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Full-Text Articles in Engineering
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 …
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 …
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 …
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 …
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 …
Remote Sensing Of High Latitude Rivers: Approaches, Insights, And Future Ramifications, Merritt E. Harlan
Remote Sensing Of High Latitude Rivers: Approaches, Insights, And Future Ramifications, Merritt E. Harlan
Doctoral Dissertations
High latitude rivers across the pan-Arctic domain are changing due to changes in climate and positive Arctic feedback loops. Understanding and contextualizing these changes is challenging due to a lack of data and methods for estimating and modeling river discharge, and mapping rivers. Remote sensing, and the availability of satellite imagery can provide ways to overcome these challenges. Through combining various forms of fieldwork, modeling, deep learning, and remote sensing, we contribute methodologies and knowledge to three key challenges associated with better understanding high latitude rivers. In the first chapter, we combine field data that can be rapidly deployed with …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Civil & Environmental Engineering Faculty Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …