Automatic Interpretation of Potential Field Data Based on Euler Deconvolution with Linear Background
Automatic Interpretation of Potential Field Data Based on Euler Deconvolution with Linear Background
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DOI：
10.3390/app13095323
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Qiang Liu;Qing Shu;Wei Gao;Yao Luo;Zelin Li;Junjun Yang;Wenqiang Xu
中科院分区：
文献类型：
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作者：
Qiang Liu;Qing Shu;Wei Gao;Yao Luo;Zelin Li;Junjun Yang;Wenqiang Xu
欧拉反褶积是一种常用的分析势场数据的技术，因为它需要很少的先验信息。然而，欧拉解的可靠性会受到邻近场源或背景场干扰的影响。在本文中，我们提出了一种有效的欧拉反卷积算法，该算法考虑了线性背景场。我们的算法，称为改进有限差分欧拉反卷积，建立在有限差分方法的基础上，不易受附近干扰源的干扰。我们使用该算法实现了源坐标、结构指数和线性背景趋势系数的联合估计。与基于差分相似变换的Euler反卷积方法（也考虑了线性背景场）相比，我们的方法更容易理解和编程实现，并且速度更快。利用二维和三维合成数据对该方法进行了测试，结果表明，该算法的计算精度优于有限差分算法，并可与基于差分相似变换的欧拉反卷积算法相媲美。此外，在实际数据上进行了测试，证明了该方法的有效性。
Euler deconvolution is a popular technique used for analyzing potential field data because it requires little prior information. However, the reliability of Euler solutions can be impacted by interference from adjacent field sources, or background fields. In this manuscript, we present an effective Euler deconvolution algorithm that accounts for linear background fields. Our algorithm, called improved finite-difference Euler deconvolution, builds upon the finite-difference method and is less susceptible to interference from nearby sources. We use this algorithm to achieve a joint estimation of the coefficients of the source coordinates, the structure index, and the linear background trend. Compared to Euler deconvolution methods based on differential similarity transformations, which also account for linear background fields, our method is easier to understand and implement programmatically and is faster. We tested our method using both 2D and 3D synthetic data, and the results indicate that our algorithm has better computational accuracy than the finite-difference algorithm and is comparable to the Euler deconvolution algorithm based on differential similarity transformations. In addition, our method was shown to be effective when tested on real data.