Real and Psuedosynthetic timeseries used in "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog"

5Hz GNSS Velocity Data for the submitted work: "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog" Dittmann et al (202?)

Datasets included:

1. Pseudosynthetic timeseries, ambient timeseries and training featuresets generated for Dittmann, et al (202?) 
2. Real GNSS 5Hz validation featuresets from Dittmann, et al. (2022) 

Timeseries and Featuresets are stored in Apache Parquet format.

Getting Started:
Notebook demos for reading using conda+ jupyterlab (easiest).
In a terminal:

1. Unzip untar (mac/linux tar -xvf psuedo_synth_gnssvel.tar.gz)

2. conda env create -f environment.yml
   conda activate pgv23_zenodo
   jupyter lab

    

3. Open the notebook “reading_data.ipynb”

Data References:

NGA-West 2 (NGAW2) Ground Motion Database 

SNIVEL GNSS Velocity Processing