At a GeoScienceWorld meeting this week. Some beautiful dark red garnets in a schist (metamorphosed shale) from Wissahickon Valley in Philadelphia.

26 Business Professionals in a multinational corporation were asked to assess their search skill prior to undertaking 2 exploratory search goal tasks (not a single right result) using their enterprise search engine. Task #1 could have potentially many results, Task #2 very few. For each task 4 high value documents were hidden in the search... Continue Reading →

Unlock the value in your oil & gas, mining, subsurface and geoscience documents. Disrupt existing business workflows. Automatically classify, extract data and names, find problems and opportunities. Assisting the subsurface professional and Geoscientist. Save time searching for information, reduce the risk of missing key information and increase the chances of ideation & discovering new knowledge.... Continue Reading →

I created a vectorspace model using 700 UK license relinquishment reports, comparing companies to risk (x-axis) and uncertainty (y-axis) using word vectors and cosine similarity. Based on patterns in text, those companies in the top right quadrant have a higher 'similarity' to risk and uncertainty; those in the bottom left - the opposite. The companies... Continue Reading →

Word embeddings in Natural Language Processing (NLP) are a representation of words in real valued vectors that encode the meaning of the word. Words closer in vector space are likely to be similar in meaning. From 5,000 reports, the cross plots above shows the word vectors for 1500 minerals to the word vectors for hydrothermal... Continue Reading →

A recent survey was undertaken of a large organisation that has been using the Natural Language Processing / Machine Learning Python algorithm OpportunityFinder for the past 12 months. They have been applying the algorithm to millions of documents to extract knowledge and ideas. Compared to their existing traditional search engines they estimated: 1. The algorithm... Continue Reading →

I have been experimenting with text analytics on 500 public Mars Geology documents. Following on from my last post spatialising data on a map, I have also explored multivariate heat map clustering. Recognition to Metsalu and Vilo (2015) for clustering visualisations originally developed for Nucleic Acid research.

#mars #nasa #geology #naturallanguageprocessing

Over 5,000 USGS reports were analysed using Natural Language Processing (NLP) and Machine Learning to detect potential environments for Copper. Over 1.5Million detections were made. The results were coarsely spatialised by country and shown on the map above. The larger the pie-chart the greater the tone of uncertainty / speculation. These data were displayed in... Continue Reading →