| 多期断层擦痕的硬划分:一种目标函数算法 |
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| 引用本文： | 单业华,林舸,孙洪斌,李志安.多期断层擦痕的硬划分:一种目标函数算法[J].地学前缘,2003,10(1):81-87. |
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| 作者姓名： | 单业华 林舸 孙洪斌 李志安 |
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| 作者单位： | 1. 青岛海洋大学,海洋地质系,山东,青岛,266003;中国科学院,长沙大地构造研究所,湖南,长沙,410013
2. 中国科学院,长沙大地构造研究所,湖南,长沙,410013 |
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| 基金项目： | 中国科学院资源环境领域知识创新工程重要方向项目(KZCX2 113 )，山东省自然科学基金项目 (Y98E0 80 78) |
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| 摘 要： | 提出一种基于硬划分的目标函数法 ,它能够识别多期断层擦痕数据。这是通过在Fry(1999)的Sigma空间里对数据内在的线性构造的识别来实现的。与其它的应力反演法不同 ,目标函数法在原理上相对完善。为了检验此方法 ,在设定的应力张量下由ManteCarlo法生成“人工”断层擦痕数据。计算结果表明 ,目标函数法能够很好地识别多期断层擦痕数据。应力估测的准确度取决于断层擦痕数据的误差和不同构造期应力张量之间的相似性。随着断层擦痕数据误差范围的加大 ,应力估测的准确度趋向降低。当断层擦痕数据存在误差时 ,越是相似的设定应力张量 ,其估计准确度也就越低。不同期构造应力的彼此相似往往与构造应力场微妙或细部的时空变化有关。在从多期断层擦痕数据里识别出相似的不同期构造应力上 ,目标函数法有所突破 ,进而有助于深入认识地质构造的形成。
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| 关 键 词： | 应力反演 断层擦痕数据 多期 算法 聚类分析 硬划分 |
| 文章编号： | 1005-2321(2003)01-0081-07 |
| 修稿时间： | 2002年10月8日 |
SEPARATION OF POLYPHASE FAULT/SLIP DATA:AN OBJECTIVE-FUNCTION ALGORITHM BASED ON HARD DIVISION |
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| SHAN Ye hua ,LIN Ge ,SUN Hong bin ,LI Zhi an.SEPARATION OF POLYPHASE FAULT/SLIP DATA:AN OBJECTIVE-FUNCTION ALGORITHM BASED ON HARD DIVISION[J].Earth Science Frontiers,2003,10(1):81-87. |
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| Authors: | SHAN Ye hua LIN Ge SUN Hong bin LI Zhi an |
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| Institution: | SHAN Ye hua 1,2,LIN Ge 2,SUN Hong bin 2,LI Zhi an 2 |
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| Abstract: | The objective function algorithm (OFA) on the basis of hard division is presented in the paper to separate polyphase fault/slip data. The separation is made by detection of linear structures existing in the data in Fry's (1999) sigma space. Different from other exhaustive search methods, the OFA is direct and robust in theory without any arbitrary assumptions. Polyphase fault/slip data are simulated under prescribed tensors in order to validate the method. The results show its efficiency in stress estimation. The accuracy of stress estimation is controlled by random errors in the orientation of fault striations and by similarity between prescribed stress vectors related to different tectonic phases. The similarity between controlling stress vectors has an obvious effect on the estimation either when random errors are large enough or when some similarity coefficients between the vectors are large enough. The accuracy of stress inversion tends to decrease as the range of errors increases. The OFA makes a very good approach to recognition of similar controlling stress vectors from polyphase fault/slip data, which are often associated with spatial and temporal variation of the tectonic stress field in a region, thus the OFA is critical in understanding the formation of geological structures. |
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| Keywords: | stress inversion fault/slip data polyphase algorithm cluster analysis hard division |
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