Author-submitted data information
| ID | 615 |
| Title | Partial codes and data for paper ' Global Distribution of Marsquakes Revealed by Deep-Learning-Based Sliding-Window and Noise-Aware Single-Station 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 Marsquakes Revealed by Deep-Learning-Based Sliding-Window and Noise-Aware Single-Station Polarization Estimation '. 'SWIFT_prediction.ipynb' is the script for predicting the polarization of marsquakes and plotting Figure S5. 'Aquality_[filter_range]_model.h5' and 'Aquality_0.15_1_model.h5' are the trained model for predicting the polarization of Quality A events. '[filter_range]' is the frequency band range used for data filtering, such as '0.15-0.5Hz' and '0.15-1Hz'. '[event_label]_[filter_range]_zne.mseed' is the three component marsquake data. [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, two example events are provided as inputs for the model. |
| Contributor | Zhuowei Xiao, JinLai Hao, Juan Li |
| Date | 2023-2024 |
| 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/20240905CodesAndMarsquakesData.zip |
| DOI | 10.12197/2024GA016 |
| Source | |
| Language | eng |
| Relation | |
| Coverage | |
| Rights | Institute of Geology and Geophysics, Chinese Academy of Sciences |