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泥石流堆积物粒度分布特征影响因素分析及分形维数预测
引用本文:董佳祺,王清,张旭东,陈剑平,单博,肖广平.泥石流堆积物粒度分布特征影响因素分析及分形维数预测[J].工程地质学报(英文版),2015,23(3):462-468.
作者姓名:董佳祺  王清  张旭东  陈剑平  单博  肖广平
作者单位:1.吉林大学建设工程学院 长春 130026;
基金项目:国家自然科学基金,水利部公益性行业科研专项经费项目(201001008)资助.
摘    要:泥石流堆积物作为泥石流发育最终的产物,含有大量与泥石流发生过程和发育特征相关的信息,能够反映泥石流灾害程度和活动强度。研究表明,泥石流堆积物颗粒具有明显的自相似性和无标度区间,运用分形理论,计算泥石流堆积物颗粒分布的分维数。分析分维数与主沟长度、泥砂补给段长度比、主沟平均比降、流域最大相对高差和松散物源量的关系,结果表明分维数与各因素之间存在较强的非线性响应关系。以乌东德库区泥石流实测数据为例,以上述的5个因素作为输入单元,建立了泥石流堆积物分维数支持向量机预测模型,并对分维数进行了预测,其预测结果的最大误差为1.25%,说明预测值与实测值吻合度较高。综合表明支持向量机预测模型能够较好地模拟和泛化数据,是一种行之有效的泥石流堆积物分形维数预测方法,可用于不具备筛析条件的泥石流堆积物粒度分布特征的预测与研究,进而可为研究泥石流的形成机理、类型、危险度和堆积物的形成演化特征及物理力学性质提供一个新思路。

关 键 词:泥石流    堆积物    颗粒分维数    支持向量机    预测
收稿时间:2014-07-27

INFLUENCING FACTORS ANALYZING OF GRAIN SIZE DISTRIBUTION CHARACTERISTICS OF DEBRIS FLOW DEPOSITION AND FRACTAL DIMENSION PREDICTION
DONG Jiaqi,WANG Qing,ZHANG Xudong,CHEN Jianping,SHAN Bo,XIAO Guangping.INFLUENCING FACTORS ANALYZING OF GRAIN SIZE DISTRIBUTION CHARACTERISTICS OF DEBRIS FLOW DEPOSITION AND FRACTAL DIMENSION PREDICTION[J].Journal of Engineering Geology,2015,23(3):462-468.
Authors:DONG Jiaqi  WANG Qing  ZHANG Xudong  CHEN Jianping  SHAN Bo  XIAO Guangping
Institution:1.College of Construction Engineering, Jilin University, Changchun 130026;2.Northeast Electric Power Design Institute, Changchun 130022
Abstract:The debris flow deposition is the final product of debris flow. It contains a lot of information of transit process and development of debris flows. The research of deposition has reflected the debris flow hazard and activity intensity. Consequently, solid grains in the debris-flow deposits display a marked self-similarity in geometrical shape and scale-invariance in size according to fractal theory. The particle fractal dimension of debris flow deposition is calculated with fractal theory. By analyzing the relationship between fractal dimension and the length of the main channel, the ratio of loose material length along the channel to the total channel length, average gradient of the main channel, and maximum elevation difference, this paper finds the nonlinear response between fractal dimension and its influencing factors. According to fractal dimension data of debris flow in the Wudongde reservoir area, taking the aforementioned influencing factors as the input units, the support vector machine forecasting model is established. The fractal dimension of debris flow deposits is predicted. The maximum error is only 1.25%,which means the predicted values are consistent with the measured values. It is indicated that the support vector machine model has a great fitting and generalization ability. It is an effective method for prediction of debris flow deposits fractal dimension, which can also be used to deduce the distribution law of particle for debris flows without sample sieve analysis. Besides it provides a new idea for researching the following subjects: formation mechanism, type, hazard of debris flow and formation characteristic, physical and mechanical properties of debris flow deposits.
Keywords:Debris flow  Deposits  Fractal dimension  Support vector machine  Prediction
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