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).
Note: Earlier versions of this software (v1.x) depended on the installation of GAMIT/GLOBK, as they used the Fortran programs VELROT and CVFRAME included in the GAMIT/GLOBK package for velocity field alignment and rotation. The current release (v2.x) replaces these dependencies with fully integrated Python implementations (PyVelrot and PyCvframe), eliminating the need to install GAMIT/GLOBK while preserving the original alignment and rotation functionality.
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 ┃ ┣ 📜frame_registry.py ┃ ┣ 📜lognorm_filter.py ┃ ┣ 📜plot_maps_filtering.py ┃ ┣ 📜plot_rotated_vels.py ┃ ┣ 📜pycvframe.py.py ┃ ┣ 📜pyvelrot.py ┃ ┗ 📜uncertainty_filter_verticals.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 ┃ ┣ 📜li_2024.raw ┃ ┣ 📜liang_2013.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
Key Steps:
-
Filtering stations affected by postseismic transient motions: Remove stations identified as being affected by postseismic transient motions
-
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).
-
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.
-
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.
-
Velocity field rotation: Rotate velocity fields to different reference frames using published Euler poles.
-
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 =
$Q3 + 1.5 * IQR$
- Lower threshold =
-
Velocity field combination: Estimate median velocities and uncertainties for the East and North velocity components at collocated stations.
-
Manual filtering: Remove outliers based on geographical coordinates and radii, generating cleaned data sets and logs of removed stations.
-
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).
-
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.
- Python: Version 3.7 or higher
- Python Libraries: numpy, scipy, matplotlib, pygmt, jupyter, pandas, os, subprocess, datetime, sys, glob, json, time, concurrent, argparse, itertools
-
Clone the Repository:
git clone https://github.com/ncperdomo/FICORO_GNSS.git cd FICORO_GNSS -
Create a virtual environment (optional but recommended):
python3 -m venv ficoro_venv source ficoro_venv/bin/activate -
Launch Jupyter Notebook:
jupyter notebook
-
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.
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:
I am committed to promoting reproducibility and open access in geodetic and tectonic research. Please cite FICORO_GNSS according to how the software or associated data products are used in your work.
Please cite the Zenodo software release if you:
- use the filtering, combination, alignment, or rotation workflows implemented in FICORO_GNSS,
- adapt or modify the code,
- incorporate parts of the methodology into another workflow, or
- use the software for scientific analyses or derived products.
Citation:
Castro-Perdomo, N. (2024). FICORO_GNSS: An open-source Python software package for filtering, combining and rotating GNSS velocity fields (Version v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13921189
BibTeX:
@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}
}Please cite the accompanying JGR: Solid Earth article if you use:
- the combined GNSS velocity fields provided with this repository,
- derived strain-rate products,
- regional subsets of the compiled velocity database, or
- analyses based on the Alpine–Himalayan GNSS compilation.
Citation:
Castro-Perdomo, N., Jónsson, S., Klinger, Y., Masson, F., Becker, T. W., & Johnson, K. (2025). Strain rates along the Alpine-Himalayan Belt from a comprehensive GNSS velocity field. Journal of Geophysical Research: Solid Earth, 130(12), e2025JB031738. https://doi.org/10.1029/2025JB031738
BibTeX:
@article{castro_perdomo_2025_JGR,
author = {Castro-Perdomo, N. and Jónsson, S. and Klinger, Y. and Masson, F. and Becker, T. W. and Johnson, K.},
title = {Strain Rates Along the Alpine-Himalayan Belt From a Comprehensive GNSS Velocity Field},
journal = {Journal of Geophysical Research: Solid Earth},
volume = {130},
number = {12},
pages = {e2025JB031738},
keywords = {strain rates, active tectonics, crustal deformation, Alpine-Himalayan belt, GNSS, velocity field},
doi = {https://doi.org/10.1029/2025JB031738},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025JB031738},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2025JB031738},
note = {e2025JB031738 2025JB031738},
year = {2025}
}This project is licensed under the MIT License.