基于地震瞬时信息与相干算法的构造识别技术研究
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摘要
随着勘探程度的深入,大型构造和易被发现的油气藏不断被开发,那些形态清楚、规模较大的构造越来越少,寻找储量接替区、开发剩余油气成为勘探发展的主要难题。所以人们把注意力转移到了那些难以发现的隐蔽圈闭上来,尤其是低幅度构造油气藏的勘探。
虽然构造比较平缓、构造幅度较低,但是低幅度构造容易富集油气,形成“小而肥”的油气藏。由于构造幅度较低,反映在地震资料上表现为反射同相轴平直而变化幅度很小,构造不易识别。
图像边缘检测是利用图像空间灰度的突变,来识别和提取图像边界信息,加强图像轮廓特征的。分别研究了Roberts、Sobel、Prewitt、Log以及Canny等五种边缘检测算子的原理,利用各算子检测实际地震剖面数据,对比分析各算子的特性及其识别效果,并对检测结果加以处理,以达到识别地震剖面同相轴的目的。经过评价得出Canny算子的检测效果最佳。
C1相干分析技术是以归一化互相关为数学基础,以计算地震道间的波形相似性来描述地层连续性的。当地层发育有构造时,横向连续性发生改变,通过寻找C1(Max)值所对应的旅行时差来确定构造起伏的层位展布。影响C1相干算法识别低幅度构造效果的因子主要有搜索范围、噪音以及时窗大小。
分别建立两层一界面不同构造幅度、高宽比的低幅度构造,以及三层两界面下伏干扰层的薄互层低幅度构造,并利用C1相干算法对其进行层位识别,确定低幅度构造的构造端点、构造高点所对应的时间深度以及水平位置。
在复数道分析的基础上研究了高分辨率复数道分析方法。经过高分辨率复数道分析处理的地震剖面,分辨率得到了显著提高,利用高分辨率三瞬剖面的各自振幅、相位、频率特性可以确定低幅度构造的空间展布。在高分辨率复数道分析的基础上,利用C1相干算法识别薄互层低幅度构造,识别结果更佳准确,提高了精度。
Fault and anticline etc. are significant features in geological structure, which function crucially in the transfer and accumulation of the oil-gas, and lead to complex features in seismic section. As the deepening in the exploration extent, the macro-structured and easy-to-find oil and gas reservoirs have continuously been developed. Those clearly-formed and large-scaled structures become less and less. How to find the replacement area of reserves and how to develop the remaining oil-gas have already been the major problems in the development of exploration. Therefore, the focus has been shifted to the hard-to-find subtle traps, especially the exploration of the low-amplitude oil and gas reservoir.
At present, there is not yet an integrated and rigorous definition concerning the low-amplitude structure in the theories and practices of the oil-gas exploration both domestically and abroad. It is a relative concept, which indicates the geological body of smooth structure and low amplitude about 10m to 50m. Although it is of low amplitude, it is easy to gather the oil-gas, and form the“tiny and rich”oil and gas reservoir. Due to its low amplitude, straightness in the reflection event and small varying amplitude are reflected in the seismic materials, with the structure hard to identify.
The edge of the image is one of the most fundamental and most significant features, which is the aggregation of the spatial-mutated pixels generated by the gray scale. The purpose of the identification and extraction of the edge is to highlight the edge information of the image and heighten the contour feature of the image.
Through the detection of the images by use of every detection operator and the comparison of the results, it is discovered that due to the lack of smoothing processing, without strong ability to resist the noise, Roberts operator’s test result is relatively rough and loses a number of initial image information, though it can exactly locate the edge; compared with the Sobel operator, Prewitt operator includes weight in the average process of filtering, but it can not detect the image completely under the interference of noise; Log detection operator aims to find the precipitous zero cross point through the calculation of second order differential. The detection result of Log operator is much more superior to the previous two methods. Canny detection operator utilizes first order differential to unveil the edge point of the image. Comparing with Roberts, Sobel and Prewitt, Canny method owns high effects in de-noising and detection, and can better detect the complete edge of the target image.
When the amplitude values of every sampling point are transformed into various levels of grey scale, the picture of seismic section becomes the picture of seismic grey scale. Thus the image edge detection can be introduced into the treatment of seismic section. Then the reflection event turns into the edge of two-dimensional numerical image. Through identifying the events in the practical seismic section by use of every operator, extracting the envelope medial axis from the identification results, it is discovered that the identification results of the events based on the Roberts operator, Prewitt operator and Sobel operator are relatively poor, and lead to a great loss in the original information and an obvious decrease in event number, and even ruptures. While the identification results of events of Log operator and Canny operator are comparatively better, which almost reserve all the horizon information of the events in the original seismic section. However, there are still a few tiny spotted noise in the detection result section, which due to the appearance of the false boundary point caused by the discontinuous grey scale. Compare the results after filtering the noise, Canny operator possesses the best detection effects, more complete horizon information and continuous event.
Coherence analysis is a technology based on the computation of the similarities of the data among seismic traces, in order to get the horizon information and stratum continuity, which functions greatly in the identification of underground small structures, faults and cracks. The mathematical theoretical foundation of C1 coherence algorithm is normalized cross correlation. Through the treatment of waveform similarities among seismic traces, C1 algorithm is easy to realize and without much computational complexity. It shows high coherent value in continuous and transversely uniform stratum, and shows low coherent value in case of faults and cracks.
The realization method of C1 coherence algorithm is as follows: 1.Open a fixed time window with appropriate size in the data of the reference trace and the surrounding seismic traces which need to be computed, according to the time shift characteristic of coherence, fix the reference trace, take the interval of the sample points as the unit, and move the adjacent traces from the top down. With every movement, calculate the coherent value of the waveform in the time window once. 2. Figure out the maximum value C1(Max) , that is, the value of position when the two traces are mostly correlated, in order to demonstrate the two traces own the maximum similarity in this position. 3. Take down the time-shift value in accordance with the maximum coherent value, so as to reflect the continuous transverse change of the wave form in horizon, i.e., the rise and fall situation of the structures.
First build a horizontal reflection interface and a tilted reflection interface. Through the detection of C1 coherence algorithm, record the searching range, the value ofτcorresponding with the C1 (Max), in the purpose of certifying the horizon trend of the events. And take the time depth of C1 (Max) as the ordinate, and the seismic trace as the abscissa, and get the tracing graph of reflection horizon. After the evaluation of the inaccuracy and comparison of the results, it is discovered that the result acquired is in complete uniformity with the theory, which verifies the practicality of C1 coherence method. Build respectively three kinds of synthetic seismogram of low-amplitude structured model. They are H=50m(λ/2),L=100m(λ),H/L=1/2;H=10m(λ/10),L=50m(λ/2),H/L=1/5;and H=5m(λ/10),L=50m(λ/2),H/L=1/10. The features presented in the seismic section are concealed structure, straight-distributed event, and with difficulty in ensuring the existence of the horizon and the size of the structure. After C1 coherence identification, from the horizon tracing graph the horizontal position of the low-amplitude structure can be obviously seen. Combining with the horizon slope rate graph, it can be ascertained exactly the time depth of the structure’s end point and its high point, and its corresponding seismic trace. Therefore, complete spatial display information of the low-amplitude structure is acquired.
The major factors influencing the C1 algorithm’s identification of low-amplitude structure are searching range, noise and the size of time window. Build a model of low-amplitude structure of two layers and one interface. The three low-amplitude structures H=50m(λ/2)、L=150m(3λ/2)、H/L=1/3,H=20m(λ/5)、L=60m(3λ/5)、H/L=1/3,H=30m(3λ/10)、L=60m(3λ/5)、H/L=1/2 are distributed on this reflection interface continuously. The searching range begins to increase fromτ=1dt, and different identification results are gained whenτ=4dt andτ=15dt. Comparing with the case whenτ=1dt, it is discovered that S3 needs larger searching range than S1 and S2. From this, it can be seen that the parameter influencing the searching range is the height-width ratio instead of the size of the structure’s amplitude. The fix of searching rangeτis decided by the height-width ratio of the real horizon of the structure, which is the maximum inclining angle of the target horizon in the work area. Add Gaussian independent noise of 10dB and 0dB respectively to the model of low-amplitude structure, and then use C1 coherence algorithm to identify, it is found that the identification result of 10dB is without any mistake, and the identification result of 0dB appears to be no more than four thousandth relative error, which would not affect the fix of the structural horizon. Therefore, under the environment of Gaussian independent noise, the identification result of C1 coherence algorithm towards low-amplitude structure is still reliable. Take 5T*/2～T*/4 as the time window to identify the low-amplitude structure under the environment of 0dB. When the time window is 3T*/2～T*/2, a complete wave crest or wave trough could be seen, which is comparatively ideal and can reflect the relatively correct spatial display of the low-amplitude structure. When the time window is less than T*/2, the view of the time window is comparatively narrow, and a complete wave crest or wave trough can not be seen. The result at this moment does not demonstrate the similarities of waves among seismic traces, but rather the noise situation of this small area. When the time window is more than 3T*/2, on the one hand, the amount of data included by the time window is enlarged, the time of calculation would be prolonged; on the other hand, the oversize time window would not be so sensitive towards the identification of the low-amplitude structure. In addition, the oversize time window would encompass other horizon information if other horizons exist in the stratum. Therefore, the value of the time window should be 3T*/2～T*/2
Build a model of low-amplitude structure of underlying interference with three layers and two interfaces. Take the layer thickness D=λ、λ/2、λ/4、λ/5 to form the seismic records containing 10dB’s noise. After the C1 coherence identification, it is discovered that when the layer thickness is reduced, within the limit of appropriate time window, the proper reduction in the width of the window can heighten the identification effects and reduce the inaccuracy of identification. When the interference layer is contained in the noise, C1 coherence algorithm still can acquire the horizon spatial display of low-amplitude structure correctly. Apply C1 coherence algorithm to the actual identification of seismic section, and the identification results reflect the event trend of reflection wave of the target horizon T1, T2 and T3 comparatively exactly. The effect of application is relatively ideal.
Complex trace analysis is three-instantaneous analysis, which aims to get the physical parameters reflecting the characteristics of seismic wave, such as the instantaneous amplitude, the instantaneous phase and the instantaneous frequency from the signal of the seismic trace. As the increase in exploration extent, the complex seismic trace can not meet the need of the exploration of the thin layers. Therefore, the complex trace analysis of high resolution is derived after the derivation of the signal, which is weighted through the spectrum of the signal x (t), taking ?ω2as the weights.
This paper builds a wedge model, through complex trace analysis of high resolution, which raises the resolution limit of the conventional seism fromλ/4 toλ/5, and functions positively to the amplitude. Do the complex trace analysis of high resolution to the model of low-amplitude structure of underlying interference with three layers and two interfaces and try to get its three-instantaneous section of high resolution. This shows the horizon of the low-amplitude structure, which can not be displayed in the original section, in the seismic section of high resolution, and fixes the existence of the structural horizon in the instantaneous amplitude of high resolution through the amplitude highlight. It also confirms the time depth of the low-amplitude structure in the instantaneous phase section of high resolution, and the end point of the structure in the instantaneous frequency section of high resolution. To identify the seismic section of high resolution by the use of C1 coherence algorithm, compared with the identification result which have not been analyzed by the complex trace analysis of high resolution, it is discovered that the identification result erases the false structure of underlying horizon, and the selected points are almost completely coincide with the true values, with the selected relative error decreasing from 0.8% to 0.2%, which is an conspicuous enhancement in the identification result and as well beneficial to the determination of the spatial display of the low-amplitude structure.
虽然构造比较平缓、构造幅度较低,但是低幅度构造容易富集油气,形成“小而肥”的油气藏。由于构造幅度较低,反映在地震资料上表现为反射同相轴平直而变化幅度很小,构造不易识别。
图像边缘检测是利用图像空间灰度的突变,来识别和提取图像边界信息,加强图像轮廓特征的。分别研究了Roberts、Sobel、Prewitt、Log以及Canny等五种边缘检测算子的原理,利用各算子检测实际地震剖面数据,对比分析各算子的特性及其识别效果,并对检测结果加以处理,以达到识别地震剖面同相轴的目的。经过评价得出Canny算子的检测效果最佳。
C1相干分析技术是以归一化互相关为数学基础,以计算地震道间的波形相似性来描述地层连续性的。当地层发育有构造时,横向连续性发生改变,通过寻找C1(Max)值所对应的旅行时差来确定构造起伏的层位展布。影响C1相干算法识别低幅度构造效果的因子主要有搜索范围、噪音以及时窗大小。
分别建立两层一界面不同构造幅度、高宽比的低幅度构造,以及三层两界面下伏干扰层的薄互层低幅度构造,并利用C1相干算法对其进行层位识别,确定低幅度构造的构造端点、构造高点所对应的时间深度以及水平位置。
在复数道分析的基础上研究了高分辨率复数道分析方法。经过高分辨率复数道分析处理的地震剖面,分辨率得到了显著提高,利用高分辨率三瞬剖面的各自振幅、相位、频率特性可以确定低幅度构造的空间展布。在高分辨率复数道分析的基础上,利用C1相干算法识别薄互层低幅度构造,识别结果更佳准确,提高了精度。
Fault and anticline etc. are significant features in geological structure, which function crucially in the transfer and accumulation of the oil-gas, and lead to complex features in seismic section. As the deepening in the exploration extent, the macro-structured and easy-to-find oil and gas reservoirs have continuously been developed. Those clearly-formed and large-scaled structures become less and less. How to find the replacement area of reserves and how to develop the remaining oil-gas have already been the major problems in the development of exploration. Therefore, the focus has been shifted to the hard-to-find subtle traps, especially the exploration of the low-amplitude oil and gas reservoir.
At present, there is not yet an integrated and rigorous definition concerning the low-amplitude structure in the theories and practices of the oil-gas exploration both domestically and abroad. It is a relative concept, which indicates the geological body of smooth structure and low amplitude about 10m to 50m. Although it is of low amplitude, it is easy to gather the oil-gas, and form the“tiny and rich”oil and gas reservoir. Due to its low amplitude, straightness in the reflection event and small varying amplitude are reflected in the seismic materials, with the structure hard to identify.
The edge of the image is one of the most fundamental and most significant features, which is the aggregation of the spatial-mutated pixels generated by the gray scale. The purpose of the identification and extraction of the edge is to highlight the edge information of the image and heighten the contour feature of the image.
Through the detection of the images by use of every detection operator and the comparison of the results, it is discovered that due to the lack of smoothing processing, without strong ability to resist the noise, Roberts operator’s test result is relatively rough and loses a number of initial image information, though it can exactly locate the edge; compared with the Sobel operator, Prewitt operator includes weight in the average process of filtering, but it can not detect the image completely under the interference of noise; Log detection operator aims to find the precipitous zero cross point through the calculation of second order differential. The detection result of Log operator is much more superior to the previous two methods. Canny detection operator utilizes first order differential to unveil the edge point of the image. Comparing with Roberts, Sobel and Prewitt, Canny method owns high effects in de-noising and detection, and can better detect the complete edge of the target image.
When the amplitude values of every sampling point are transformed into various levels of grey scale, the picture of seismic section becomes the picture of seismic grey scale. Thus the image edge detection can be introduced into the treatment of seismic section. Then the reflection event turns into the edge of two-dimensional numerical image. Through identifying the events in the practical seismic section by use of every operator, extracting the envelope medial axis from the identification results, it is discovered that the identification results of the events based on the Roberts operator, Prewitt operator and Sobel operator are relatively poor, and lead to a great loss in the original information and an obvious decrease in event number, and even ruptures. While the identification results of events of Log operator and Canny operator are comparatively better, which almost reserve all the horizon information of the events in the original seismic section. However, there are still a few tiny spotted noise in the detection result section, which due to the appearance of the false boundary point caused by the discontinuous grey scale. Compare the results after filtering the noise, Canny operator possesses the best detection effects, more complete horizon information and continuous event.
Coherence analysis is a technology based on the computation of the similarities of the data among seismic traces, in order to get the horizon information and stratum continuity, which functions greatly in the identification of underground small structures, faults and cracks. The mathematical theoretical foundation of C1 coherence algorithm is normalized cross correlation. Through the treatment of waveform similarities among seismic traces, C1 algorithm is easy to realize and without much computational complexity. It shows high coherent value in continuous and transversely uniform stratum, and shows low coherent value in case of faults and cracks.
The realization method of C1 coherence algorithm is as follows: 1.Open a fixed time window with appropriate size in the data of the reference trace and the surrounding seismic traces which need to be computed, according to the time shift characteristic of coherence, fix the reference trace, take the interval of the sample points as the unit, and move the adjacent traces from the top down. With every movement, calculate the coherent value of the waveform in the time window once. 2. Figure out the maximum value C1(Max) , that is, the value of position when the two traces are mostly correlated, in order to demonstrate the two traces own the maximum similarity in this position. 3. Take down the time-shift value in accordance with the maximum coherent value, so as to reflect the continuous transverse change of the wave form in horizon, i.e., the rise and fall situation of the structures.
First build a horizontal reflection interface and a tilted reflection interface. Through the detection of C1 coherence algorithm, record the searching range, the value ofτcorresponding with the C1 (Max), in the purpose of certifying the horizon trend of the events. And take the time depth of C1 (Max) as the ordinate, and the seismic trace as the abscissa, and get the tracing graph of reflection horizon. After the evaluation of the inaccuracy and comparison of the results, it is discovered that the result acquired is in complete uniformity with the theory, which verifies the practicality of C1 coherence method. Build respectively three kinds of synthetic seismogram of low-amplitude structured model. They are H=50m(λ/2),L=100m(λ),H/L=1/2;H=10m(λ/10),L=50m(λ/2),H/L=1/5;and H=5m(λ/10),L=50m(λ/2),H/L=1/10. The features presented in the seismic section are concealed structure, straight-distributed event, and with difficulty in ensuring the existence of the horizon and the size of the structure. After C1 coherence identification, from the horizon tracing graph the horizontal position of the low-amplitude structure can be obviously seen. Combining with the horizon slope rate graph, it can be ascertained exactly the time depth of the structure’s end point and its high point, and its corresponding seismic trace. Therefore, complete spatial display information of the low-amplitude structure is acquired.
The major factors influencing the C1 algorithm’s identification of low-amplitude structure are searching range, noise and the size of time window. Build a model of low-amplitude structure of two layers and one interface. The three low-amplitude structures H=50m(λ/2)、L=150m(3λ/2)、H/L=1/3,H=20m(λ/5)、L=60m(3λ/5)、H/L=1/3,H=30m(3λ/10)、L=60m(3λ/5)、H/L=1/2 are distributed on this reflection interface continuously. The searching range begins to increase fromτ=1dt, and different identification results are gained whenτ=4dt andτ=15dt. Comparing with the case whenτ=1dt, it is discovered that S3 needs larger searching range than S1 and S2. From this, it can be seen that the parameter influencing the searching range is the height-width ratio instead of the size of the structure’s amplitude. The fix of searching rangeτis decided by the height-width ratio of the real horizon of the structure, which is the maximum inclining angle of the target horizon in the work area. Add Gaussian independent noise of 10dB and 0dB respectively to the model of low-amplitude structure, and then use C1 coherence algorithm to identify, it is found that the identification result of 10dB is without any mistake, and the identification result of 0dB appears to be no more than four thousandth relative error, which would not affect the fix of the structural horizon. Therefore, under the environment of Gaussian independent noise, the identification result of C1 coherence algorithm towards low-amplitude structure is still reliable. Take 5T*/2～T*/4 as the time window to identify the low-amplitude structure under the environment of 0dB. When the time window is 3T*/2～T*/2, a complete wave crest or wave trough could be seen, which is comparatively ideal and can reflect the relatively correct spatial display of the low-amplitude structure. When the time window is less than T*/2, the view of the time window is comparatively narrow, and a complete wave crest or wave trough can not be seen. The result at this moment does not demonstrate the similarities of waves among seismic traces, but rather the noise situation of this small area. When the time window is more than 3T*/2, on the one hand, the amount of data included by the time window is enlarged, the time of calculation would be prolonged; on the other hand, the oversize time window would not be so sensitive towards the identification of the low-amplitude structure. In addition, the oversize time window would encompass other horizon information if other horizons exist in the stratum. Therefore, the value of the time window should be 3T*/2～T*/2
Build a model of low-amplitude structure of underlying interference with three layers and two interfaces. Take the layer thickness D=λ、λ/2、λ/4、λ/5 to form the seismic records containing 10dB’s noise. After the C1 coherence identification, it is discovered that when the layer thickness is reduced, within the limit of appropriate time window, the proper reduction in the width of the window can heighten the identification effects and reduce the inaccuracy of identification. When the interference layer is contained in the noise, C1 coherence algorithm still can acquire the horizon spatial display of low-amplitude structure correctly. Apply C1 coherence algorithm to the actual identification of seismic section, and the identification results reflect the event trend of reflection wave of the target horizon T1, T2 and T3 comparatively exactly. The effect of application is relatively ideal.
Complex trace analysis is three-instantaneous analysis, which aims to get the physical parameters reflecting the characteristics of seismic wave, such as the instantaneous amplitude, the instantaneous phase and the instantaneous frequency from the signal of the seismic trace. As the increase in exploration extent, the complex seismic trace can not meet the need of the exploration of the thin layers. Therefore, the complex trace analysis of high resolution is derived after the derivation of the signal, which is weighted through the spectrum of the signal x (t), taking ?ω2as the weights.
This paper builds a wedge model, through complex trace analysis of high resolution, which raises the resolution limit of the conventional seism fromλ/4 toλ/5, and functions positively to the amplitude. Do the complex trace analysis of high resolution to the model of low-amplitude structure of underlying interference with three layers and two interfaces and try to get its three-instantaneous section of high resolution. This shows the horizon of the low-amplitude structure, which can not be displayed in the original section, in the seismic section of high resolution, and fixes the existence of the structural horizon in the instantaneous amplitude of high resolution through the amplitude highlight. It also confirms the time depth of the low-amplitude structure in the instantaneous phase section of high resolution, and the end point of the structure in the instantaneous frequency section of high resolution. To identify the seismic section of high resolution by the use of C1 coherence algorithm, compared with the identification result which have not been analyzed by the complex trace analysis of high resolution, it is discovered that the identification result erases the false structure of underlying horizon, and the selected points are almost completely coincide with the true values, with the selected relative error decreasing from 0.8% to 0.2%, which is an conspicuous enhancement in the identification result and as well beneficial to the determination of the spatial display of the low-amplitude structure.
引文
[1]柳开洋,濮剑锋,等.断层两侧层位识别中同相轴定位和提取的实现[J].石油仪器,2001,15(6):55-56,59.
[2]宁松华,漆立新.低幅构造分析方法研究_以塔里木盆地三道桥构造为例[J].石油天然气学报,2005,27(3):341-344.
[3]孙明江,路慎强,刘彤滨,等.复杂断块油藏的地震识别与描述[J].断块油气田,2003,10(6):4-6.
[4]唐建华,郭勇,虞敏.准噶尔盆地腹部沙漠区低幅度构造的正演模拟与识别[C].2003,中国地球物理—中国地球物理学会第十九届年会论文集.
[5]王发现.浩绕沼地区低幅度构造速度场研究[D].长春:吉林大学,2007.构造断层的相干识别技术.
[6]李启昆.构造地质学[M].北京:冶金工业出版社,1992.
[7]戴启德,黄玉杰.油田开发地质学[M].北京:石油大学出版社,2002.
[8]周文,谢洪光,陈红.低幅度圈闭识别方法探讨[J].江汉石油科技,2006,16(3):4-8.
[9]陈广军,宋国奇.低幅度构造地震解释探讨[J].石油物探,2003,42(3):395-398.
[10]盛湘.塔河油田AT11井区低幅度构造圈闭的识别方法[J].石油物探,2009,48(2):171-174.
[11]林勇刚,陈恭洋,李陶,等.低幅度构造油藏滚动勘探开发及储层横向预测_以陆梁油田陆9_陆22井区为例[J].内蒙古石油化工,2007,3:206-209.
[12]申龙斌,汤冬梅,王传忠.构造低平区低幅度油藏的滚动勘探研究[J].胜利油田职工大学学报,2003,17(3):1-3.
[13]李庆忠.走向精确勘探的道路[M].北京:石油工业出版社,1994,3-21.
[14]卢圣祥.对地震属性处理应用的思考[J].石油物探,2004,43(增刊):4-9.
[15]张健,黄芸,杨迪生,等.二维地震资料落实低幅度圈闭方法探讨[J].中国石油勘探,2002,7(3):54-59.
[16]荆文波,李道阳,宋学良,等.低幅度构造油藏滚动勘探开发实践[J].特种油气藏,2004,11(5):69-71.
[17]滕佃波,汪鹏程,王贇,等.利用叠前Kirchhoff积分偏移识别小断裂与低幅度构造[J].地球物理学进展,2005,20(4):1035-1038.
[18]张建宁,于建国.地震属性应用中的不确定性[J].油气地球物理,2006,4(1):1-7.
[19]曹辉,吴永栓,佘德平,等.一种减少地震属性不确定性的方法[J].石油物探,2002,41(4):422-424,438.
[20]严又生.速度横向变化对小幅度构造解释的影响[J].石油地球物理勘探,1994,29(6):758-768,798.
[21]何新贞,尚新民,石林光.低幅度构造圈闭地震资料处理中的几个关键点[J].油气地球物理,2003,1(3):41-44.
[22]吴琳,帕提幔,薛慧.静校正对低幅度构造资料处理的影响[J].石油地球物理勘探,2008,43(增刊1):24～28.
[23]陈照光,汪瑞良,庞雄等.南海低幅度构造解释中Paradigm软件的应用[J].石油物探,2002,41(增刊):331-335.
[24]郭泽清,李本亮,张林等.低幅度构造天然气成藏的闭合度下限探讨[J].地质科学,2008,43(1):34-49.
[25]段润梅,赵颖,刘广华.中浅层复杂构造、微幅度圈闭地震地质解释技术在大港滩海区的应用[J].华南地质与矿产,2003(3):70-72.
[26]漆立新.一种提高低幅度构造分析的方法_以塔里木盆地SDQ为例[J].工程地球物理学报,2005,2(3):228-231.
[27]王延章,林承焰,温长云等.低幅度构造油藏研究方法[J].新疆石油地质,2006,27(4):407-409.
[28]刘彤滨,崔永北,张磊.宁海油田沙二段低幅构造三维地震精细解释及描述[J].石油地质,2009(2):169-170.
[29]张书法.三维地震资料解释的水平切片作图法[J].断块油气田,1995,2(6):6-11.
[30]蒲仁海.溢出点约束速度场法_提高低幅度构造成图精度的方法[J].石油与天然气地质,1998,19(2):106-109.
[31]雷卫兵,曹坚明,查树贵.变速成图技术在二连盆地赛汉塔拉凹陷低幅度构造解释中应用[J].内蒙古石油化工,2005,(10),91-94.
[32]满益志,王兴军,张耀堂等.复杂逆掩构造及低幅度构造变速成图技术与应用[J].天然气工业,2007,27(增刊A):205-208.
[33] F.Bergholm,Edge focusing.IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(6):726-741.
[34]胡晓飞.灰度值不连续图像的边缘检测方法研究[J].视频应用,2002,11:72-77.
[35]张小琳.图像边缘检测技术综述[J].高能量密度物理,2007.03(01):37-40.
[36]周道炳,朱卫纲.几种边缘检测算子的评估[J].指挥技术学院学报,2000.02,11(01):59-63.
[37]马艳,张志辉.几种边缘检测算子的比较[J].工矿自动化,2004.02(1):54-56.
[38] ROBERTS L G.Machine perception of three dimension solids[A].Optical and Electro-Optimal Information Processing[C].Cambridge,MA;MIT Press,1965.
[39] SOBELL.Camera model sand machine perception[D].StanfordUniversity,CA,1970.
[40]曾欢,王浩.图像边缘检测算法的性能比较与分析[J].现代电子技术,2006(14):53-56.
[41] PREWITT J.Object enbarcemeni and extraction[A].Picture Processing and Psychopictoric Prese[C].New York,1970:75-149.
[42]董梁,王忠民.图像边缘检测算法研究[J].现代电子技术,2007(15):179-180.
[43] HaralickRM and ShapiroLG.Survey:Image Segmentation.Comput Vision Graphics Image Processing,1985,129:100-132.
[44]沈洁,杜宇人,高浩军.图像边缘检测技术研究[J].信息技术,2005(12):32-34.
[45]叶俊.基于Canny算子的图像边缘检测与提取的算法[J].科技咨询,2007,34:69-70.
[46] CANN YJ . A computational approach to edge detection.IEEE - PAMI ,1986.
[47]何新英,王家忠,孙晨霞,等.基于数学形态学和Canny算子的边缘提取方法[J].计算机应用,2008,28(2):477-483.
[48]张树忠.基于Canny理论的彩色图像边缘检测[J].成都理工大学硕士学位论文[D],2006.05.
[49]李枉,孟祥萍.自适应双阂值Canny算子的图像边缘检测[J].长春工程学院学报,2007,8(3):44-53.
[50]李牧,闫继红,李戈,等.自适应Canny算子边缘检测技术[J].哈尔滨工程大学学报, 2007,28(9):1002-1007.
[51]杨振亚,白治江,王成道.自适应Canny边缘检测算法[J].上海海运学院学报,2003,24(4):373-377.
[52]梅跃松,杨树兴,莫波.基于Canny算子的改进的图像边缘检测方法[J].激光与红外,2006,36(6):501-503.
[53]向富强.地震相干分析技术和时频分析方法及其在储层描述中的应用[D].成都:成都理工大学,2008.
[54]吕小惠.三维地震资料中断层识别方法研究[D].南京:南京理工大学,2004.
[55]李明,侯连华,邹才能等.岩性地层油气藏地球物理勘探技术与应用[M].北京:石油工业出版社,2005.
[56]王永刚,谢东,乐友喜等.地震属性分析技术在储层预测中的应用[J].石油大学学报(自然科学版),2003,27(3):30-32.
[57]张延玲,杨长春,贾曙光.地震属性技术的研究和应用[J].地球物理学进展,2005,20(4):1129-1133.
[58]罗桂纯.利用相关检测法进行地震波速及其变化的精确测量[D].中国地震局地震预测研究所,2006.
[59]王一重.构造断层的相干识别技术[D].长春:吉林大学,2009.
[60]何建军.致密碳酸盐岩缝洞储层地震检测方法研究[D].成都:成都理工大学,2008.
[61] Bahorich M S,Bridges S R.The seismic sequence at tribute map[J].Expanded Abstracts of the 62nd Annual Internat SEG Meeting,1992:227-230.
[62] Bahorich M S,Lopez J A.Stratigraphic and structural interpre-tation with 3-D coherence[J].Expanded Abstracts of the 65th Annual Internat SEG Meeting,1995,97-100.
[63] Bahorich M S,Farmer S L.3-D seismic coherency for faults andstratigrAphi features[J].The Leading Edge,1995,14(10):1053-1058.
[64]余德平,曹辉,王咸彬等.相干数据体及其在三维地震解释中的应用[J].石油物探,1998,37(4):75-79.
[65] Marfurt K J,Kirlin R L,Farmer S L,etal.3-D Seismic Attributes Using a Semblance-Based Coherency Algorithm[J].Geophysics,1998,63(4):1150-1165.
[66] Gersztenkorn A,Marfurt K J.Eigenstructure-Based Coherence Computations as an Aid to 3-D Structural and Stratigraphic Mapping[J].Geophysics,1999,64(5):1468-1479.
[67] Marfurt K J,Sudhaker V,Gersztenkorn A,et al.Coherency Calculations in the Presence of Structural Dip[J].Geophysics,1999,64(1):104-111.
[68]叶增炉.小波相干技术及在地震解释中的应用[D].成都:成都理工大学,2006.
[69]孙夕平,杜世通.乘幂法在地震属性分析中的应用[J].物探化探计算技术,2000,22(4):361-321.
[70]王西文,杨孔庆,周立宏,等.基于小波变换的地震相干体算法研究[J].地球物理学报,2002,45(6):847-852.
[71]宋维琪,刘江华.地震多矢量属性相干数据体计算及应用[J].物探与化探,2003,27(2):128-130.
[72] Al-Dossary S,Simon Y,Marfurt L J.Inter Azimuth Coherence Attribute for Fracture Detection[J].SEG Technical Program Expanded Abstract,2004,23:183-186.
[73]门德庆,庞留彦,仲其涛等.C1相干算法及其在三维地震勘探解释中的应用[J].物探化探计算技术,2001,23(3):206-209,271.
[74]余德平,曹辉,王咸彬,等.相干数据体及其在三维地震解释中的应用[J].石油物探,1998,37(4):75-79.
[75]许庆山,李秀人.信号与系统[M].航空工业出版社,1995,147-153.
[76] Lu W,Li Y,Xiao H. Higher-order statisticsbased coherency estimation algorithm[J]. Expanded Abstracts of the 73th Annual Internat SEG Meeting,2003,22:1732-1735.
[77] Gersztenkorn A,Marfurt K J. Eigenstructure based coherence computationns[J]. Expanded Abstracts of the 66th Annual Internat SEG Meeting,1996:328-331.
[78] Gersztenkorn A,Marfurt K J.Eigenstructure based coherence computations as an aid to 3-D structural and stratigraphic mapping[J]Geophysics,1999,64(5):1468-1479.
[79] Cohen I,Coifman R R. Local discontinuity measures for 3-D seismicdata[J]. Geophysics,2002,67(6):1933-1945.
[80] Marfurt K J,Sndbakar V.Gerszrenkorn A,et al.Coherency calculations in the presence of structural dip[J].Geophysics.1999,64(1):104-111.
[81]程乾生.数字信号处理的数学原理[M].石油工业出版社,1979,172-203.
[82]宗孔德,胡广书.数字信号处理[M].清华大学出版社,1999,70-84.
[83]闫德庆,庞留彦,等.C1相干算法及其在三维地震勘探解释中的应用[J].物探化探计算技术,2001,23(3):206-209,271.
[84]丛茂祥,李洪亮.C1相干算法及其在三维地震资料处理解释中的应用[J].中国煤田地质,2005,17(2):49-50,70.
[85]王大伟,刘震,陈小宏,等.地震相干技术的进展及其在油气勘探中的应用[J].地质科技情报,2005,24(2):71-76.
[86]侯伯刚,乌达巴拉,杨再岩.地震相干体技术简介及其应用[J].现代地质,1999,13(1):121-124.
[87]边树涛,董艳蕾,苏晓军,等.地震相干体技术识别低序级断层方法研究[J].世界地质,2007,26(3):368-374.
[88]陈国俊,王纬,张宏.地震相干体软件开发及其应用效果分析[J].工程地球物理学报,2004,1(5):399-403.
[89]佘德平,曹辉,郭全仕.断裂系统解释与储层预测中的三维相干技术[J].石油与天然气地质,2007,28(1):100-105.
[90]贺振华.地震波复数道分析的计算方法[J].石油物探,1980,19(3):64-80.
[91]石颖,刘洪.地震信号的复地震道分析及应用[J].地球物理学进展,2008,23(5):1538-1543.
[92]高静怀,廖祖文,陈文超.高分辨地震资料瞬时频率提取方法研究[J].西安交通大学学报,2005,39(4):331-334.
[93]冯智慧,王世煜,刘财,等.高分辨率地震信号瞬时特征分析方法[J].吉林大学学报(地球科学版),2007,37(增刊):82-85.
[94]刘财,周辉,杨宝俊,等.高分辨率复数道分析方法[J].石油地球物理勘探,1995,30(增刊1):24-29.
[95]冯智慧.高分辨率复数道分析方法在油藏描述中的应用[D].吉林大学,2008.
[2]宁松华,漆立新.低幅构造分析方法研究_以塔里木盆地三道桥构造为例[J].石油天然气学报,2005,27(3):341-344.
[3]孙明江,路慎强,刘彤滨,等.复杂断块油藏的地震识别与描述[J].断块油气田,2003,10(6):4-6.
[4]唐建华,郭勇,虞敏.准噶尔盆地腹部沙漠区低幅度构造的正演模拟与识别[C].2003,中国地球物理—中国地球物理学会第十九届年会论文集.
[5]王发现.浩绕沼地区低幅度构造速度场研究[D].长春:吉林大学,2007.构造断层的相干识别技术.
[6]李启昆.构造地质学[M].北京:冶金工业出版社,1992.
[7]戴启德,黄玉杰.油田开发地质学[M].北京:石油大学出版社,2002.
[8]周文,谢洪光,陈红.低幅度圈闭识别方法探讨[J].江汉石油科技,2006,16(3):4-8.
[9]陈广军,宋国奇.低幅度构造地震解释探讨[J].石油物探,2003,42(3):395-398.
[10]盛湘.塔河油田AT11井区低幅度构造圈闭的识别方法[J].石油物探,2009,48(2):171-174.
[11]林勇刚,陈恭洋,李陶,等.低幅度构造油藏滚动勘探开发及储层横向预测_以陆梁油田陆9_陆22井区为例[J].内蒙古石油化工,2007,3:206-209.
[12]申龙斌,汤冬梅,王传忠.构造低平区低幅度油藏的滚动勘探研究[J].胜利油田职工大学学报,2003,17(3):1-3.
[13]李庆忠.走向精确勘探的道路[M].北京:石油工业出版社,1994,3-21.
[14]卢圣祥.对地震属性处理应用的思考[J].石油物探,2004,43(增刊):4-9.
[15]张健,黄芸,杨迪生,等.二维地震资料落实低幅度圈闭方法探讨[J].中国石油勘探,2002,7(3):54-59.
[16]荆文波,李道阳,宋学良,等.低幅度构造油藏滚动勘探开发实践[J].特种油气藏,2004,11(5):69-71.
[17]滕佃波,汪鹏程,王贇,等.利用叠前Kirchhoff积分偏移识别小断裂与低幅度构造[J].地球物理学进展,2005,20(4):1035-1038.
[18]张建宁,于建国.地震属性应用中的不确定性[J].油气地球物理,2006,4(1):1-7.
[19]曹辉,吴永栓,佘德平,等.一种减少地震属性不确定性的方法[J].石油物探,2002,41(4):422-424,438.
[20]严又生.速度横向变化对小幅度构造解释的影响[J].石油地球物理勘探,1994,29(6):758-768,798.
[21]何新贞,尚新民,石林光.低幅度构造圈闭地震资料处理中的几个关键点[J].油气地球物理,2003,1(3):41-44.
[22]吴琳,帕提幔,薛慧.静校正对低幅度构造资料处理的影响[J].石油地球物理勘探,2008,43(增刊1):24～28.
[23]陈照光,汪瑞良,庞雄等.南海低幅度构造解释中Paradigm软件的应用[J].石油物探,2002,41(增刊):331-335.
[24]郭泽清,李本亮,张林等.低幅度构造天然气成藏的闭合度下限探讨[J].地质科学,2008,43(1):34-49.
[25]段润梅,赵颖,刘广华.中浅层复杂构造、微幅度圈闭地震地质解释技术在大港滩海区的应用[J].华南地质与矿产,2003(3):70-72.
[26]漆立新.一种提高低幅度构造分析的方法_以塔里木盆地SDQ为例[J].工程地球物理学报,2005,2(3):228-231.
[27]王延章,林承焰,温长云等.低幅度构造油藏研究方法[J].新疆石油地质,2006,27(4):407-409.
[28]刘彤滨,崔永北,张磊.宁海油田沙二段低幅构造三维地震精细解释及描述[J].石油地质,2009(2):169-170.
[29]张书法.三维地震资料解释的水平切片作图法[J].断块油气田,1995,2(6):6-11.
[30]蒲仁海.溢出点约束速度场法_提高低幅度构造成图精度的方法[J].石油与天然气地质,1998,19(2):106-109.
[31]雷卫兵,曹坚明,查树贵.变速成图技术在二连盆地赛汉塔拉凹陷低幅度构造解释中应用[J].内蒙古石油化工,2005,(10),91-94.
[32]满益志,王兴军,张耀堂等.复杂逆掩构造及低幅度构造变速成图技术与应用[J].天然气工业,2007,27(增刊A):205-208.
[33] F.Bergholm,Edge focusing.IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(6):726-741.
[34]胡晓飞.灰度值不连续图像的边缘检测方法研究[J].视频应用,2002,11:72-77.
[35]张小琳.图像边缘检测技术综述[J].高能量密度物理,2007.03(01):37-40.
[36]周道炳,朱卫纲.几种边缘检测算子的评估[J].指挥技术学院学报,2000.02,11(01):59-63.
[37]马艳,张志辉.几种边缘检测算子的比较[J].工矿自动化,2004.02(1):54-56.
[38] ROBERTS L G.Machine perception of three dimension solids[A].Optical and Electro-Optimal Information Processing[C].Cambridge,MA;MIT Press,1965.
[39] SOBELL.Camera model sand machine perception[D].StanfordUniversity,CA,1970.
[40]曾欢,王浩.图像边缘检测算法的性能比较与分析[J].现代电子技术,2006(14):53-56.
[41] PREWITT J.Object enbarcemeni and extraction[A].Picture Processing and Psychopictoric Prese[C].New York,1970:75-149.
[42]董梁,王忠民.图像边缘检测算法研究[J].现代电子技术,2007(15):179-180.
[43] HaralickRM and ShapiroLG.Survey:Image Segmentation.Comput Vision Graphics Image Processing,1985,129:100-132.
[44]沈洁,杜宇人,高浩军.图像边缘检测技术研究[J].信息技术,2005(12):32-34.
[45]叶俊.基于Canny算子的图像边缘检测与提取的算法[J].科技咨询,2007,34:69-70.
[46] CANN YJ . A computational approach to edge detection.IEEE - PAMI ,1986.
[47]何新英,王家忠,孙晨霞,等.基于数学形态学和Canny算子的边缘提取方法[J].计算机应用,2008,28(2):477-483.
[48]张树忠.基于Canny理论的彩色图像边缘检测[J].成都理工大学硕士学位论文[D],2006.05.
[49]李枉,孟祥萍.自适应双阂值Canny算子的图像边缘检测[J].长春工程学院学报,2007,8(3):44-53.
[50]李牧,闫继红,李戈,等.自适应Canny算子边缘检测技术[J].哈尔滨工程大学学报, 2007,28(9):1002-1007.
[51]杨振亚,白治江,王成道.自适应Canny边缘检测算法[J].上海海运学院学报,2003,24(4):373-377.
[52]梅跃松,杨树兴,莫波.基于Canny算子的改进的图像边缘检测方法[J].激光与红外,2006,36(6):501-503.
[53]向富强.地震相干分析技术和时频分析方法及其在储层描述中的应用[D].成都:成都理工大学,2008.
[54]吕小惠.三维地震资料中断层识别方法研究[D].南京:南京理工大学,2004.
[55]李明,侯连华,邹才能等.岩性地层油气藏地球物理勘探技术与应用[M].北京:石油工业出版社,2005.
[56]王永刚,谢东,乐友喜等.地震属性分析技术在储层预测中的应用[J].石油大学学报(自然科学版),2003,27(3):30-32.
[57]张延玲,杨长春,贾曙光.地震属性技术的研究和应用[J].地球物理学进展,2005,20(4):1129-1133.
[58]罗桂纯.利用相关检测法进行地震波速及其变化的精确测量[D].中国地震局地震预测研究所,2006.
[59]王一重.构造断层的相干识别技术[D].长春:吉林大学,2009.
[60]何建军.致密碳酸盐岩缝洞储层地震检测方法研究[D].成都:成都理工大学,2008.
[61] Bahorich M S,Bridges S R.The seismic sequence at tribute map[J].Expanded Abstracts of the 62nd Annual Internat SEG Meeting,1992:227-230.
[62] Bahorich M S,Lopez J A.Stratigraphic and structural interpre-tation with 3-D coherence[J].Expanded Abstracts of the 65th Annual Internat SEG Meeting,1995,97-100.
[63] Bahorich M S,Farmer S L.3-D seismic coherency for faults andstratigrAphi features[J].The Leading Edge,1995,14(10):1053-1058.
[64]余德平,曹辉,王咸彬等.相干数据体及其在三维地震解释中的应用[J].石油物探,1998,37(4):75-79.
[65] Marfurt K J,Kirlin R L,Farmer S L,etal.3-D Seismic Attributes Using a Semblance-Based Coherency Algorithm[J].Geophysics,1998,63(4):1150-1165.
[66] Gersztenkorn A,Marfurt K J.Eigenstructure-Based Coherence Computations as an Aid to 3-D Structural and Stratigraphic Mapping[J].Geophysics,1999,64(5):1468-1479.
[67] Marfurt K J,Sudhaker V,Gersztenkorn A,et al.Coherency Calculations in the Presence of Structural Dip[J].Geophysics,1999,64(1):104-111.
[68]叶增炉.小波相干技术及在地震解释中的应用[D].成都:成都理工大学,2006.
[69]孙夕平,杜世通.乘幂法在地震属性分析中的应用[J].物探化探计算技术,2000,22(4):361-321.
[70]王西文,杨孔庆,周立宏,等.基于小波变换的地震相干体算法研究[J].地球物理学报,2002,45(6):847-852.
[71]宋维琪,刘江华.地震多矢量属性相干数据体计算及应用[J].物探与化探,2003,27(2):128-130.
[72] Al-Dossary S,Simon Y,Marfurt L J.Inter Azimuth Coherence Attribute for Fracture Detection[J].SEG Technical Program Expanded Abstract,2004,23:183-186.
[73]门德庆,庞留彦,仲其涛等.C1相干算法及其在三维地震勘探解释中的应用[J].物探化探计算技术,2001,23(3):206-209,271.
[74]余德平,曹辉,王咸彬,等.相干数据体及其在三维地震解释中的应用[J].石油物探,1998,37(4):75-79.
[75]许庆山,李秀人.信号与系统[M].航空工业出版社,1995,147-153.
[76] Lu W,Li Y,Xiao H. Higher-order statisticsbased coherency estimation algorithm[J]. Expanded Abstracts of the 73th Annual Internat SEG Meeting,2003,22:1732-1735.
[77] Gersztenkorn A,Marfurt K J. Eigenstructure based coherence computationns[J]. Expanded Abstracts of the 66th Annual Internat SEG Meeting,1996:328-331.
[78] Gersztenkorn A,Marfurt K J.Eigenstructure based coherence computations as an aid to 3-D structural and stratigraphic mapping[J]Geophysics,1999,64(5):1468-1479.
[79] Cohen I,Coifman R R. Local discontinuity measures for 3-D seismicdata[J]. Geophysics,2002,67(6):1933-1945.
[80] Marfurt K J,Sndbakar V.Gerszrenkorn A,et al.Coherency calculations in the presence of structural dip[J].Geophysics.1999,64(1):104-111.
[81]程乾生.数字信号处理的数学原理[M].石油工业出版社,1979,172-203.
[82]宗孔德,胡广书.数字信号处理[M].清华大学出版社,1999,70-84.
[83]闫德庆,庞留彦,等.C1相干算法及其在三维地震勘探解释中的应用[J].物探化探计算技术,2001,23(3):206-209,271.
[84]丛茂祥,李洪亮.C1相干算法及其在三维地震资料处理解释中的应用[J].中国煤田地质,2005,17(2):49-50,70.
[85]王大伟,刘震,陈小宏,等.地震相干技术的进展及其在油气勘探中的应用[J].地质科技情报,2005,24(2):71-76.
[86]侯伯刚,乌达巴拉,杨再岩.地震相干体技术简介及其应用[J].现代地质,1999,13(1):121-124.
[87]边树涛,董艳蕾,苏晓军,等.地震相干体技术识别低序级断层方法研究[J].世界地质,2007,26(3):368-374.
[88]陈国俊,王纬,张宏.地震相干体软件开发及其应用效果分析[J].工程地球物理学报,2004,1(5):399-403.
[89]佘德平,曹辉,郭全仕.断裂系统解释与储层预测中的三维相干技术[J].石油与天然气地质,2007,28(1):100-105.
[90]贺振华.地震波复数道分析的计算方法[J].石油物探,1980,19(3):64-80.
[91]石颖,刘洪.地震信号的复地震道分析及应用[J].地球物理学进展,2008,23(5):1538-1543.
[92]高静怀,廖祖文,陈文超.高分辨地震资料瞬时频率提取方法研究[J].西安交通大学学报,2005,39(4):331-334.
[93]冯智慧,王世煜,刘财,等.高分辨率地震信号瞬时特征分析方法[J].吉林大学学报(地球科学版),2007,37(增刊):82-85.
[94]刘财,周辉,杨宝俊,等.高分辨率复数道分析方法[J].石油地球物理勘探,1995,30(增刊1):24-29.
[95]冯智慧.高分辨率复数道分析方法在油藏描述中的应用[D].吉林大学,2008.