联立不同高度数据的有限差分欧拉反褶积方法

    Finite-difference Euler deconvolution method based on data from different heights

    • 摘要: 在位场勘探中,欧拉反褶积方法因其对先验信息依赖较少而得到广泛应用。然而,受外部干扰的影响,其结果往往存在较大的发散性。为此,本文对欧拉反褶积方法开展了进一步研究,并提出了一种简单有效的确定场源深度和构造指数的方法。该方法充分利用欧拉方程中深度值与构造指数之间的线性关系,通过在场源潜在位置联合多个不同延拓高度的数据,运用有限差分方法构建方程组并求解系数,进而确定场源深度和构造指数。在反演过程中,本文仅在场源潜在水平位置进行反演,有效地减少了反演结果的数量;通过采取联立不同高度数据进行反演的策略,减小了噪声等因素对结果的影响。最后,模型实验和实际数据验证了本文方法的有效性,为后续相关工作提供支持与参考。

       

      Abstract: In potential field exploration, the Euler deconvolution method has been extensively utilized due to its minimal requirement for prior information. However, it yields significant dispersed results due to external interference. Based on a further investigation of the Euler deconvolution method, this study proposed a simple, effective finite-difference Euler deconvolution method for determining the depth and structural index of field sources. The proposed method leveraged the linear relationship between depth and structural index in the Euler equation. Combining data from multiple continuation heights at potential field source locations, a system of equations was constructed using the finite difference method to solve for coefficients, thereby determining the depth and structural index of field sources. The inversion was limited to potential horizontal locations of field sources, effectively reducing the number of inversion results. Additionally, the strategy of combining data from different heights for inversion mitigated the impacts of noise and other disturbances. Finally, the proposed method proved effective through model experiments and actual data.

       

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