大数据背景下粒度分布沉积信息挖掘方法进展  

Progress on Mining Methods of Sedimentological Information from Grain-size Distribution under the Background of Big Data

作　　者：袁瑞[1] YUAN Rui(School of Geophysics and Petroleum Resources,Yangtze University,Wuhan 430100,China)

机构地区：[1]长江大学地球物理与石油资源学院,武汉430100

出　　处：《沉积学报》2025年第2期361-375,共15页Acta Sedimentologica Sinica

基　　金：国家自然科学基金项目(42202113)。

摘　　要：【意义】沉积物颗粒的大小反映了颗粒的搬运方式、沉积过程和沉积环境等沉积因素,利用粒度分布数据揭示现代和古代沉积环境是沉积学研究的基础之一。经典的粒度分析方法一直存在定量化不足和多解性突出的缺陷。随着数学理论的完善和计算机的发展,非传统的粒度分布沉积学分析技术为定量表征沉积属性提供了新思路。【进展】系统梳理了沉积物粒级划分标准、粒度参数计算和传统沉积环境分析方法,重点介绍了粒度分布聚类和多重分形的基本原理和应用方法,对比论述了基于概率密度函数的单个粒度分布分解和基于端元模型的粒度分布数据集分解的次总体分离方法及工具。【结论与展望】最终归纳了粒度分布沉积学分析面临的问题及其大数据特点,展望了粒度分布沉积学研究的两个发展方向,包括粒度分布沉积信息的智能挖掘和大数据库的建设。在大数据背景下,粒度分布大数据技术将为深度挖掘沉积属性提供新引擎。[Significance]The grain sizes of sediments contain information on multiple factors:transport path,depositional process,and environment.Grain-size distribution(GSD)is defined in sedimentology and geology as the frequency of occurrence of different-diameter particles.GSD is a record of the original sedimentological information.It is one aspect of the basic data used to reveal modern and ancient depositional environments in rivers,lakes,oceans,deserts,loess,etc.The traditional GSD analytical methods adopted to describe the overall features of depositional processes and environments,either qualitatively or semi-quantitatively,may not overcome problems of quantification and multiple solutions.[Progress]This study summarizes the range of different classification standards of grain-size scale,and compares moment and graphical frequency-curve methods of describing GSDs with morphological description standards.The applicability and usage of traditional methods of sedimentary environment analysis by GSD are reviewed,and some unconventional approaches are developed using mathematical methodology to tackle the entire range of GSD.Unsupervised clustering algorithms calculate the similarity of GSDs using their frequency,cumulative frequency or statistical parameters,then depositional environments are sorted according to the classes of clustering.Multifractal analysis is used to extract fractal parameters that represent the complexity of GSD frequency data.The different fractal structures reveal different depositional properties.When applied to multiple sedimentary processes in different sedimentary environments and dynamics and the GSD is superposed by multi-subpopulations,the corresponding frequency curve is found to be bimodal or multimodal.This implies that an inverse unmixing model of the sediments is ideally suited for obtaining genetically meaningful interpretations of these subpopulations.Two techniques are used to separate the grain-size component from GSD frequency data.To apply the statistical finite-mixture model,si

关 键 词：大数据 粒度分布 沉积信息 智能挖掘 

分 类 号：P512.2[天文地球—地质学]