Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were deve...Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were developed based on similar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSCStd and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and paleo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.展开更多
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 num...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 Garzêloess 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.展开更多
基金supported by project funding from Chongqing Normal University (No. 12XLB009)Key Projects in the National Science & Technology Program (No. 2006BAD26B0302)
文摘Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were developed based on similar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSCStd and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and paleo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.
基金supported by the National Natural Science Foundation of China(Grant Nos.41072176,41371496)the National Science and Technology Supporting Program of China(Grant No.2013BAK05B04)the Fundamental Research Funds for the Central Universities(Grant No.201261006)
文摘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 Garzêloess 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.
