| 利用加强水平方向总水平导数对位场全张量数据进行边界识别 |
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| 引用本文： | 袁园,黄大年,余青露.利用加强水平方向总水平导数对位场全张量数据进行边界识别[J].地球物理学报,2015,58(7):2556-2565. |
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| 作者姓名： | 袁园 黄大年 余青露 |
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| 作者单位： | 1. 吉林大学地球探测科学与技术学院, 长春 130026;2. 国家海洋局第二海洋研究所, 杭州 310012;3. 国家海洋局海底科学重点实验室, 杭州 310012;4. 中国石化石油物探技术研究院, 南京 211103 |
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| 基金项目： | 国家高技术研究发展计划(863计划)课题(2014AA06A613),国家重点基础研究发展计划项目(973项目)西南印度洋洋中脊热液成矿过程与硫化物矿区预测的第五课题"硫化物矿区特征和找矿标志"(2012CB417305),大洋十二五项目西南印度洋脊合同区多金属硫化物资源评价联合资助. |
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| 摘 要： | 位场全张量梯度数据以其信息量大、含有更高频的信号成分,能更好地描述小的异常特征等优点在地球物理领域中得到广泛应用.边界检测是位场解释中不可缺少的任务,需要新的边界探测器来处理位场梯度张量数据.为了充分利用位场梯度张量数据的多信息成分,本文定义了方向总水平导数和加强方向总水平导数,并利用其定义新的边界检测器.为了能同时显示不同振幅大小异常的边界,本文对其进行了归一化处理.通过模型试验,证明了归一化方法能更加清晰准确地显示浅部和深部的地质体边界信息.最后将该边界检测方法用于加拿大圣乔治湾实际测得全张量重力梯度数据和中国朱日和地区的磁异常数据中,并得到了较好的边界检测结果.
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| 关 键 词： | 边界检测 位场梯度张量 加强方向总水平导数 归一化 |
| 收稿时间： | 2015-02-22 |
Using enhanced directional total horizontal derivatives to detect the edges of potential-field full tensor data |
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| YUAN Yuan,HUANG Da-Nian,YU Qing-Lu.Using enhanced directional total horizontal derivatives to detect the edges of potential-field full tensor data[J].Chinese Journal of Geophysics,2015,58(7):2556-2565. |
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| Authors: | YUAN Yuan HUANG Da-Nian YU Qing-Lu |
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| Institution: | 1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;2. Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;3. Key Laboratory of Submarine Geoscience, State Oceanic Administration, Hangzhou 310012, China;4. Sinopec Geophysical Research Institute, Nanjing 211103, China |
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| Abstract: | With the ongoing development of the full potential field gradient techniques, more and more gradient tensor data have been widely used in geophysical exploration for its large amount of information and containing higher frequency signals than potential field data. The full potential field gradient technique can simultaneously measure six gradient components. Each component has its own geophysical meaning. The high frequency gradient tensor data can be used to delineate small scale anomalies.Edge detection is required for interpretation of potential field data, and has been used in exploration technology for discovery of mineral resources. The main geological edges are fault lines and the borders of geological or rock bodies of different density, magnetic nature, etc. On account of the higher-frequency signals, their interpretation enables high-resolution and detailed investigation of geological structures. Development of new methods is required to enable interpretation of these data, especially for edge detection for small geological structures.In order to make use of multiple component information, we define directional total horizontal derivatives and enhanced directional total horizontal derivatives and use them to define new edge detectors. However, the new defined edge detectors can balance the detected edge signals of different amplitude anomalies. In order to balance the edge signal amplitude, we present a normalization method, where we divide the new edge detectors by the vertical potential field gradient data. Besides, we introduce a constant parameter in the denominator of the normalization method, which can effectively avoid bringing some additional false edges when real geological bodies contain positive and negative anomalies simultaneously.These methods have been tested with synthetic data to verify that the new methods can delineate the edges of different amplitude anomalies clearly. The results show that new defined directional total horizontal derivatives and enhanced directional total horizontal derivatives have higher resolution than directional analytic signal. The normalized edge detectors can display the edges of large and small amplitude anomalies simultaneously, and avoid introducing additional false edges. To further test the stability, we demonstrate the new edge detectors with the model data corrupted with 5% Gaussian noise. Finally, we apply these methods to real full gravity gradient tensor data in St. Georges Bay, Canada and magnetic anomalies in Zhurihe area, China, and get good edge results. |
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| Keywords: | Edge detection Potential field gradient tensor Enhanced directional total horizontal derivative Normalization |
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