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

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 .