Periodic revisions of the Global Heat Flow Database (GHFD) take place under the auspices of the International Heat Flow Commission (IHFC) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). A growing number of heat-flow values, advances in scientific methods, digitization, and improvements in database technologies all warrant a revision of the structure of the GHFD that was last amended in 1976. We present a new structure for the GHFD, which will provide a basis for a reassessment and revision of the existing global heat-flow data set. The database fields within the new structure are described in detail to ensure a common understanding of the respective database entries. The new structure of the database takes advantage of today's possibilities for data management. It supports FAIR and open data principles, including interoperability with external data services, and links to DOI and IGSN numbers and other data resources (e.g., world geological map, world stratigraphic system, and International Ocean Drilling Program data). Aligned with this publication, a restructured version of the existing database is published, which provides a starting point for the upcoming collaborative process of data screening, quality control and revision. In parallel, the IHFC will work on criteria for a new quality scheme that will allow future users of the database to evaluate the quality of the collated heat-flow data based on specific criteria.

Geophysical joint inversions seek to exploit the statistical fact that a model that simultaneously satisfies two or more independent data sets is more likely to represent geological ‘reality’ than a model that only satisfies a single data set. Interpreting geophysical data directly rapidly exceeds the capacity of a human as more data are added, so some form of machine assistance is usually required. Conventional inversion techniques can produce a ‘best fit’ model but this might only be one of a large range of possible models that fit the data. Bayesian inference provides a tool to evaluate the relative probability of all possible geological models in a given set, thereby quantifying the amount of information the data is actually providing. Over 2012–2014, National ICT Australia (NICTA; now Data 61) worked with a number of university, government and industry partners, with support from the Australian Renewable Energy Agency, to build a Bayesian inference software tool for geophysical joint inversions. The tool was initially directed at geothermal energy exploration but is equally applicable to investigating other geological problems. For one geothermal exploration problem, Bayesian inference allowed us to jointly invert gravity, magnetics, magnetotelluric soundings and borehole temperature records to map in three dimensions the probability of encountering granite >270°C beneath the Moomba region of South Australia. The results correlated well with an independent deterministic inversion carried out by Geoscience Australia, but provided a much richer interpretation in probability space. NICTA released the software tools as open source code on the GitHub platform.

Coal-bearing basins have increased economic potential for enhanced geothermal systems owing to thermal insulation provided by the coal and associated organic-rich sediments. In such insulation-dominated prospects, heat refraction effects associated with buried insulators can produce negative surface heat-flow anomalies in the most prospective areas. The Latrobe Valley in Victoria, Australia, is an archetypal coal basin. Using numerical simulations incorporating the coal geometries, we show that the Latrobe Valley coals might elevate the temperature of the rocks at 4 km depth by some 30–35°C relative to a ‘base’ condition with no coal. This effectively boosts the average geothermal gradient by about 30%, with a corresponding improvement in the economic case for geothermal energy in the Latrobe Valley.