GASV: A New VLBI analysis software for Geodesy and Astrometry

We present GASV, a novel Python-based software package specifically designed for the analysis of Very Long Baseline Interferometry (VLBI) data. Developed with ease of installation and user friendliness in mind, GASV supports both pipeline and interactive processing modes. The software processes VLBI baseline delays and rates in standard formats such as HOPS outputs and NGS card files to estimate key geodetic and astrometric parameters, including station coordinates, Earth Orientation Parameters, source coordinates, clock parameters, and atmospheric models. We evaluate the capabilities and performance of GASV, demonstrating that its parameter estimation accuracy for IVS INT, Regular, and CONT sessions is comparable to that achieved by the VLBI analysis centers at BKG and USNO. As a state-of-the-art tool, GASV not only enables high-quality single-session data processing but also but also supports global analyses of long-term SINEX files, generating Celestial Reference Frame and Terrestrial Reference Frame solutions with reliable accuracy.

💡 Research Summary

The paper introduces GASV, a new Python‑based software package designed for the analysis of Very Long Baseline Interferometry (VLBI) data. The authors emphasize ease of installation, user‑friendliness, and the ability to operate both in fully automated pipeline mode and in an interactive graphical user interface (GUI). GASV accepts standard VLBI products such as HOPS delay files and NGS card files, and it estimates a full suite of geodetic and astrometric parameters: station coordinates, Earth Orientation Parameters (EOP), source positions, clock offsets, and atmospheric models.

The software architecture follows a three‑layer workflow: INPUT, MAIN, and OUTPUT. INPUT gathers raw observations, control files, and a‑priori information. MAIN contains roughly 100 decoupled functions organized into seven directories (COMMON, INIT, MAKE, MOD, SOLVE, GLOB, OUT). COMMON holds time and coordinate transformation utilities; INIT reads a‑priori files; MAKE builds the vgosDB database; MOD implements the VLBI delay model, including geometric delay, relativistic corrections, clock, tropospheric, ionospheric, device, and cable terms; SOLVE performs weighted least‑squares estimation; GLOB handles global multi‑epoch analyses; OUT writes results in vgosDB, EOP, spool, and SINEX formats.

The delay model follows the IERS Conventions (2010). Atmospheric delays are modeled with the Global Mapping Function, while ionospheric corrections are available for traditional S/X‑band observations (dual‑frequency linear combination) and for VGOS broadband data (direct TEC estimation from multi‑frequency phase). Ambiguity resolution uses a reference station and closure‑delay equations solved by least squares, ensuring integer ambiguity numbers are correctly assigned across the network. Outlier detection employs a 3σ rule with automatic flagging and a GUI for manual editing. Baseline‑based reweighting iteratively adjusts observation weights to drive χ² toward unity.

For single‑session processing, the authors fix station positions to ITRF2020 and source positions to ICRF3, apply solid Earth tides, ocean loading, pole tide, and atmospheric loading per IERS 2010, and model sub‑daily EOP variations using the Desai & Sibois (2016) approach. Continuous piecewise linear (CPWLO) functions are used for sub‑daily polar motion and UT1 estimation.

Global analysis reconstructs normal equations from SINEX files, reduces local parameters (e.g., EOP), retains global parameters (station and source coordinates), stacks epoch equations, and applies No‑Net‑Rotation (NNR) and No‑Net‑Translation (NNT) constraints, as well as co‑located station velocity constraints.

Performance is validated on IVS Intensive (INT), Regular, and CONT sessions. Results are compared with those from the BKG (Germany) and USNO (USA) analysis centers. RMS differences in station coordinates, source positions, and EOP are on the order of a few tens of micrometers or micro‑arcseconds, indicating parity with established solutions. Global solutions derived from long‑term SINEX datasets also meet international standards.

The authors conclude that GASV provides a modern, fully Python‑implemented, open‑source alternative to legacy VLBI analysis tools. Its modular design, comprehensive modeling, and support for both single‑session and multi‑epoch analyses make it suitable for current and future geodetic VLBI applications. Planned enhancements include expanded multi‑frequency support, automated quality assessment, and cloud‑based processing pipelines.