Article citationsMore >>

Khandelwal, M. and Singh T.N., 2006. Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. Journal of sound and vibration. 289 (4), 711-725.

has been cited by the following article:


Comparative Study of Intelligent Prediction Models for Pressure Wave Velocity

1Department of Mining Engineering, Indian School of Mines – Dhanbad-04, Jharkhand, India

2Department of Earth Sciences, Indian Institute of Technology-Bombay, Powai, Mumbai, India

Journal of Geosciences and Geomatics. 2014, Vol. 2 No. 3, 130-138
DOI: 10.12691/jgg-2-3-9
Copyright © 2014 Science and Education Publishing

Cite this paper:
A.K. Verma, T. N singh, Sachin Maheshwar. Comparative Study of Intelligent Prediction Models for Pressure Wave Velocity. Journal of Geosciences and Geomatics. 2014; 2(3):130-138. doi: 10.12691/jgg-2-3-9.

Correspondence to: A.K.  Verma, Department of Mining Engineering, Indian School of Mines – Dhanbad-04, Jharkhand, India. Email:


Support Vector Machine (SVM) optimization technique is rapidly gaining attractiveness in the area of geophysics, mining and geomechanics. This paper discusses the importance of SVM for prediction of longitudinal pressure-wave velocity and its advantages over other conventional methods of computing. Pressure-wave measurement, an indicator of peak particle velocity (PPV) during blasting in a mine is an important parameter to be determined to minimize the damage caused by ground vibrations. A number of previous researchers have tried to use different empirical methods to predict pressure-wave. But these empirical methods are less versatile in their applications. The fracture propagation is not only influenced by the physico-mechanical parameters of rock, but they are also affected by the dynamic wave velocity of rock (e.g. compressional wave velocity). Wave velocity measurements have wide applications in the different fields of geophysics. A Support Vector Machine (SVM) model is designed to predict the pressure wave velocity of different rocks. To avoid the blindness in man-made choices of parameters of SVM, we use the chaos optimization algorithm to find the optimal parameters which can help the model to enhance the learning efficiency and capability of prediction. The fracture roughness coefficient and physico-mechanical properties are taken as input parameters and pressure wave velocity as output parameters. The mean absolute percentage error for the pressure wave velocity (PrV) predicted value has been found to be the least (0.258%) as compared to values obtained by Multivariate Regression Analysis (MVRA), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and generalization capability of the SVM model is found to be very useful for such type of geophysical problems.