Python tool for computing the Curie depth from magnetic data
Magnetic data is one of the most common geophysics datasets available on the surface of the Earth. Curie depth is the depth at which rocks lose their magnetism. The most prevalent magnetic mineral is magnetite, which has a Curie point of 580°C, thus the Curie depth is often interpreted as the 580°C isotherm.
Current methods to derive Curie depth first compute the (fast) Fourier transform over a square window of a magnetic anomaly that has been reduced to the pole. The depth and thickness of magnetic sources is estimated from the slope of the radial power spectrum.
pycurious implements the Tanaka et al. (1999) and Bouligand et al. (2009) methods for computing the thickness of a buried magnetic source.
pycurious ingests maps of the magnetic anomaly and distributes the computation of Curie depth across multiple CPUs. Common computational workflows and geospatial manipulation of magnetic data are covered in the Jupyter notebooks bundled with this package.
Launch the demonstration at mybinder.org
Mather, B. and Delhaye, R. (2019). PyCurious: A Python module for computing the Curie depth from the magnetic anomaly. Journal of Open Source Software, 4(39), 1544, https://doi.org/10.21105/joss.01544
Navigation / Notebooks
There are two matching sets of Jupyter notebooks - one set for the Tanaka and one for Bouligand implementations. The Bouligand set of noteboks are a natural choice for Bayesian inference applications.
Note, these examples can be installed from the package itself by running:
import pycurious pycurious.install_documentation(path="Notebooks")
You will need Python 2.7 or 3.5+. Also, the following packages are required:
Optional dependencies for mapping module and running the Notebooks:
Installing using pip
You can install
pycurious using the
pip package manager with either version of Python:
python2 -m pip install pycurious python3 -m pip install pycurious
All the dependencies will be automatically installed by
Installing with conda
You can install
pycurious using the conda package manager.
Its required dependencies can be easily installed with:
conda install numpy scipy cython
And the full set of dependencies with:
conda install numpy scipy cython matplotlib pyproj cartopy
pycurious can be installed with
pip install pycurious
Alternatively, you can create a custom
pycurious can be installed along with its dependencies.
Clone the repository:
git clone https://github.com/brmather/pycurious cd pycurious
Create the environment from the
conda env create -f environment.yml
Activate the newly created environment:
conda activate pycurious
pip install pycurious
Issue with gcc
pycurious installation fails due to an issue with
Anaconda, you just
need to install
gxx_linux-64 with conda:
conda install gxx_linux-64
And then install
Installing using Docker
A more straightforward installation for
pycurious and all of its dependencies may be deployed with Docker.
To install the docker image and start the Jupyter notebook examples:
docker run --name pycurious -p 127.0.0.1:8888:8888 brmather/pycurious:latest
PyCurious consists of 2 classes:
CurieGrid: base class that computes radial power spectrum, centroids for processing, decomposition of subgrids.
CurieOptimise: optimisation module for fitting the synthetic power spectrum (inherits CurieGrid).
Also included is a
mapping module for gridding scattered data points, and converting between coordinate reference systems (CRS).
Below is a simple workflow to calculate the radial power spectrum:
import pycurious # initialise CurieOptimise object with 2D magnetic anomaly grid = pycurious.CurieOptimise(mag_anomaly, xmin, xmax, ymin, ymax) # extract a square window of the magnetic anomaly subgrid = grid.subgrid(window_size, x, y) # compute the radial power spectrum k, Phi, sigma_Phi = grid.radial_spectrum(subgrid)
A series of tests are located in the tests subdirectory.
In order to perform these tests, clone the repository and run
git checkout https://github.com/brmather/pycurious.git cd pycurious pytest -v
The API for all functions and classes in
pycurious can be accessed from https://brmather.github.io/pycurious/.
- Bouligand, C., Glen, J. M. G., & Blakely, R. J. (2009). Mapping Curie temperature depth in the western United States with a fractal model for crustal magnetization. Journal of Geophysical Research, 114(B11104), 1–25. https://doi.org/10.1029/2009JB006494
- Tanaka, A., Okubo, Y., & Matsubayashi, O. (1999). Curie point depth based on spectrum analysis of the magnetic anomaly data in East and Southeast Asia. Tectonophysics, 306(3–4), 461–470. https://doi.org/10.1016/S0040-1951(99)00072-4
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