Comparative Study of Intelligent Prediction Models for Pressure Wave Velocity

A.K. Verma^1, , T. N singh² and Sachin Maheshwar¹

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

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

Cite this paper:
A.K. Verma, T. N singh and 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

Abstract

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.

Keywords:
SVM ANFIS ANN pressure wave velocity hardness porosity

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References:

[1]	Aveline M., 1964. Experimental results on the relation between micro-fissuration and speed of propagation of ultrasounds in the granites of Sidbore, Sci. Terre. 9 (4), 439-448.
[2]	Berezkin V M & Mikhaylov I N.,1964. On the correlational relationship between the density of rocks and their velocities of elastic wave propagation for the central and eastern regions of Russian platform, Geofiz, Razvedka. 16, 83-91.
[3]	Boadu, F.K., 1997. Fractured rock mass characterization parameters and seismic properties: analytical studies. J. Appl. Geophys. 36, 1-19.
[4]	Chiu S., 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent and fuzzy systems. 2 (3), 267-278.
[5]	Goodman R.E., 1989. Introduction to Rock Mechanics. (2nd ed.), Wiley, New York, 562.
[6]	H. Kern, 1990. Laboratory seismic measurements: an aid in the interpretation of seismic field data, Terra Nova. 2, 203-617.
[7]	Hogstrom K., 1994. A study on strength parameter for aggregate from south western Sweden rocks, Res Rep. (Chamer Univ of Technology, Goteborg, Sweden), 123-134.
[8]	Hudson, T.A., Jones, E.T.W., New, B.M., 1980. P-wave velocity measurements in a machine bored chalk tunnels. Q. J. Eng. Geol, 13, 33-43.
[9]	Karpuz, C. and Pasamehmetoglu, A.G., 1997. Field characterization of weathered Ankara andesites. Eng. Geol. 46, 1-17.
[10]	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.
[11]	Monjezi, M. and Dehghani,H., 2008. Evaluation of effect of blasting pattern parameters on back break using neural networks. Int. Jol. of Rock Mech. and Mining Sci. 45 (8), 1446-1453.
[12]	Price, D.G., Malone, A.W., Knill, T.L., 1970. The application of seismic methods in the design of rock bolt system. Proc. 1st Int. Congr., Int. Assoc. Eng. Geol., 2, 740-752.
[13]	Richard, MD and Lippmann, RP, 1991. Neural network classifiers estimate Bayesian a posteriori probabilities, Neural computation. 3 (4), 461-483
[14]	S Sinha, TN Singh, VK Singh, AK Verma, 2010. Epoch determination for neural network by self-organized map (SOM). Computational Geosciences. 14 (1), 199-206.
[15]	Singh, T.N., Kanchan, R. and Verma, A.K., 2004b. Prediction of blast induced ground vibration and frequency using an artificial intelligence technique, Int J. Noise and Vibration Worldwide, Multi Science Pub. UK, 35 (11), 7-14.
[16]	Singh, T.N. and Verma, A.K., 2005. Prediction of creep characteristic of rock under varying environment. Environmental Geology. 48 (4), 559-568
[17]	Singh, T.N., Kanchan, R., Saigal, K. and Verma, A.K. 2004a. Prediction of P-wave Velocity and Anisotropic properties of rock using Artificial Neural Networks technique, J. Scientific and Industrial research, CSIR Publication, 63 (1), 32-38.
[18]	Takagi T. and Sugeno M.,1985. Fuzzy identification of systems and its applications to modeling and control. IEEE.Transactions on systems, man, and cybernetics, SMC-15. 1, 116-132.
[19]	Vapnik, V., 1995. The Nature of Statistical Learning Theory. New York. Springer.
[20]	Vapnik, V. and A. Lerner, 1963. Pattern recognition using generalized portrait method. Automation and Remote Control. 24, 774-780.
[21]	Vapnik, V. N. and A. Y. Chervonenkis, 1991. The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis 1 (3), 283-305.
[22]	Verma, A.K. and Singh, T.N. 2009. A Neuro-Genetic approach for prediction of compressional wave velocity of rock and its sensitivity analysis. Int. J.of Earth Sci. and Engg., 2 (2), 81-94.
[23]	Young, R.P., Hill, T.T., Bryan, I.R. and Middleton, R., 1985. Seismic spectroscopy in fracture characterization. Q. J. Eng. Geol., 18, 59-479.
[24]	Yudborovsky I and Vilenskaya S M., 1962. Some results of study of the elastic properties of rocks of western central Asia; Akad Nauk Turkmen SSR Ser Fiz-Tekh Khim Geol Nauk. 3, 26-31.