OLMoEarth - open-source (training data, code, models) Earth Observation V1.1 released. OlmoEarth is an Earth-observation AI system built by Ai2 (the Allen Institute for AI), the earth image counterpart to Ai2's OLMo open language models.There is a family of foundation models trained on roughly 10 terabytes of global satellite and sensor data, designed to turn... Continue Reading →

Taking part in an AI Ethics panel at the Science Council in London next month with the Department for Science, Innovation and Technology and many other esteemed scholars and practitioners.“As AI rapidly reshapes how science is conducted, taught, and applied, there is a clear need for authoritative leadership to help frame the conversation about the... Continue Reading →

Delighted to serve as an associate editor of Earth Science, Systems and Society.Look forward to working with the editorial team and reconnect with my good friend Gene Rankey the Editor in Chief.

Fantastic presentations, panel sessions and audience participation at the AI in the Geosciences conference at the Geological Society of London. Day 2 today!

Just finished taking part in the Great Debate at the European Geosciences Union (EGU) in Vienna on Opportunities and Risks of Ethical AI. Fantastic well attended event organised by the Geoethics Commission of the IUGS with EGU. Everything from earth observation, seismology, mining through to large language models, agriculture and environmental - and what it... Continue Reading →

Presented AI Ethics Recommendations for Geoscientists at the European Geosciences Union (EGU) in Vienna yesterday.At around 20,000 attendees, the European Geosciences Union (EGU) is a huge event with researchers world-wide attending. As well as geology and the geological sciences in general, the conference covers hydrology, biogeosciences, remote sensing, oceanography, planetary, earth, space and atmospheric sciences... Continue Reading →

Porosity and permeability prediction from petrographic point-counting data using machine learning. A new study from the Karlsruhe Institute of Technology has demonstrated that machine learning can accurately predict porosity and permeability in reservoirs using microscopic rock descriptions from samples.The researchers (Sadrikhanloo et al., 2026) trained models on petrographic point-counting data, mineral-by-mineral descriptions of rock composition that geoscientists... Continue Reading →

Open-source data: A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System. Paper out this week in Nature from Bäthge et al (2026), introduces GROW (Global-scale integrated GROundWater dataset), a large, standardised, analysis-ready dataset designed to improve understanding of groundwater dynamics within the Earth system.Groundwater is critical (~99% of accessible freshwater), but its... Continue Reading →

Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference. Interesting paper this week from Menke et al (2026) at the Institute of GeoEnergy Engineering, and Subsurface Energy Transition and Innovation Centre, Heriot-Watt University, Edinburgh, UK.Abstract:Geoenergy projects (CO2 storage, geothermal, subsurface H2 generation/storage, critical minerals from subsurface fluids, or nuclear waste disposal) increasingly follow a petroleum-style funnel... Continue Reading →