Synthetic total-field magnetic anomaly data and code to perform Euler deconvolution on it

Synthetic data, source code, and supplementary text for the article "Euler deconvolution of potential field data" by Leonardo Uieda, Vanderlei C. Oliveira Jr., and Valéria C. F. Barbosa. This is part of a tutorial submitted to The Leading Edge (http://library.seg.org/journal/tle). Results were generated using the open-source Python package Fatiando a Terra version 0.2 (http://www.fatiando.org). This material along with the manuscript can also be found at https://github.com/pinga-lab/paper-tle-euler-tutorial <strong>Synthetic data and model</strong> Examples in the tutorial use synthetic data generated with the IPython notebook create_synthetic_data.ipynb. File synthetic_data.txt has 4 columns: x (north), y (east), z (down) and the total field magnetic anomaly. x, y, and z are in meters. The total field anomaly is in nanoTesla (nT). File metadata.json contains extra information about the data, such as inclination and declination of the inducing field (in degrees), shape of the data grid (number of points in y and x, respectively), the area containing the data (W, E, S, N, in meters), and the model boundaries (W, E, S, N, top, bottom, in meters). File model.pickle is a serialized version of the model used to generate the data. It contains a list of instances of the PolygonalPrism class of Fatiando a Terra. The serialization was done using the cPickle Python module. <strong>Reproducing the results in the tutorial</strong> The notebook euler-deconvolution-examples.ipynb runs the Euler deconvolution on the synthetic data and generates the figures for the manuscript. It also presents a more detailed explanation of the method and more tests than went into the finished manuscript.

本数据集包含莱昂纳多·乌耶达、万德雷·C·奥利韦拉 Jr.、瓦莱丽亚·C·F·巴博萨合著的论文《位场数据的欧拉反褶积（Euler deconvolution）》的合成数据、源代码与补充文本。本内容为提交至《The Leading Edge》（http://library.seg.org/journal/tle）的教程的组成部分。研究结果通过开源Python软件包Fatiando a Terra 0.2版（http://www.fatiando.org）生成。相关材料与论文原稿亦可通过https://github.com/pinga-lab/paper-tle-euler-tutorial获取。

**合成数据与模型**
本教程示例采用由IPython笔记本`create_synthetic_data.ipynb`生成的合成数据。`synthetic_data.txt`文件包含四列数据：x（北向坐标）、y（东向坐标）、z（向下坐标）与总磁场异常值。x、y、z的单位为米，总磁场异常值的单位为纳特斯拉（nanoTesla，nT）。`metadata.json`文件包含该数据集的额外元数据信息，例如激发磁场的倾角与偏角（单位为度）、数据网格形状（分别为y与x方向的点数）、数据覆盖区域（西、东、南、北，单位为米）以及模型边界（西、东、南、北、顶部、底部，单位为米）。`model.pickle`文件为生成该数据所用模型的序列化版本，其中包含Fatiando a Terra的PolygonalPrism（多边形棱柱）类的多个实例，序列化过程通过Python的cPickle模块完成。

**复现教程结果**
IPython笔记本`euler-deconvolution-examples.ipynb`可对合成数据执行欧拉反褶积（Euler deconvolution），并生成论文所需的图表。该笔记本还对该方法提供了比最终论文更为详尽的解释与更多测试内容。