Performance assessment of genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of seismic ultrasonic attenuation

The determination of seismic attenuation (<i<s</i<) (dB/cm) is a challenging task in earthquake science. This article employs genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of <i<s</i<. 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 (<i<n</i<) (%), permeability (<i<k</i<) (millidarcy), grain size (<i<d</i<) (μm), and clay content (<i<c</i<) (%) have been considered as inputs of GP and MPMR. The output of GP and MPMR is <i<s</i<. The developed GP gives an equation for prediction of <i<s</i<. 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 <i<s</i<.