基于尾波包络特征提取的超声波海底沉积物分类识别研究

【作者】 张超；

【导师】 卜英勇；

【作者基本信息】 中南大学 ， 机械电子工程， 2008， 硕士

【摘要】 随着世界经济和科学技术的飞速发展,人类对矿产资源的需求量与日俱增,由于陆地资源的日趋枯竭,人们开始把目光转向海洋,深海钴结壳成为21世纪最具有商业开采价值的战略资源,世界主要发达国家已经开始了开采研究工作。面对国际社会对海洋资源争夺的形势,为维护我国的海洋权益,开辟我国新的矿产资源来源,本文在在国家自然科学基金项目“深海钴结壳微地形监测技术与最佳采集深度建模研究”的资助下,对深海海底沉积物的分类与识别做了相关研究。本文借鉴地震学中尾波的定义,提出了回波信号的尾波这一概念,并提出了一种基于小波变换提取尾波包络特征进行海底沉积物分类识别的方法。为了建立海底沉积物的分类模型,以Folk海底沉积物分类方法为依据,配比建立了14种模拟自然界中海底沉积物的实物样本,对这14种样本进行超声波回波采样,在互相关法的理论基础上,截取了采样信号中的尾波信号,再通过离散小波变换提取了尾波信号的24维包络特征,最后运用基于Fisher准则的最佳鉴别矢量法抽取了13维目标识别特征矢量,并以此为基础建立了海底沉积物的分类模型。为了验证模型的正确性,采集了两种真实的湘江河床沉积物,对采集样本进行筛分,通过Folk方法判断其实际类别;然后通过模型对其进行分类,得到的分类结果与实际类别一致,模型的正确识别率在80%左右,由此证明了本文所得模型的正确性。本文的研究为深海海底沉积物的分类与识别提供了良好的理论基础,对海底地貌及其性质的识别具有实际意义,为我国的深海采矿事业提供了有效的技术支持。更多还原

【Abstract】 Along with the fast development of economy and science in the world,the need of mineral resources is growing day by day,however,the land resources are becoming less and less.In this situation,people tend to mine the resources in the ocean. Oceanic cobalt crust resource has been a commercial foreground strategically resource in 21 century and the head developed countries in the world have been doing the work of investigation and exploitation.In order to mine the new resources by our country independently, the investigation is done of the identification and classific-tion of the abyssalbenthic sediment in the dissertation, imbursed by the national natural science fund item "study on the Abyssalbenthic Cobalt-rich Crusts Tiny terrain Detecting Technology and the Best collection deepness model".The echoed tail wave is proposed based on the define of tail wave in seismology,and the envelop method of echoed tail wave is used in the classification of sediment in the dissertation. In order to found a classification model of sediment, fourteen actual swatchs are made to simulate the sediment in nature,based on the classification theory of Folk. The echo signals of these swatchs were gained,and the echoed tail waves were cropped from the signals by using the correlation principle. Then do wavelets transform to the tail wave and pick out the envelop feature vector,whose dimension is twenty-four. The discriminating feature vector whose dimension is thirteen,is extracted by optimal-set-of-discriminant-vectors method based on Fisher theory. At last,a classification model of sediment is founded by the discriminating feature vectors. In order to validate the classification model, two kinds of the sediment were sampled from the XiangJiang riverbed. Classify the samples by using our model,at the same time, classify them through Folk’s classification theory.Two classification results are consilient, the exactness identification rate of our model is about 80 percents.The research of the dissertation affords favorable theoretic value for the identification and classification of the abyssalbenthic sediment. It also has realistic significance for the abyssalbenthic physiognomy and characters. The important is that the research furnishes effective technique sustain for the country abyssal mining.更多还原