【摘要】：本課題來源于國家自然科學基金項目“基于連續(xù)震源的煤層反射式槽波精細探測理論”。針對國家自然科學基金項目中,需要在對煤系地層所接收的彈性波信號的混合模型和源信號無法精確獲知的情況下,從觀測信號中分離出各源信號。本課題旨在研究設計適于煤系地層下接收彈性波信號的盲源分離算法。(1)詳細敘述了盲源分離的國內(nèi)外發(fā)展歷程并對不同算法的不同應用進行分類;闡述了盲源分離的基礎知識和分離要求,確定煤系地層的噪聲環(huán)境和彈性波的傳輸特性,明確了煤系地層接收彈性波信號盲源分離算法的設計要求。(2)選取合適的基于負熵的FastICA算法的非線性函數(shù)和正交化公式,與自然梯度算法分別處理煤系地層接收到的混合彈性波信號,經(jīng)過Matlab仿真實驗成功實現(xiàn)混合信號分離。驗證非高斯性判據(jù)和似然度判據(jù)與互信息判據(jù)的本質(zhì)相同性,從而證明基于負熵的FastICA算法、自然梯度算法和基于互信息的FastICA算法均能實現(xiàn)煤系地層下接收彈性波信號的盲源分離,并驗證了獨立分量分析算法存在的分離順序和分離信號幅值不確定的兩個固有問題。(3)提出一種新的非正交分解算法。傳統(tǒng)經(jīng)典算法獨立分量分析等需要滿足觀測信號數(shù)不少于源信號、獨立成分必須是非高斯分布等前提條件,大大降低了獨立分量分析在實際煤系地層下彈性波信號盲源分離的實用性。提出的新的非正交分解算法無需滿足以上前提條件,利用相關分析從單一觀測信號中選擇初等函數(shù),然后,用這些功能函數(shù)作為非正交信號分解算法的基從1個混合信號中逐一分離出不同的源信號。利用該算法對由方波、正弦波、衰減波調(diào)制信號和隨機噪聲合成的單個典型觀測信號進行了仿真實驗,實驗表明,該算法不僅可以從單一觀測信號中準確提取出所有源信號,且相比目前普遍認可的獨立分量分析具有分離順序確定、分離信號能量符號確定等更為優(yōu)良的性能。
[Abstract]:This subject comes from the project of National Natural Science Foundation of China "the fine detection theory of coal bed reflection trough wave based on continuous source". In the project of National Natural Science Foundation of China, it is necessary to separate the source signals from the observed signals when the mixed model and the source signals of the elastic wave signals received by the coal measures stratum cannot be accurately obtained. The purpose of this paper is to study and design a blind source separation algorithm which is suitable for receiving elastic wave signals in coal measure strata. (1) the development of blind source separation at home and abroad is described in detail and the different applications of different algorithms are classified. The basic knowledge and separation requirements of blind source separation are expounded, and the noise environment of coal measure strata and the transmission characteristics of elastic waves are determined. The design requirements of blind source separation algorithm for receiving elastic wave signals in coal measure strata are defined. (2) the nonlinear function and orthogonalization formula of FastICA algorithm based on negative entropy are selected. The mixed elastic wave signal received by coal measure stratum is processed separately with natural gradient algorithm, and the mixed signal separation is successfully realized by Matlab simulation experiment. It is proved that the non-Gaussian criterion, likelihood criterion and mutual information criterion are essentially identical to each other, so that the FastICA algorithm based on negative entropy is proved. Both the natural gradient algorithm and the FastICA algorithm based on mutual information can realize the blind source separation of receiving elastic wave signals in coal measure strata. Two inherent problems of the separation sequence and the uncertainty of the amplitude of the separated signals are verified. (3) A new non-orthogonal decomposition algorithm is proposed. The traditional independent component analysis (ICA) algorithm needs to satisfy the premise that the number of observed signals is no less than that of the source signals, and the independent components must be non-Gao Si distribution and so on. It greatly reduces the practicability of independent component analysis in blind source separation of elastic wave signals in actual coal measure strata. The proposed new non-orthogonal decomposition algorithm does not need to satisfy the above prerequisites. The correlation analysis is used to select the elementary function from a single observed signal, and then, These function functions are used as the basis of non-orthogonal signal decomposition algorithm to separate different source signals from one mixed signal one by one. Simulation experiments are carried out on a single typical observation signal composed of square wave, sine wave, attenuation wave modulation signal and random noise. The experiments show that the algorithm can extract all the source signals accurately from a single observation signal. Compared with the independent component analysis (ICA), which is generally accepted at present, it has better performance such as separation sequence determination, separation signal energy symbol determination and so on.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7

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