Skip to content


Repository files navigation

Euler deconvolution of potential field data

Leonardo Uieda, Vanderlei C. Oliveira Jr., and Valéria C. F. Barbosa

This tutorial was published on the April 2014 issue of The Leading Edge.

Results were generated using the open-source Python package Fatiando a Terra version 0.2.

The IPython notebooks and data files are also on figshare at

Reading the tutorial

You can view the final edited version at

The tutorial is also openly available at the SEG wiki. If you're a SEG member, you can help improve the article by adding more information or correcting any mistakes that you find. If you're not, submit an issue to this repository to start a discussion. We can add the relevant information to the wiki after. Hurray for openness!

If you don't want to leave this repository, you can read a pre-print of the tutorial (manuscript.pdf) or have a quick look at the Markdown source See below for instructions on how to convert the Markdown source to PDF.

Synthetic data and model

Examples in the tutorial use synthetic data generated with the IPython notebook create_synthetic_data.ipynb. The data can be found in the data directory of this repository. 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):

{"shape": [100, 100],
 "dec": 30,
 "inc": -15,
 "bounds": [0, 30000, 0, 30000, 0, 5000],
 "area": [5000, 25000, 5000, 25000]}

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. To load this module in a Python session, run:

import cPickle as pickle
with open('model.pickle') as f:
    model = pickle.load(f)

Reproducing the results

The notebook euler-deconvolution-examples.ipynb runs the Euler deconvolution on the synthetic data and generates the figures for the manuscript. Also presents a more detailed explanation of the method and more tests than went into the finished manuscript.

Compiling the manuscript

The text ( is written using Markdown and compiled to PDF and Microsoft Word (doc) formats using pandoc. To produce the PDF, run:

make pdf

and to produce doc:

make doc


Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Tutorial about Euler deconvolution for The Leading Edge by @leouieda, @birocoles and @valcris






No releases published


No packages published