超小波稀疏表示與相干體技術(shù)研究
發(fā)布時間：2018-08-03 21:10
【摘要】：人們對自然能源的開發(fā)和利用大大推動了社會的發(fā)展，提高了社會生產(chǎn)力，為人們生活提供了極大的便利，為了保持能源，尤其是煤炭、石油、天然氣這些地下能源的可持續(xù)利用，人們必須盡可能最大程度的合理開發(fā)和利用這些地下的天然寶藏。隨著能源需求度的越來越大，對地下能源的尋找勘探變得愈發(fā)重要，與此同時尋找新的油氣儲藏變得越來越困難，這就需要人們高效快速準確的尋找油氣儲藏區(qū)域，以便及時開發(fā)并合理利用自然能源。地震勘探是分析、構(gòu)造地下地質(zhì)結(jié)構(gòu)的重要方法之一，地震數(shù)據(jù)包含了包括振幅、波形、頻率、衰減、能量等多種地震屬性在內(nèi)的豐富的信息，對地震屬性的解釋能有效解決地質(zhì)構(gòu)造問題，尤其有利于尋找石油和天然氣藏。其中斷層解釋是地震解釋中最重要的問題之一。斷層線的精確識別能夠幫助研究人員對地下構(gòu)造、油氣儲藏等做出有正確分析和判斷，因此提高斷層線識別的精度具有重要的現(xiàn)實意義。 小波對一維信號的點奇異的捕捉是很有力的，但是對高維信號中線奇異和面奇異的捕捉便不是那么令人滿意了，而超小波的誕生解決了這個問題。近年來在地震勘探領(lǐng)域，，超小波的使用越來越深入。Surfacelet變換是指先對信號進行多分辨率分解變換，然后利用多維方向濾波器組對相同方向上的變換系數(shù)進行合并，Surfacelet變換能有效的對高維信號中的曲面奇異進行刻畫。Surfacelet能很方便的處理離散的三維信號，這一特性十分適合處理三維地震數(shù)據(jù)，提高地震資料信噪比，本文采用Surfacelet變換處理地震數(shù)據(jù)，對各向異性這一性質(zhì)進行充分利用。在三維地震數(shù)據(jù)的預(yù)處理上，取得了比較好稀疏表示和降噪效果，對后續(xù)提取斷層信息提供了更多幫助。相干體方法的的基本原理是通過在一定時窗內(nèi)計算三維地震數(shù)據(jù)體中每一道上每一點所在地震道與相鄰地震震道的相似程度，這個相似程度可以用一個具體的數(shù)值來表示，最后會形成一個新的三維數(shù)據(jù)體，數(shù)據(jù)體中的每一點都代表該點與鄰域內(nèi)點的相關(guān)程度。其中分析時窗中心某一道的某一點與相鄰若干道的相似程度表明了該點所在的地震道與相鄰地震道的相關(guān)程度，相關(guān)程度大則相干值大，相關(guān)程度小則相干值小，這個結(jié)果體現(xiàn)了地震道之間的不連續(xù)程度，有利于識別斷層、裂縫和特殊巖體等。 本文的主要研究內(nèi)容包括一下幾點： 1.研究了地震勘探領(lǐng)域的相關(guān)知識，包括地震數(shù)據(jù)的采集，地震數(shù)據(jù)處理等，并研究了圖像稀疏表示領(lǐng)域中Surfacelet變換的主要理論知識，對Surfcelet變換進行了全面而深入的學習，包括方向濾波器（DFB）的起源和多維方向濾波器（NDFB）的具體概念及操作流程，然后對Surfacelet變換的過程進行了詳細的研究和分析； 2．提出了基于Surfacelet變換的地震數(shù)據(jù)去噪方法。針對三維地震數(shù)據(jù)體，先對目標區(qū)域進行Surfacelet頻域分解變換，得到地震數(shù)據(jù)不同尺度下不同方向的頻率子帶，這些子帶都是由一系列系數(shù)構(gòu)成的，由于地震數(shù)據(jù)中的噪聲往往分布在數(shù)據(jù)的高頻部分，在此基礎(chǔ)上，對精細尺度層系數(shù)進行處理，然后對系數(shù)處理后的數(shù)據(jù)進行重構(gòu)，達到了提高地震數(shù)據(jù)信噪比的目的； 3．對地震勘探領(lǐng)域斷層識別用到的主要技術(shù)進行深入學習和研究，包括第一代基于互相關(guān)的相干體技術(shù)，第二代基于相似的相干體技術(shù)，第三代基于本征結(jié)構(gòu)的相干體技術(shù)等，并對相干體技術(shù)在斷層識別上的應(yīng)用進行分析和總結(jié)； 4．提出了改進的多特征值相干體算法。在第三代相干體技術(shù)的基礎(chǔ)上，對地震道內(nèi)每一點地震數(shù)據(jù)的相干值計算的方法進行改進，使用多個特征值計算相干性，提高了對地震數(shù)據(jù)能量的保持程度。實驗結(jié)果表明，與傳統(tǒng)的第三代相干體算法比，改進的多特征值相干體算法更適于提取斷層信息，此外當與Surfacelet變換結(jié)合時，效果更為明顯。
[Abstract]:The development and utilization of natural energy has greatly promoted the development of society, improved the social productivity and provided great convenience for people's life. In order to keep energy, especially coal, oil and natural gas, the sustainable utilization of underground energy, such as coal, oil and natural gas, must be used to the maximum reasonable development and utilization of these underground days. As the energy demand is increasing, it is becoming more and more important for the exploration and exploration of underground energy. At the same time, it is becoming more and more difficult to find new oil and gas storage. This requires people to find oil and gas storage areas efficiently, quickly and accurately, so as to develop and utilize natural energy in time. One of the important methods of geological structure, seismic data contains abundant information including amplitude, waveform, frequency, attenuation, energy and many other seismic attributes. The interpretation of seismic attributes can effectively solve the problem of geological structure, especially in the search of oil and natural gas reservoirs. Fault interpretation is the most important problem in seismic interpretation. The accurate identification of fault lines can help the researchers to make correct analysis and judgment on the underground structure and oil and gas storage. Therefore, it is of great practical significance to improve the accuracy of fault line recognition.
The capture of point singularity of one-dimensional signals is very strong, but the capture of line singularities and surface singularity in high dimensional signals is not so satisfactory, and the birth of super wavelet solves this problem. In recent years, in the field of seismic exploration, the use of super wavelet is more and more deep into the.Surfacelet transform, which means that the signal is first divided into multiple points. Discrimination rate decomposition transformation, and then the use of multidimensional directional filter banks to merge the transformation coefficients in the same direction, Surfacelet transform can effectively describe the surface singularity in high dimensional signals,.Surfacelet can easily handle discrete three-dimensional signals. This characteristic is suitable to deal with 3D seismic data and improve seismic data letter. In this paper, the Surfacelet transform is used to deal with seismic data and make full use of the properties of anisotropy. In the preprocessing of 3D seismic data, a better sparse representation and noise reduction effect is obtained, and more help is provided for the subsequent extraction of fault information. The basic principle of the coherent body method is to be calculated in a certain time window. The similarity degree of the seismic channel and the adjacent seismic channel in each point in the 3D seismic data body can be represented by a specific value. Finally, a new three-dimensional data body is formed, and each point in the data body represents the degree of correlation between the point and the neighborhood point. The similarity between one point of the road and the adjacent main road shows the degree of correlation between the seismic channel and the adjacent seismic channel in which the point is located. The correlation is large and the correlation is small, the coherence is small. This result shows the discontinuity between the seismic channels, and is beneficial to the identification of faults, cracks and special rock masses.
The main contents of this paper include a few points:
1. study the related knowledge in the field of seismic exploration, including seismic data acquisition, seismic data processing, and study the main theoretical knowledge of Surfacelet transform in the field of image sparse representation, and make a comprehensive and in-depth study of Surfcelet transform, including the origin of directional filter (DFB) and the concrete of multidimensional directional filter (NDFB). Concept and operation process, and then the process of Surfacelet transformation is studied and analyzed in detail.
2. the method of seismic data de-noising based on Surfacelet transform is proposed. For 3D seismic data, the Surfacelet frequency domain decomposition transformation of the target area is first carried out to obtain the frequency subbands of different directions under different scales of seismic data. All these subbands are made up of a series of coefficients, because the noise in the seismic data is often distributed in the number of data. According to the high frequency part, the fine scale layer coefficient is processed on this basis, and then the data after the coefficient processing are reconstructed to improve the signal to noise ratio of seismic data.
3. the main techniques used in fault recognition in the field of seismic exploration are studied and studied in depth, including the first generation of coherent coherent technology based on cross correlation, the second generation based on the similar coherent body technology and the third generation of the coherent body based on the eigenstructure, and the application of the coherent body technology in fault recognition;
4. an improved multi eigenvalue coherent body algorithm is proposed. On the basis of the third generation coherent body technology, the method of calculating the coherence value of each seismic data in the seismic channel is improved. The coherence of the seismic data is calculated by using multiple eigenvalues. The experimental results show that the third generation coherency body is with the traditional third generation coherence body. Compared with the improved multi-eigenvalue coherence cube algorithm, the improved multi-eigenvalue coherence cube algorithm is more suitable for extracting fault information, and the effect is more obvious when combined with Surfacelet transform.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.4
本文編號：2163005
[Abstract]:The development and utilization of natural energy has greatly promoted the development of society, improved the social productivity and provided great convenience for people's life. In order to keep energy, especially coal, oil and natural gas, the sustainable utilization of underground energy, such as coal, oil and natural gas, must be used to the maximum reasonable development and utilization of these underground days. As the energy demand is increasing, it is becoming more and more important for the exploration and exploration of underground energy. At the same time, it is becoming more and more difficult to find new oil and gas storage. This requires people to find oil and gas storage areas efficiently, quickly and accurately, so as to develop and utilize natural energy in time. One of the important methods of geological structure, seismic data contains abundant information including amplitude, waveform, frequency, attenuation, energy and many other seismic attributes. The interpretation of seismic attributes can effectively solve the problem of geological structure, especially in the search of oil and natural gas reservoirs. Fault interpretation is the most important problem in seismic interpretation. The accurate identification of fault lines can help the researchers to make correct analysis and judgment on the underground structure and oil and gas storage. Therefore, it is of great practical significance to improve the accuracy of fault line recognition.
The capture of point singularity of one-dimensional signals is very strong, but the capture of line singularities and surface singularity in high dimensional signals is not so satisfactory, and the birth of super wavelet solves this problem. In recent years, in the field of seismic exploration, the use of super wavelet is more and more deep into the.Surfacelet transform, which means that the signal is first divided into multiple points. Discrimination rate decomposition transformation, and then the use of multidimensional directional filter banks to merge the transformation coefficients in the same direction, Surfacelet transform can effectively describe the surface singularity in high dimensional signals,.Surfacelet can easily handle discrete three-dimensional signals. This characteristic is suitable to deal with 3D seismic data and improve seismic data letter. In this paper, the Surfacelet transform is used to deal with seismic data and make full use of the properties of anisotropy. In the preprocessing of 3D seismic data, a better sparse representation and noise reduction effect is obtained, and more help is provided for the subsequent extraction of fault information. The basic principle of the coherent body method is to be calculated in a certain time window. The similarity degree of the seismic channel and the adjacent seismic channel in each point in the 3D seismic data body can be represented by a specific value. Finally, a new three-dimensional data body is formed, and each point in the data body represents the degree of correlation between the point and the neighborhood point. The similarity between one point of the road and the adjacent main road shows the degree of correlation between the seismic channel and the adjacent seismic channel in which the point is located. The correlation is large and the correlation is small, the coherence is small. This result shows the discontinuity between the seismic channels, and is beneficial to the identification of faults, cracks and special rock masses.
The main contents of this paper include a few points:
1. study the related knowledge in the field of seismic exploration, including seismic data acquisition, seismic data processing, and study the main theoretical knowledge of Surfacelet transform in the field of image sparse representation, and make a comprehensive and in-depth study of Surfcelet transform, including the origin of directional filter (DFB) and the concrete of multidimensional directional filter (NDFB). Concept and operation process, and then the process of Surfacelet transformation is studied and analyzed in detail.
2. the method of seismic data de-noising based on Surfacelet transform is proposed. For 3D seismic data, the Surfacelet frequency domain decomposition transformation of the target area is first carried out to obtain the frequency subbands of different directions under different scales of seismic data. All these subbands are made up of a series of coefficients, because the noise in the seismic data is often distributed in the number of data. According to the high frequency part, the fine scale layer coefficient is processed on this basis, and then the data after the coefficient processing are reconstructed to improve the signal to noise ratio of seismic data.
3. the main techniques used in fault recognition in the field of seismic exploration are studied and studied in depth, including the first generation of coherent coherent technology based on cross correlation, the second generation based on the similar coherent body technology and the third generation of the coherent body based on the eigenstructure, and the application of the coherent body technology in fault recognition;
4. an improved multi eigenvalue coherent body algorithm is proposed. On the basis of the third generation coherent body technology, the method of calculating the coherence value of each seismic data in the seismic channel is improved. The coherence of the seismic data is calculated by using multiple eigenvalues. The experimental results show that the third generation coherency body is with the traditional third generation coherence body. Compared with the improved multi-eigenvalue coherence cube algorithm, the improved multi-eigenvalue coherence cube algorithm is more suitable for extracting fault information, and the effect is more obvious when combined with Surfacelet transform.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.4
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