hfqa_tool is a python package for validating heat-flow data against the IHFC Global Heat Flow Database (GHFDB) schema and assessing data quality according to International Heat Flow Commission standards, including quality scoring based on the methodology described in Fuchs et al., 2024 (https://doi.org/10.1016/j.tecto.2023.229976).

This is an updated and fully revised version of Chishti et al. (2025, https://doi.org/10.5880/fidgeo.2025.043)

The quality assessment follows the framework established by Fuchs et al. (2023) and evaluates each heat-flow determination through three independent components:

Uncertainty (U-score) quantifies the numerical reliability of a measurement using the coefficient of variation (COV = uncertainty / mean × 100%), yielding scores U1–U4 from excellent (25%).

Methodology (M-score) assesses the quality of the measurement approach separately for temperature gradient and thermal conductivity, then combines them multiplicatively into a score between 0 and 1 (M1–M4). The scoring path differs depending on whether the data was acquired via borehole/mine drilling or marine probe-sensing, accounting for factors such as temperature measurement method, number of measurements, probe penetration depth, water depth, and thermal conductivity saturation and pT correction conditions.

Perturbation flags (P-flags) produce a 7-character code capturing the status of environmental disturbances: sedimentation, erosion, topography, paleoclimate, bottom water variation, convection, and heat refraction, each marked as corrected, uncorrected, not significant, unrecognized, or unspecified.

The three components are combined into a final quality code of the form U·M·P (e.g. U1M2x.SxxxCxR), where the x suffix on the M-score flags incomplete metadata.

MIT License

Copyright (c) the authors, GFZ Helmholtz Centre for Geosciences

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