Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm

Abstract

During the last several decades, researchers have made significant advances in sedimentary environment interpretation of grain-size analysis, but these improvements have often depended on the subjective experience of the researcher and were usually combined with other methods. Currently, researchers have been using a larger number of data mining and knowledge discovering methods to explore the potential relationships in sediment grain-size analysis. In this paper, we will apply bipartite graph theory to construct a Sample/Grain-Size network model and then construct a Sample network model projected from this bipartite network. Furthermore, we will use the Mini Batch K-means algorithm with the most appropriate parameters (reassignment ratio ϵ=0.025 and mini batch = 25) to cluster the sediment samples. We will use four representative evaluation indices to verify the precision of the clustering result. Simulation results demonstrate that this algorithm can divide the Sample network into three sedimentary categorical clusters: marine, fluvial, and lacustrine. According to the results of previous studies obtained from a variety of indices, the precision of experimental results about sediment grain-size category is up to 0.92254367, a fact which shows that this method of analyzing sedimentary environment by grain size is extremely effective and accurate.