地球物理学进展 ›› 2015, Vol. 30 ›› Issue (1): 466-470.doi: 10.6038/pg20150169

• 应用地球物理学Ⅱ • 上一篇    

多参数层序地层的边缘最优智能划分算法及其应用

朱常坤1,2,3, 梁杏1,4   

  1. 1. 中国地质大学(武汉)环境学院, 武汉 430074;
    2. 江苏省地质调查研究院, 南京 210018;
    3. 国土资源部地裂缝地质灾害重点实验室, 南京 210018;
    4. 中国地质大学(武汉)环境学院湿地演化与生态恢复湖北省重点实验室, 武汉 430074
  • 收稿日期:2014-08-06 修回日期:2014-11-16 出版日期:2015-02-20 发布日期:2015-02-20
  • 通讯作者: 梁杏, 女, 1958年生, 教授, 博士生导师, 主要从事地下水流系统理论与工程水文地质方向的教学与科研工作. (E-mail:xliang@cug.edu.cn) E-mail:xliang@cug.edu.cn
  • 作者简介:朱常坤, 男, 1987年生, 地下水科学与工程专业硕士研究生, 主要从事三维地质建模及入渗补给科研工作. (E-mail:willing1104@163.com)
  • 基金资助:

    国家重点基础研究发展计划“973”项目(2010CB428802)和国家自然科学基金项目(41272258)联合资助.

An edge detection optimum intelligent division method and its application for multi-parameter log data

ZHU Chang-kun1,2,3, LIANG Xing1,4   

  1. 1. School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan, Hubei 430074, China;
    2. Geological Survey of Jiangsu Province, Nanjing 210018, China;
    3. Key laboratory of Earth Fissures Geological Disaster, Ministry of Land and Resources, Nanjing 210018, China;
    4. Hubei Key Laboratory of Wetland Evolution & Ecological Restoration, School of Environment Studies, China University of Geosciences, Wuhan 430074, China
  • Received:2014-08-06 Revised:2014-11-16 Online:2015-02-20 Published:2015-02-20

摘要:

将最优分割、边缘检测与多种群遗传算法相结合, 设计了一种适用于多参数层序地层自动划分的边缘最优智能划分算法.该算法综合利用测井参数的Fisher比与表征测点奇异性的Lipschitz指数(Lipschitz Exponent, LE)构造优化指标;以钻孔参数测点为基因, 以有序测点组为染色体, 通过参数控制实现不同种群同时进行优化搜索, 采用移民算子沟通各种群协同进化;实现综合利用多参数测井数据求取最优化地层分界线.在河北平原第四纪地层岩性划分中的实际应用, 表明该方法的自动分层结果符合地质实际, 计算速度快, 分层效率高, 对河北平原山前冲积洪积、中部冲积湖积、东部冲积海积等不同沉积类型地层的划分工作都有较好适用性.

Abstract:

Sequence stratigraphy division is the important part of geological work, especially in the field of geologic modeling, inversion of sedimentary environment and mineral resources exploration. Well logs can be used for dividing a well (or a segment of it) into a group of sub-sections. In this paper, an edge detection optimum intelligent division method is designed for multi-parameter sequence stratigraphic division. The algorithm incorporates the Fisher optimal division and edge detection methods into Multi-Population Genetic Algorithm (MPGA). The optimization criterion involves the following steps. To begin with, Fisher Ratio and Lipschitz Exponent (LE), which respectively characterize the cycle and singularity of the measuring points, of the Logging parameters are utilized for optimization index constructing. Fisher ratio can be calculated by the sum of the deviation within and between layers, and LE can be calculated by Wavelet Transform Modulus Maxima (WTMM). Then logging measurement points are treated as genes and the group of ordered measurement points are regarded as chromosomes. In order to achieve different populations optimizing searching simultaneously, the parameters of crossover probability and mutation probability are controlled in a reasonable range. Meanwhile, immigrant operators are utilized for coevolving between various populations, which finally realize automatic dividing of sequence stratigraphy basing on multi-parameter logging data. This method mentioned before is applied on division of the Quaternary lithology in the Hebei Plain. It is shown that, the stratigraphic division results accord with the geologically fact. Moreover, the method proves faster and more efficient than the ordinary Fisher optimal division method in computation. It can be conclude that the edge detection optimum intelligent division method is highly applicable in Hebei Plain stratigraphic division of different sediment types, including piedmont area of diluvial alluvial plains, central area of alluvial lacustrine plain and eastern area of alluvial marine plain.

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