PyCurious: A Python module for computing the Curie depth from the magnetic anomaly.

Abstract

Multiple geophysical methods have been proposed to resolve the thermal structure of the Earth's lithosphere with varying degrees of precision. Geotherms may be constructed from heat flow or xenolith data, but the spatial coverage of these are often limited. Seismic velocity has proven effective to infer upper-mantle temperature, but its application relies on building a compositional model suitable for the geological context and estimating attenuation from grainsize and water content. In contrast, magnetic data are among the most widespread geophysical datasets available on the surface of the Earth and can offer useful insight into its thermal structure.

Crustal and Thermal Heterogeneities Across the Fram Strait and the Svalbard Margin

“…Derived from the magnetic data, the Curie point depth (Figure 4a) was estimated using the Pycurious Python code (Mather & Delhaye, 2019) including the Bouligand et al. (2009) algorithm.…”

“…Derived from the magnetic data, the Curie point depth (Figure 4a) was estimated using the Pycurious Python code (Mather & Delhaye, 2019) including the Bouligand et al (2009) algorithm. The Curie point depth is the depth of the isotherm of the Curie temperature of magnetite (580°C).…”

Geothermal as resource for megacities

“…4) and assure trends in the interpolation process. Estimations of the CPDs reported in this work follow the approach proposed by Bouligand et al (2009) and use the code Pycurious (Mather and Delhaye, 2019). Details of the method and parameters used are provided in the online data repository.…”

Mapping the thermal structure of southern Africa from Curie depth estimates based on wavelet analysis of magnetic data with uncertainties

“…for some constant C. This distribution is then sampled via a Metropolis-Hasting algorithm in order to obtain the posterior mean and variance of the parameters m, in particular of z b . For some more details, we refer the reader to Audet and Gosselin (2019); Mather and Fullea (2019); Mather and Delhaye (2019). In particular, we will be using the code of Audet (2020) for our later computations.…”

“…Curie depth estimation from the power spectrum is traditionally performed using a windowed Fourier transform approach (e.g., Bouligand et al, 2009;Li et al, 2017) which is known to cause estimation problems (e.g., Audet & Gosselin, 2019). Here we combine the inverse spectral method with a 2-D wavelet approach (Gaudreau et al, 2019;Kirby, 2005) and a Bayesian framework (Audet & Gosselin, 2019;Mather & Delhaye, 2019;Mather & Fullea, 2019) to alleviate some of those problems and to provide uncertainties for the obtained Curie depths.…”