Karimi Kurosh, Kletetschka Gunther
Institute of Hydrogeology, Engineering Geology and Applied Geophysics, Faculty of Science, Charles University, Prague, 12843, Czech Republic.
Geophysical Institute, University of Alaska-Fairbanks, 903 N Koyukuk Drive, Fairbanks, AK, 99709, USA.
Sci Rep. 2024 Jan 30;14(1):2440. doi: 10.1038/s41598-024-52843-5.
Geophysics aims to locate bodies with varying density. We discovered an innovative approach for estimation of the location, in particular depth of a causative body, based on its relative horizontal dimensions, using a dimensionality indicator (I). The method divides the causative bodies into two types based on their horizontal spread: line of poles and point pole (LOP-PP) category, and line of poles and plane of poles (LOP-POP) category; such division allows for two distinct solutions. The method's depth estimate relates to the relative variations of the causative body's horizontal extent and leads to the solutions of the Euler Deconvolution method in specific cases. For causative bodies with limited and small depth extent, the estimated depth (z) corresponds to the center of mass, while for those with a large depth extent, z relates to the center of top surface. Both the depth extent and the dimensionality of the causative body influence the depth estimates. As the depth extent increases, the influence of I on the estimated depth is more pronounced. Furthermore, the behavior of z exhibits lower errors for larger values of I in LOP-POP solutions compared with LOP-PP solutions. We tested several specific model scenarios, including isolated and interfering sources with and without artificial noise. We also tested our approach on real lunar data containing two substantial linear structures and their surrounding impact basins and compared our results with the Euler deconvolution method. The lunar results align well with geology, supporting the effectiveness of this approach. The only assumption in this method is that we should choose between whether the gravity signal originates from a body within the LOP-PP category or the LOP-POP category. The depth estimation requires just one data point. Moreover, the method excels in accurately estimating the depth of anomalous causative bodies across a broad spectrum of dimensionality, from 2 to 3D. Furthermore, this approach is mathematically straightforward and reliable. As a result, it provides an efficient means of depth estimation for anomalous bodies, delivering insights into subsurface structures applicable in both planetary and engineering domains.
地球物理学旨在定位具有不同密度的物体。我们发现了一种创新方法，用于根据致因体的相对水平尺寸，使用维度指标（I）来估计其位置，特别是致因体的深度。该方法根据致因体的水平分布将其分为两类：极线和点极（LOP - PP）类别，以及极线和极面（LOP - POP）类别；这种划分允许有两种不同的解决方案。该方法的深度估计与致因体水平范围的相对变化有关，并在特定情况下得出欧拉反褶积方法的解。对于深度范围有限且较小的致因体，估计深度（z）对应于质心，而对于深度范围较大的致因体，z与顶面中心有关。致因体的深度范围和维度都会影响深度估计。随着深度范围的增加，I对估计深度的影响更为明显。此外，与LOP - PP解相比，在LOP - POP解中，对于较大的I值，z的行为表现出较低的误差。我们测试了几种特定的模型场景，包括有和没有人工噪声的孤立源和干扰源。我们还在包含两个大型线性结构及其周围撞击盆地的真实月球数据上测试了我们的方法，并将我们的结果与欧拉反褶积方法进行了比较。月球结果与地质学吻合良好，支持了这种方法的有效性。该方法的唯一假设是我们应在重力信号源自LOP - PP类别内的物体还是LOP - POP类别内的物体之间做出选择。深度估计仅需要一个数据点。此外，该方法擅长在从2D到3D的广泛维度范围内准确估计异常致因体的深度。此外，这种方法在数学上简单直接且可靠。因此，它为异常体提供了一种有效的深度估计手段，为适用于行星和工程领域的地下结构提供了见解。
