Author-submitted data information
| ID | 704 |
| Title | Contaminated zircon dataset and Random Forest-based Element Recovery Algorithm |
| Creator | Pengfei Lv |
| Subject | Machine Learning, Geochemistry, Zircon, Solid Earth |
| Publisher | Xiukuan Zhao |
| Description | Data set S1 Data original. (separate file) Zircons from granite with full REE, complied from Georoc Database (DIGIS TEAM, 2024) Data set S2 Data processing. (separate file) Input data for machine learning training, obtained by preprocessing Data set S1. Data set S3 updated_data_RF. (separate file) Data set S1 data filled by the trained RF model. Data set S4 updated_data_SVR. (separate file) Data set S1 data filled by the trained SVR model Data set S5 updated_data_XGB. (separate file) Data set S1 data filled by the trained XGB model Data set S6 Jack_Hills. (separate file) Jack Hills zircons with full REE, compiled from Bell et al. (2016） Data set S7 updated_JH. (separate file) Data set S6 data filled by the trained random forest model Data set S8 Global_detrital_zircon. (separate file) Detrital zircons with full REE, compiled from Balica et al. (2020) Data set S9 updated_data_GDZ. (separate file) Data set S8 data filled by the trained random forest model Code: This project contains two main scripts: Code_Train Train and evaluate models (Random Forest, XGBoost, and SVR) to predict rare earth element concentrations (e.g., La, Pr, Nd, Sm) using KFold cross-validation. Code_Predicted Use the previously trained models to predict and correct missing or low-quality rare earth element data (e.g., in `Jack_Hills.xlsx`) More details please see README.md |
| Contributor | Xinyu Zou |
| Date | 13 March, 2025 |
| Type | |
| Format | .xlsx, .py, .pkl |
| URL | http://www.geophys.ac.cn/ArticleData/20250313PollutedZircons.zip |
| DOI | 10.12197/2025GA005 |
| Source | |
| Language | eng |
| Relation | |
| Coverage | |
| Rights | Institute of Geology and Geophysics, Chinese Academy of Sciences |