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

作     者：Manoj Kumar Manav Mittal Pijush Samui 

作者机构：National Institute of Rock MechanicsKolar Gold Fields 563117 Karnataka India School of Mechanical and Building Science VIT University Centre for Disaster Mitigation and Management VIT University 

出 版 物：《Earthquake Science》 (地震学报（英文版）)

年 卷 期：2013年第26卷第2期

页      面：147-150页

学科分类：0709[理学-地质学] 0819[工学-矿业工程] 07[理学] 070801[理学-固体地球物理学] 0707[理学-海洋科学] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0815[工学-水利工程] 0816[工学-测绘科学与技术] 0813[工学-建筑学] 0814[工学-土木工程] 0825[工学-航空宇航科学与技术] 0704[理学-天文学] 

主　　题：Seismic attenuation Geneticprogramming Minimax probability machineregression Artificial neural network Prediction 

摘      要：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 algo- rithm. 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 net- work. This article gives robust models based on GP and MPMR for prediction of s.