...). vibration time-frequency characteristics are extracted into time frequency images with the application of short-time Fourier transform (STFT). This paper develops lithology classification models using new data sources based on convolutional neural network (CNN) combining with Mobilenet and ResNet...

.... Then, some proposals have been done how to characterize the geometry, fracturing, morphology, and internal structure of fault/shear zones and some examples are given using some rock classifications. Then, some direct shear tests were performed on the actual fault gouges and their actual characteristics have...

... of the predictions with the actual data. Besides, supervised classification of the geological layers was done using decision trees and support vector machine algorithms. The optimum number of branches that construct the decision tree is found to be 20. The best quality of fitting was obtained using decision trees...

... of the slope. Mountainous slopes are large and are sinuous in terms of sloping direction. lithology criticality envelop discontinuity karchham orientation continuous kinematic criticality classification sarkar reservoir characterization discontinuity orientation approach rahman niyogi...

.... artificial intelligence dataset neural network machine learning probability correspond algorithm revisiting rockburst upstream oil & gas metals & mining classification rockburst engineering reservoir characterization adoko accuracy heal prediction underground space technology...

... by verifying the applicability of machine learning to rock surface evaluation. Support vector machine (SVM) is implemented to build the model for the classification of four support patterns based on data collected from different tunnel construction sites. As a result, the proposed model has shown high accuracy...

.... prediction accuracy estimation upstream oil & gas machine learning reservoir characterization predictor evaluation artificial intelligence strength application classification is50 rock strength classifier core logging data quantity pierce differentiate ARMA 22 283 Application...

... excavation subscript zhang underground space technology variation classification ARMA 22 516 A new analysis method for the influencing factors of surrounding rock stability and its application Peitao, Li. And Quansheng, Liu.* 1. The Key Laboratory of Safety for Geotechnical and Structural Engineering...

... characterization bieniawski shear strength tunnel stability himalaya wellbore design larger span overhanging cliff constructed strength upstream oil & gas span classification engineering assessment safety figure 6 modeling ARMA 22 0534 Design of Larger Span Overhanging Cliffs Constructed...

... embedded in a half space (Comninou and Dunders, 1975; Okada, 1992; Thomas, 1993; Jonsson, 2002; Maerton et al., 2005; Meade, 2007; Nikkhoo and Classification: General Use General Use computational efficiency and stability are sensitive to the The structural framework (Figure 1-2) is based on the shape...

... of empirical support design methods using the following rock mass classifications: Barton s Q System; Bieniawski s RMR System; and Warren s Weak-RMR. The Weak-RMR classification system, and empirical support design procedures proposed by Warren et al., 2018 are explored in more detail with a series...

...); Chesapeake reported casing damages in the Eagle Ford and the Powder River Basin (Hoffman et al., 2020). In many cases, operators have had to skip fracturing stages or even abandon wells. These have negative impacts on stimulation efficiency and production. saudi arabia government classification...

... provided by transportation authorities and other governing bodies have been analyzed (McCauley et al., 1985; Delonca et al., 2014). us government rockfall activity artificial intelligence calculation classification rock slope machine learning rockfall bedrock floyd hill point cloud...

.... Two models were developed: a predictive (fitting) model and a classification model. The results indicate that ANN, as a prediction model, is not appropriate for dilution because of a low prediction capability observed (R 2 = 0.57). On the other hand, the classifier model showed good classification...

... underground excavation damages induced by mine seismicity in order to efficiently control a ground prone to rockburst. machine learning artificial intelligence reservoir characterization evolutionary algorithm upstream oil & gas classification performance corresponding classification rock...

...ARMA 21 1623 Classification and mechanism analysis of wellbore failure based on cavings Jian Jin1, Yan Jin1 Yunhu Lu1, Huiwen Pang1,2 1 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China 2 College of Science, China University...

... ABSTRACT: Lithological classification of geological formations usually depends on the geologist’ expertise through a laborious and time consuming process. Automating the rock classification procedure is a step towards fast and accurate characterization of geological formations where large...

.... In this paper, a recent case study is presented which highlights the challenges associated with the geotechnical characterization of a rock mass containing vein stockwork from a deep block cave mine. Associated data collection difficulties are detailed, including classification of the different structure types...

... information from the back analysis of the same stopes and using the software c. Reservoir Characterization Upstream Oil & Gas metals & mining stope knowledge management reservoir geomechanics classification orientation Mathew major geological fault information Stability Graph Method...

..." layout is shown on the right. The RQD values from recovered core logging were mainly 60-90. With respect to tunneling conditions, and according to standard empirical classification systems (RMR [4], and Q, [5 the rock mass was estimated to be mainly fair to good. However, it was anticipated that in some...