Physics of the Earth and Planetary Interiors

AMS raw data of carbonates sampled in the Central High Atlas (Morocco), in the Imilchil-Toumliline-Amouguer area. You can use anisoft software to visualize the data Calvín, P., Villalaín, J. J., & Casas-Sainz, A. M. (2018). The carriers of AMS in remagnetized carbonates. Insights for remagnetization mechanism and basin evolution. Physics of the Earth and Planetary Interiors, 282(March), 1–20. https://doi.org/10.1016/j.pepi.2018.06.003

Data compilation for the heat diffusion code and iSALE numerical models with: - the heat diffusion code Heat_Diffusion/heat_diffusion.py - a plotting tool, Heat_Diffusion/plot.py - material parameters in folder Heat_Diffusion/materials - configuration files for all models of this publication compiled in Heat_Diffusion/ - Read README.txt in Heat_Diffusion/ for more details on the structure of files, how to use the code... - configuration files of the corresponding iSALE models in iSALE/ from which the results are used in the diffusion code - Read README.txt in iSALE/ for more details on the structure of files, how to use the code... -> This part of the publication is a reproduction of models and variants used in Moreau et al. (2018, 2019a) with scripts reproduced from Moreau et al. (2019a)

Electrical conductivity of shoshonitic melt at various temperatures, pressures, and H2O contents.

Jupyter notebooks written in python to identify strombolian eruptions in infrared images in the Ray lava lake atop Mount Erebus in Antarctica and cross-correlate volcano seismic data from the strombolian eruptions. These notebooks are related to the research by Brian Dye and Gabriele Morra titled, "Machine Learning as a Detection Method of Strombolian Eruptions in Infrared Images from Mount Erebus, Antarctica." The notebooks were written to be ran in Google CoLab with the current file names under the "Colab Notebooks" folder located in the "My Drive" folder of Google Drive. Some notebooks require ObsPy miniseed data, those should be run outside of Google CoLab. All outputs are contained within the folder so any notebook may be ran in or out of numerical order.

Jupyter notebooks written in python to identify strombolian eruptions in infrared images in the Ray lava lake atop Mount Erebus in Antarctica and cross-correlate volcano seismic data from the strombolian eruptions. These notebooks are related to the research by Brian Dye and Gabriele Morra titled, "Machine Learning as a Detection Method of Strombolian Eruptions in Infrared Images from Mount Erebus, Antarctica." The notebooks were written to be ran in Google CoLab with the current file names under the "Colab Notebooks" folder located in the "My Drive" folder of Google Drive. Some notebooks require ObsPy miniseed data, those should be run outside of Google CoLab. All outputs are contained within the folder so any notebook may be ran in or out of numerical order.

Supplementary information for High Pressure Thermoelasticity and Sound Velocities of Fe-Ni-Si Alloys, including 1) raw NRIXS spectra, 2) additional figures pertaining to the vibrational Grüneisen parameter, 3) an approximation for the vibrational Grüneisen parameter via an alternate determination method, 4) determination of Lamb-Mössbauer factors, 5) an additional figure pertaining to the vibrational entropy and thermal expansion, 6) determination of vibrational kinetic energy and specific heat capacity, 7) figures comparing our re-analysis of data published in Murphy et al. 2011a, 2013 with the original values, and 8) additional data tables including experimental conditions, equation of state parameters, molecular masses, sound velocity results corrected for natural enrichment, and additional vibrational quantities derived from the phonon density of states

P-wave and S-wave velocity model and final Temperature model.

The FTIR spectra of garnet were obtained at room temperature in the range 650 to 4000 cm^-1 on a Nicolet 6700 spectrometer. Measurements were carried out using unpolarized radiation with an IR light source, a KBr beam-splitter, and an MCT-A liquid N2-cooled detector. For each analysis, 128 scans at a resolution of 4 cm^-1 were recorded.

Relocated local earthquakes after applying the new computed 1-D velocity model.

The dataset includes the Python2 scripts for calculating the lunar interior heat production and temperature, along with the text files of the reference temperatures as the input data. The reference temperatures are obtained from the Fig. 3 in Hood and Sonett (1982) "LIMITS ON THE LUNAR TEMPERATURE PROFILE". The inversion algorithm is based on the particle swarm optimization proposed by Kennedy and Eberhart (1995).