Graph Neural Network Daily GNSS Denoising

Repository for the daily GNSS time series denoising using a graph neural network

Preprocessing

In the preprocessing folder, you will find all the scripts and notebook to prepare the data:

2 download script for UNR and CWU
2 raw to netcdf to convert and concatenate all stations together for both processing centers
station maintenance correction notebook to remove trend using the station maintenance logs, and remove outliers
add tremor notebook to co-locate tremors in time and space with the stations (possibility to tune the parameters)
resource folder with the necessary files to download and add info to the stations

Scalers and trends are saved and important to keep for rescaling the results

Graph construction

create_graph: main script to launch the graph construction
GNSS_daily_clas: pytorch geometric dataset class for the GNSS dataset construction
graphs utils: utility functions for the graph construction

training and prediction

NN_architecture: pytorch geometric GNN class
GNN training script: automatically trains on the first n graphs of the dataset and saves the last 400 for testing, equivalent to saving 2022 and 2023 to testing in our use case.
- Command Line Arguments:
  - 'lr' (float): Learning rate for the optimization algorithm.
  - 'hidden_layer' (int): Size of the hidden layer in the neural network.
  - 'nb_epoch' (int): Maximum number of epochs for training.
  - 'dataset_id' (str): Identifier for the dataset being used.
  - 'lambda_center' (float): Lambda parameter for the center part of the loss function.
  - 'post' (str): Additional string to add at the end of the output filename.
  - Example: python GNN_training.py 0.001 64 100 my_dataset _experiment_1 This example sets lr=0.001, hidden_layer=64, nb_epoch=100, dataset_id='my_dataset', lambda_center=0.1, and post='_experiment_1'.
- Then predicts for all the graphs in the dataset

Results

results_to_nc_daily: Notebook to read the graph results from the GNN and generate a netcdf
- read graphs
- average the overlapping windows
- rescale and add trend
- some simple verification figures
- final cleanup and save

References

TODO Add paper ref TODO Add result dataset zenodo