小波结合幂次变换方法在边界识别中的应用

在位场数据边界识别传统方法理论的基础上，前人研究了各种场源边界增强技术来提高信噪比和定位精度，但仍然存在受噪声影响大、场源边界不够收敛等问题。本文在传统边界识别方法的基础上进行改进，利用小波变换与传统方法相结合来增强对噪声的压制能力，并且提出了幂次变换法对识别出的边界进行有效收敛。将改进方法与传统方法应用于地质体的边界识别；同时选取3种传统数值计算方法，结合模型数据、四川盆地重力异常数据及朱日和地区磁异常数据进行对比分析。结果表明：小波结合幂次变换法能够有效识别出研究区域内地质体的边界，能很好地起到压制噪声的作用；并且识别出的边界收敛，提高了边界识别的精度，在边界识别中取得了良好的效果。

Application of Wavelet Transform Combined with Power Transform Method in Edge Detection

On the basis of the traditional methods for edge detection of potential-field data,various field source edge enhancement techniques have been studied to improve signal-to-noise ratio (SNR)and localization accuracy.But problems still exist,such as noise interference and divergence of source edges. The authors made an improvement based on the traditional methods of edge detection using wavelet combined with traditional methods to enhance the ability of noise suppressing,and put forward power transform to make edges convergent effectively.In this paper,the principle was introduced and the method was applied to edge detection.Three commonly used numerical methods for edge detection were selected and compared with the modified methods in edge detection effect with the model data,gravity anomalies of Sichuan basin,and the magnetic anomalies in Zhurihe area.The results showed that the method of wavelet combined with power transform could effectively detect the edges of geological bodies in the study area.It plays a good role in noise suppressing,and can be used to identify the edges with convergence.The proposed method improves the accuracy of edge detection and obtains satisfactory results.