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| Performance assessment of genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of seismic ultrasonic attenuation |
| Manoj Kumar1, Manav Mittal2, Pijush Samui3 |
1 National Institute of Rock Mechanics, Kolar Gold Fields 563117, Karnataka, India
2 School of Mechanical and Building Science, VIT University, Vellore 632014, Tamil Nadu, India
3 Centre for Disaster Mitigation and Management, VIT University, Vellore 632014, India |
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Abstract The determination of seismic attenuation (s) (dB/cm) is a challenging task in earthquake science. This article employs genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of s. GP is developed based on genetic algorithm. MPMR maximizes the minimum probability of future predictions being within some bound of the true regression function. Porosity (n) (%), permeability (k) (millidarcy), grain size (d) (μm), and clay content (c) (%) have been considered as inputs of GP and MPMR. The output of GP and MPMR is s. The developed GP gives an equation for prediction of s. The results of GP and MPMR have been compared with the artificial neural network. This article gives robust models based on GP and MPMR for prediction of s.
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Received: 15 July 2013
Published: 30 October 2013
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Corresponding Authors:
Manoj Kumar
E-mail: manojgeologist84@gmail.com
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