Release for JGR: SE paper publication

This release accompanies the publication of "Machine Learning and Big Data Mining Reveal Earth’s Deep Time Crustal Thickness and Tectonic Evolution: A New Chemical Mohometry Approach" in the Journal of Geophysical Research: Solid Earth. It includes all scripts and data used to generate the results in the paper, specifically focusing on:

Crustal Thickness Prediction:

Contains Python code that predicts crustal thickness based on geochemical element data.
Utilizes machine learning models to explore the relationships between crustal thickness and geochemical elements.
Includes data preprocessing, model training, and evaluation steps.

Spatial and Temporal Evolution:

Provides tools to visualize the spatial-temporal evolution of crustal thickness across different geologic ages.
Features scatter plots showing the correlation between longitude, latitude, and crustal thickness over time, along with median crustal thickness values.
Includes PDF exports of visualizations related to the Southern Tibet area and the South China region.
Instructions:

For details on running the code and setting up the environment, please refer to the README.md file.
Data files and instructions for reproducing the figures in the paper are also included.