Crustal magnetic anomalies carry information on the source distribution of magnetization in the Earth's crust (Blakely, 1995). The Curie point corresponds to the depth at which crustal rocks loose their magnetization where they reach their Curie temperature, and is obtained by fitting the power spectral density (PSD) of magnetic anomaly data with a model where magnetic anomalies are confined within a layer (Bouligand et al., 2009; Audet and Gosselin, 2019; Mather and Fullea, 2019). Mapping the Curie point provides important information on geothermal gradients in the Earth; however, mapping Curie depth is a spatio-spectral localization problem because the PSD needs to be calculated within moving windows at wavelengths long enough to capture the greatest possible depth to the bottom of the magnetic layer. The wavelet transform is particularly well suited to overcome this problem because it avoids splitting the grids into small windows and can therefore produce PSD functions at each point of the input grid (Gaudreau et al., 2019).
This package extends the package plateflex, which contains python modules to calculate
the wavelet transform and scalogram of 2D gridded data, by providing a new class
MagGrid that inherits from plateflex.classes.Grid with methods to estimate the properties
of the magnetic layer (depth to top of layer (zt), thickness
of layer (dz), and power-law exponent of fractal magnetization (β))
using Bayesian inference. Common computational workflows are covered in the Jupyter
notebooks bundled with this package. The software contains methods to make beautiful and
insightful plots using the seaborn package.
Installation, Usage, API documentation and Jupyter Notebooks are described at https://paudetseis.github.io/PlateCurie/
Author: Pascal Audet (Developer and Maintainer)
If you use PlateCurie in your work, please cite the Zenodo DOI.
All constructive contributions are welcome, e.g. bug reports, discussions or suggestions for new features. You can either open an issue on GitHub or make a pull request with your proposed changes. Before making a pull request, check if there is a corresponding issue opened and reference it in the pull request. If there isn't one, it is recommended to open one with your rationale for the change. New functionality or significant changes to the code that alter its behavior should come with corresponding tests and documentation. If you are new to contributing, you can open a work-in-progress pull request and have it iteratively reviewed.
Examples of straightforward contributions include notebooks that describe published examples of elastic thickness results. Suggestions for improvements (speed, accuracy, etc.) are also welcome.
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Audet, P. and Gosselin, J.M. (2019). Curie depth estimation from magnetic anomaly data: a re-assessment using multitaper spectral analysis and Bayesian inference. Geophysical Journal International, 218, 494-507. https://doi.org/10.1093/gji/ggz166
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Blakely, R.J. (1995). Potential Theory in Gravity and Magnetic Applications, Cambridge University Press.
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Bouligand, C., Glen, J.M.G. and 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, https://doi.org/10.1029/2009JB006494
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Gaudreau, E., Audet, P., and Schneider, D.A. (2019). Mapping Curie depth across western Canada from a wavelet analysis of magnetic anomaly data. Journal of Geophysical Research, 124, 4365-4385. https://doi.org/10.1029/ 2018JB016726
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Mather, B., and Fullea, J. (2019). Constraining the geotherm beneath the British Isles from Bayesian inversion of Curie depth: integrated modelling of magnetic, geothermal, and seismic data. Solid Earth, 10, 839-850. https://doi.org/10.5194/se-10-839-2019