Small Faults Research Articles

Review of Methods for Prediction and Identification of Small Faults

Fault system is the oil and gas reservoir is one of the main control factors of characteristic changes especially, for dense reservoirs, so it is very important for quality reservoir prediction. However, due to the complex relationship and contradiction between the genesis and scale problems of small faults and the characteristics and resolution of seismic response, the seismic detection technology of small fault development zones is one of the current research hotspots. Seismic attribute analysis is one of the effective means for small fault identification and prediction. Various types of seismic attributes can be obtained by doing different mathematical and physical transformations on seismic data, and various different kinds of seismic attributes can be obtained based on seismic data. Different seismic attributes can describe different geological features, and a single seismic attribute has good ability to identify faults, but a single seismic attribute to predict reservoir The single seismic attribute has good ability to identify faults, but the single seismic attribute may have multiple solutions when predicting reservoir parameters. Therefore, by preferring multiple attributes that can reflect the reservoir characteristics in the work area for analysis, multiple solutions can be reduced and the prediction accuracy can be improved, which is very practical in fault prediction.

Frictional Fault Strength Analysis of Palu-Koro and Matano Faults, Sulawesi, Indonesia, from Earthquake Focal Mechanism Data

ABSTRACT Sulawesi’s complex tectonic setting generates active Palu-Koro and Matano faults. Understanding earthquake mechanic is important for mitigation purpose. We performed fault strength analysis by examining its friction, through joint stress inversion and stress magnitude estimation approach, using focal mechanism data from International Seismological Centre bulletin. Friction was analyzed in four segments of the Palu-Koro Fault and two segments of the Matano Fault. Bootstrap and Jack-knife are applied to assess its uncertainty. From north to south, the friction pattern increases at the Palu-Koro Fault segments: Donggala (0.35<μs <0.56) to Palu segment (0.59<μs <0.76); then decreases sequentially: at Saluki (0.49<μs <0.55), Moa segment (0.31< μs <0.35), and further at the Matano Fault segment (Pamsoa-Ballawai) (0.26<μs <0.3). Regarding the 2018 Mw 7.5 Palu earthquake rupture, high friction at the Saluki segment coincides with and indicates its rupture termination. Seismicity records from 1977 to 2011 showed that the largest event occurred at Matano Fault has Mw less than 7.5. Low friction observed at Matano Fault correlates with small magnitude events and low fault strength.

A dynamic rifting model of the Caroline Ridge, West Pacific

The dynamic rifting model of the Caroline Ridge, an oceanic plateau in the West Pacific, remains unclear. Previous studies have revealed that the crustal width of the Caroline Ridge clearly varies from the northwest to the southeast. Here, we investigate Caroline Ridge rifting using numerical simulation and reveal the relationships between different plateau sizes and rifting. For small-scale oceanic plateaus, the initial rift migration forms a symmetric structure. The strain transfers toward one side of the margin. The rifting manifests as a process from landward migration to seaward migration, accompanied by a transition from a symmetric to an asymmetric structure. With larger initial lithospheric thinning, larger ranges of crustal deformation appear with less basement tectonic subsidence. For large-scale oceanic plateaus, basement tectonic subsidence occurs within narrower ranges. The proximal half-graben structures rapidly abandon with relatively horizontal basement and small fault offsets on the rift flank, leading to a symmetric structure during breakup. With larger initial lithospheric thinning, crustal deformation occurs within a narrower region during the early stage. Landward rift migration dominates the deformation, resulting in an asymmetric structure. On the basis of these results, for the large-scale section of the Caroline Ridge, NW-trending normal faults originated from the landward rift migration. At present, seaward rift migration dominates the Caroline Ridge deformation, forming a relatively symmetric structure. For the small-scale section of the Caroline Ridge, the initial landward rift migration occurred. Larger initial lithospheric thinning may exist beneath the Caroline Ridge during the initiation of Miocene rifting.

Tectonic and sedimentary evolution of the Northern Cameroon Cretaceous rift basins: A study of the Babouri-Figuil and Mayo Oulo-Lere basins

The Babouri-Figuil and Mayo Oulo-Lere basins located in Northern Cameroon are small elongated sedimentary asymmetric half-grabens associated with the development of the West and Central African Rift System (WCARS). The evolution of these basins began during the Precambrian pre-rift period and continued through the Late Jurassic - Early Cretaceous periods, although details of their development remain unclear. The aim of this study is to understand the evolution and development of the border faults and their control on syn-rift geometry, accommodation space, and the resulting stratigraphic architecture. Using a combination of multiples surface and subsurface well logs data, we demonstrate the relationship and evolution of various stages of rifting and the associated sedimentary architecture. The basin is composed of an alluvial to lacustrine succession that developed during its tectonic evolution. A 3-stage tectonic model is proposed for the evolution of the basin that uses fault propagation analysis to describe the sedimentary architecture and evolution of basin geometry. The three stages of the model are the early rift, rift climax, and late rift; here, the early rifting phase is marked by the reactivation of faults in the Lower Cretaceous. The rift climax phase resulted in the interconnection of small segmented border faults leading to the high rate of activation of major border faults and to the development of the half graben structure of the basins. The late rift stage is marked by a decrease in border fault activity and accommodation. Sediment distribution and the facies architecture are controlled by tectonic subsidence created throughout the rifting phases. The stratigraphic signature of the Babouri-Figuil and Mayo Oulo-Lere basins are punctuated by the lacustrine water transgression-regression cycles, and controlled by the balance between the rate of sediment supply and tectonic accommodation. The progressive change between overfilled to starved sedimentary architecture is illustrated by this stratigraphic succession. Additional age assessment and sub-surface data in future work could help confirm the structures identified on the surface.

Line Selection of Single-Phase Fault Grounding System Based on the Deviation Criterion of Composite Zero-Sequence Admittance

Abstract The current methods for line selection of small current fault grounding system faults within distribution networks have some limitations. This paper proposes a method based on the deviation criterion of composite zero-sequence admittance for line selection of single-phase fault grounding systems with small currents. First, the derivation formula of zero-sequence admittance for small current grounding systems considering asymmetrical transmission lines is presented. Then, a method based on the deviation criterion of composite zero-sequence admittance is proposed for accurate fault line selection. Finally, a fault model is built by using simulation software and simulation experiments are conducted to verify that this method can rightly select the fault line by significant feature values.

Three-dimensional structural models, evolution and petroleum geological significances of transtensional faults in the Ziyang area, central Sichuan Basin, SW China

With drilling and seismic data of Transtensional (strike-slip) Fault System in the Ziyang area of the central Sichuan Basin, SW China plane-section integrated structural interpretation, 3-D fault framework model building, fault throw analyzing, and balanced profile restoration, it is pointed out that the transtensional fault system in the Ziyang 3-D seismic survey consists of the northeast-trending FI19 and FI20 fault zones dominated by extensional deformation, as well as 3 sets of northwest-trending en echelon normal faults experienced dextral shear deformation. Among them, the FI19 and FI20 fault zones cut through the Neoproterozoic to Lower Triassic Jialingjiang Formation, presenting a 3-D structure of an “S”-shaped ribbon. And before Permian and during the Early Triassic, the FI19 and FI20 fault zones underwent at least two periods of structural superimposition. Besides, the 3 sets of northwest-trending en echelon normal faults are composed of small normal faults arranged in pairs, with opposite dip directions and partially left-stepped arrangement. And before Permian, they had formed almost, restricting the eastward growth and propagation of the FI19 fault zone. The FI19 and FI20 fault zones communicate multiple sets of source rocks and reservoirs from deep to shallow, and the timing of fault activity matches well with oil and gas generation peaks. If there were favorable Cambrian–Triassic sedimentary facies and reservoirs developing on the local anticlinal belts of both sides of the FI19 and FI20 fault zones, the major reservoirs in this area are expected to achieve breakthroughs in oil and gas exploration.

Direct Dynamic Triggering Scenarios of the Southern San Andreas Fault by Moderate-Magnitude Cross-Fault Earthquakes in the Brawley Seismic Zone, California

Abstract Intersections between small faults and larger faults are ubiquitous throughout the world, including the strike-slip San Andreas system in southern California. In particular, orthogonal intersections may exist in the Brawley seismic zone (BSZ) in the Salton Sea region between small left-lateral strike-slip faults and the main southern San Andreas fault (SSAF). This area often experiences earthquake swarms, which poses the question of whether moderate earthquakes on these left-lateral cross faults (CFs) may propagate to the nearby SSAF, triggering a large, damaging event. To address this question, we present a collection of dynamic rupture scenarios describing the interaction of a representative CF intersecting the highly prestressed SSAF in the BSZ. Our models span a variety of CF earthquake rupture scenarios that vary in magnitude (Mw∼5.2–6.1), rupture depth, location, and directivity to test their potential to trigger the SSAF. We use our models to investigate how the above parameters play an interconnected role in developing ruptures that might trigger the SSAF. Our results highlight that adjacency to the SSAF and shallow rupture enhance the ability of moderate-size CF earthquakes to propagate onto the SSAF. We also show that earthquakes starting at the opposite edge of the CF from the intersection are less likely to trigger the SSAF unless they propagate over at least half of the CF length. Our experiments provide for the first time a benchmark of comparison and insights into rupture parameters that might control the initiation of a significant SSAF event from a smaller CF earthquake. They may also give insight into the general interactions of small faults with larger intersecting faults, such as in the case of the recent 2023 Kahramanmaraş, Türkiye, earthquake.

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

Time series diffusion method: A denoising diffusion probabilistic model for vibration signal generation

Diffusion models have demonstrated powerful data generation capabilities in various research fields such as image generation. However, in the field of vibration signal generation, the criteria for evaluating the quality of the generated signal are different from that of image generation and there is a fundamental difference between them. At present, there is no research on the ability of diffusion model to generate vibration signal. In this paper, a Time Series Diffusion Method (TSDM) is proposed for vibration signal generation, leveraging the foundational principles of diffusion models. The TSDM uses an improved U-net architecture with attention block, ResBlock and TimeEmbedding to effectively segment and extract features from one-dimensional time series data. It operates based on forward diffusion and reverse denoising processes for time-series generation. Experimental validation is conducted using single-frequency, multi-frequency datasets, and bearing fault datasets. The results show that TSDM can accurately generate the single-frequency and multi-frequency features in the time series and retain the basic frequency features for the diffusion generation results of the bearing fault series. It is also found that the original DDPM could not generate high quality vibration signals, but the improved U-net in TSDM, which applied the combination of attention block and ResBlock, could effectively improve the quality of vibration signal generation. Finally, TSDM is applied to the small sample fault diagnosis of three public bearing fault datasets, and the results show that the accuracy of small sample fault diagnosis of the three datasets is improved by 32.380%, 18.355% and 9.298% at most, respectively.

Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis

Aiming at the problems of low information utilization and lack of feature mining capability in multi-sensor fusion networks, this study presents a multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis. Firstly, a metric termed integrated cliff entropy is devised to assign weights to vibration signals from diverse sensor channels. It aims to enhance the cyclic impulse characteristics within the fused signals, thereby facilitating more precise fault identification. Secondly, a lightweight Diwaveformer architecture is constructed as the backbone of contrast learning. It enables the global and local features of faulty signals to be comprehensively extracted with less computational effort. Finally, a double contrast loss is constructed to optimize the distribution of intra-class and inter-class features to improve the fault identification ability of the network with small samples. Additionally, a discard regularization method is designed to remove the projection head during the contrast learning process, further advancing the model lightweight. Our method achieved accuracies of 95.54% and 92.56% on two aerospace bearing datasets with extremely sparse training samples, which proved its superior performance.

Impact of supercritical carbon dioxide on the frictional strength of and the transport through thin cracks in shale

Understanding the mechanical and transport behavior of thin (i.e. small aperture) cracks slipping under supercritical carbon dioxide (sc-CO2) conditions is essential to evaluate the integrity of sealing formations with buoyant sc-CO2 below and the success of waterless fracturing. The two major items of interest in this work are frictional strength and permeability change of the crack. We used a triaxial cell that permits in situ visualization to conduct and monitor slippage along the faces of narrow cracks subjected to triaxial stresses. Such cracks are analogs to small geological faults. We tested carbonate-rich, 1-inch diameter Wolfcamp shale samples that are saw cut 30° to vertical to create a thin crack. Friction coefficients ranged from about 0.6 to 0.8 consistent with expectations for brittle rocks. The sc-CO2 generally did not alter friction coefficient over the time scale of experiments. From a transport perspective, saturating cracks with sc-CO2 substantially decreased permeability of the crack by 26%–52%, while slip resulted in a variety of permeability responses. Overall, the combined impact of sc-CO2 saturation and slip reduced fault permeability for all tests. Our observations support the notion that the sealing capacity of some caprocks improves when saturated with sc-CO2 and that some slip of small fractures is not necessarily detrimental to caprock integrity.

CBAM-CRLSGAN: A novel fault diagnosis method for planetary transmission systems under small samples scenarios

In various and complex industrial scenarios, the fault data acquisition of planetary transmission system is expensive and unavailable, thus intelligent fault diagnosis under small fault samples scenarios is a challenging task. Therefore, we propose a Convolutional Block Attention Mechanism Conditional Regularized Least Squares Generative Adversarial Network (CBAM-CRLSGAN) method for intelligent diagnosis of planetary transmission systems. First, the diversity of original samples is increased by an overlapping segmentation strategy. Then, a novel data augmentation method is proposed via incorporating the CBAM module and conditional regularized least squares loss function into least squares generative adversarial network, which enables the proposed method to extract data features efficiently and improve training stability. Finally, the real and obtained fake samples are input into the classifier to realize fault diagnosis. The experimental results on the planetary transmission system test rig show that the proposed CBAM-CRLSGAN can obtain superior diagnostic performance with a diagnosis accuracy of 99.35%.

A high speed and sensitive protection for distribution network with DGs using ferrite rings as line boundary

AbstractThere exist problems of hard coordination and threshold setting as well as low sensitivity in overcurrent relay due to the bidirectional and small fault current when distributed generators are integrated into the distribution network. This paper introduces ferrite rings (FRs) to act as boundaries of distribution feeders, and a peak time of voltage traveling wave‐based protection is proposed. The characteristics of FRs are analyzed and their feasibility as line boundary is validated. The fault characteristics of internal and external faults are analyzed when FRs are installed at both ends of the protected feeder. The results show that the peak time of the voltage travelling wave of the internal fault is far smaller than that of the external faults. According to the difference, the fault detection method and the protection scheme are proposed. Simulations in PSCAD and experiment validate that the proposed protection principle can reliably distinguish between internal and external faults. Besides, the proposed protection scheme has higher sensitivity compared to ROCOV‐based protection.

Extracting geological and alteration information and predicting antimony ore based on multisource remote sensing data in Huangyangling, Xinjiang

Introduction: China has the world’s largest amount of antimony resources and plays a vital role in the global antimony industry. The Huangyangling area in Xinjiang belongs to the Lazhulong-Huangyangling antimony mineralization belt; this area is rich in antimony ore resources, with potential for discovery of additional ore. This high-elevation mountainous area is located in the northwestern region of the northern Tibetan Plateau, with a harsh natural environment and poor transportation access; thus, the use of traditional geological exploration methods is limited, and remote sensing technology provides unique advantages.Methods: Using Landsat 8 OLI and Gaofen-6 (GF-6) data, we extracted stratigraphic, lithologic and fault information from the study area through information enhancement processing of the images. Based on Landsat 8 OLI, Sentinel-2 and ASTER images and the use of remote sensing image preprocessing and interference information masking, the spectral characteristics of limonite, as well as hydroxyl, carbonate, and silicified alteration minerals were utilized; the characteristics included reflection or absorption in specific spectral bands. The spectral characteristics were then combined with principal component analysis (PCA) and the band ratio method to extract the alteration information for the study area.Results: The stratigraphy and boundaries between stratigraphic and lithologic units extracted in this study were defined, and numerous small faults were identified based on a comparison with 1:250,000 geologic maps. And the overall distribution trends of the indicators of alteration extracted in this study were located along fault zones; additionally, the hydroxyl and carbonate alteration anomalies matched well with known deposits (occurrences). Thus, the alteration information is a good indication of antimony ore bodies. Based on the geological interpretation, alteration information extraction and geochemical exploration data, four prospecting areas were identified.Discussion: The method of extracting geological and hydrothermal alteration information by remote sensing has practical significance because it provides the possibility for geological work and mineral exploration in difficult high-elevation environments.

Detection of Rotating Stall Inception of Axial Compressors Based on Deep Dilated Causal Convolutional Neural Networks

The anomaly detection of outliers and fault diagnosis in the imperfect time-series data is crucial in aero-engine industry. Early detection of the rotating stall is significant for the active stabilization control of the axial compressors, because the rotating stall and surge can cause enormous vibratory stresses in the blades and gives rise to surge to limit the performance of compressors. An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as stall inception in axial compressors is observed in aero-engine. Based on deep learning theory, this paper conducts the accurate and rapid detection of stall precursors based on deep neural network. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. The LR-WaveNet model can implement fast anomaly detection and stall prediction in long-time term series data. Furthermore, a single LR-WaveNet can be trained to capture and learn the time-domain statistical characteristics of many different stall inception training data with equal fidelity. The trained LR-WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis and the wavelet analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of the stall detection approach based on LR-WaveNet. Especially, the LR-WaveNet can detect the irregular and imperfect stall inception and capture the small fault features when the characteristics of stall inception sigals or data are not very unobvious, irregular and imperfect. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Note to Practitioners–This paper was motivated by the problem of detecting the stall and surge of axial compressor for aeroengine, but it also applies to other small fault diagnosis with imperfect data or time series prediction problem. The stall Inception is very difficult to detect for the reason of imperfect data with small fault or features. These existing detection methods of stall inception can only be applied under certain conditions, because they can only extract the pre-stall features partly. Meanwhile, the excessive number of compressor sensors is demanded in this method, so the application of these traditional methods are limited in practical situations in the stall inception detection. This paper suggests a new approach using Deep Dilated Causal Convolutional Neural Networks. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. In this paper, the new approach is proved to be suitable and feasible to all the types of stall inception including the irregular stall inception with imperfect data. We then show how the deep learning method can be efficiently applied in the imperfect data situation with small fault and time series prediction. The experiments suggest that this approach is feasible. In future research, we will create the novel approach and theory for the detection of stall inception and small fault diagnostics with the imperfect data.

Meta-model based sequential importance sampling method for structural reliability analysis under high dimensional small failure probability

Reliability analysis poses a significant challenge for complex structures with stringent reliability requirements. While Sequential Importance Sampling (SIS) and Subset Simulation (SUS) have proven highly effective in addressing high-dimensional problems with small failure probabilities, the computational burden of mechanical simulations remains substantial due to the time-consuming nature of numerical simulation processes. Consequently, this paper introduces a novel approach, denoted as AK-SIS, which combines SIS with Kriging metamodeling specifically designed to address computational challenges associated with small failure probabilities. The fundamental principle of this approach involves utilizing AK-MCS technology (Echard et al., 2011) [1] as a precursor to the SIS approach to initially generate metamodels. These metamodels are then employed in lieu of performance functions in subsequent steps, significantly reducing the number of function calls required to simulate complex engineering problems when applying SIS techniques directly. By inheriting the advantages of SIS, AK-SIS has demonstrated its suitability for reliability analysis in scenarios involving high-dimensional spaces and small fault probabilities. Furthermore, AK-SIS is not limited by the shape of the failure domain, eliminates the need to solve the design point, and is particularly well-suited for analyzing reliability in cases of discontinuous failure domains, multiple failure domains, as well as complex failure domains and rare events. The efficacy of AK-SIS is substantiated through rigorous evaluation encompassing nonlinear, high-dimensional examples, and an engineering application. These empirical validations collectively contribute to a robust methodological framework for reliability analysis of intricate structures characterized by stringent reliability requirements.

A digital twin library of mechanical transmission system for the application of small sample fault diagnosis problem

Timely and accurate fault diagnosis of transmission systems is crucial to ensuring the systems’ reliability, safety, and economic viability. However, intelligent fault diagnosis algorithms require a lot of labeled data for training, which may not be available and accessible, especially for many critical devices. This hinders the application of some excellent diagnosis methods in real industry. Digital twin (DT), as an advanced cyber-physical integration method, can be utilized to generate rich fidelity data with virtual models to overcome the dilemma of insufficient data, especially for the small sample problem. We propose the DT library to model the mechanical transmission system with various faults for the data augmentation of the small sample problem. In the library, common components in mechanical transmission systems are modular and digitalized into several differential equations. They can compose a mechanical transmission system digital twin (TSDT) and be injected with various faults to simulate the transmission signal, and even replace the physical experimental platform. The simulation data is used as a pre-training dataset, which can be imported into the transfer learning method for the fault diagnosis. After several verifications, it can be concluded that the simulation data from TSDT is effective in transfer ability and fault feature learning, which significantly improves fault recognition accuracy in the small sample problem.

A small sample bearing fault diagnosis method based on novel Zernike moment feature attention convolutional neural network

Bearings are one of the core components of rotating machine machinery. Monitoring their health status can ensure the safe and stable operation of rotating machine equipment. The limited nature of bearing fault samples makes it difficult to meet the demand for sufficient samples based on deep learning methods. Therefore, how to solve the problem of small- samples is the key to achieving intelligent fault diagnosis. In bearing failures based on vibration signals, the complex operating environment causes the vibration signals to inevitably mix with noise. The mixing of fault signature features and noise intensifies the strong spatial coupling of different types of fault features. In addition, diagnosing bearing failures under different loads is challenging because of the complex working conditions of bearings. Given the above problems, a small sample-bearing fault diagnosis method based on a high and low-frequency layered algorithm (HLFLA) and a novel Zernike moment feature attention convolutional neural network (ZMFA-CNN) is proposed. First, the proposed HLFLA converts one-dimensional time series signals into two-dimensional signals distributed rectangularly according to different frequency bands, and is used to simplify network feature screening, reduce the impact of noise, and retain adjacent signal constraint information. In addition, a new ZMFA-CNN is proposed to further extract multi-order moment features and attention weights, and can significantly improve the model generalization ability without increasing model parameters. At the same time, it is combined with FilterResponseNorm2d and thresholded linear unit to further improve model performance. Finally, sufficient experiments verified that the algorithm proposed in this paper can solve the above problems and has excellent transfer generalization ability and noise robustness. In addition, the experimental results of applying the algorithm proposed in this article to gas turbine main bearing fault diagnosis prove the reliability of the algorithm proposed in this article.

Prediction of geomechanical changes in faulted rock mass around underground structures subjected to earthquakes

Faults are a type of geological structure that often leads to geological disasters in underground structures under normal conditions and especially when earthquakes occur, thereby requiring careful study.This article introduces some numerical simulation results on the seismic wave propagation process, on changes in stress and displacement states in the rock mass with a small fault of a dip angle of 45o, around a circular cross-section tunnel, using the discrete element method (Universal distinct element Code - UDEC). Three cases are simulated: a) The tunnel is located in the hanging wall rock, the fault is located 10m from the centre of the tunnel on the vertical axis; b) The fault runs through the centre of the tunnel and c) The tunnel is located in the footing wall rock, the fault is located 10m from the centre of the tunnel on the vertical axis. The results obtained show a very clear influence of the position and location of the fault on the geomechanical changes in the rock mass around the tunnel, contributing to the reasonable design of tunnels subjected to earthquakes.

Integrated geophysical methods to constrain subsurface structures of Tulu Moye-Bora-Berecha axial volcanic complex, main Ethiopia rift: Implications for geothermal resources

The Main Ethiopian Rift (MER) is a well-known continental rift whose axial sector is characterized by the occurrence of regularly spaced silicic caldera complexes and central stratovolcanoes, interspersed with large fields of fissural basalts, small mafic scoria cones and numerous young normal faults and fissures. The Tulu Moye-Bora-Berecha volcanic complex is found in the central portion of the MER and includes the Tulu Moye geothermal prospect area. A combination of gravity and magnetic methods was used to better constrain the subsurface volcanic stratigraphy and tectonic structures. Regional and residual anomaly maps were produced from the gravity data and the magnetic data were corrected to produce anomaly and enhanced maps. A complete Bouguer anomaly contour map was produced after the necessary reduction was applied to the gravity data. Due to the geomagnetic field's dipolar nature and since the study area is in the equatorial region (<15°), the reduced-to-equator (RTE) technique was used to minimize external effects and correct the data as if the body had been laid at the magnetic equator. The anomalous source's depth was calculated using a Euler depth solution and spectral analysis approach. Joint 2D forward models on three profiles were developed using GM-SYS of Oasis Montaj software. From the interpretation of the geophysical results, the following conclusions have been reached: (1) the crystalline basement is more raised around Salen ridge and west of it; (2) the main heat source of the geothermal system appears to be a central region of the studied area near Salen ridge and is estimated to be at 4–5 km depth, (3) under Gnaro obsidian dome, the basaltic and silicic volcanic horizon is thin, and the deep-seated regional fault serves as the main conduit for the passage of hot fluid to the surface and (4) the gravity and magnetic anomaly plots, regional-residual maps and enhanced data plots all indicate towards the existence of a major geological feature-a large caldera-comprising of Tulu Moye, Bora and Berecha volcanic centers.