【摘要】：隨著海洋地質(zhì)學(xué)等學(xué)科的發(fā)展,以及海洋工程和海洋開發(fā)的需要,海底沉積物聲學(xué)特性研究具有重要的現(xiàn)實(shí)意義,并受到越來越廣泛的重視。海底沉積物通常被認(rèn)為是一種固液雙相介質(zhì),其結(jié)構(gòu)和物理性質(zhì)直接決定了聲波在其中的傳播速度,是聲波傳播的物理基礎(chǔ)。構(gòu)建明確、統(tǒng)一的海底沉積物聲速與物理參數(shù)模型,對(duì)于開展聲速反演、地聲模型建立、工程實(shí)踐等方面的研究都具有重要的意義。國(guó)內(nèi)外的研究學(xué)者對(duì)縱波聲速與沉積物物理參數(shù)之間的相關(guān)關(guān)系進(jìn)行了大量實(shí)際調(diào)查工作,建立了適用于不同海域沉積物的聲速與物理參數(shù)之間的經(jīng)驗(yàn)公式。這些經(jīng)驗(yàn)公式的建立在一定程度上揭示了兩者之間的相互關(guān)系,但由于經(jīng)驗(yàn)公式大多采用簡(jiǎn)單的回歸擬合得到,再加上海洋沉積環(huán)境的多樣性及復(fù)雜性,在進(jìn)行聲速預(yù)報(bào)時(shí),存在回歸誤差過大、適用范圍有限、缺乏物理意義等問題。針對(duì)這些問題,本文將在已有BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)的基礎(chǔ)上,運(yùn)用遺傳算法優(yōu)化其初始權(quán)值和閾值的方法,構(gòu)建出基于含水量、孔隙度的聲速預(yù)報(bào)模型進(jìn)行聲速預(yù)報(bào)。同時(shí),將南沙海域采集得到的海底沉積物樣品分為兩部分,隨機(jī)抽取120組涵蓋陸架、陸坡、海槽等地貌單元的樣品作為訓(xùn)練數(shù)據(jù),另外剩余6組作為測(cè)試數(shù)據(jù)。經(jīng)試驗(yàn)對(duì)比后發(fā)現(xiàn),在對(duì)本區(qū)域進(jìn)行聲速預(yù)報(bào)時(shí),宜采用遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò),其要優(yōu)于傳統(tǒng)的單參數(shù)、雙參數(shù)回歸擬合預(yù)報(bào)方法和國(guó)內(nèi)外其他學(xué)者所得到的經(jīng)驗(yàn)公式。此種預(yù)報(bào)方法具有一定的科學(xué)依據(jù)和廣泛的應(yīng)用前景,可在今后為建立明確、統(tǒng)一的聲速預(yù)報(bào)模型提供參考。
[Abstract]:With the development of marine geology and the need of marine engineering and marine development, the study of acoustic characteristics of seabed sediments has important practical significance and has been paid more and more attention. Seafloor sediments are generally considered as a solid-liquid biphasic medium, whose structure and physical properties directly determine the velocity of sound wave propagation in which, is the physical basis of acoustic wave propagation. The establishment of a clear and unified model of acoustic velocity and physical parameters of seabed sediment is of great significance for the research of acoustic velocity inversion, the establishment of a geoacoustic model, and engineering practice. Researchers at home and abroad have carried out a lot of practical investigations on the correlation between longitudinal wave velocity and sediment physical parameters, and established empirical formulas between sound velocity and physical parameters suitable for sediment in different sea areas. The establishment of these empirical formulas reveals the relationship between them to some extent. However, because most of the empirical formulas are obtained by simple regression fitting, coupled with the diversity and complexity of the marine sedimentary environment, in the prediction of sound velocity, There are some problems such as too large error of regression, limited scope of application and lack of physical meaning. Aiming at these problems, based on the existing BP neural network prediction, this paper uses genetic algorithm to optimize its initial weight and threshold value, and constructs a sound velocity prediction model based on water content and porosity to predict sound velocity. At the same time, the samples collected from Nansha sea area were divided into two parts. 120 groups of geomorphologic units including shelf, slope and trough were randomly selected as training data, and the remaining 6 groups were used as test data. It is found that the BP neural network, which is optimized by genetic algorithm, is superior to the traditional regression forecasting method with single parameter, double parameter and the empirical formula obtained by other scholars at home and abroad in the prediction of sound velocity in this region. This forecasting method has certain scientific basis and wide application prospect, which can be used as reference for the establishment of a clear and unified sound velocity prediction model in the future.
【學(xué)位授予單位】：中國(guó)科學(xué)院研究生院(海洋研究所)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：P733.2;P714

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本文編號(hào)：2197853