本文選題：去噪 + 數(shù)學(xué)形態(tài)學(xué)�。� 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：自從大地電磁測深法問世以來,因其方法簡單、實(shí)現(xiàn)便利、效果顯著等諸多優(yōu)點(diǎn)在越來越多的領(lǐng)域得到應(yīng)用。而隨之而來的大地電磁信噪分離方法研究也成為信號分析領(lǐng)域的一個(gè)熱點(diǎn)和難點(diǎn)問題,特別長周期大地電磁信號因其信號能量弱,頻域?qū)?容易被干擾等因素,成為信號的信噪分離的一個(gè)難點(diǎn)問題。而受干擾的大地電磁信號將會(huì)對后續(xù)資料解釋產(chǎn)生嚴(yán)重影響,所以在野外采集的長周期大地電磁信號都需要先經(jīng)過去噪處理后才能更可靠的使用。目前針對長周期大地電磁信號的去噪雖然方法有很多,諸如小波變換、加窗傅里葉變換、源參考法等,也取得了許多研究成果,但目前的方法都有一定的缺陷,且大部分都是基于頻域去噪提出來的。因此提出更多更適應(yīng)長周期大地電磁信號的處理方法一直是大地電磁測深領(lǐng)域內(nèi)的熱點(diǎn),本文基于此背景,提出了數(shù)學(xué)形態(tài)學(xué)原理對長周期大地電磁信號進(jìn)行處理。本文提出的數(shù)學(xué)形態(tài)學(xué)方法與過去對長周期大地電磁信號處理方法的不同之處是數(shù)學(xué)形態(tài)學(xué)去噪是基于時(shí)域進(jìn)行處理,適合大地電磁信號頻域?qū)挼奶攸c(diǎn),而且此方法數(shù)學(xué)理論基礎(chǔ)扎實(shí),原理簡單,發(fā)展空間大。本文的仿真實(shí)驗(yàn)也表明此方法是十分有效的。本文主要內(nèi)容如下:(1)總結(jié)了大地電磁信號的場源,分析了大地電磁的各類噪聲干擾。并分析每種噪聲干擾對大地電磁信號造成的不同影響。指出長周期大地電磁數(shù)據(jù)存在數(shù)據(jù)確實(shí)現(xiàn)象。(2)對信號的分類和信號的一般處理方法進(jìn)行了簡要的介紹。對不同信號的性質(zhì)也做了簡明扼要的說明,介紹了信號的各種分析方法中的典型算法。同時(shí)介紹的信號頻譜,幅度等特征(3)介紹了形態(tài)學(xué)的基本數(shù)學(xué)理論,詳細(xì)闡述了形態(tài)學(xué)濾波中的四種基本算子的定義與數(shù)學(xué)含義。并用實(shí)驗(yàn)仿真了四種基本算子的濾波效果,討論了它們具體的去噪性能。然后對分析了形態(tài)學(xué)濾波中的傳統(tǒng)的結(jié)構(gòu)元素類型,用幾何和二值化參數(shù)對它們進(jìn)行了解釋。模擬和對比了傳統(tǒng)幾種結(jié)構(gòu)元素的去噪性能,并自定義結(jié)構(gòu)元素來對信號進(jìn)行處理。(4)在數(shù)學(xué)形態(tài)學(xué)濾波的理論基礎(chǔ)上對長周期大地電磁實(shí)測信號做了去噪處理,分析了不同類型結(jié)構(gòu)元素對長周期大地電磁的去噪效果,分析了數(shù)學(xué)形態(tài)學(xué)的數(shù)據(jù)數(shù)據(jù)填補(bǔ)功能。并在前面的基礎(chǔ)上探討了改進(jìn)型的數(shù)學(xué)形態(tài)學(xué)濾波器,用實(shí)測信號對其進(jìn)行分析。在基于數(shù)學(xué)形態(tài)學(xué)去噪的基礎(chǔ)上結(jié)合中值濾波對長周期大地電磁信號進(jìn)行處理,實(shí)驗(yàn)結(jié)果表明這種方法也是有效的。(5)最后用本文提出的數(shù)學(xué)形態(tài)學(xué)的處理方法與目前去噪領(lǐng)域中常用的小波包去去噪方法同時(shí)對同一信號進(jìn)行處理,實(shí)驗(yàn)結(jié)果也表明在此信號的處理上本文提出的方法略優(yōu)于小波包去噪方法。
[Abstract]:Since the advent of magnetotelluric sounding (MT), it has been applied in more and more fields because of its simplicity, convenience and remarkable effect. The research of magnetotelluric signal noise separation has become a hot and difficult problem in the field of signal analysis, especially because of its weak signal energy, wide frequency domain and easy to be interfered. It is a difficult problem for the separation of signal and noise. The disturbed magnetotelluric signal will have a serious impact on the interpretation of the subsequent data, so the long period magnetotelluric signals collected in the field need to be de-noised before they can be used more reliably. Although there are many methods of de-noising for long-period magnetotelluric signals, such as wavelet transform, windowed Fourier transform, source reference method and so on, many achievements have been made, but the present methods have some defects. And most of them are based on frequency domain denoising. Therefore, it has been a hot spot in the field of magnetotelluric sounding to put forward more and more suitable processing methods for longperiod magnetotelluric signals. Based on this background, this paper puts forward the mathematical morphology principle to process long-period magnetotelluric signals. The difference between the mathematical morphology method proposed in this paper and the previous methods of long period magnetotelluric signal processing is that the mathematical morphology denoising is based on time domain processing, which is suitable for the characteristics of magnetotelluric signal frequency domain width. Moreover, this method has a solid foundation of mathematics theory, simple principle and large space for development. The simulation results also show that this method is very effective. The main contents of this paper are as follows: (1) the field source of magnetotelluric signal is summarized, and the noise interference of magnetotelluric signal is analyzed. The different effects of each noise disturbance on magnetotelluric signal are analyzed. It is pointed out that long period magnetotelluric data exist in real data. (2) the classification of signals and the general processing methods of signals are briefly introduced. The properties of different signals are also explained concisely, and the typical algorithms in various signal analysis methods are introduced. The basic mathematical theory of morphology is introduced, and the definition and mathematical meaning of four basic operators in morphological filtering are described in detail. The filtering effect of four basic operators is simulated by experiments, and their specific denoising performance is discussed. Then, the traditional structural elements in morphological filtering are analyzed, and the geometric and binary parameters are used to explain them. This paper simulates and compares the de-noising performance of several traditional structural elements, and defines the structural elements to process the signal. (4) on the basis of mathematical morphological filtering theory, the long-period magnetotelluric signal is de-noised. The de-noising effect of different structural elements to long-period magnetotelluric (LPM) is analyzed, and the data filling function of mathematical morphology is analyzed. On the basis of the above, the improved mathematical morphological filter is discussed, and the measured signal is used to analyze the filter. The long period magnetotelluric signal is processed based on mathematical morphology denoising and median filtering. The experimental results show that this method is also effective. Finally, the same signal is processed simultaneously by the mathematical morphology method proposed in this paper and the wavelet packet de-noising method commonly used in the field of denoising. The experimental results also show that the proposed method is slightly better than the wavelet packet denoising method in the signal processing.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.7

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本文編號：1813464