基于模糊集的地震图像增强方法研究与实现
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摘要
随着工业化的发展,石油资源越来越少,勘探开发新的油田成为当务之急。地震勘探是钻探前勘测石油与天然气资源的重要手段,地震图像的清晰程度直接关系着地震解释的结果。因此,地震图像的增强成为一个重要的研究课题。
地震图像增强处理中常用的均值滤波和中值滤波等方法有较强的抑制噪声的能力,但在一定程度上会导致地震图像模糊,影响地震解释的效果。直方图均衡化是目前地震图像增强的主要方法,但它存在着图像细节信息丢失和噪声放大的缺点。随着油气勘探开发对象复杂程度的增加,现有的地震图像增强方法已不能满足正确解释的要求。而基于模糊集的图像增强方法逐渐被应用到实际的图像处理中,并且显示出它优于传统图像增强算法的特点,因此,本文提出了基于模糊集的地震图像增强方法。
本文首先对传统的Pal-King基于模糊集的图像增强算法进行了研究,并概述了近30年来Pal-King算法的发展和改进情况。本文在前人的基础上,针对Pal-King算法的缺点和不足,提出了两种改进算法,并且选用lena图像对改进算法的增强效果进行了仿真实验。实验结果表明,改进算法确实比Pal-King算法取得了更好的增强效果。其次,本文对BMP图像的数据格式和各种颜色模型进行了研究,将HSI和RGB两种颜色模型相结合,研究出了一种适合人眼视觉特征的地震图像颜色配置方案。最后,本文结合地震图像的特点,设计并开发出了基于模糊集的地震图像增强系统,将研究出的地震图像的颜色配置方案应用到地震属性图像中,并对改进后的基于模糊集的图像增强算法进行了仿真实验,将其应用到大庆油田ao9、shj、pu等多个区块中。实验结果表明,本文的改进算法确实比传统的地震图像增强方法取得了更好的增强效果。这为地震解释奠定了良好的基础,对探明地质储量具有十分重要的意义,有利于石油资源的勘探开发,进而为我国的工业化发展提供更多的能源支持。
With the development of industrialization, oil resources are fewer and fewer and exploration new oil fields become a top priority. Seismic exploration is an important means of surveying oil and natural gas resources before drilling. The clarity of the seismic image is directly related to seismic interpretation results. Therefore, the seismic image enhancement has become an important research topic.
Mean filtering and median filtering methods,which are commonly used in seismic image enhancement, have a stronger ability to suppress noise, but to some extent, it will blur the seismic image and affect the seismic interpretation results. Histogram equalization is the primary means of seismic image enhancement, but there are some shortcomings of image detail information loss and noise amplification existing in it. With the increasing complexity of the object of oil and gas exploration, the current seismic image enhancement algorithms cannot satisfy the need of right interpretation. The image enhancement based on fuzzy sets have been gradually applied to the actual image processing, and shown that it is superior to the traditional image enhancement algorithm. Consequently, this thesis first proposes the seismic image enhancement method based on fuzzy sets.
Firstly, in this thesis, the traditional Pal-King image enhancement algorithm based on fuzzy sets has been studied, and the development and improvement situation of Pal-King algorithm in the past 30 years has been summarized. On the basis of our predecessors, two improved algorithms are proposed in this thesis to overcome the shortcomings and deficiencies of the Pal-King algorithm, and Lena image is choose for simulation experiments. And the experiments results show that the enhancement effect of the improved algorithm indeed better than that of Pal-King algorithm. Secondly, data format of BMP image and a variety of color models have been studied in this paper, and on the basis of combination of HSI model and RGB model, a new color configuration, which is suitable for human visual features, has been worked out. Thirdly, in this thesis, combining the characteristics of seismic image, seismic image enhancement system based on fuzzy sets has been designed and developed and the new seismic image color configuration has been applied to the seismic attribute image and simulation experiments are carried out with the improved image enhancement algorithms based on fuzzy sets, which have been applied to ao9, shj, pu and other block in Daqing oil field. Experimental results show that the improved algorithms proposed in this thesis indeed acquire better enhancement effect than the traditional seismic image enhancement methods. It has laid a good foundation for seismic interpretation and has a great significance with ascertaining geological reserves, and is conducive to exploration and development of oil resources, and thus provides more and more energy support to accelerate the industrialization of our country.
地震图像增强处理中常用的均值滤波和中值滤波等方法有较强的抑制噪声的能力,但在一定程度上会导致地震图像模糊,影响地震解释的效果。直方图均衡化是目前地震图像增强的主要方法,但它存在着图像细节信息丢失和噪声放大的缺点。随着油气勘探开发对象复杂程度的增加,现有的地震图像增强方法已不能满足正确解释的要求。而基于模糊集的图像增强方法逐渐被应用到实际的图像处理中,并且显示出它优于传统图像增强算法的特点,因此,本文提出了基于模糊集的地震图像增强方法。
本文首先对传统的Pal-King基于模糊集的图像增强算法进行了研究,并概述了近30年来Pal-King算法的发展和改进情况。本文在前人的基础上,针对Pal-King算法的缺点和不足,提出了两种改进算法,并且选用lena图像对改进算法的增强效果进行了仿真实验。实验结果表明,改进算法确实比Pal-King算法取得了更好的增强效果。其次,本文对BMP图像的数据格式和各种颜色模型进行了研究,将HSI和RGB两种颜色模型相结合,研究出了一种适合人眼视觉特征的地震图像颜色配置方案。最后,本文结合地震图像的特点,设计并开发出了基于模糊集的地震图像增强系统,将研究出的地震图像的颜色配置方案应用到地震属性图像中,并对改进后的基于模糊集的图像增强算法进行了仿真实验,将其应用到大庆油田ao9、shj、pu等多个区块中。实验结果表明,本文的改进算法确实比传统的地震图像增强方法取得了更好的增强效果。这为地震解释奠定了良好的基础,对探明地质储量具有十分重要的意义,有利于石油资源的勘探开发,进而为我国的工业化发展提供更多的能源支持。
With the development of industrialization, oil resources are fewer and fewer and exploration new oil fields become a top priority. Seismic exploration is an important means of surveying oil and natural gas resources before drilling. The clarity of the seismic image is directly related to seismic interpretation results. Therefore, the seismic image enhancement has become an important research topic.
Mean filtering and median filtering methods,which are commonly used in seismic image enhancement, have a stronger ability to suppress noise, but to some extent, it will blur the seismic image and affect the seismic interpretation results. Histogram equalization is the primary means of seismic image enhancement, but there are some shortcomings of image detail information loss and noise amplification existing in it. With the increasing complexity of the object of oil and gas exploration, the current seismic image enhancement algorithms cannot satisfy the need of right interpretation. The image enhancement based on fuzzy sets have been gradually applied to the actual image processing, and shown that it is superior to the traditional image enhancement algorithm. Consequently, this thesis first proposes the seismic image enhancement method based on fuzzy sets.
Firstly, in this thesis, the traditional Pal-King image enhancement algorithm based on fuzzy sets has been studied, and the development and improvement situation of Pal-King algorithm in the past 30 years has been summarized. On the basis of our predecessors, two improved algorithms are proposed in this thesis to overcome the shortcomings and deficiencies of the Pal-King algorithm, and Lena image is choose for simulation experiments. And the experiments results show that the enhancement effect of the improved algorithm indeed better than that of Pal-King algorithm. Secondly, data format of BMP image and a variety of color models have been studied in this paper, and on the basis of combination of HSI model and RGB model, a new color configuration, which is suitable for human visual features, has been worked out. Thirdly, in this thesis, combining the characteristics of seismic image, seismic image enhancement system based on fuzzy sets has been designed and developed and the new seismic image color configuration has been applied to the seismic attribute image and simulation experiments are carried out with the improved image enhancement algorithms based on fuzzy sets, which have been applied to ao9, shj, pu and other block in Daqing oil field. Experimental results show that the improved algorithms proposed in this thesis indeed acquire better enhancement effect than the traditional seismic image enhancement methods. It has laid a good foundation for seismic interpretation and has a great significance with ascertaining geological reserves, and is conducive to exploration and development of oil resources, and thus provides more and more energy support to accelerate the industrialization of our country.
引文
[1] Hua Li, Hyun S. Yang. Fast and reliable image enhancement using fuzzy relaxation technique. IEEE Transactions on Systems, Man and Cybernetics, 1989, 19(5): 1276-1281.
[2]李弼程等.智能图像处理技术[M].北京:电子工业出版社,2004.
[3]高彦平.图像增强方法的研究与实现[D].济南:山东科技大学,2005.
[4]李刚.数字图像的模糊增强方法[D].武汉:武汉理工大学,2005.
[5]鱼海涛.基于模糊集理论的图像增强算法研究[D].西安:西安科技大学,2005.
[6] J. S. Lee, Digital Enhancement and Noising Filtering by Using of Local Statistics [J],IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, VOL.PAMI-2: 165-168.
[7]李宏刚.基于模糊集的车辆图像增强方法研究[D].哈尔滨:哈尔滨工业大学,2006.
[8] A. Lev. S. W. Zucker., A. Rosentfeld, Iterative Enhancement of Noisy Images, IEEE Trans.Syst.Man.Cybern, 1977, Vol. SMC-7, pp.435-441.
[9] L. S Davis and A. Rosenfeld, Noise Cleaning by Iterated Local Averaging, IEEE Trans. Syst., Man. Cybern.,1978, Vol. SMC-8, pp.705-710.
[10] P. Saint-Marc. J. Chen. G Mendioni, Adaptive Smoothing: A General Tool for Early Vision, IEEE Trans. Pattern Anal. Machine Intell,1993,Vol.13,No.6,pp.514-529.
[11] M.Nitzberg, T.Shiota, Nonlinear Image Filtering with Edge and Corner Enhancement, IEEE Trans. Pattern Anal. Machine Intell., 1993, Vol.14, pp.826-832.
[12] R. Whitaker, S. M. Pizer, A Multi-scale Approach to Nonuniform Diffusion, CVGIP: Image Understanding, 1993, Vol.57, pp.99-110.
[13] Pal. S. K, King. R. A. Image Enhancement using Smoothing width Fuzzy Set. IEEE Trans, Syst, Man, Cybern, 1981, SMC-11, 7: 494-501.
[14] X. Yang, P. S. Ton, Adaptive Fuzzy Multilevel Median Filter, IEEE Trans. Image Processing, May 1995, Vol.4, pp.680-682.
[15] F. Russo, G. Rampom. Edge Extraction by FIRE Operators, In Proc. Third IEEE Int. Conf. Fuzzy Systems, Orlando. FL. 1994, pp.249-253.
[16] M.van der Schaar, H. Radha. Adaptive Motion-Compensation Fine-Granular-Scalablity (AMC-FGS) for Wireless Video. IEEE Trans. Circuit and systems for video technology, 12(6), 2002: 360-371.
[17]赵巍,张向前.遥感图像解译中的增强技术[J].测绘信息与工程,1997,No.3,15-18.
[18]王胜军.图像增强方法的研究[D].大连:大连海事大学,2005,19-23.
[19] Cristian Munteanu, Agostinho Rosa, Gray-Scale Image Enhancement as an Automatic Process Driven by Evolution, IEEE Transactions on systems. Man and cybernetics-Part B: cybernetics, vol.34, No.2, pp.1292-1298, April, 2004.
[20]徐建华.图像处理与分析[M].北京:科学出版社,1992.
[21] Lotfi A. Zadeh, A fuzzy-set-theoretic interpretation of linguistic hedges, Journal of Cybernetic, 1972, 64(2): 4-34.
[22]陈守煜.模糊水文学的基本理论模型与应用[J].大连理工大学学报,1992,(02).
[23]刘应明.拓扑学与模糊性的数学处理[J].大自然探索,1996,15(58):5-8.
[24]王国俊.Lukasiewicz语义集上的紧Hausdorff拓扑[J].数学学报,2002,45(5):919-924.
[25]王培庄,李洪兴.Fuzzy计算机的设计思想[J].北京师范大学学报:自然科学版,1995,31(2):189-196.
[26]王光远,欧进萍.结构模糊随机振动的理论与应用[J].地震工程与工程振动,1992,12(3):16-25.
[27]吴从炘，段延正．Fuzzy拓朴线性空间的归纳极限[J].模糊系统与数学, 1987,(00).
[28]张继国,张文修.模糊随机变量及其概率分布[J].模糊系统与数学,1996,10(4):76-82.
[29]徐立中.数字图像的智能信息处理.北京:国防工业出版社,2001,41-47,63-93.
[30]王保平.基于模糊技术的图像处理方法研究[D].西安:西安电子科技大学,2004.
[31]靳蕃.神经计算智能基础原理和方法.成都:西南交通大学出版社,2000,177-215.
[32]范九伦.模糊墒理论.西安:西北大学出版社,1999.
[33] S. K. Pal,R. A. King. Image Enhancement Using Fuzzy Sets. Electron. Lett., 1980, 16(9): 376-378.
[34]王章伟,郑昌琼,王景熙,等.一种新型自适应中值滤波其在超声医学图像中的应用[J].四川人学学报,2000,32(5):92-95.
[35]刘兴伟,股国富,罗小宾,等.工程扫描图的一种新型的自适应中值滤波算法[J].中国机械工程,2002,13(5): 403-405.
[36] L. A. Zadeh and K S Fu. Fuzzy Sets and Their Applications to Cognition and Decision Processes, London: Academic, 1975.
[37] C. C. Wang and C. C. Li. Digital Image Enhancement: A Survey, Computer Vision, Graphics and Image Processing, 1983, Vol.24: 363-381.
[38] Pal S. K., King R. A. Image enhancement using fuzzy sets. Electron. Lett, 1980, 16(9).
[39]史卉萍,耿国华,周明全,董建民.基于模糊集的图像增强[J].微计算机信息,2008,24(8-3):291-292,254.
[40] http://en.wikipedia.org/wiki/Principle_of_maximum_entropy.
[41] H.D. Cheng, Yen-Hung Chen, Ying Sun. A novel fuzzy entropy approach to image enhancement and thresholding. Signal Processing, 1999, 75: 277-301.
[42]何东健.数字图像处理[M].西安:西安电子科技大学出版社,2006.
[43]魏宝钢,鲁东明,潘云鹤,等.多颜色空间上的交互式图像分割[J].计算机学报,2001,24(7):770-775.
[44] KIM W S, PARK R H. Color Image Palette Construction Based on the HSI Color System for Minimixing the Reconstruction Error[J]. IEEE International Conference on Image Processing[C]. 1996(3): 1041-1044.
[45]章毓晋.图像处理和分析基础[M].北京:高等教育出版社,2002.
[46]王有新,王延光.地震勘探技术概述[J].油气地球物理,2007,5(1).
[47] Michael W. Norris and Alan K. Faichney. SEG Y rev 1 Data Exchange format[S]. SEG Technical Standards Committee, May, 2002.
[48]颜世磊,陈世军,史玉平.地震剖面波形的自动识别与提取[J].中国图象图形学报,2003,8(11):1291-1294.
[2]李弼程等.智能图像处理技术[M].北京:电子工业出版社,2004.
[3]高彦平.图像增强方法的研究与实现[D].济南:山东科技大学,2005.
[4]李刚.数字图像的模糊增强方法[D].武汉:武汉理工大学,2005.
[5]鱼海涛.基于模糊集理论的图像增强算法研究[D].西安:西安科技大学,2005.
[6] J. S. Lee, Digital Enhancement and Noising Filtering by Using of Local Statistics [J],IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, VOL.PAMI-2: 165-168.
[7]李宏刚.基于模糊集的车辆图像增强方法研究[D].哈尔滨:哈尔滨工业大学,2006.
[8] A. Lev. S. W. Zucker., A. Rosentfeld, Iterative Enhancement of Noisy Images, IEEE Trans.Syst.Man.Cybern, 1977, Vol. SMC-7, pp.435-441.
[9] L. S Davis and A. Rosenfeld, Noise Cleaning by Iterated Local Averaging, IEEE Trans. Syst., Man. Cybern.,1978, Vol. SMC-8, pp.705-710.
[10] P. Saint-Marc. J. Chen. G Mendioni, Adaptive Smoothing: A General Tool for Early Vision, IEEE Trans. Pattern Anal. Machine Intell,1993,Vol.13,No.6,pp.514-529.
[11] M.Nitzberg, T.Shiota, Nonlinear Image Filtering with Edge and Corner Enhancement, IEEE Trans. Pattern Anal. Machine Intell., 1993, Vol.14, pp.826-832.
[12] R. Whitaker, S. M. Pizer, A Multi-scale Approach to Nonuniform Diffusion, CVGIP: Image Understanding, 1993, Vol.57, pp.99-110.
[13] Pal. S. K, King. R. A. Image Enhancement using Smoothing width Fuzzy Set. IEEE Trans, Syst, Man, Cybern, 1981, SMC-11, 7: 494-501.
[14] X. Yang, P. S. Ton, Adaptive Fuzzy Multilevel Median Filter, IEEE Trans. Image Processing, May 1995, Vol.4, pp.680-682.
[15] F. Russo, G. Rampom. Edge Extraction by FIRE Operators, In Proc. Third IEEE Int. Conf. Fuzzy Systems, Orlando. FL. 1994, pp.249-253.
[16] M.van der Schaar, H. Radha. Adaptive Motion-Compensation Fine-Granular-Scalablity (AMC-FGS) for Wireless Video. IEEE Trans. Circuit and systems for video technology, 12(6), 2002: 360-371.
[17]赵巍,张向前.遥感图像解译中的增强技术[J].测绘信息与工程,1997,No.3,15-18.
[18]王胜军.图像增强方法的研究[D].大连:大连海事大学,2005,19-23.
[19] Cristian Munteanu, Agostinho Rosa, Gray-Scale Image Enhancement as an Automatic Process Driven by Evolution, IEEE Transactions on systems. Man and cybernetics-Part B: cybernetics, vol.34, No.2, pp.1292-1298, April, 2004.
[20]徐建华.图像处理与分析[M].北京:科学出版社,1992.
[21] Lotfi A. Zadeh, A fuzzy-set-theoretic interpretation of linguistic hedges, Journal of Cybernetic, 1972, 64(2): 4-34.
[22]陈守煜.模糊水文学的基本理论模型与应用[J].大连理工大学学报,1992,(02).
[23]刘应明.拓扑学与模糊性的数学处理[J].大自然探索,1996,15(58):5-8.
[24]王国俊.Lukasiewicz语义集上的紧Hausdorff拓扑[J].数学学报,2002,45(5):919-924.
[25]王培庄,李洪兴.Fuzzy计算机的设计思想[J].北京师范大学学报:自然科学版,1995,31(2):189-196.
[26]王光远,欧进萍.结构模糊随机振动的理论与应用[J].地震工程与工程振动,1992,12(3):16-25.
[27]吴从炘，段延正．Fuzzy拓朴线性空间的归纳极限[J].模糊系统与数学, 1987,(00).
[28]张继国,张文修.模糊随机变量及其概率分布[J].模糊系统与数学,1996,10(4):76-82.
[29]徐立中.数字图像的智能信息处理.北京:国防工业出版社,2001,41-47,63-93.
[30]王保平.基于模糊技术的图像处理方法研究[D].西安:西安电子科技大学,2004.
[31]靳蕃.神经计算智能基础原理和方法.成都:西南交通大学出版社,2000,177-215.
[32]范九伦.模糊墒理论.西安:西北大学出版社,1999.
[33] S. K. Pal,R. A. King. Image Enhancement Using Fuzzy Sets. Electron. Lett., 1980, 16(9): 376-378.
[34]王章伟,郑昌琼,王景熙,等.一种新型自适应中值滤波其在超声医学图像中的应用[J].四川人学学报,2000,32(5):92-95.
[35]刘兴伟,股国富,罗小宾,等.工程扫描图的一种新型的自适应中值滤波算法[J].中国机械工程,2002,13(5): 403-405.
[36] L. A. Zadeh and K S Fu. Fuzzy Sets and Their Applications to Cognition and Decision Processes, London: Academic, 1975.
[37] C. C. Wang and C. C. Li. Digital Image Enhancement: A Survey, Computer Vision, Graphics and Image Processing, 1983, Vol.24: 363-381.
[38] Pal S. K., King R. A. Image enhancement using fuzzy sets. Electron. Lett, 1980, 16(9).
[39]史卉萍,耿国华,周明全,董建民.基于模糊集的图像增强[J].微计算机信息,2008,24(8-3):291-292,254.
[40] http://en.wikipedia.org/wiki/Principle_of_maximum_entropy.
[41] H.D. Cheng, Yen-Hung Chen, Ying Sun. A novel fuzzy entropy approach to image enhancement and thresholding. Signal Processing, 1999, 75: 277-301.
[42]何东健.数字图像处理[M].西安:西安电子科技大学出版社,2006.
[43]魏宝钢,鲁东明,潘云鹤,等.多颜色空间上的交互式图像分割[J].计算机学报,2001,24(7):770-775.
[44] KIM W S, PARK R H. Color Image Palette Construction Based on the HSI Color System for Minimixing the Reconstruction Error[J]. IEEE International Conference on Image Processing[C]. 1996(3): 1041-1044.
[45]章毓晋.图像处理和分析基础[M].北京:高等教育出版社,2002.
[46]王有新,王延光.地震勘探技术概述[J].油气地球物理,2007,5(1).
[47] Michael W. Norris and Alan K. Faichney. SEG Y rev 1 Data Exchange format[S]. SEG Technical Standards Committee, May, 2002.
[48]颜世磊,陈世军,史玉平.地震剖面波形的自动识别与提取[J].中国图象图形学报,2003,8(11):1291-1294.
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