A novel type of neural networks for feature engineering of geological data:Case studies of coal and gas hydrate-bearing sediments  

作　　者：Lishuai Jiang Yang Zhao Naser Golsanami Lianjun Chen Weichao Yan 

机构地区：[1]State Key Laboratory of Mining Disaster Prevention and Control,Shandong University of Science and Technology,Qingdao 266590,China [2]College of Energy and Mining Engineering,Shandong University of Science and Technology,Qingdao 266590,China [3]College of Safety and Environmental Engineering,Shandong University of Science and Technology,Qingdao 266590,China [4]Department of Well Logging,School of Geosciences,China University of Petroleum(East China),Qingdao 266580,China

出　　处：《Geoscience Frontiers》2020年第5期1511-1531,共21页地学前缘（英文版）

摘　　要：The nature of the measured data varies among different disciplines of geosciences.In rock engineering,features of data play a leading role in determining the feasible methods of its proper manipulation.The present study focuses on resolving one of the major deficiencies of conventional neural networks(NNs)in dealing with rock engineering data.Herein,since the samples are obtained from hundreds of meters below the surface with the utmost difficulty,the number of samples is always limited.Meanwhile,the experimental analysis of these samples may result in many repetitive values and 0 s.However,conventional neural networks are incapable of making robust models in the presence of such data.On the other hand,these networks strongly depend on the initial weights and bias values for making reliable predictions.With this in mind,the current research introduces a novel kind of neural network processing framework for the geological that does not suffer from the limitations of the conventional NNs.The introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other points.This method,being completely different from the conventional NNs,re-arranges all the basic elements of the neuron model into a new structure.Therefore,its mathematical calculations were performed from the very beginning.Moreover,the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time being.This new kind of network was first evaluated through computer-based simulations of rock cracks in the 3 DEC environment.After the model’s reliability was confirmed,it was adopted in two case studies for estimating respectively tensile strength and shear strength of real rock samples.These samples were coal core samples from the Southern Qinshui Basin of China,and gas hydrate-bearing sediment(GHBS)samples from the Nankai Trough of Japan.The coal samples used in the experiments underwent nu

关 键 词：Tensile strength Shear strength Gas Hydrate Feature engineering Rock engineering data Neuron model 

分 类 号：P618.13[天文地球—矿床学]