Fantastic first day at the Digital Geoscience conference at the Geological Society of London. Insightful presentations from the British Geological Survey, Government Office for Science , Arup, AtkinsRéalis, VRGeoscience Limited, Earth Science Analytics AS, University of Exeter and University of Cambridge . I presented on the application of AI to Geoscience Documentation. Also some fascinating posters ranging from Carbon Storage and Water AI to nuclear waste... Continue Reading →

Inspired by Isaac Asimov's Three Laws of Robotics from the 1940's. I've applied these to Data Management in a world of Frontier AI. This is to provoke debate and discussion in a light hearted way in what is a fast moving emerging field. The first law is about the governance of Data Management (including documents... Continue Reading →

Towards more meaningful Generative AI: There’s more to understanding language than just statistics. More meaningful answers and summaries can be generated by automatically pre-labelling document text chunks with entities and thematic topics before the vector cosine similarity step to feed organisational content to Large Language Models (LLM). As with Google-like searches, when there are vast amounts... Continue Reading →

I'll be giving a talk on Artificial Intelligence applied to Geoscience Information, at the British Computer Society Search Solutions event on the 22nd November in London. There is an excellent programme with other topics including: The dangers of using LLM's by Professor Julie Weeds (University of Sussex), Using AI tools for discovery by Hong Zhou (Wiley Scientific)... Continue Reading →

As well as classifying images within documents it is also possible to detect geological features on them. I built a deep learning model to detect salt diapirs and applied it to unseen data as shown in the montage. This can be useful to enhance search & discovery, especially if there is little/no text on or... Continue Reading →

This is what (a subset) of 5,000 labelled subsurface / geoscience images looks like which are typically found in documentation! Happy to share freely with anyone for non-commercial use supporting the geoscience community. These can be used to train a machine vision classifier to help geoscientists sift through this vast amount of information in their... Continue Reading →

Results so far of my deep learning object detection efforts for fossil ammonites. Video here: https://www.linkedin.com/posts/paulhcleverley_geology-fossils-palaeontology-activity-7118884143685349376-3mt4?utm_source=share&utm_medium=member_desktop I wanted a model that could work on my mobile phone to pick out fossils in-situ on the beach, to experiment and see what is possible. More work to do but encouraging signs as a a side hobby!I'm using... Continue Reading →

Topseal performance is critical for geological disposal sites, whether that is carbon capture & storage or radioactive storage, as well as natural hydrogen or oil & gas exploration. I've used patterns in text only using machine automated techniques, to visualise Lithostratigraphic Formations by their association to clues for 'seal' (y-axis), by the overall sentiment of... Continue Reading →

Latest hobby project is detecting fossils (in this case ammonites) from photographs. Labelling examples in existing images, building a deep learning model and applying it to new information. Whether this actually assists me scan the shingle using my mobile phone on my next fossil collecting trip remains to be seen! There is quite a bit... Continue Reading →