小波降噪在核磁共振測(cè)井中的應(yīng)用研究
本文選題:核磁測(cè)井 切入點(diǎn):回波串 出處:《華中科技大學(xué)》2015年碩士論文 論文類型:學(xué)位論文
【摘要】:降低核磁共振測(cè)井信號(hào)中的噪聲是提高測(cè)井信噪比的一項(xiàng)基本任務(wù)。被動(dòng)降噪一般處于核磁共振測(cè)井回波串形成之后的數(shù)字信號(hào)處理階段。相關(guān)應(yīng)用資料表明,小波降噪算法是一種消除數(shù)據(jù)噪聲的有效方法。本文在介紹核磁共振測(cè)井原理的基礎(chǔ)上,對(duì)測(cè)井信號(hào)及其回波串的構(gòu)成機(jī)制進(jìn)行了必要的闡述,并分析了噪聲在測(cè)量系統(tǒng)中產(chǎn)生的機(jī)理、存在形式及其對(duì)測(cè)量主要參量精度的影響。論文介紹了小波降噪算法的數(shù)學(xué)基礎(chǔ)和小波降噪算法的構(gòu)成,選用了兩種小波降噪算法:Mallat算法和Lifting Scheme(提升框架)算法,利用兩種算法對(duì)多組不同信噪比(10-30dB)的數(shù)據(jù)進(jìn)行降噪處理,并對(duì)相關(guān)性能指標(biāo)進(jìn)行了分析,仿真結(jié)果表明,Lifting Scheme算法在整體性能指標(biāo)上略優(yōu)于Mallat算法。另外,應(yīng)用測(cè)井工程中用于標(biāo)定的一組柴油數(shù)據(jù)作為參照標(biāo)準(zhǔn),分別對(duì)模擬窄帶濾波方法,Mallat算法,Lifting Scheme算法的降噪效果進(jìn)行對(duì)比實(shí)驗(yàn)測(cè)試,結(jié)果表明兩種小波算法均優(yōu)于窄帶濾波方法并且Lifting Scheme算法要優(yōu)于Mallat算法。論文還分別對(duì)所選兩種算法的現(xiàn)場(chǎng)可編程門陣列(Field-Programmable Gate Array,FPGA)設(shè)計(jì)實(shí)現(xiàn)問題進(jìn)行了分析探討,在計(jì)算量相同的條件下,Lifting Scheme算法在計(jì)算速度、硬件資源占用量等方面也優(yōu)于Mallat算法,從而從算法性能及其在FPGA設(shè)計(jì)實(shí)現(xiàn)兩方面驗(yàn)證了Lifting Scheme算法在核磁共振測(cè)井系統(tǒng)中應(yīng)用的可行性。
[Abstract]:Reducing noise in nuclear magnetic resonance logging signal is a basic task to improve signal-to-noise ratio. Passive noise reduction is generally in the digital signal processing stage after the formation of nuclear magnetic resonance logging echo string. Wavelet denoising algorithm is an effective method to eliminate data noise. Based on the introduction of nuclear magnetic resonance (NMR) logging principle, the formation mechanism of logging signal and its echo string is expounded. The mechanism of noise generation in measurement system, its existing form and its influence on the accuracy of main measurement parameters are analyzed. The mathematical basis of wavelet denoising algorithm and the constitution of wavelet denoising algorithm are introduced in this paper. Two kinds of wavelet denoising algorithms: Mallat algorithm and Lifting Schema algorithm are selected. The two algorithms are used to deal with multiple groups of data with different signal-to-noise ratio (SNR) of 10-30dB, and the related performance indexes are analyzed. The simulation results show that lifting Scheme algorithm is slightly better than Mallat algorithm in overall performance index. In addition, a set of diesel oil data used for calibration in logging engineering is used as a reference standard. The effect of noise reduction in the proposed lifting Scheme algorithm, an analog narrowband filtering method, is tested by a comparative experiment. The results show that both wavelet algorithms are superior to narrow band filtering methods and Lifting Scheme algorithm is superior to Mallat algorithm. The design and implementation of field programmable gate array Field-Programmable Gate FPGA are also discussed in this paper. The lifting Scheme algorithm is better than the Mallat algorithm in computing speed and the amount of hardware resources, etc. Thus, the feasibility of Lifting Scheme algorithm in NMR logging system is verified from the performance of the algorithm and its design and implementation in FPGA.
【學(xué)位授予單位】:華中科技大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2015
【分類號(hào)】:TN791
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 肖立志;謝慶明;謝然紅;潘衛(wèi)國(guó);;核磁共振測(cè)井的正則化-啟發(fā)式閾值降噪研究[J];地球物理學(xué)報(bào);2013年11期
2 吳磊;程晶晶;孔力;張本庭;;核磁共振測(cè)井儀脈沖刻度設(shè)計(jì)[J];儀表技術(shù)與傳感器;2011年07期
3 王希武;董光波;謝桂海;;基于小波變換的核磁共振FID信號(hào)的去噪方法研究[J];核電子學(xué)與探測(cè)技術(shù);2008年02期
4 徐立強(qiáng);何宗斌;郭書生;;MRIL_Primer回波信號(hào)生成關(guān)鍵技術(shù)[J];工程地球物理學(xué)報(bào);2008年01期
5 趙軍龍;譚成仟;焦積田;李慶春;;小波域閾值濾波在測(cè)井信號(hào)去噪中的應(yīng)用[J];西安科技大學(xué)學(xué)報(bào);2007年02期
6 鄭傳行;張一鳴;;基于小波變換的低場(chǎng)脈沖核磁共振系統(tǒng)高斯白噪聲估計(jì)[J];分析儀器;2006年04期
7 李杏梅;陳亮;;小波閾值去噪在圖像去噪中的應(yīng)用[J];現(xiàn)代計(jì)算機(jī);2006年10期
8 何峰江,陶果,羅厚義;應(yīng)用小波分析估計(jì)核磁共振測(cè)井信噪比[J];核電子學(xué)與探測(cè)技術(shù);2005年05期
9 何雨丹,毛志強(qiáng),肖立志,任小軍;核磁共振T_2分布評(píng)價(jià)巖石孔徑分布的改進(jìn)方法[J];地球物理學(xué)報(bào);2005年02期
10 黃思齊,楊魯平,劉橋;JPEG2000的5/3離散小波變換FPGA硬件實(shí)現(xiàn)[J];貴州大學(xué)學(xué)報(bào)(自然科學(xué)版);2004年04期
,本文編號(hào):1641482
本文鏈接:http://sikaile.net/kejilunwen/dianzigongchenglunwen/1641482.html