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Full-Text Articles in Mining Engineering
A Fault Identification Method For Deep Coalbed Methane Reservoirs Based On An Improved Transunet, Gao Congshun, Wu Haibo, Liu Lei, Wang Zijian, Huang Xuanyu, Jia Lilan, Xu Aiguo
A Fault Identification Method For Deep Coalbed Methane Reservoirs Based On An Improved Transunet, Gao Congshun, Wu Haibo, Liu Lei, Wang Zijian, Huang Xuanyu, Jia Lilan, Xu Aiguo
Coal Geology & Exploration
Objective Small-scale faults in deep coalbed methane (CBM) reservoirs significantly affect CBM enrichment and the deployment of development wells. However, deep seismic data tend to suffer from low signal-to-noise ratios (SNRs) and poor continuity of reflection events, while conventional methods are likely to miss small-scale faults. To address these limitations, this study proposes a fault identification method based on an improved TransUNet. Methods The proposed method is developed based on the R50-ViT-B_16 TransUNet architecture. A coordinate attention (CA) module is introduced before the skip connections enter the decoder to enhance the feature representation of the locations and directional continuity of …
Comparison Of Log-Based Coal Structure Identification Methods For Structurally Complex Areas: A Case Study Of The Huainan Mining Area, Zhang Zixin, Zhou Xiaozhi, Chen Xuehua
Comparison Of Log-Based Coal Structure Identification Methods For Structurally Complex Areas: A Case Study Of The Huainan Mining Area, Zhang Zixin, Zhou Xiaozhi, Chen Xuehua
Coal Geology & Exploration
Objective CO2 displacing coalbed methane (CBM) provides substantial environmental and energy benefits. However, differences in coal structures represent a key geological factor influencing the effectiveness of this technique. Constrained by the complex geological conditions for CBM occurrence and the impacts of multiple factors on the log responses of coal structures, traditional log-based methods for coal structure identification face challenges such as much human intervention, complex feature engineering, and poor adaptability. This study aims to achieve effective identification of complex coal structures within a single coal seam. Methods An asymmetric convolution kernel-based convolutional neural network (CNN) model for coal structure …
Key Technologies For Artificial Intelligence-Based Inversion Of The Borehole Transient Electromagnetic Method In Coal Mining Areas, Fan Tao, Hao Yue, Zhang Peng, Li Ping, Zhao Zhao, Zhao Rui, Liu Qiang, Yan Junsheng, Song Xianzhe, Liu Borui, Chen Changyuan, Li Bo
Key Technologies For Artificial Intelligence-Based Inversion Of The Borehole Transient Electromagnetic Method In Coal Mining Areas, Fan Tao, Hao Yue, Zhang Peng, Li Ping, Zhao Zhao, Zhao Rui, Liu Qiang, Yan Junsheng, Song Xianzhe, Liu Borui, Chen Changyuan, Li Bo
Coal Geology & Exploration
Background Using directional boreholes, the borehole transient electromagnetic (TEM) method enables near-field excitation and the simultaneous reception of three components within a borehole. This method can effectively avoid interference from ferromagnetic materials in roadways, thereby significantly enhancing the detection accuracy and range. Therefore, this method is widely applied to the detection of underground concealed water hazards in coal mines. However, there is a conflict between the accuracy and efficiency of current inversion methods for borehole TEM data, and existing technologies are insufficient to simultaneously meet the demands for high efficiency and high precision in the detection of concealed water hazards. …
Transformer-Driven Intelligent Prediction For The Time-Series Features Of Mine Pressure In Fully Mechanized Mining Face, Du Feng, Chen Bo, Wang Wenqiang, Pu Hai, Du Xueming, Li Guodong, Qiao Rui, Li Xinlei, Xu Jie, Cao Yu
Transformer-Driven Intelligent Prediction For The Time-Series Features Of Mine Pressure In Fully Mechanized Mining Face, Du Feng, Chen Bo, Wang Wenqiang, Pu Hai, Du Xueming, Li Guodong, Qiao Rui, Li Xinlei, Xu Jie, Cao Yu
Coal Geology & Exploration
Objective Mine pressure prediction represents an important means for early warning and management of disasters in coal seam roofs, serving as the prerequisite and foundation for safe production in intelligent mines. However, the complex and variable conditions of fully mechanized mining face lead to significant variations in the distribution of support pressure data acquired using electro-hydraulic control systems, complicating mine pressure prediction. Methods This study developed a Transformer-based mine pressure prediction model. Specifically, missing mine pressure data were filled using linear interpolation, and the data structure of mine pressure was adjusted using a sliding window algorithm. Based on the time-series …
Coal Body Structure Identification Method Based On Bayesian-Optimized Cnn-Bilstm-Attention, Bian Huiyuan, Ji Jiajun, Duan Chaowei, Zhou Jun, Li Kun, Ma Yuhan
Coal Body Structure Identification Method Based On Bayesian-Optimized Cnn-Bilstm-Attention, Bian Huiyuan, Ji Jiajun, Duan Chaowei, Zhou Jun, Li Kun, Ma Yuhan
Coal Geology & Exploration
Background Coal-bearing basins contain primary and tectonically deformed coals due to multistage tectonic deformations. However, the gas-bearing properties of coal seams differ significantly due to varying pore and fracture densities, permeability, and mechanical properties. This makes coal structure assessment critical to coalbed methane (CBM) exploration and production. Objective and Method To enhance the accuracy and intelligence of coal structure identification, this study constructed a CNN-BiLSTM-Attention hybrid model that integrated a Bayesian optimization strategy. This model allowed for efficient fusion and representation of multi-scale log data by combining the local feature extraction capability of the convolutional neural network (CNN), the temporal …
Attenuation Characteristics And Wave Impedance Inversion Of Depth-Domain Nonstationary Seismic Data, Ma Ming, Ma Feng
Attenuation Characteristics And Wave Impedance Inversion Of Depth-Domain Nonstationary Seismic Data, Ma Ming, Ma Feng
Coal Geology & Exploration
Background The efficient application of depth migration methods has enabled the extensive implementation of depth-domain seismic data interpretation. Previous studies on the impacts of time-to-depth conversion and velocity models merely determine that an increase in depth corresponds to reduced dominant wavenumber of seismic waves and waveform stretching, without considering the changes in amplitude and phase. Objective This study aims to accurately characterize the waveforms of nonstationary seismic signals in the depth domain and enhance the impedance inversion accuracy. Methods First, based on energy dissipation and frequency dispersion effects during the propagation of seismic waves, this study investigated the complex mapping …
High-Precision Reconstruction Of Seismic Data Based On Improved U-Net++, Wang Minling, Zhou Fei, Wang Honghua, He Xiang, Hou Zhiyang
High-Precision Reconstruction Of Seismic Data Based On Improved U-Net++, Wang Minling, Zhou Fei, Wang Honghua, He Xiang, Hou Zhiyang
Coal Geology & Exploration
Objective Conventional reconstruction methods are insufficient for the reconstruction of seismic data with missing consecutive traces, producing a negative impact on subsequent processing accuracy. Hence, this study proposed CU-Net++, a deep learning network based on the U-Net++ architecture combined with the convolutional block attention module (CBAM). Methods During the reconstruction of missing data, the independent decoder for each sub-U-Net in the nested U-Net++ architecture enables the utilization of information from different depths. The long and short skip connections can effectively enhance the network's capability to extract multi-scale features from data. The core innovation of CU-Net++ is the introduction of CBAM, …
A Time Series Forecasting Model Integrating Spatial Features For Inter-Borehole Resistivity And Its Application, Wang Jianghong, Liu Shuo, Wang Gang, Xue Wuqiang, Li Bofan
A Time Series Forecasting Model Integrating Spatial Features For Inter-Borehole Resistivity And Its Application, Wang Jianghong, Liu Shuo, Wang Gang, Xue Wuqiang, Li Bofan
Coal Geology & Exploration
Background Mine resistivity prediction serves as a core technique for water hazards monitoring in coal mines. However, due to sparse monitoring points and insufficient spatial resolution, conventional prediction approaches fail to capture dynamic resistivity variations under complex geological conditions.Objective and Methods To address this challenge, this study developed a deep learning-based time series forecasting model integrating spatial features for inter-borehole resistivity. Using a prediction framework constructed based on long short-term memory (LSTM) and critical spatial monitoring points selected through Pearson correlation analysis, this model (also referred to as the LSTM model) enabled high-precision prediction of resistivity in unknown areas. …
Enhancing Autonomous Truck Navigation In Underground Mines: A Review Of 3d Object Detection Systems, Challenges, And Future Trends, Ellen Essien, Samuel Frimpong
Enhancing Autonomous Truck Navigation In Underground Mines: A Review Of 3d Object Detection Systems, Challenges, And Future Trends, Ellen Essien, Samuel Frimpong
Mining Engineering Faculty Research & Creative Works
Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal …
A Seismic Random Noise Suppression Method Based On Cnn-Mamba, Wei Xiujuan, Liu Xingye, Zhou Huailai
A Seismic Random Noise Suppression Method Based On Cnn-Mamba, Wei Xiujuan, Liu Xingye, Zhou Huailai
Coal Geology & Exploration
Background Seismic random noise suppression is recognized as a key step to improve the quality of seismic data. Data-driven deep learning provides an intelligent solution for the noise suppression. However, mainstream random noise intelligent methods based on convolutional neural networks (CNNs) are constrained by their local receptive fields. This limitation results in insufficient collaborative optimization between local details and macroscopic structures during denoising, further reducing the noise suppression accuracy. Transformer models, which are widely applied to global feature extraction, can effectively capture long-distance dependencies through the self-attention mechanism, theoretically overcoming the limitations of CNNs in global modeling. However, these models …
Application Of A Generative Adversarial Network Algorithm To Filling Blank Strips Of Fractures In Formation Microresistivity Imaging Images, Kang Zhengming, Wu Chensheng, Yang Guodong, Wu Disheng, Wang Ruifei, Yang Xiangyu, Gan Wei
Application Of A Generative Adversarial Network Algorithm To Filling Blank Strips Of Fractures In Formation Microresistivity Imaging Images, Kang Zhengming, Wu Chensheng, Yang Guodong, Wu Disheng, Wang Ruifei, Yang Xiangyu, Gan Wei
Coal Geology & Exploration
Objective Gaps between the electrodes of formation microresistivity imaging (FMI) imagers lead to blank strips in the resistivity images of borehole walls, significantly influencing the parameter assessment for fractures near borehole walls. The absence of full-borehole images renders it challenging to assess the blank strip filling quality for fractures in FMI images. Using a dataset constructed utilizing both simulated and actual data, this study proposed a generative adversarial network (GAN)-based method for filling the blank strips of fractures in FMI images. Methods First, the resistivity logging responses of a fractured formation were simulated using the 3D finite element method. Actual …
A Method For Intelligent Information Extraction Of Coal Fractures Based On Μct And Deep Learning, Hu Zhazha, Zhang Xun, Jin Yi, Gong Linxian, Huang Wenhui, Ren Jianji, Norbert Klitzsch
A Method For Intelligent Information Extraction Of Coal Fractures Based On Μct And Deep Learning, Hu Zhazha, Zhang Xun, Jin Yi, Gong Linxian, Huang Wenhui, Ren Jianji, Norbert Klitzsch
Coal Geology & Exploration
Objective The fine-scale characterization of fractures in coal reservoirs is significant for the exploration and exploitation of coalbed methane (CBM) resources. Given that the size, orientation, and density of fractures directly affect the permeability of coal seams, the accurate information identification and extraction of fractures in coal seams plays a key role in revealing the formation and propagation mechanisms of fracture networks during reservoir volume fracturing. Conventional methods for fracture information extraction typically rely on manual labeling and feature extraction based on image processing techniques, exhibiting significantly limited accuracy and efficiency. Methods This study proposed a method for fracture information …
A U-Net-Based Denoising Method For Semi-Airborne Transient Electromagnetic Data And Its Application, Liu Dong, Feng Hao, Wang Yongxin, Zhou Xiaosheng, Yao Yuhong, Sun Huaifeng
A U-Net-Based Denoising Method For Semi-Airborne Transient Electromagnetic Data And Its Application, Liu Dong, Feng Hao, Wang Yongxin, Zhou Xiaosheng, Yao Yuhong, Sun Huaifeng
Coal Geology & Exploration
Objective and Methods The semi-airborne transient electromagnetic (SATEM) method, an efficient geophysical exploration technique, has been extensively applied to mineral resource exploration, groundwater surveys, and geothermal resource surveys. However, the collected data are frequently susceptible to noise interference, significantly affecting the accuracy of subsequent data processing and interpretation. To address issues such as residual noise and the loss of effective signals, enhance denoising effects, and reduce the influence of subjective factors, this study proposed a denoising method for SATEM data based on the U-Net deep learning architecture (also referred to as the U-Net-based method) by applying U-Net to SATEM data …
Predicted Water Yield Of Open-Pit Metal Mines Based On A Bi-Rnn And Gms Coupling Model, Zhao Yuxing, Li Xiangwen
Predicted Water Yield Of Open-Pit Metal Mines Based On A Bi-Rnn And Gms Coupling Model, Zhao Yuxing, Li Xiangwen
Coal Geology & Exploration
Objective Accurately predicting water yield of mine before mining can provide directive guidance for preventing potential water hazards and ensuring safe production. Methods To enhance the prediction accuracy and stability of water yield of open-pit metal mines, for which atmospheric precipitation acts as the primary recharge source of water, this study developed a prediction model that coupled a bidirectional recurrent neural network (Bi-RNN) and the Groundwater Modeling System (GMS) software. Specifically, based on historical forecasted precipitation data provided by the Global Forecast System (GFS), the fluctuation pattern of differences between predicted forecasted and actual precipitation was analyzed. After being corrected …
Fine And Intelligent Interpretation Of Key Geological Structures In The Metal Metallogenesis Of Coal Measures, Yang Yi, Zhao Jingtao, Li Wenyu, Shi Suzhen
Fine And Intelligent Interpretation Of Key Geological Structures In The Metal Metallogenesis Of Coal Measures, Yang Yi, Zhao Jingtao, Li Wenyu, Shi Suzhen
Coal Geology & Exploration
Objective In order to identify the key geological structures in the metallogenic process of coal measures metal deposits, the Linxing block of Ordos Basin was taken as the research object. Based on the previous research results, geological data of the block, seismic and logging data, a geological model including tectonic volcanic activities and other events was constructed to analyze the key geological factors of deposit formation. Methods Firstly, based on the post-stack seismic data, the diffraction wave separation and imaging are realized by using the plane wave destruction filter technology, and the small-scale key geological information is extracted. Secondly, the …
Acoustic Velocity Inversion Based On Convolutional Autoencoder Embedded With Fourier Neural Operator, Li Chen, Zhao Haixia, Bai Zhaowei, Hao Yufan
Acoustic Velocity Inversion Based On Convolutional Autoencoder Embedded With Fourier Neural Operator, Li Chen, Zhao Haixia, Bai Zhaowei, Hao Yufan
Coal Geology & Exploration
Background Seismic wave inversion serves as an effective method for obtaining the characteristics of structures, lithology, and physical properties of subsurface media using the arrival times, amplitude, and waveforms of seismic waves. Seismic inversion methods based on wave equations iteratively update model parameters using forward modeling. This generally involves extensive numerical simulations and optimization calculations, requiring large quantities of computational resources and time. In recent years, neural operators for deep learning, represented by the Fourier neural operator (FNO), have gained widespread attention. However, the original FNO structure fails to effectively learn the wavefield information with sharp changes in geological structures …
Intelligent Noise Suppression For 3d Post-Stack Seismic Data Of The Junggar Basin, Mao Haibo, Zhou Xin, Li Xiaofeng, Pan Long, Lin Juan, Liu Dawei, Wang Xiaokai
Intelligent Noise Suppression For 3d Post-Stack Seismic Data Of The Junggar Basin, Mao Haibo, Zhou Xin, Li Xiaofeng, Pan Long, Lin Juan, Liu Dawei, Wang Xiaokai
Coal Geology & Exploration
Objective The Junggar Basin is recognized as a significant petroliferous basin in China, and its hydrocarbon exploration targets have shifted to deeper strata. However, the 3D seismic data of this basin suffer from low signal-to-noise ratios (SNRs) and high data volumes due to the basin's complex near-surface conditions, the great depths of exploration targets, and seismic data acquisition methods characterized by wide azimuths, broadbands, and high density. This complicates the identification of hydrocarbon exploration targets, rendering the improvement in the quality of the 3D seismic data by noise suppression vitally important. Methods The progress in the deep learning theory and …
Transient Electromagnetic Numerical Simulation Based On The Transformer Neural Network, Wang Yunhong
Transient Electromagnetic Numerical Simulation Based On The Transformer Neural Network, Wang Yunhong
Coal Geology & Exploration
Objective The deep learning-based transient electromagnetic (TEM) forward and inverse modeling methods are data-driven, requiring considerable numerical simulation results as supervisory data to train and assess neural networks. The conventional finite-difference time-domain (FDTD) method for TEM numerical simulation necessitates an iterative solution of time-domain Maxwell's equations. Therefore, this method is time-consuming and computationally intensive, failing to meet the data demand of deep learning-based TEM inversion. Methods This study introduced deep learning for TEM numerical simulation. Based on the transformer neural network architecture, a neural network for deep learning-based TEM numerical simulation was designed using an encoder-decoder structure. This neural network …
An Intelligent Identifying-While-Drilling Method For Geological Features Of Roof Strata In Coal Roadways Based On A 1dcnn-Bilstm-Cbam Model, Lei Zhiyong, Wang Jiawen, Fan Dong, Lu Feifei, Chen Weiming
An Intelligent Identifying-While-Drilling Method For Geological Features Of Roof Strata In Coal Roadways Based On A 1dcnn-Bilstm-Cbam Model, Lei Zhiyong, Wang Jiawen, Fan Dong, Lu Feifei, Chen Weiming
Coal Geology & Exploration
Objective Most roof accidents in coal roadways occur in potential caving zones such as primary fissure-bearing zones and rock fracture zones. A significant approach to preventing roof accidents is to understand the geological features of roof strata in an accurate and timely manner and optimize support schemes and parameters of roofs. However, current methods for identifying the geological features of roof strata in roadways suffer from issues such as slow identification speeds, low efficiency, and high costs, thus failing to meet the demand for safe, efficient, and intelligent coal mining. Methods This study proposed a neural network model based on …
A Neural Network-Based Method For Analyzing Diffracted Wave Velocity, Tao Junhong, Zhao Jingtao, Sheng Tongjie
A Neural Network-Based Method For Analyzing Diffracted Wave Velocity, Tao Junhong, Zhao Jingtao, Sheng Tongjie
Coal Geology & Exploration
Background In seismic imaging, accurate velocity models are crucial for characterizing subsurface structures in a fine-scale manner. Notably, in coal mining, small-scale geological structures like faults and collapse columns are closely associated with mining accidents. These structures typically occur as diffracted waves in seismograms.Objective and Methods To effectively image these small-scale geological bodies, fine-scale velocity modeling using diffracted wave information is particularly important. Hence, this study proposed a neural network-based method for analyzing diffracted-wave velocity. First, diffracted-wave velocity spectra were generated using the gathers of diffracted waves that were separated in the common virtual source domain. Second, a gamma-ray …
A Non-Uniform Interpolation Method For Seismic Data Based On A Diffusion Probabilistic Model, Chen Yao, Yu Siwei, Lin Rongzhi
A Non-Uniform Interpolation Method For Seismic Data Based On A Diffusion Probabilistic Model, Chen Yao, Yu Siwei, Lin Rongzhi
Coal Geology & Exploration
Objective The non-uniform interpolation of seismic data is identified as a prolonged challenge in energy exploration. Since geophones cannot be precisely placed at positions corresponding to theoretical grid points, current uniform interpolation techniques frequently suffer deviations and detail distortion. Methods This study proposed a novel non-uniform interpolation method based on a diffusion probabilistic model, which is an emerging generative model in deep learning that involves the diffusion and generation processes. In the diffusion process, noise is added to the complete seismic data iteratively to train the denoising capability of the neural network. In the generation process, the neural network is …
A Petrophysical Modeling-Guided Method For Predicting Parameters Of Low-Permeability Reservoirs, Wang Rui, Li Fang, Liu Shiyou, Sun Wanyuan, Li Songling, Huang Sheng
A Petrophysical Modeling-Guided Method For Predicting Parameters Of Low-Permeability Reservoirs, Wang Rui, Li Fang, Liu Shiyou, Sun Wanyuan, Li Songling, Huang Sheng
Coal Geology & Exploration
Backgroud Accurately predicting reservoir parameters is significant for characterizing subsurface reservoirs, establishing gas accumulation patterns, releasing production capacity, and understanding fluid migration. The traditional approaches based on core measurement or mathematical-petrophysical modeling are limited by the strong multiplicity of solutions and low accuracy of elastic parameters inversion results, making it difficult to meet the demands of modern exploration.Objective and Methods To more effectively predict reservoir parameters, this study proposed a petrophysical modeling-guided method for predicting parameters of low-permeability reservoirs. With the convolutional neural network (CNN) as a deep learning framework, the proposed method can predict water saturation, clay content, …
Seismic Data Denoising Based On The Convolutional Neural Network With An Attention Mechanism In The Curvelet Domain, Bao Qianzong, Zhou Mei, Qiu Yi
Seismic Data Denoising Based On The Convolutional Neural Network With An Attention Mechanism In The Curvelet Domain, Bao Qianzong, Zhou Mei, Qiu Yi
Coal Geology & Exploration
[Objective] Noise in seismic data significantly affects the accurate interpretation of subsurface stratigraphic information. Given that effective signals with pronounced lateral correlations in seismic data are distributed in specific coefficients but random noise typically spreads uniformly over all coefficients in the curvelet domain, more effective separation of signals can be achieved. [Methods] The convolutional neural network based on the attention mechanism can adaptively extract key information by focusing on important features of images. Hence, this study proposed a noise attenuation method for seismic data using a convolutional neural network based on the curvelet transform and attention mechanism (Curvelet-AU-Net). First, the …
Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao
Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao
Coal Geology & Exploration
Rockburst is one of the urgent problems to be addressed in the process of deep resource extraction. In order to predict the rockburst disasters safely and efficiently, a rockburst intensity grade prediction model (MICE-CNN) based on the Multiple Imputation by Chained Equations (MICE) and Convolutional Neural Network (CNN) was proposed. Specifically, a predictive indicator system was established based on the main influencing factors and the acquisition conditions of rockburst. A total of 120 sets of raw data from rockburst cases were collected, with the outliers processed by pauta criterion. Then, the missing data were interpolated with the four interpolation models …
Drilling Core Identification Based On Natural Image, Gao Hui, Wu Zhenkun, Ke Yu, Tan Songcheng, He Siqi, Duan Longchen
Drilling Core Identification Based On Natural Image, Gao Hui, Wu Zhenkun, Ke Yu, Tan Songcheng, He Siqi, Duan Longchen
Coal Geology & Exploration
The traditional on-site core identification and recording mainly rely on the experience of technicians, and there are many uncertain factors. Limited by the site conditions, using mobile phones or cameras to capture the natural images is the most convenient way to collect the core information. Therefore, it is necessary to study the feature information extraction technology of core image and apply it to the identification and prediction of core type and other information. Specifically, a large number of core samples were collected, the thin-section identification method was employed to determine the core types and names, and then the core images …
An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao
An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao
Coal Geology & Exploration
The accurate characterization and dynamic analysis of drilling string mechanics are essential to ensure the safe and efficient drilling. In the classical soft/rigid string model for drag & torque of drilling string, the friction coefficient of the drilling string is determined by empirical estimation or post-drilling inversion, of which the accuracy and timeliness needs to be improved. Based on the effectiveness of artificial intelligence technology applied in complex nonlinear mapping, a drag and torque prediction method of drill string with mechanism-data fusion was proposed by predicting the friction coefficient. Firstly, the friction coefficient was inversed using the drilled and logged …
Intelligent Lithology Prediction Method Based On Vibration Signal While Drilling And Deep Learning, Wang Sheng, Lai Kun, Zhang Zheng, Bai Jun, Luo Zhongbin, Li Bingle, Zhang Jie
Intelligent Lithology Prediction Method Based On Vibration Signal While Drilling And Deep Learning, Wang Sheng, Lai Kun, Zhang Zheng, Bai Jun, Luo Zhongbin, Li Bingle, Zhang Jie
Coal Geology & Exploration
Intelligent lithology prediction is of great importance in geological drilling, capable of improving exploration and mining efficiency, as well as the quality of results. In this study, a method of intelligent lithology prediction while drilling was proposed based on the vibration signals produced by the drill bit breaking rocks during drilling. Specifically, seven types of rocks with the same size and different lithologies were selected, and a micro-drilling experiment was designed to apply different drilling rates and rotary speeds to the rocks, in order to collect the triaxial vibration signals while drilling under multiple drilling conditions. The signals were preprocessed …
Mine Water Inrush Prediction Method Based On Vmd-Dbn Model, Liu Hui, Liu Guiqin, Ning Dianyan, Fan Juan, Chen Weiming
Mine Water Inrush Prediction Method Based On Vmd-Dbn Model, Liu Hui, Liu Guiqin, Ning Dianyan, Fan Juan, Chen Weiming
Coal Geology & Exploration
In the process of coal mining, the loss of people and property caused by mine water inrush is extremely serious. To prevent the occurrence of water inrush accidents and grasp the law of change of water inrush, the water inrush prediction and forecasting, especially the accurate estimation of mine water inrush, is very important, which is also an important task in the prevention and control of mine water damage. To increase the prediction accuracy of mine water inrush, an efficient time series prediction model combining Variational Mode Decomposition (VMD) and Deep Belief Network (DBN) was proposed for the series of …
Terrain Characterization Via Machine Vs. Deep Learning Using Remote Sensing, Jordan Ewing, Thomas Oommen, Jobin Thomas, Anush Kasaragod, Richard Dobson, Colin Brooks, Paramsothy Jayakumar, Michael Cole, Tulga Ersal
Terrain Characterization Via Machine Vs. Deep Learning Using Remote Sensing, Jordan Ewing, Thomas Oommen, Jobin Thomas, Anush Kasaragod, Richard Dobson, Colin Brooks, Paramsothy Jayakumar, Michael Cole, Tulga Ersal
Michigan Tech Publications, Part 1
Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning …
Recognition Of Land Use On Open-Pit Coal Mining Area Based On Deeplabv3+ And Gf-2 High-Resolution Images, Zhang Chengye, Li Feiyue, Li Jun, Xing Jianghe, Yang Jinzhong, Guo Junting, Du Shouhang
Recognition Of Land Use On Open-Pit Coal Mining Area Based On Deeplabv3+ And Gf-2 High-Resolution Images, Zhang Chengye, Li Feiyue, Li Jun, Xing Jianghe, Yang Jinzhong, Guo Junting, Du Shouhang
Coal Geology & Exploration
A highly efficient means is provided by remote sensing and deep learning to keep tracking of land use in open-pit coal mining area. Based on the high–resolution images from the domestic GF-2 satellite, a DeepLabv3+ model was utilized to achieve recognition of land use on open-pit coal mining area. In addition, a comparison was made among Deeplabv3+, U-Net, FCN, Random Forest, Support Vector Machine, and Maximum Likelihood Method. Firstly, samples data from high-resolution images were produced and sensitivity tests were conducted to determine the optimal cutting size and mode of the sample. Then, the deep neural network model (DeepLabv3+) was …