Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models (original) (raw)
Regression Analysis and Neural Network Fitting of Rock Mass Classification Systems
Ferid Oge
2018
View PDFchevron_right
Delineation of geologic facies with statistical learning theory
Brendt Wohlberg, Daniel Tartakovsky
Geophysical Research Letters, 2004
View PDFchevron_right
Artificial Neural Networks and Kriging Method for Slope Geomechanical Characterization
Romina Secci, Alessandro Mazzella, A. Montisci
Engineering Geology for Society and Territory - Volume 2, 2015
View PDFchevron_right
Lithology prediction in the subsurface by artificial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method
Iva Kolenković Močilac
GEM - International Journal on Geomathematics, 2020
View PDFchevron_right
239 Artificial Neural Networks and Kriging Method for Slope Geomechanical Characterization
Gabriele Uras, Alessandro Mazzella, A. Montisci
View PDFchevron_right
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers
Longjun Dong
Mathematical Problems in Engineering, 2013
View PDFchevron_right
Prediction Model Based on an Artificial Neural Network for Rock Porosity
Hany Gamal
View PDFchevron_right
Machine learning algorithms for applications in geotechnical engineering
javad manashti
2018
View PDFchevron_right
Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study
Abu Bakarr Jalloh
International Journal of Mining Science and Technology, 2016
View PDFchevron_right
The application of artificial neural network in geotechnical engineering
nghiem tran uy
IOP Conference Series: Earth and Environmental Science, 2018
View PDFchevron_right
MACHINE LEARNING ALGORITHMS APPLIED TO SEISMIC ACTIVITY
Qasim Khan
2019
View PDFchevron_right
Site Characterization Model Using Support Vector Machine and Ordinary Kriging
SARAT KUMAR DAS
Journal of Intelligent Systems, 2011
View PDFchevron_right
Neural network modelling and classification of lithofacies using well log data: a case study from KTB borehole site
Saumen Maiti
View PDFchevron_right
Application of artificial neural networks for lithofacies determination based on limited well data
Bruno Saftic
Central European Geology
View PDFchevron_right
Support vector machines and gradient boosting for graphical estimation of a slate deposit
Antonio Vaamonde Liste
Stochastic Environmental Research and Risk Assessment, 2004
View PDFchevron_right
Artificial neural networks as a basis for new generation of rock failure criteria
Md Jakaria
International Journal of Rock Mechanics and Mining Sciences, 2011
View PDFchevron_right
Applicability of Statistical Learning Algorithms for Seismic Attenuation Prediction
Sarat Kumar Das
civil.iitb.ac.in
View PDFchevron_right
Leveraging machine learning for precision prediction of geomechanical properties of granitic rocks: a comparative analysis of MLR, ANN, and ANFIS models
Samad Narimani
Earth Science Informatics, 2025
View PDFchevron_right
Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type
Nafees Ali
Energies
View PDFchevron_right
Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation
C. Gokceoglu
International Journal of Rock Mechanics and Mining Sciences, 2006
View PDFchevron_right
Machine learning methods for rockburst prediction-state-of-the-art review
Hani Mitri
International Journal of Mining Science and Technology, 2019
View PDFchevron_right
Determination of Earthquake Depth Using Seismic Records of a Single Station, Implementing Machine Learning Techniques
Ingeniería e Investigación
View PDFchevron_right
The artificial neural network for the rockfall susceptibility assessment. A case study in Basilicata (Southern Italy)
Francesco Sdao
Geomatics, Natural Hazards and Risk
View PDFchevron_right
ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING
Mohamed Shahin, M. Jaksa
2001
View PDFchevron_right
Support vector machines for delineation of geologic facies from poorly differentiated data
Brendt Wohlberg
2006
View PDFchevron_right
Machine learning in geosciences and remote sensing
Amir Alavi, David J Lary
View PDFchevron_right
Rock Fragmentation Prediction Using an Artificial Neural Network and Support Vector Regression Hybrid Approach
richard amoako
Mining
View PDFchevron_right
State of the Art of Artificial Neural Networks in Geotechnical Engineering
Mohamed Shahin
2000
View PDFchevron_right
Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
Reza Tarinejad
Applied Sciences
View PDFchevron_right
Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site
Saumen Maiti
View PDFchevron_right
The determination of information point distribution and classfication of confidence level estimation of geotechnic solid rock kriging estimation parameter based on variogram analysis
Irfan Marwanza
3RD INTERNATIONAL POSTGRADUATE CONFERENCE ON MATERIALS, MINERALS & POLYMER (MAMIP) 2019, 2020
View PDFchevron_right
Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data
Hui Wang
View PDFchevron_right