by Felipe F. Melo, Shayane P. Gonzalez, Valéria C. F. Barbosa and Vanderlei C. Oliveira Jr

This paper has been published in the journal in the journal Journal of Applied Geophysics. Melo, F.F., Gonzalez, S.P., Barbosa, V.C.F., Oliveira Jr., V.C, 2021. Amplitude of the magnetic anomaly vector in the interpretation of total-field anomaly at low magnetic latitudes. Journal of Applied Geophysics, v. 190, p. 104339. https://doi.org/10.1016/j.jappgeo.2021.104339.

This repository contains a first version of the source code to perform the synthetic tests presented. The generate_input.py generates the synthetic data, the codes High_I_D.py, Mid_I_D.py and Low_I_D.py compute the amplitude of the magnetic anomaly vector andthe codes High_I_D_plot.py, Mid_I_D_plot.py and Low_I_D_plot.py generate the figures of the synthetic test.

The programs are compatible with Python 2.7 programming language.

We propose the use of the amplitude of the magnetic anomaly vector (amplitude data) for qualitative interpretation of large areas at low magnetic latitudes. The amplitude data are weakly dependent on the magnetization direction. Hence, the amplitude data require no prior knowledge of the source magnetization direction. The amplitude data produce maxima over the causative sources, allowing the definition of the horizontal projections of the sources. This characteristic is attractive for interpretation at low magnetic latitudes because at these regions the interpretation of the total-field anomaly is not straightforward. We compute the amplitude data using the equivalent-layer technique to transform the total-field anomaly into the three orthogonal components of the magnetic anomaly vector. We analyze the results of tests in synthetic data simulating a main geomagnetic field at high, mid and low latitudes, with sources ranging from symmetric to elongated forms, including a dipping source. These sources, that give rise to the simulated anomalies, have both induced and strong remanent magnetizations. By comparing the amplitude data with the total gradient, we show that the amplitude data locate the horizontal projections of the sources. Thus, the amplitude data can be used to delineate the boundaries of the sources. We apply both the amplitude data and the total gradient to a real total-field anomaly over a large area of the Amazonian Craton, northern Brazil, located at low magnetic latitudes. The amplitude data show a better performance in delineating geologic bodies that are in agreement with the outcropping intrusions in the geologic map. Moreover, the amplitude data revealed new geologic bodies that were not present in the geologic map. The clear alignment of these new bodies with the outcropping intrusions suggested the continuity of these intrusions in depth. This result is a step forward in understanding this area for which the geology is poorly known. Hence, the amplitude data can provide an apparent-geologic map especially in areas at low latitudes with remanently magnetized bodies.

• generate_input.py: Python code to compute the forward model.

• X_I_D.py: General Python module containing the functions to compute the equivalent layer, estimate the parameters, the three orthogonal vectors and the amplitude. The "X" stands for the latitude of the test, eg., "High", "Mid" or "Low".

• X_I_D_plot.py: Plot the results.

Test data:

• input_mag.dat: Synthetic total-field anomaly data generated using the Python packaged "Fatiando a Terra": http://fatiando.org/. This data is an example used in the current publication.

You can download a copy of all the files in this repository by cloning the git repository:

git clone https://github.com/ffigura/amplitude-vector-interpretation.git

or download a zip archive.

The Python programs for compute the syntehtic data - "generate_input.py" - and the amplitude of the magnetic anomaly vector - "X_I_D.py" - require the Python library "numpy" and the last development version of the free Python packaged "Fatiando a Terra" ( https://www.fatiando.org/dev/install.html#installing-the-latest-development-version ). The script "X_plot.py" requires the same Python packages in addtion to "matplotlib".

The easier way to get Python and all libraries installed is through the Anaconda Python distribution (https://www.anaconda.com/distribution/). After installed Anaconda, install the libraries by running the following command in your terminal:

conda install numpy matplotlib

The programs are compatible with Python 2.7.

The results and figures for the synthetic test are reproducible from the folders /High_I_D, /Mid_I_D and /Low_I_D. Running the code generate_input.py will generate the synthetic data, the code X_I_D.py will allow the reprodution of the results and the code X_I_D_plot.py will generate the figures. For more information read the file README.MD or README.txt in the folder /code.

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.md for the full license text.

The manuscript text is not open source. The authors reserve the rights to the article content, which is currently submitted for publication in the Journal of Applied Geophysics.