FICORO_GNSS

An open-source Python software package for filtering, combining and rotating GNSS velocity fields

1. Introduction
2. Overview of the code
3. Usage
4. Example outputs
5. How to cite
6. License

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1) Introduction

The expansion of regional GNSS networks and the availability of published velocities have significantly enhanced our understanding of active tectonics. However, despite these advancements, few attempts have been made to integrate the available GNSS velocities at continental and global scales (e.g., Nocquet J.M, 2012; Kreemer et al., 2014; Graham et al., 2018; Piña-Valdez., et al., (2022); Zeng et al., (2022)), and integrating multiple GNSS velocity fields remains challenging due to the lack of standardised methods for filtering and harmonising these data sets.

FICORO_GNSS is a Python-based suite of tools designed to filter, combine, and rotate multiple GNSS velocity fields into a comprehensive and self-consistent dataset. Each input GNSS velocity file adheres to the GAMIT\GLOBK velocity file format, containing 13 columns:

Lon Lat E.vel N.vel E.adj N.adj E.sig N.sig Corr U.vel U.adj U.sig Stat 

The methodology implemented in the code combines some of the approaches by previous research on combined GNSS velocity fields, including Piña-Valdez., et al., (2022) and Zeng et al., (2022).

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Note: This code currently relies on the installation of GAMIT/GLOBK, as it utilises the Fortran codes VELROT and CVFRAME included with GAMIT/GLOBK to align and rotate velocity fields. In upcoming releases, I plan to provide Python scripts that will eliminate the need for GAMIT/GLOBK. Until then, please ensure that GAMIT/GLOBK is installed before running this code. Considering that VELROT limits the maximum number of sites allowed in the input files to 4096, I increased the default value in the Velrot module to 10,000, thus allowing to align input velocity fields with a larger number of stations.

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2) Overview of the code

This software comprises a main Jupyter notebook named FICORO_GNSS.ipynb and an input folder titled raw_input. Within the raw_input folder, you'll find the input velocity fields stored as column-formatted text files with the .raw extension. To facilitate data processing, there's a scripts folder containing additional Python scripts designed for filtering and combining GNSS velocity fields. If you need to manually remove outliers from the data, you can utilise the manual_filter folder, which houses a CSV file that enables you to define specific geographic coordinates (latitude and longitude) and corresponding radii (in kilometers) for the removal of outliers from the combined velocity field.

The folder structure is organised as follows:

📦FICORO_GNSS
 ┣ 📜FICORO_GNSS.ipynb
 ┗ 📜README.md
  ┣ 📂scripts
 ┃ ┣ 📜coherence_filter.py
 ┃ ┣ 📜combine_vel.py
 ┃ ┣ 📜lognorm_filter.py
 ┃ ┣ 📜plot_maps_filtering.py
 ┃ ┣ 📜plot_rotated_vels.py
 ┃ ┗ 📜uncertainty_scaling_combined.py
 ┣ 📂manual_filter
 ┃ ┗ 📜filter_criteria.csv
 ┣ 📂raw_input
 ┃ ┣ 📂levelling_verticals
 ┃ ┃ ┗ 📜wu_2022.raw
 ┃ ┣ 📜alchalbi_2013.raw
 ┃ ┣ 📜bahrouni_2020.raw
 ┃ ┣ 📜billi_2023.raw
 ┃ ┣ 📜bougrine_2019.raw
 ┃ ┣ 📜briole_2021.raw
 ┃ ┣ 📜castro_2021.raw
 ┃ ┣ 📜england_2016.raw
 ┃ ┣ 📜ergintav_2023.raw
 ┃ ┣ 📜euref_all.raw
 ┃ ┣ 📜euref_ch8.raw
 ┃ ┣ 📜floyd_2023.raw
 ┃ ┣ 📜gomez_2020.raw
 ┃ ┣ 📜graham_2021.raw
 ┃ ┣ 📜hamiel_2021.raw
 ┃ ┣ 📜jolivet_2023.raw
 ┃ ┣ 📜kadirov_rot_karakhanyan_2013.raw
 ┃ ┣ 📜karakhanyan_2013.raw
 ┃ ┣ 📜khorrami_2019.raw
 ┃ ┣ 📜kurt_2023.raw
 ┃ ┣ 📜liang_2013.raw
 ┃ ┣ 📜lindsey_2023.raw
 ┃ ┣ 📜mcclusky_2010.raw
 ┃ ┣ 📜nocquet_2012.raw
 ┃ ┣ 📜ozarpaci_2020.raw
 ┃ ┣ 📜ozbey_2024.raw
 ┃ ┣ 📜ozdemir_2019.raw
 ┃ ┣ 📜ozkan_2022.raw
 ┃ ┣ 📜perry_2018.raw
 ┃ ┣ 📜pinaValdes_2022.raw
 ┃ ┣ 📜reilinger_2006.raw
 ┃ ┣ 📜saleh_2015.raw
 ┃ ┣ 📜serpelloni_2022.raw
 ┃ ┣ 📜sokhadze_2018.raw
 ┃ ┣ 📜stamps_2018.raw
 ┃ ┣ 📜tatar_2012.raw
 ┃ ┣ 📜viltres_2020.raw
 ┃ ┣ 📜viltres_2022.raw
 ┃ ┣ 📜wang_barbot_2023.raw
 ┃ ┣ 📜wang_shen_2020.raw
 ┃ ┣ 📜wedmore_2021.raw
 ┃ ┣ 📜zheng_2017.raw
 ┃ ┗ 📜zubovich_2010.raw

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FICORO_GNSS in a nutshell:

Key Steps:

1. Filtering stations affected by postseismic transient motions: Remove stations identified as being affected by postseismic transient motions

2. Filtering by uncertainty distribution: GNSS stations with velocity uncertainties exceeding the 99th percentile of the scaled log-normal distribution are removed from input velocity fields, following the approach by Piña-Valdez., et al., (2022).

3. Filtering based on spatial coherence of velocity magnitudes: Remove stations if velocity magnitudes in the East and North velocity components diverge over 2 sigma from the mean, considering a radius of 20 km. Additionally, the code allows applying geographic-based stringency levels (n-sigma), allowing for a customisable approach to data filtering.

4. Velocity field alignment to a common reference frame: Implement a least squares approach to align all the data sets to a reference velocity field using a 6-parameter Helmert transformation (3 translations and 3 rotations), leveraging on repeated stations in both the input and reference data sets.

5. Velocity field rotation: Rotate velocity fields to different reference frames using published Euler poles.

6. Filtering by velocity magnitude and azimuthal direction: Implement the Interquartile Range (IQR) method to detect outliers, omitting solutions displaying disparities in magnitude, azimuthal direction, or both. The thresholds for outlier detection are set as:

   • Lower threshold = $Q1 -1.5 * IQR$
   • Upper threshold = $Q3 + 1.5 * IQR$

7. Velocity field combination: Estimate median velocities and uncertainties for the East and North velocity components at collocated stations.

8. Manual filtering: Remove outliers based on geographical coordinates and radii, generating cleaned data sets and logs of removed stations.

9. Scaling velocity uncertainties: Horizontal velocity uncertainties are adjusted to match the same percentile in a subjectively chosen target log-normal distribution, following the approach by Piña-Valdez., et al., (2022).

10. Final filtering based on uncertainty distribution: Stations with velocity uncertainties exceeding the 99th percentile of the scaled log-normal distribution are removed from the final combined velocity field.

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3) Usage

Prerequisites

• Python: Version 3.7 or higher
• Python Libraries: numpy, scipy, matplotlib, pygmt, jupyter, pandas, os, subprocess, datetime, sys, glob, json, time, concurrent, argparse, itertools
• GAMIT/GLOBK: Required for FICORO_GNSS v1.0.0 (see GAMIT/GLOBK documentation)

Steps

1. Clone the Repository:

   git clone https://github.com/ncperdomo/FICORO_GNSS.git
   cd FICORO_GNSS

2. Create a virtual environment (optional but recommended):

   python3 -m venv ficoro_venv
   source ficoro_venv/bin/activate 

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open and run FICORO_GNSS.ipynb:

• Navigate to the repository directory in the Jupyter interface and open the main notebook to start processing GNSS velocity fields.

• Input Data: Place your .raw GNSS velocity files in the raw_input/ folder. Each file should follow the GAMIT velocity file format. E-N-U adjustments and E-N velocity correlation values can be set to zero if not available.

• Manual Filtering: Use the manual_filter/ folder to define specific geographic coordinates and radii for outlier removal. Modify the provided CSV file to specify the criteria.

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4) Example outputs:

FICORO_GNSS includes a set of input velocity fields for the Alpine-Himalayan region. By running the main Jupyter notebook, you can filter, rotate, and combine these datasets to generate publication-quality maps using PyGMT. Below are examples of the outputs you can expect:

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5) How to cite:

I am committed to promoting reproducibility and open data access in science. Please cite FICORO_GNSS as follows:

• If you use, adapt, modify, or are inspired by the filtering and combination methods implemented in FICORO_GNSS, or if you use the combined velocity field for the Alpine-Himalayan Belt provided with FICORO_GNSS:

Castro-Perdomo N. (2024). FICORO_GNSS: An open-source Python software package for filtering, combining and rotating GNSS Velocity Fields. Retrieved from https://doi.org/10.5281/zenodo.13921189. DOI: 10.5281/zenodo.13921189

-BibTex citation:

@software{castro_perdomo_ficoro_gnss_2024,
  author       = {Nicolás Castro-Perdomo},
  title        = {{FICORO\_GNSS: An open-source Python software package for filtering, combining and rotating GNSS velocity fields}},
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.13921189},
  url          = {https://doi.org/10.5281/zenodo.13921189}
}

6) License

This project is licensed under the MIT License.

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