重磁异常的小波模极大值分解及其在北衙矿区中的应用

位场数据异常存在的线性特征往往对应着地下断裂构造、不同岩性地质体的边界接触带或者其他具有一定密度或磁性差异的构造特征。对这些线性特征进行增强、提取并进行半定量地解释是重磁资料处理的主要内容。然而,位场数据中若混入噪声干扰,再利用总水平导数、总水平梯度倾斜角等方法进行高次求导运算会把噪声放大,导致提取的线性构造位置发生偏离甚至出现错误。为了获取较为准确的线性构造及边界位置,选取对噪声干扰不敏感的小波模极大值方法,将该方法用于模型试验和实际矿区数据处理中,都能较好地定位出异常体模型边界和矿体的投影边界,表明小波模极大值是一种有效的重磁异常线性特征增强与提取方法。

Wavelet modular maximum of gravity and magnetic anomalies and its application for Beiyagold mine

The linear characteristics of abnormal field data often correspond to underground fault structures,boundary contact zones of different lithologic geological bodies,or other structural features with certain density or magnetic difference.The enhancement,extraction and semi quantitative interpretation of these linear features are the main contents of gravity and magnetic data processing.However,if the field data is mixed with noise interference,then using the general horizontal derivative,the total horizontal gradient,the tilt angle and other methods,the higher derivative operation will magnify the noise,cause the extraction of linear construction positions to deviate from and even make errors.In order to obtain more accurate linear structure and boundary position,we select wavelet modular maximum,it is not sensitive to noise interference.This method is used in model test and actual mining in data processing,This can better locate the projection boundary model of abnormal boundary and orebody,Our results shown that wavelet modular maximum is an effective linear feature enhancement and extraction method for gravity and magnetic anomalies.