The monogenic signal of potential-field data: A Python Implementation
Source code, data and manuscript for the article "The monogenic signal of potential-field data: A Python Implementation". Published in the GEOPHYSICS VOL. 82, NO. 3 (MAY-JUNE 2017), DOI:10.1190/GEO2016-0099.1 by Marlon C. Hidalgo-Gato and Valéria C. F. Barbosa
This repository contains the manuscript, supplementary source code and data for a paper about the open-source program Monogenic.
The Monogenic program is written in the Python 2.7/3.5 programming language. This program calculates the non-scale and the Poisson scale-space monogenic signal attributes based on the definitions introduced by Hidalgo-Gato and Barbosa(2015). In addition, we make available the Python script “synthetic.py” and the data “data.dat” to run our synthetic test example.
We have presented the program Monogenic, a Python 2.7/3.5 package of two functions to calculate the non-scale and the Poisson scale-space monogenic signals of a 2D data. Both monogenic signal functions return the local amplitude, the local phase and the local orientation of a potential-field data or any kind of 2D array. Compared with the Poisson scale-space monogenic signal, the non-scale monogenic signal is easier to use because the former requires band-pass filtering the data whereas non-scale monogenic signal requires only the original data set. However, the Poisson scale-space monogenic signal yields a sharper image of boundaries of the geologic bodies than the non-scale monogenic signal. We demonstrated the use of the monogenic signal by applying it to a synthetic magnetic data. The Python script “synthetic.py” contains the algorithm to run our synthetic example. This example was run using specific Poisson scale-space parameters. However, the users can try different Poisson scale-space parameters for the same synthetic model. Finally, we stress that the application of the non-scale and the Poisson scale-space monogenic signals to enhance other geophysical data (e.g., seismic, GPR, gravity, multiple-component gravity gradiometry, magnetic gradient data) has no methodological obstacles.
The Monogenic program is a research software made by scientists. Your citations help us justify the effort that goes into developing open-source codes.
If you use the Monogenic program in your research, please cite it in your publications as:
Hidalgo-Gato, M.C. and V. C. F. Barbosa (2017), The monogenic signal of potential-field data: A Python Implementation, Geophysics, 82(3), F9-F14, DOI: 10.1190/GEO2016-0099.1.
Please, also cite the main method Hidalgo-Gato and Barbosa (2015):
Hidalgo-Gato, M.C. and V. C. F. Barbosa (2015), Edge detection of potential-field sources using scale-space monogenic signal: Fundamental principles, Geophysics, 80(5), J27–J36, DOI: 10.1190/GEO2015-0025.1
monogenic.py: General Python module containing the two functions to calculate the local amplitude, local phase and local orientation in the Poisson scale-space Monogenic Signal and the local amplitude, local phase and local orientation of the non-scale Monogenic Signal.
synthetic.py: Python script to generate the published results. The script loads the total-field anomaly of a synthetic model from the file data.dat and calculates the local amplitude, local phase and local orientation of the non-scale and in the Poisson scale-space monogenic signals using the functions in monogenic.py
- data.dat: Synthetic magnetic data generated using the Python packaged Fatiando a Terra. This data is an example used in the current publication.
Getting the files
To actually run the code, you will need to have the files on your machine. You can download the files "monogenic.py", "synthetic.py" and "data.dat" on your machine. Also, you can download a zip archive of this repository to get everything. Alternatively, you can download a zip archive of this repository at: software.seg.org.
Installing the software
The Python program Monogenic v1.0 - "monogenic.py" requires the Python library "numpy" and the script "synthetic.py" requires the Python packages "numpy" and "matplotlib".
The easiest way to get Python and all libraries installed is through the Anaconda Python distribution. After installed Anaconda, install the libraries by running the following command in your terminal:
conda install numpy matplotlib
The program Monogenic and the script "synthetic.py" were tested on Windows and Linux platforms using Python 2.7 and 3.5 as well.
Running the files
Once Anaconda and the libraries are installed, download the files "monogenic.py", "synthetic.py" and "data.dat" on your machine. Alternatively, you can download a zip archive of this repository at: software.seg.org. Following, be sure that all files are in the same directory. There are two easy ways to run the synthetic test: "synthetic.py"
Open the command prompt windows (terminal) and run the command:
Open the Spyder program that comes with the Ananconda installation. In the Spyder, open the example script "synthetic.py" and click in "play" inside the Spyder to execute it.
If everything runs ok, a set of figures shown in the publication will show up.
The module "monogenic.py" can be imported to an interactive Python session or called from an external Python module. Their functionalities are documented using standard Python docstrings.
The file "synthetic.py" is a Python-based command line script. By using this script, the figures presented in the publication can be reproduced.
All source code is made available under a BSD 3-clause license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors. See LICENSE.txt for the full license text.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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