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首页> 外文期刊>The Leading Edge >A source-depth separation filter: Using the Euler method on the derivatives of total intensity magnetic anomaly data
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A source-depth separation filter: Using the Euler method on the derivatives of total intensity magnetic anomaly data

机译：源深度分离滤波器：使用Euler方法处理总强度磁异常数据的导数

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摘要

Derivatives of potential-field anomalies (or the anomaly gradients) enhance the field associated with shallow features and de-emphasize the field from deeper sources. The derivative approach of separating anomalies of shallow, intermediate, and deep sources is, however, qualitative. Semiautomatic source location methods, such as the Euler method (also variously referred to in the literature as Euler's theorem on homogeneous functions, Euler's differential equation, EULDPH, and Euler deconvolution), the analytic signal method, and Werner deconvolution, developed since the 1980s use anomaly gradients to characterize sources of anomalies (i.e., the type of sources and their locations). In this article, we investigate the benefits of applying the Euler method on derivatives of anomalies to enhance the location of shallow and deep sources. Used appropriately, the method is suitable for characterizing sources from all potential-field data and/or their derivatives, as long as the data can be regarded mathematically as "continuous." We also explain the reasons why the use of the Euler method on derivatives of anomalies is particularly helpful in the analysis and interpretation of shallow features.
机译：势场异常（或异常梯度）的导数增强了与浅层特征关联的场，并从较深的源中不再强调该场。但是，区分浅，中和深源异常的派生方法是定性的。半自动源定位方法，例如自1980年代使用以来开发的Euler方法（在文献中也被称为Euler定理，关于齐次函数的Euler定理，Euler微分方程，EULDPH和Euler反卷积），解析信号方法和Werner反卷积异常梯度以表征异常源（即，源的类型及其位置）。在本文中，我们研究了对异常导数应用Euler方法以增强浅源和深源位置的好处。适当地使用该方法适用于表征所有势场数据和/或其派生词的来源，只要该数据在数学上可以被视为“连续的”即可。我们还解释了为什么对异常导数使用Euler方法特别有助于浅层特征的分析和解释。

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