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Normalized full gradient of full tensor gravity gradient based on adaptive iterative Tikhonov regularization downward continuation

机译:基于自适应迭代Tikhonov正则化向下延续的全张量重力梯度的归一化全梯度

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摘要

Normalized full gradient (NFG) method depends on the downward continuation of NFG values of gravity data In this paper, I deduce an improved NFG method of full tensor gravity gradient (FfG) data by using x-, y- and z-directional analytic signals of FTG data. During the calculation, I introduce the adaptive iterative Tikhonov regularization downward continuation method in the calculation process to improve the stability of the NFG method. The new approach is tested on various model data with and without noise, and satisfactory results are obtained. It demonstrates that the new NFG method of FTG can improve the lateral resolution and describe the gravity bodies in more detail. In addition, the method is applied to a real field FfG data acquired over the Vinton Salt Dome, Louisiana, USA. All results demonstrate that the-new method can accurately detect the depth of the geologic sources while providing enhanced information of the sources simultaneously. (C) 2015 Elsevier B.V. All rights reserved.
机译:归一化全梯度(NFG)方法取决于重力数据的NFG值的向下连续性本文中,我通过使用x,y和z方向的分析信号得出了一种改进的全张量重力梯度(FfG)数据的NFG方法FTG数据。在计算过程中,我在计算过程中引入了自适应迭代Tikhonov正则化向下连续方法,以提高NFG方法的稳定性。该新方法在有噪声和无噪声的各种模型数据上进行了测试,并获得了令人满意的结果。结果表明,FTG的新的NFG方法可以提高横向分辨率并更详细地描述重力体。此外,该方法还应用于在美国路易斯安那州的Vinton Salt Dome上采集的真实FfG数据。所有结果表明,该新方法可以准确检测地质源的深度,同时提供增强的地质源信息。 (C)2015 Elsevier B.V.保留所有权利。

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