Numerical partitioning of components for four-modal sedimentary grain-size distribution based on gradient descent method

Abstract

The gradient descent (GD) method is used to fit the measured data (i.e., the laser grain-size distribution of the sediments) with a sum of four weighted lognormal functions. The method is calibrated by a series of ideal numerical experiments. The numerical results indicate that the GD method not only is easy to operate but also could effectively optimize the parameters of the fitting function with the error decreasing steadily. The method is applied to numerical partitioning of laser grain-size components of a series of Garze loess samples and three bottom sedimentary samples of submarine turbidity currents modeled in an open channel laboratory flume. The overall fitting results are satisfactory. As a new approach of data fitting, the GD method could also be adapted to solve other optimization problems.