CN1165562A - 自动地震图形识别方法 - Google Patents
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Abstract
一种自动地震图形(seismic pattern)识别方法,其特征在于包括下列步骤:确定给定数量的要被识别的地震图形;提供一组所述区域的地震部分;规定对所有的道部分公用的图形识别参数,并对所述组的每个道部分确定所述参数的值;在所述组中选择道部分;选择含有和要被识别的图形数量一样多的细胞的一维神经网络,每个细胞被分配给一个识别参数值;利用选择的道部分使神经网络实现学习过程,使得在所述过程结束时,每个细胞和要被识别的一个图形(pattern)对应,并对所述图形逐次地排序;把要被处理的所述组的每个道部分提供给分类的和排序的神经网络;并对提供给网络的每个道部分赋以和其最接近的细胞号数。本发明尤其适用于识别通过取一地震剖面而确定的两个地层间的地震图形。
Description
本发明涉及一种在地质区的两个地层之间或一个地层范围内的地震相(seismic facies)的自动识别方法,尤其涉及在和所述地质区有关的地震剖面上确定的两个地层之间的或一个地层范围内的地震相的自动识别方法。
现在,几乎所有的关于地震相的地质和地球物质勘探解释都在解释站进行,并且属于地震地层学的专门领域。
在地震地层学中,一般在图(图形)上识别并表示被调查地质区的给定部分中的地震相的变化,所述的部分处在或不处在两个被标志的地层之间。
地震相单位(seismic facies unit)是一组地震反射(seismicreflection),一个相单位和另一个相单位之间,即使在两个相邻的或连续的相单位之间,地震相单位的结构(configuration)、外部形状与内部参数互不相同。
通常通过分析三类参数定义地震相单位:
·反射的结构(平行的,发散的,S形的等),
·外部形状(上凸形,下凹形,有皱纹的等),
·反射的内部参数(幅值,频率等)。
在给定的地质区中地震相的识别是非常重要的,这是因为尤其对沉积层的类型和预测岩石学它能够提供有用的信息。
为了成功地识别给定的地质区的地震相,必须首先通过单独地分析至少上述定义中的三个参数,并且然后通过对所述的研究进行综合,从而收集关于在所述地质区中存在的地震相的大量数据或信息。
进行这种处理以及所用的装置,尤其是数据处理装置的花费是非常大的,并且和获得的结果根本不成比例。
实际上,如果想要识别的地震相属于地层夹断(stratigraphicpinchout)与/或混浊通道(turbiditic channel)时,当异常现象(anomaly)出现在通常的地震剖面上时,要在它们之间进行识别是非常困难的,即使这些异常现象通过在有关地区内的钻井地震调查被识别,这显然是以利用所述区域中的井为条件的,但所述异常现象并不见得如所识别的那样。
在EP-0561492中描述了一种利用神经网络改善测井的方法。所描述的具体神经网络是一种分层的网络。然而,从统计的观点看来,分层网络是一种类(class)之间的边界的通用近似器(approximator),尤其是监督网络,即,在这种神经网络的输出获得的值和由其它方法确定的已知的值进行比较,直到得到这些值之间的一致或准一致为止。
由于Kohonen使拓扑图(topological)被应用于其它领域中,尤其在医学领域中用于确定敏感的模型,以便通过再现脑的基本结构来模拟脑的大量功能,地球物理学家已试图将其应用于地球物理领域中。
一种具体的应用在US-5373486中描述了,其中涉及使用Kohonen对抗(antagonistic)网络对地震事件分类,在US 5355313中描述了空中探测地磁数据的解释,在US-5181171中描述了适用于在地震道(seismictrace)上检测初至时间(first arriral)的相互作用的神经网络。
本发明的目的在于提供一种通过无监督的神经网络自动地识别和一个地质区域有关的地震剖面中的地震相的方法。
本发明涉及用于识别一个地质区域的两个地层之间或一个地层范围内的地震相的方法,包括下列步骤:
·确定要被识别的给定数量的地震相,
·取和所述区域有关的一组地震道部分(seismic trace portion),
·确定对所有道部分(trace portion)通用的相识别参数,并对所述组的每个道部分确定所述参数的值,
·从所述组选择道部分,
·选择含有和要被识别的相数一样多的细胞的一维神经网络,每个细胞被分配一个识别参数值,
·通过所选的道部分实现神经网络的学习,使得当学习过程完成时,每个细胞相应于要被识别的一个相,并被逐次地排序,
·向分类和排序的神经网络提供要被处理的所述组的每个道部分,以及
·对提供给网络的每个道部分分配最接近的细胞号数。
本发明的一个优点在于,例如,现在能够识别相应于地层夹断或可能被解释为故障的地貌的地震相的变化。
按照另一个特点,本发明的神经网络是无监督型的,并由一维Kohonen拓扑图构成。
按照再一个特点,道部分包括相同的取样数量,并且识别参数按包括在在一个地震剖面上确定的两个地层之间或所述地层范围内的取样序列确定。
按照再一个特点,使用道部分确定对所有道部分通用的总体识别参数。
按照再一个特点,每个相被分配一个颜色代码,在给定的颜色范围内,利用所述范围的任何两个连续的颜色之间的深浅缓慢变化,不同的颜色被逐次地排序。
按照再一个特点,识别的地震相在图上用其相应的颜色表示。
本发明的另一个优点在于,按照关于被调查的地质区域的一般知识和用其它方法获得的知识,可以使用和地震道有关的特征,这些特征或者是从道元素(trace element)的顶部和底部之间的取样序列得到的,例如在所述区域的两个地层之间或一个地层范围内的情况下,或者从表示有关的地质区域的取样的分布的总体统计的参数中得到,所述这些总体参数是,例如,幅值,频率,层速度等。
本发明的上述以及其它优点与特点,结合附图阅读本发明的说明之后将更加清楚,其中:
图1是一个地震剖面,上面确定了关于被标志的地层的一个混浊通道,
图2示意地表示按照本发明获得的最后的拓扑图,
图3示意地表示和相应于一组被处理的地震道部分的地质区域有关的地震相图。
本发明的方法使用一种无监督的一维神经网络,它包括和在给定的地质区域内两个地层之间或一个地层范围内的要被识别的地震相的数量一样多的细胞,所述地层通过取一地震剖面确定,所述地震剖面含有大量的地震道,其数量范围至少为几百个道,例如图1中的地层H。在所述道上,限定一组地震道部分,它们以所述的两个地层或围绕所述的地层为界限,这些地震道部分按下述方式用于制备拓扑图。
在另一步中,确定地震相识别参数,所述参数对全部道部分是通用的。在图2的情况下,该参数由信号的形状确定。在所示的例子中,有编号为0至14的15个信号形状,每个和一个地震相相对应。显然,两个信号形状可能彼此相同:这说明相应的地震相在性质上和/或连续性上是相似的。确定识别参数的值,在信号形状的例子中,所述的值由相关的道部分上的取样序列构成。每个道部分被以相同方式取样,即,它包括相同数量的取样,但在同一序列或不同序列中一个采样和另一个采样的大小可以不同。
从一组要被处理的道部分中选择一些道部分,例如每4个道部分中选择一个,或通过随机或伪随机选择方法进行选择。
在下一步,通过选择的道部分实现神经网络的学习,使得当学习过程完成时,每个组相应于要被识别的一个相。这在图2中示出了,其中编号为0到14的15个细胞相应于给定的要被识别的15个相,每个相由和要被识别的15个相相应的15个类中的一个类所示的信号的形关确定。为实现在其中进行分类(classing)并对分类进行排序(ordering)的双重目的,进行下述的学习过程。
设E是选择的要被分类的道的集合,M拓扑图中的细胞的集合。
在第一学习阶段,以随机方式对拓扑图的细胞的加权值(weight)进行初始化。
在第二学习阶段,对拓扑图进行检索,以便找出对于集合E的每个道部分Ei为最接近的细胞Mi,并然后更新属于Mi邻近的Mj细胞的加权值。
这一阶段用下式表示:
Mj=Mj(t-1)+f[ε(t),d,σ(t)]*[Ei(t)-Mj(t-1)]其中:
[ε(t),d,σ(t)]=ε(t)*exp [(-d2/σ2(t)]d是细胞Mi和Mj之间的距离,S(t)是邻近参数,e(t)是增益系数。
按照本发明的另一个特点,e(t)小于1,在第一次迭代时最好等于0.7,在道部分的表示或迭代的每个循环之后,e(t)和s(t)减少。当达到所要求的收敛时,则认为迭代完成,即这时所选的道部分的新的表示不再修正或只稍微修正细胞的排序。
当学习过程完成时,要被处理的所有道部分都出现在拓扑图上,以便对它们进行分类,并相对于在所述拓扑图中规定的类对它的排序。
提供给拓扑图的每个道部分被分配给和其相应的细胞号数,即所述号数的细胞的信号的形状最接近于被提供的所述道的信号的形状。
在本发明的最佳实施例中,在对所有道部分进行所述表示之前,拓扑图的每个类或每个细胞被分配一个给定的颜色代替所述的号数。在图2中的拓扑图上的编号从0到14的15个细胞可以相应于15种不同颜色,其范围例如逐渐地从棕(0类)到紫(14类),任何给定颜色的不同的色调表示相应的类是互相接近的。图2的右边还表示要被分类的道部分C。如果它被提供给拓扑图,则它在细胞7中被分类,或者如果需要,在细胞6中分类,它基本上相应于和由细胞7确定的相相似的相。
图3示意地表示一个地理区域或调查的层的地震相的图,每个地震相相应于最后的拓扑图的类0到14中的一个。可以看到,不同的类呈鳞状并/或被包括在其它的类中。编号100到114分别相应于图2中的拓扑图的类0到类14。
Claims (7)
1.一种用于给定地质区域的两个地层之间或一个地层范围内的地震相的自动识别方法,其特征在于包括下列步骤:
确定要被识别的给定数量的地震相,
取一组和所述区域有关的地震道部分,
确定对所有的道部分公用的相识别参数,并对所述组的每个道部分确定所述参数的值,
从所述组中选择道部分,
选择含有和要被识别的相数一样多的细胞的一维神经网络,每个细胞被分配给识别参数的一个值,
通过选择的道部分实现神经网络的学习,使得当学习过程完成时,每个细胞相应于要被识别的一个相,并使得所述的相被逐次排序,
把要被处理的所述组的每个道部分提供给分类的和排序的神经网络,以及
对提供给网络的每个道部分分配一个最接近的细胞号数。
2.如权利要求1所述的方法,其特征在于,所述神经网络是无监督网络。
3.如权利要求2所述的方法,其特征在于,所述无监督神经网络是一维的Kohonen拓扑图。
4.如权利权利1所述的方法,其特征在于,所述道部分包括相同的取样数,并且所述识别参数通过在两个地层之间的或在所述地层范围内包括的一系列取样确定。
5.如权利要求1所述的方法,其特征在于,确定对所有的道部分公用的总体识别参数。
6.如权利要求1所述的方法,其特征在于,每个细胞相应于一个被分配给一种颜色编码的类,在给定的颜色范围内,利用在所述范围的任何两个连续的颜色之间的深浅的缓慢变化,对不同颜色进行逐次地排序。
7.如权利要求1和6中任何一个所述的方法,其特征在于对识别的地震相在图上用它们相应的颜色表示。
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FR9510962A FR2738920B1 (fr) | 1995-09-19 | 1995-09-19 | Methode de reconnaissance automatique de facies sismiques |
FR95/10962 | 1995-09-19 |
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CN1165562A true CN1165562A (zh) | 1997-11-19 |
CN1110710C CN1110710C (zh) | 2003-06-04 |
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US (1) | US5940777A (zh) |
EP (1) | EP0808467B1 (zh) |
CN (1) | CN1110710C (zh) |
AU (1) | AU6991796A (zh) |
CA (1) | CA2205457C (zh) |
FR (1) | FR2738920B1 (zh) |
NO (1) | NO316984B1 (zh) |
OA (1) | OA10484A (zh) |
RU (1) | RU2155971C2 (zh) |
WO (1) | WO1997011392A1 (zh) |
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- 1996-09-11 CN CN96191086A patent/CN1110710C/zh not_active Expired - Lifetime
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- 1996-09-11 AU AU69917/96A patent/AU6991796A/en not_active Abandoned
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
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CN101663667B (zh) * | 2007-02-08 | 2012-07-18 | 雪佛龙美国公司 | 利用综合地层柱状图生成储层模型的方法 |
CN106707340A (zh) * | 2016-12-13 | 2017-05-24 | 中国石油天然气股份有限公司大港油田分公司 | 一种火山岩岩相的预测方法 |
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FR2738920B1 (fr) | 1997-11-14 |
AU6991796A (en) | 1997-04-09 |
CN1110710C (zh) | 2003-06-04 |
ZA967823B (en) | 1997-04-07 |
EP0808467A1 (fr) | 1997-11-26 |
CA2205457A1 (en) | 1997-03-27 |
NO972234D0 (no) | 1997-05-15 |
NO972234L (no) | 1997-05-15 |
OA10484A (en) | 2002-04-12 |
FR2738920A1 (fr) | 1997-03-21 |
EP0808467B1 (fr) | 2000-12-20 |
RU2155971C2 (ru) | 2000-09-10 |
US5940777A (en) | 1999-08-17 |
NO316984B1 (no) | 2004-07-19 |
WO1997011392A1 (fr) | 1997-03-27 |
CA2205457C (en) | 2003-11-18 |
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