A New Nonlinear Smoothing and Recognizing Method of Well-Log Time Series
Yong Liu · 2005
In this paper, a type of asymmetric Gaussian function fitted by nonlinear least square method is used for data smoothing of time series and this method is applied to the smoothing of well--log time series. A simplified algorithm of smoothing well--log curves of oil-layers is presented, which can reduce the number of parameters needed to optimize. Varying model parameters can describe the different one peak of well--log curves. The model parameters are directly used as feature values to classify the shape of one peak of well-log curves into bell, case, egg, funnel and date core. The recognition results are satisfactory. This method also helps for the smoothing and shape feature extraction of other time series.