3, 9.6 ± 2.0, and 19.7 ± 4.2 ka, respectively. The single paleoevent (EQ1) from TC2 was constrained to about 6.7 ± 0.1 ka, and the paleoevents EW1, EW2, and EW3 from TC3 were constrained to 2.3 ± 0.4, 6.0 ± 1.0, and before 7.0 ka, respectively. With reference to previous research, the Holocene earthquake sequence of the LRF can be established as 2.30–2.43 (E1), 3.06–4.41 (E2), 6.71–6.80 (E3), 7.60–9.81 (E4), and 19.70 ± 4.20 (E5) ka BP. Events E1, E3, and E4 might have been caused by events with magnitudes of Mw 7.6–7.8 that ruptured the entire LRF. Event E2 might have been smaller magnitude, about M7.0, and ruptured only a portion of the fault. The vertical slip rate of the LRF at the Qingshan site is inferred as 0.9 or 1.4–1.6 mm/year in the last 6.8 ka. The slip rate at Wulanhashao is considered to have been close to, but not <0.66 mm/year since 15 ka BP. We deem the LRF is an unsegmented active fault. Although the possibility of missing events in Late Pleistocene can not be dismissed, we argue the Holocene paleoearthquake history is complete, indicating an average recurrence interval of ~2500 years. [ABSTRACT FROM AUTHOR]

Langshan, a monoclinic mountain, which started to uplift since Oligocene, bounds the northwest margin of the Hetao Basin. The continuous activity of the active normal Langshan range-front fault forms the typical basin-and-range landform in Langshan area and controls the landform evolution of Langshan. Langshan is an ideal place to study relationship between quantitative geomorphological index and active deformation. According to study on knickpoints, fitting on longitudinal channel profiles and steepness index, we demonstrate that the main controlling factors on distribution of normalized steepness index of channels are not climate (precipitation), lithology, sediment flux, but tectonic factor, or the activity of Langshan range-front fault. The short channels in southeast flank, whose lengths are shorter than 16 km, may be still in the non-steady status. If not considering these short channels, the distribution of normalized steepness index along the Langshan range-front fault appears like M-shape pattern, while the normalized steepness index in the middle section is higher than those at both ends. This pattern is well consistent with geometrical segmentation model of the Langshan range-front fault. Combining previous active tectonic research on Langshan range-front fault, which demonstrates the Langshan range-front fault has been in the stage of linkup, we reasonably infer the Langshan range-front fault now is the result of linkup of both fault which continuously bilaterally extended independently. Our tectonic geomorphological study also supports the conclusion that the Langshan range-front fault has been in the stage of linkup. The formation of several knickpoints due to tectonic factor may have been caused by slip-rate variation because of linkup of both independent faults. Based on cognition above, we also proposed the geological and geomorphological evolutionary model of the Langshan range-front fault since Oligocene. [ABSTRACT FROM AUTHOR]

Paleoseismic studies are critical for the assessment and prediction of large earthquakes (M ≥ 7). These studies involve excavating and analyzing evidence from surface–rupturing earthquakes. However, they often face limitations in spatiotemporal resolution. The Yuguang Graben, located in the northern Shanxi Grabens of North China, contains an abundance of Quaternary loess that can be utilized for extending paleoseismic records. Fine-grained deposits of loess contrast sharply with gravel deposits of the colluvial wedge, making the wedge shape apparent. To enhance our understanding of earthquake behavior in the Yuguang Graben, we excavated a trench on terrace T2 of the Songzhikou segment of the Yuguang Graben Fault (YGF). We used LiDAR to obtain a high–resolution orthophoto of the trench wall. Through OSL dating of colluvial wedges and loess, we identified at least four large paleoseismic events with a magnitude of M ≥ 7 that occurred along the YGF. These events took place at >77.6 ± 5.1, 63.9 ± 5.1, 51.0 ± 4.3, and 37.3 ± 0.3 kyr, respectively. Combined with previous paleoseismic studies, we identified seven paleoseismic events since the late Pleistocene. These findings reveal that earthquakes occurring on YGF follow a quasiperiodic pattern with an average recurrence interval of about 12 ± 3 kyr. The elapsed time of the most recent earthquake near the recurrence interval suggests an increased risk of a large earthquake along the YGF. We also analyzed earthquake clusters generated by neighboring faults in the active northeastern boundary of the Ordos block. Our analysis indicated that fault interactions can affect the recurrence interval of earthquakes along single faults, while regional seismic activity tends to concentrate in time during a seismically active period. These observations underscore the importance of expanding paleoseismic records and exploring regional seismic hazards to gain a more comprehensive understanding of earthquakes. • Colluvial wedges on normal fault reveal new paleoseismic events. • Establishment of the paleoseismic sequence of the Yuguang Graben Fault for 77,000 years. • Improve the understanding of fault behavior located at the boundary of the Ordos block. [ABSTRACT FROM AUTHOR]

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The Lenglongling fault in the NE Tibetan Plateau hosts fresh scarps along the entire ∼120 km length. • UAV-based data and satellite images reveal 192 left-laterally offset geomorphic markers along the Lenglongling fault. • A binned cumulative offset probability density distribution reveals at least four paleoearthquakes. Abstract The Lenglongling Fault (LLLF) is located in the western section of the "Tianzhu seismic gap" along the Qilian-Haiyuan fault zone in the NE Tibetan Plateau, between the 1920 M8.5 Haiyuan and the 1927 M8.0 Gulang earthquakes. There is little information about the paleoseismic history of the LLLF and understanding the faulting history of the LLLF is important for determining the seismic hazards of both the fault and the seismic gap. Using UAV-based photogrammetry, high-resolution satellite imagery, and field investigations, we mapped the surface trace of the fault in detail and measured 192 left-lateral offsets of geomorphic markers along the fault trace. A binned cumulative offset probability density (COPD) distribution was calculated to analyze single-event and multi-event cumulative offsets. As a result, we find that the LLLF has fresh scarps along the entire ∼120 km length, indicating that the most recent event might have ruptured the full-segment of the fault. The coseismic offset of the most recent event changes along the fault trace and has an asymmetrical bell-shaped distribution. The maximum offset was ∼7 m, located in the middle-eastern section of the fault, and the offset decreases progressively towards both ends. The minimum offset, located at the western end, was ∼3.0 m, and the average coseismic offset was ∼4.8 m. The cumulative offsets of the four most recent paleoearthquakes are multiples of the coseismic offset of the most recent event, and show similar slip distributions, indicating that the fault ruptures with a characteristic slip. The fault slip distribution, estimated recurrence interval, and estimated moment magnitude imply that the LLLF releases accumulated strain energy mainly by repeatedly producing Mw 7.5–7.8 earthquakes every 1000 ± 200 years. We also show that the binned COPD technique performs well to separate single-event and multi-event cumulative offsets. [ABSTRACT FROM AUTHOR]

Long Short-Term Memory (LSTM) networks are a significant branch of Recurrent Neural Networks (RNN), capable of learning long-term dependencies. In recent years, vanilla LSTM (a variation of original LSTM above) has become the state-of-the-art model for a variety of machine learning problems, especially Natural Language Processing (NLP). However, in industry, this powerful Deep Neural Network (DNN) has not aroused wide concern. In research focusing on Prognostics and Health Management (PHM) technology for complex engineered systems, Remaining Useful Life (RUL) estimation is one of the most challenging problems, which can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize economic losses of the systems. Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises. In addition, to promote cognition ability about model degradation processes, a dynamic differential technology was proposed to extract inter-frame information. The whole proposition is illustrated and discussed by performing tests on a case of the health monitoring of aircraft turbofan engines which have four different issues. Performances of vanilla LSTM are benchmarked with standard RNN and Gated Recurrent Unit (GRU) LSTM. Results show the significance of performance improvement achieved by vanilla LSTM. [ABSTRACT FROM AUTHOR]

Abstract: As one of the most critical tasks in structural damage monitoring, real-time fatigue crack monitoring plays an important role in improving the durability of a structure. In this paper, an online fatigue crack detection method is investigated based on the fiber Bragg grating (FBG) crack monitoring test bed (FBG-CMTB). Aiming at detecting ultrasonic spread in the structure when the crack is growing, the spectrum cross-correlation analysis algorithm and the cross-correlation function sequence are two methods that we will investigate in detail. Considering the singularity characteristic of the crack detecting signals when crack initiates, the wavelet packet analysis method is applied for feature extraction and two damage factors, crack initiation factor (CIF) and crack propagation factor (CPF), are constructed for damage initiation and propagation degree identification. To analyze the efficiency of this method, this paper presents the comparison tests based on different sensors array, FBG and acoustic emission (AE). Experimental results shows a satisfactory performance of the proposed spectrum cross-correlation analysis (SCCA) and damage feature factors on the fatigue crack online monitoring. [Copyright &y& Elsevier]

The temporal slip behavior of a fault from displaced landforms when there are no chronological data remains poorly understood. The southern segment of the Xiaojiang fault zone (XJFZ) plays an important role in accommodating the lateral extrusion of the SE Tibetan plateau. However, there are few reports on the evolution of the offset landforms and slip behavior of the fault due to the dense vegetation in the region. Here, offset landforms along the Jianshui fault (JSF) in the southern segment of the XJFZ are systematically interpreted and measured using high-resolution satellite imagery, field investigations, and airborne lidar. The risers on the right banks of three stream channels feature similar left-lateral offset characteristics near the town of Dongshanzhai. The left-lateral offsets consist of multiple inflections produced by seismic events, and the offset of each event is similar. These inflections are distributed downstream in a stair-stepped manner. The newly formed inflections are located close to the fault, and the earlier formed ones are eroded by flowing water and migrate downstream. The difference between the amount of downstream erosion of two adjacent inflections varies. Assuming the stream's long-term erosion rate remains steady, the estimated time intervals between seismic events are different. Combined with the cumulative offset probability density calculation for 92 offsets, the JSF is considered to show a nonperiodic characteristic earthquake recurrence pattern. We also propose a multistage offset evolution model of the stream channel riser. This provides a new way to analyze the seismic recurrence pattern of the fault over a relative time scale. [ABSTRACT FROM AUTHOR]

The Lenglongling fault (LLLF) is located along the northeastern margin of the Tibetan Plateau and forms a part of the Tianzhu seismic gap along the Qilian‐Haiyuan fault zone. Little is known about the recurrence of large earthquakes along the LLLF nor the associated seismic hazards of the gap. Here the six most recent surface rupturing paleoearthquakes of the LLLF are revealed by measurement of offset landforms, trench excavations, and radiocarbon dating. They are labeled E1–E6 from youngest to oldest, and their timings are constrained to the following time ranges: 636–498 to present, 2951–1155, 4016–3609, 5325–4476, 7284–6690, and 8483–7989 years BP, respectively. The LLLF displays evidence of fresh, recent surface rupture, and the trench sections reveal that the fault ruptured to the ground surface during the most recent event. Based on this fresh surface rupture and historical earthquake records, the latest event E1 was most likely the 1927 M8.0 Gulang earthquake. In conjunction with previous studies, Gulang earthquake might be a complicated event characterized by the combined rupture of both strike‐slip and thrust faults. The average recurrence interval of the six paleoearthquakes is 1643 ± 568 years, and the coefficient of variation is 0.34, indicating that the LLLF follows a quasiperiodic recurrence model. Based on this new understanding of the last event, the LLLF may not be a part of the Tianzhu seismic gap. However, an earthquake of up to MW7.6 could still rupture the gap sections composed of the Jinqianghe, Maomaoshan, and Laohushan faults. Key Points: Six recent surface rupturing paleoearthquakes of the Lenglongling fault were revealed by trench excavationsThe most recent event of the Lenglongling fault was most likely the 1927 M8.0 Gulang earthquakeThe Lenglongling fault may follow a quasiperiodic recurrence model [ABSTRACT FROM AUTHOR]

Although current deep learning-based fault diagnosis methods have made great progress, the accuracy of these models is usually attained based on many balanced training samples, which is quite difficult to meet in real industrial scenarios. In this paper, a Siamese Neural Network (SNN) based fault diagnosis method under small sample conditions is proposed and a novel hierarchical training architecture is designed to train model efficiently. Through pre-training and secondary training of the feature extractor, the SNN training stagnation problem caused by input data is addressed, which fully exploits the deep features of small sample data. Then, a classifier is added to construct the final fault diagnosis network, which improves the diagnosis speed under large test samples, and realizes accurate and efficient fault diagnosis under small sample conditions. Finally, experiments using real rotating machinery datasets show the feasibility and accuracy of the proposed method under small sample. • A SNN-based fault diagnosis method under small sample without using auxiliary data. • A hierarchical training architecture solves the SNN training stagnation problem. • A mechanism transforms the SNN into a high-efficiency classification model. • Comparison experiments demonstrate the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]