Author-submitted data information
| ID | 615 |
| Title | Partial codes and data for paper 'Global Distribution of Low Frequency Family Marsquakes From Deep-Learning-Based Polarization Estimation' |
| Creator | Quanhong Li |
| Subject | Deep-learning-based SWIFT method, Single-station polarization estimation, Marsquake |
| Publisher | Xiukuan Zhao |
| Description | Codes and data for demonstrating the process of using SWIFT method to obtain the final prediction value for marsquakes data in the paper 'Global Distribution of Low Frequency Family Marsquakes From Deep-Learning-Based Polarization Estimation'. 'SWIFT_prediction.ipynb' is the script for predicting the polarization of marsquakes and plotting Figures S6-S8. 'ML_Models' is the trained model for predicting the polarization of events. It includes 8 trained models, with filenames in the format: '[Quality]quality_[filter_range]_model.h5'. '[filter_range]' is the frequency band range used for data filtering, such as '0.3_0.5Hz' and '0.3_1Hz'. 'Marsquake_data' includes three component marsquake data, with filenames in the format: '[event_label]_[filter_range]_ [Quality]_zne.mseed'. [event_label] is the event label, such as 'S1000a', 'S1094b'. The raw data was downloaded from IRIS and preprocessed according to the description in the article. Here, 56 events are provided as inputs for the model. |
| Contributor | Zhuowei Xiao, JinLai Hao, Juan Li |
| Date | 2023-2025 |
| Type | Python scripts, seismic data and trained deep-learning models |
| Format | Source codes are in python ipynb file format. Data are in miniseed formats. Trained models are in hdf5 format. |
| URL | http://www.geophys.ac.cn/ArticleData/20250403CodesAndMarsquakesData.zip |
| DOI | 10.12197/2024GA016 |
| Source | |
| Language | eng |
| Relation | |
| Coverage | |
| Rights | Institute of Geology and Geophysics, Chinese Academy of Sciences |