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梯度反褶积法及其在矿产勘探中的应用
引用本文:马国庆,杜晓娟,李丽丽,李文成,王凤刚. 梯度反褶积法及其在矿产勘探中的应用[J]. 吉林大学学报(地球科学版), 2013, 43(1): 259-266
作者姓名:马国庆  杜晓娟  李丽丽  李文成  王凤刚
作者单位:1.吉林大学地球探测科学与技术学院,长春130026;2.首钢地质勘查院地质研究所,北京100144
基金项目:中国地质调查局项目(GZH003-07-03);国家科技支撑计划项目(2009BAB43B00)
摘    要:梯度探测技术以其信息量大、精度高、干扰小等优点在地球物理领域中得到广泛应用,但现今针对梯度数据的反演方法还比较少。笔者提出一种新的梯度数据解释方法--梯度反褶积法,该方法以梯度数据为基础获得场源体的位置信息,在计算公式中消除了构造指数,避免了因构造指数选取不当引起的误差。通过理论模型试验证明,梯度反褶积法能有效地完成目标体的反演工作,反演结果与理论值之间的误差小于5%,且相对于常规欧拉反褶积法更加稳定、准确。将梯度反褶积法应用于实测航磁梯度异常的解释,获得了地下铁矿的分布状态。

关 键 词:梯度探测  反褶积  构造指数  勘探  
收稿时间:2012-05-24

Gradient Deconvolution Method and Its Application in Mineral Exploration
Ma Guoqing,Du Xiaojuan,Li Lili,Li Wencheng,Wang Fenggang. Gradient Deconvolution Method and Its Application in Mineral Exploration[J]. Journal of Jilin Unviersity:Earth Science Edition, 2013, 43(1): 259-266
Authors:Ma Guoqing  Du Xiaojuan  Li Lili  Li Wencheng  Wang Fenggang
Affiliation:1.College of GeoExploration Science and Technology, Jilin University, Changchun130026, China;
2.Institute of Geological Research,Shougang Geological Exploration,Beijing100144,China
Abstract:Gradient detection technology is widely used in the geophysical measurement for its great capacity, high precision and low noise level et al, but there are few methods for interpreting gradient data now. The authors presented a new gradient interpretation method here, called gradient deconvolution method. The new method can obtain the locations of the causative sources with gradient data, and eliminate structural index from the calculation equation and thus avoids the errors produced by improper structural index selection. We tested the gradient deconvolution method with synthetic potential data.The inversion results show that the misfit between the theory and inversion result is less than 5%, and the inversion results are more stable and correct compared to the conventional Euler deconvolution method. At last, we applied the gradient Euler deconvolution method to measured aeromagnetic gradient data, and obtained the distribution of underground iron mine.
Keywords:gradient detection  deconvolution  structural index  exploration  
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