ELEKTRONIKA IR ELEKTROTECHNIKA (ISSN 1392-1215) is a peer-reviewed open access bimonthly research journal of Kaunas University of Technology. The research journal ELEKTRONIKA IR ELEKTROTECHNIKA publishes original research papers on featuring practical developments that might have a significant impact in the field of electronics and electrical engineering, and focuses on automation, robotics & control; automotive electronics; electric vehicles; electrical engineering; electronic measurements; electronics; high frequency technologies, microwaves; micro & nano-electronics; power electronics; renewable energy; signal technologies; telecommunications engineering. It is aimed not only to researchers of certain field , but also to the wider public. WoS: Impact Factor 1.3 (2022); Scopus: SCImago Journal Rank 0.32 (2022)
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed. By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.
DynaEdge-Net: Dynamic Feature Gating and Edge Enhancement for Precise Road Crack Segmentation
Automatic detection of road cracks is essential for the long-term safety maintenance of roads, bridges, and other infrastructure. Although existing deep learning-based crack segmentation methods have improved detection accuracy, challenges remain in terms of high computational complexity and inadequate capture of fine cracks and edge details. To address these issues, this study proposes an enhanced UNet-based architecture, termed DynaEdge-Net. In the encoder and decoder stages, a Residual Detail Enhancement Block (RDEB) and a Cascaded Group Attention (CGA) module are incorporated to strengthen edge feature representation and focus on critical regions, respectively. In the skip connections, a Group-wise Dynamic Gating (GDG) module is introduced to adaptively suppress background noise and optimize feature transmission. During decoding, a Dynamic Upsampling (DySample) strategy replaces conventional interpolation, enabling high-fidelity reconstruction of crack structures. Experimental results show that DynaEdge-Net achieves IoU, F1-score, boundary F1-score, and recall rates of 82.34%, 90.83%, 83.27%, and 90.12%, respectively, outperforming several state-of-the-art road segmentation algorithms. The proposed method not only improves the continuity and accuracy of crack extraction but also demonstrates strong robustness and generalization capability, providing a reliable solution for intelligent inspection and maintenance of transportation infrastructure.
Copyright (c) 2025 Lingjie Cao, Hailing Sun, Sunwen Du
An Approach to CFOA-based Second-Order Universal Filters
In this work, two universal current-mode (CM) filters are proposed. These filters are single-input three-output filter and three-input single-output filter. The proposed single-input CM filter simultaneously provides low-pass filter (LPF), high-pass filter (HPF), and band-pass filter (BPF) responses without requiring passive component matching. The LPF and BPF provide gains. Additionally, a notch filter (NF) response can be obtained by subtracting the output current of the LPF from the output current of the HPF, while an all-pass filter (APF) response can be achieved by subtracting the output current of the BPF from the output current of the NF. The proposed three-input CM filter generates LPF, HPF, BPF, NF, and APF responses at its output when the inputs are appropriately selected. The BPF response of the three-input CM filter has a gain. However, HPF, NF, and APF responses require a passive component matching condition. Both proposed circuits contain two grounded capacitors, which makes them suitable for integrated circuit technology and exhibit low active and passive sensitivities. The performance of the proposed filters is verified through SPICE simulations using 0.18 µm Taiwan Semiconductor Manufacturing Company (TSMC) complementary metal-oxide-semiconductor (CMOS) technology parameters. Additionally, experimental validation is performed using AD844s to demonstrate their practical performance.
Copyright (c) 2021 Mehmet Dogan, Erkan Yuce, Shahram Minaei
Identification of Ground Fissures in Mining Areas from UAV Images Based on RDC-UNet
As coal mining deepens, ground fissures in mining areas pose significant risks to safety and the environment. Traditional geological exploration methods are inefficient and costly, making the precise detection of large-scale fissures difficult. Deep learning methods using unmanned aerial vehicle (UAV) images have become a popular approach for fissure detection, although challenges such as insufficient accuracy and poor adaptability in complex backgrounds remain. This study proposes an residual-depthwise separable convolution UNet (RDC-UNet) model to address these issues, building on U-Net by incorporating residual connections (RC), depthwise separable convolutions (DSC), and the convolutional block attention module (CBAM) attention mechanism. The model was trained on a dataset of 300 UAV images from a DJI Mavic 3e and outperformed the benchmark models, achieving 86.12 % mPrecision, 75.01 % mRecall, 70.72 % mIoU, and 0.7951 mF1. Ablation experiments show that removing any core module leads to a performance drop, with the DSC module reducing mF1 by 3.28 %, CBAM decreasing mIoU by 2.67 %, and RC lowering mRecall by 3.96 %. RDC-UNet is highly efficient, requiring only 7.8 million parameters, with an inference time of 75 milliseconds and a memory footprint of 180 MB, much lower than other models. This makes it well-suited for real-time UAV-based fissure monitoring. The RDC-UNet model offers high accuracy and efficiency, making it an ideal solution for cost-effective real-time monitoring of ground fissures in mining areas.
BSPR-Net: Dual-Branch Feature Extraction Network for LiDAR Place Recognition in Unstructured Environments
LiDAR point cloud-based place recognition (LPR) in unstructured natural environments remains an open challenge with limited existing research. To address the limitations of unstructured environments, such as sparse structural features, uneven point cloud density, and significant viewpoint variations, we present BSPR-Net, a dual-branch point cloud feature extraction approach for point cloud place recognition, which consists of a BEV - projection rotation - invariant convolution branch and a point cloud sparse convolution branch. This design enhances the representation capability of geometric structural features while aggregating rotation-invariant characteristics of point clouds, thereby better addressing the challenge of large viewpoint disparities in reverse-revisited unstructured environments. The proposed network was tested on multiple reverse-revisited sequences of the Wild-Places data set, a benchmark for unstructured natural environment place recognition. It achieved a maximum F1 score of 85.46 %, exceeding other classical methods by more than 4 %. The ablation experiments further confirmed the effectiveness of each module in improving place recognition performance.
Copyright (c) 2021 Zhichao Zhang, Youchun Xu, Zhenhuan Yuan, Yulin Ma
Enhancing Intrusion Detection and Mitigation in Ad Hoc Networks Using an AI-Driven Deep Learning Approach
Ad hoc networks are increasingly deployed in critical applications due to their flexibility and scalability. However, their decentralised and dynamic nature makes them highly vulnerable to a range of sophisticated security threats. This paper aims to improve the efficiency of intrusion detection and mitigation in ad hoc networks using an AI-driven deep learning approach. A hybrid deep learning model is proposed, integrating convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for temporal analysis to effectively detect malicious activities. Reinforcement learning, particularly using a deep Q-network (DQN), is applied to dynamically select optimal mitigation strategies. Federated learning is also used to train the model in a distributed manner, ensuring privacy while allowing scalability across network nodes. The proposed approach shows significant improvements in intrusion detection accuracy, exceeding 90 %, and offers effective real-time mitigation strategies. These results provide a comprehensive and adaptive framework for securing ad hoc networks against evolving threats.
Copyright (c) 2025 Mohamed Abbas, Mohammed I. Al-Rayif
A Comprehensive Review of Transformer Fault Diagnosis Studies Based on Dissolved Gas Analysis: Classical Methods, Historical Development of the Devices Used, Artificial Intelligence Based Methods, Accuracy of Classifications of Predictions
Transformers are critical and expensive components of power systems. Therefore, it is important that these systems operate at optimum performance levels and sustainable economic conditions. In the normal operating environments, mineral oil passes on a slow and natural deterioration, while under conditions of thermal or electrical stress, the deterioration ratio increases. Due to breakdown, the hydrocarbon gases H2, CH4, C2H6, C2H4, C2H2, CO, and CO2) are composed in the transformer mineral oil. There are several conventional methods for identifying and classifying incipient faults in power transformers based on dissolved gas analysis (DGA). However, these methods have the disadvantage of not being able to distinguish situations where multiple electrical or thermal faults occur simultaneously. Due to the serious disadvantage of traditional DGA methods in terms of accuracy and consistency estimation compared to algorithm calculations made with artificial intelligence techniques, researchers have started to work intensively on artificial intelligence techniques in recent years. This comprehensive review aims to combine and present in a single source basic information about classical methods of power transformer fault diagnosis for DGA, historical development of the devices used, artificial intelligence-based methods, accuracy classifications of predictions. This investigation also revealed the contribution of the parameter optimisation process to eliminate the imbalance of the dataset in the accuracy of prediction when applying artificial intelligence techniques in DGA. In this study, the prediction performance of each research method performed with artificial intelligence techniques in fault diagnosis among the compared methods was analysed. This review emphasises the importance of eliminating dataset imbalance by performing parameter optimisation in artificial intelligence technique with an in-depth research-orientated perspective. As a result, this study not only encourages new ideas, but also provides a comprehensive source of literature for future accessibility of the subject.
Copyright (c) 2025 Nurullah Acikgoz, Murat Husnu Sazli, Ilhan Kosalay
Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis
This study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). The main goal is to improve energy efficiency and bolster the reliability of solar power plants by forecasting their performance through real-time data analysis and modelling essential operational variables. The research was carried out in a solar field in AOULEF-ADRAR (South of Algeria), which covers six hectares and consists of 20,460 solar panels with an efficiency of 15 % to 20 %. The cumulative installed capacity is 5 MW, and the system is connected to a 30 kV electrical grid. The experimental findings validated the efficacy of the suggested ANN-based fault detection method. Subsequent to a sandstorm, the system exceeded standard operational limits, culminating in a total power overshoot of 200 KW. This procedure facilitated the identification of system faults and the execution of corrective measures, including the cleaning of PV modules to restore efficiency. The research highlights the importance of artificial intelligence (AI)-based monitoring systems to reduce downtime and maintenance expenses and guarantee consistent operation of photovoltaic plants under various environmental conditions. Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. Augmenting the data set by integrating data from various PV stations in different regions may improve the adaptability of the model to different environmental conditions. This method improves the creation of intelligent self-diagnosing solar power systems, promoting increased reliability and efficiency in the integration of global renewable energy.
Copyright (c) 2025 Mohammed Bouzidi, Abdelfatah Nasri, Omar Ouledali, Messaoud Hamouda
Furtherance of Multilevel Inverter and Evolution of a Packed Inverter Unit for Dynamic Loads
Multilevel inverters (MLIs) provide a solution for high-power applications due to the production of a high-quality output voltage with low harmonic distortion. This technology is gaining popularity in the power industry for applications such as renewable energy systems, motor drives, and electric vehicles. MLIs are categorised on the basis of the number of levels in their output waveform. This article includes a discussion of two, three, and higher-level inverters with respect to design aspects and their application in the power industry, contributing to the development of sustainable and efficient systems. In addition, a packed inverter unit (PIU) with 11 IGBT switches and three uneven DC voltages is used to generate 11 voltage steps. From the suggested axioms, the amplitude of the DC sources is chosen to generate larger voltage steps with the fewest possible circuit components. Additionally, an 11-level inverter is simulated for fixed and variable R/RL loads. A comparison analysis of the circuit components between the developed circuit and existing MLIs is carried out. Finally, 11-level inverters are evaluated in real time for constant, fluctuating R/RL loads, and various performance metrics are noticed.
Copyright (c) 2025 Premkumar R, Vimal Juliet A, Vijayaraja L
An Innovative Multiparametric Sensor Design for Detecting Microplastics and Heavy Metals
Microplastics and heavy metals are materials that harm the environment and living organisms. Rapid detection enables their control or the identification of their sources. Conventional detection methods are expensive and require expert interpretation. The proposed sensor system detects these materials and evaluates their concentrations using a trainable multilayer perceptron algorithm. The system consists of twenty-two different light spectrum LEDs and eighteen narrow bandwidth photodiodes. The absorbance of incoming light and the shifted bandwidths in the spectrum can be evaluated by assessing multiparametric optical events. The study examines water samples containing eight different heavy metals, namely arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), along with three types of microplastics: melamine particles with a diameter of 8 µm, polystyrene particles with a diameter of 8 µm, and polystyrene particles with a diameter of 10 µm. Classification was performed in a concentration-dependent and concentration-independent manner. The system performance was improved by selecting features using the Ranker method of the InfoGain algorithm. Measurements were performed without applying any indicator chemicals. The system demonstrated high success in concentration-independent evaluation and acceptable concentration accuracy for heavy metals.
Copyright (c) 2025 Ekrem Kursad Dal, Adem Gunes, Recai Kilic
Coalition-based Routing and Resource Optimisation Method for 5G User Communications
The fifth generation (5G) technology provides high transmission and low latency communication to users by integrating heterogeneous devices and maximum radio resource utilisation. Prolonged longevity and effective communication are achieved through active resource routing. In addition, interference-less routing and lossless resource allocation are essential for ensuring successful communication. The transmitting and receiving users rely on shared channels to maximise resource utilisation where interference has a significant issue. In this article, a coalition-based routing and resource optimisation (CRRO) method is proposed to improve the performance of 5G communications. The proposed method relies on the cooperative agreement between resource providers and allocation channels to distinguish the sharing intervals. Maximum routing conditions for devices, service providers, and user routing are met with high longevity. In the coalition-based resource selection, synchronisation between the communicating is considered to maximum interference-less routing efficiency. A modified deep transfer learning is included in the interference level verification based on longevity and low interference to verify the synchronised behaviour of the routing process. The proposed method significantly improves the performance of 5G networks. By dynamically optimising resource allocation, minimising interference, and extending the longevity of the routing, it adapts resource utilisation to the varying conditions of the network and the demands of the users. The proposed CRRO improves resource utilisation and routing longevity by 10.02 %, and 14.41 %, respectively, and reduces latency by 10.47 % for the maximum interval.
Copyright (c) 2025 S. Pragadeswaran, B. Nataraj
Active Tracking of Moving Objects with Speed Variations Using a Novel PTZ Camera-based Machine Vision Technique
Most existing methods of active tracking focus mainly on slow-moving objects, resulting in limited adaptability to objects with variable speeds. To bridge this research gap, a novel spherical coordinate guided adaptive active tracking (SCAAT) approach based on the pan–tilt–zoom (PTZ) camera machine vision system is proposed in this study. For object detection and tracking, YOLOv5 and DeepSORT are employed in the PTZ vision system. The spherical coordinates and angular speeds of the moving object can be acquired under the spherical coordinate system. For practical application, the start-time and start-angle delay of the PTZ cameras are calibrated, and a speed control equation is conducted in the spherical coordinate system to reduce rapid location deviation between the PTZ and the moving object. To adapt different speeds of the object and avoid camera shaking under different zooms, an adaptive tracking window is designed to keep the object within the camera field of view. Experimental testing has been performed to evaluate the proposed SCAAT method. The results indicate that the SCATT can not only expand the effective following distance and zoom of the PTZ camera, but also effectively improve the accuracy and stability of active tracking for the moving object with large speed variations.
Copyright (c) 2025 Feng Lu, Youchun Xu, Yuan Zhu, Zhichao Zhang, Yulin Ma, Kang Mao
Damage Identification of Conduit Rack in Offshore Platform Structures Based on a Novel Composite Neural Network
Structural health monitoring (SHM) of offshore jacket platforms is crucial, and currently traditional deep learning methods such as artificial neural networks (ANNs) are widely used in damage identification of offshore conduit rack platform structures, which focusses on mapping feature information caused by damage to structural damage patterns. However, traditional methods have limitations in dealing with the time series data in the feature information. To improve the application of the time series information generated from offshore platform structures in damage modes, we propose a new integrated deep learning network model, which is used to improve the accuracy of the damage mode recognition based on the acceleration information of the conduit rack structure. First, the temporal convolutional network (TCN) breaks through the localisation of traditional convolutional neural networks in modelling the temporal dimension by efficiently extracting the long-term time since of the structural vibration response through an expansive causal convolution mechanism. Second, the bidirectional long short-term memory network (BiLSTM) further extracts the contextual information and global features of the data by extracting feature information in both directions and fusing the before and after correlations of vibration response signals. In addition, we adopt the Newton-Raphson-based optimiser (NRBO) optimisation algorithm for global optimisation of the hyperparameters of TCN and BiLSTM to avoid the subjectivity of manual parameter tuning, which significantly improves the model convergence speed and generalisation performance. Experimentally validated by finite element model simulation and testbed construction, our proposed NRBO-TCN-BiLSTM combined neural network damage identification accuracy is as high as 99 % on average, exceeding existing deep learning methods. The method has a wide range of applications in SHM for offshore platforms.
Copyright (c) 2025 Jiaqiang Yan, Yuanchao Qiu, Renhe Shao, Ziqiao Ling, Ruixiang Zhang
An Exact Analysis of Fine Resolution Frequency Estimation Method from Three DFT Samples: Windowed Data Case
Frequency estimation for a single complex sinusoid in noise is a fundamental problem in signal processing. A suboptimal but simple frequency estimator, known as Jacobsen estimator, which is based on three discrete Fourier transform (DFT) samples, gives good bias performance without the need to increase the DFT size. Candan has modified the Jacobsen estimator by adding a so-called bias correction factor to further reduce the bias of the estimator. In addition to bias considerations, a number of asymptotic variance expressions of the estimators were performed in the literature. However, these expressions are valid only for signal frequencies located very near a DFT bin index. In this paper, with the use of a simple variance analysis technique, an accurate general variance expression for arbitrary frequency locations is derived for the case of windowed data. A general method for calculating the bias correction factor is also proposed. The variance expression is examined for the cosine-sum window family. An approximate variance formula for sufficiently large data record lengths is also given for windows from this family. Computer simulations are included to validate the theoretical results.
Copyright (c) 2025 Betofe Mboyo Keyta, Erdogan Dilaveroglu
Exploring the Impact of Imaging Resolution and Sharpness on Dermatological Diagnostics Using eSFR Measurements
High-resolution, small-form-factor image sensors enable the integration of mobile device cameras, which are increasingly being used for photographic documentation in many fields, including medicine. With the interface and handheld dermatoscopy, the smartphone camera forms an alternative tool to professional dermatoscopic systems for performing teledermatology and teledermoscopy. For the accurate diagnosis of skin diseases, image quality is essential, with sharpness and resolution being essential criteria. This paper focusses on measuring the sharpness and resolution of cameras used for image acquisition in dermatology using the spatial frequency response (SFR) method, which is based on standardised test charts featuring characteristic slanted contrast edges, known as edge SFR (eSFR) charts. The images were captured with mirrorless and DSLR cameras, smartphones, and a professional dermatoscopy video camera under typical dermatological conditions with digital cameras, mobile phones, and professional video dermatoscopes. Captured images were analysed, and the modulation transfer function (MTF) was defined to evaluate the performance of different camera optical systems applied for dermatological imaging. The results provide insight into the strengths and limitations of the various imaging devices and highlight their effectiveness in meeting the requirements of dermatological practice.
Copyright (c) 2025 Bogdan Dugonik, Marjan Golob
Energy Demand and CO2 Emission Forecast Model for Turkey with Deep Learning and Machine Learning Algorithms
This study has conducted a forecast analysis of the energy demand and carbon dioxide (CO2) emissions of Turkey, a developing country. Considering Turkey’s rapidly increasing energy demand, various economic and social parameters have been used for the years 1990-2024. Both machine learning and deep learning methods have been applied, and artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and linear regression (LR) algorithms have been used for two models. The performance of these models has been assessed using various error metrics. The ANN has demonstrated the highest accuracy in modelling energy demand, achieving a coefficient of determination of 98.89 %, while the RNN has shown the best performance in modelling CO2 emissions, with a coefficient of determination of 96.80 %. The findings have shown that the growth rates in energy demand and CO2 emissions are high in the early years but slowed in the following years. However, it has been determined that the general trend continued to increase. The study emphasises the need for Turkey to diversify its energy sources and increase the use of renewable energy to meet its increasing energy demand. It also has concluded that accelerating efforts to achieve net zero emission targets are critical to long-term energy security and environmental sustainability.
Copyright (c) 2025 Emre Bolat, Yagmur Arikan Yildiz
Ground Fissure Identification in Mining Areas from UAV Images Based on DN-CAMSCBNet
The development and use of mine resources have had many adverse impacts on the environment of mining areas. Among them, ground fissures are the most serious. They not only threaten the ecological protection of mining areas but also hinder the sustainable exploitation of energy. To mitigate the damage to the ecological environment caused by mining areas and ensure sustainable long-term resource exploitation, it is of particular importance to identify ground fissures in mining areas efficiently. Therefore, this paper proposes a ground fissure identification model for UAV images in mining areas named DN-CAMSCBNet. This method integrates the channel attention mechanism and the dropout mechanism on the basis of the traditional U-Net. Meanwhile, it introduces the multiscale convolution block and Nesterov-accelerated adaptive moment estimation. These are used to enhance its ability to capture complex image features, expand the receptive field of the original model, reduce the number of parameters, and reduce computational complexity. To verify the segmentation performance of the model, it is compared with U-Net, D-CAMNet, and D-MSCBNet models. The experimental results show that the accuracy and precision of the DN-CAMSCBNet model can reach 99.47 % and 92.25 %, respectively, and the F1 score is 0.7699. All these are superior to comparison models and can provide strong support for the identification of ground fissures in mining areas.
Copyright (c) 2025 Haibin Hu, Xinhui Guo, Jie Xiao
Incremental Gate State Output Decomposition Model for Highway Traffic Forecasting Using Toll Collection Data
Traffic flow on long-distance highways, especially at sections with multi-interchanges and ramps, exhibits nonlinear trends affected by long-term and short-term spatiotemporal dependencies, resulting limited fitting capabilities for the major applied spatiotemporal forecasting models in use. This paper tackles this challenge by integrating an incremental gate state output decomposition (IGOD) mechanism into the recurrent neural network (RNN) model framework, accounting for the interdependencies of spatiotemporal traffic data. The proposed method improves the ability of the RNN model to estimate traffic data series by segmenting consecutive time intervals and accumulating incremental changes across these time intervals, allowing for more precise traffic predictions. This study also explores how threshold amplitudes affect prediction effectiveness. We applied it to real traffic data from segment k602+630 to k625+420 on the Changjiu Highway. The results demonstrate that the proposed model consistently exhibits robustness, with variations in threshold magnitude having little impact on its prediction accuracy.
Incremental gate state output decomposition
Copyright (c) 2025 Liang Yu, Ming Li, Kaifeng Liu, Xiangping Cheng
Performance Analysis of Two 8-Bit Floating-Point-based Piecewise Uniform Quantizers for a Laplacian Data Source
In this paper, we employ the analogy between the representation of the floating-point (FP) format and the representation level distribution of the piecewise uniform quantizer (PWUQ) to assess the performance of FP-based solutions more thoroughly. We present theoretical derivations to assess the performance of the FP format and the PWUQ determined by this format for input data from the Laplacian source. We also provide a performance comparison of two selected 8-bit FP-based PWUQs. Beyond the typical evaluation of the applied FP format, through the accuracy degradation caused by the application of both FP8 solutions in neural network compression, we also use objective quantization measures. This approach offers insights into the robustness of these 8-bit FP-based solutions with respect to changes in input variance, which can be important when the input variance changes. The results demonstrate that the allocation of bits to encode the exponent and mantissa in the FP8 format is important, as it can significantly impact overall performance.
Copyright (c) 2025 Jelena R. Nikolic, Zoran H. Peric, Aleksandra Z. Jovanovic, Stefan S. Tomic, Sofija Z. Peric
A New Fault Recognition Method Based on Empirical Mode Decomposition and Texture Attributes
Small faults developed in coal seams are one of the major causes of coal mine accidents. Accurately predicting small faults in coal fields is an urgent requirement for efficient and safe production in coal mines. This article proposes a new small fault identification method that combines the empirical mode decomposition method and the seismic texture attribute extraction method to address the problem of large errors caused by noise in the results of small fault prediction. Firstly, the basic principles of the empirical mode method and the texture attribute method were studied, and then the fault recognition ability of this method was tested and analysed based on a small fault seismic forward modelling. Meanwhile, empirical mode decomposition is performed on actual seismic data to identify small faults by using texture attributes and by adding noise to the seismic record; this article compared the seismic record of texture properties in the presence and absence of noise. The results indicate that the texture attribute method can predict small faults well, but this method is easily disturbed by noise. The empirical mode decomposition method used in this paper can remove noise interference and highlight characteristics of the texture attribute. Therefore, the small fault prediction method that combines empirical mode decomposition with texture attributes can effectively identify small faults and play an important geological guarantee role in ensuring safe and efficient production in coal mines.
Copyright (c) 2025 Hui Qiao, Bingxin Chen, Yaping Huang, Xuemei Qi, Hongming Fan, Aiping Zeng