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在石油和金属矿勘探中,相对于重力数据,重力梯度张量数据含有高频的信号成分,能更好的描述小的异常特征。然而,全张量重力梯度仪测量值中含有高频随机噪声。从高频信号成分中分离出噪声将是处理重力梯度张量数据的一个挑战。本文在拉普拉斯方程约束条件下推导了重力梯度张量的笛卡尔方程和位场的表达式,然后应用笛卡尔方程通过最优线性反演方法拟合测量的重力梯度张量值。从而去除测量值中的噪声。通过模型实验,证明了这种方法不仅能很好的去除高频的随机噪声,而且能增强被噪声淹没的弱异常信号。与传统的低通滤波方法相比,避免了通过牺牲分辨率来达到去除噪声的缺点。最后将该方法应用到Bell Geospace在Vinton Dome测得的Air-FTG梯度张量数据中,并取得了很好的效果。
In oil and metal exploration, gravitational gradient tensor data contain high-frequency signal components relative to gravity data that better describe small anomalies. However, full tensor gravity gradiometer measurements contain high frequency random noise. Separating noise from high-frequency signal components will be a challenge in dealing with gravity-gradient tensor data. In this paper, the Cartesian equations of gravity gradient tensor and the expression of the potential field are deduced under the constraints of Laplace equation. Then the Cartesian equations are used to fit the measured gravity gradient tensors by the optimal linear inversion method. Thus removing the noise in the measurement. Through the model experiment, it is proved that this method can not only remove high frequency random noise well, but also enhance the weak anomaly signal submerged by noise. Compared with the traditional low-pass filtering method, the disadvantage of removing noise by sacrificing the resolution is avoided. Finally, this method is applied to Bell-Geospace’s Air-FTG gradient tensor data measured by Vinton Dome and achieved good results.