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 dx.doi.org/10.6084/m9.figshare.923450
You can view the final edited version at http://dx.doi.org/10.1190/tle33040448.1
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
manuscript.md.
See below for instructions on how to convert the Markdown source to PDF.
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)
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.
The text (manuscript.md
)
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
This work is
licensed under a Creative Commons
Attribution 4.0 International License.