Figure 1 - Association (Text) Frequency between chemical elements (by group) and hydrocarbon source rock (x-axis) and hydrocarbon occurrence (y-axis). Deriving Hydrocarbon to Metal / Mineral associations found in unstructured text for use as potential ore analogues and exploratory data analysis: Applying Machine Learning and Natural Language Processing (NLP) to 16 Million Geoscience Sentences. Hydrocarbon... Continue Reading →

Fascinated by this Merycoidodon (Oreodont) fossil skull.  An extinct herbivore from the Oligocene (~25 Million Years Ago) with no known close living relatives today. Related to the camel but 'sheep-like', it would have likely resembled a pig in appearance, but with a longer body, at about 1.5 metres with short limbs and probably moved in herds... Continue Reading →

We are using Deep Learning to leverage the unique 45,000 petroleum system related textual clues in OpportunityFinder®. Designed for automation, the clues combined with auto-annotation of millions of sentences allow a deep learning model to generalise (learn). This enables the detection of valid clues in geoscience text (reports, presentations, papers) not present in the original... Continue Reading →

OpportunityFinder® is being tested within a renewables geothermal project in collaboration with the British Geological Survey. BGS are investigating mine water in underground abandoned coal mines as a low carbon sustainable heat source for housing and manufacturing, and have several other potential use cases for knowledge extraction from their data archives to meet the challenges... Continue Reading →

Announcing V2.0 released. Discover new oil & gas exploration ideas, leads, plays and opportunities in your unstructured text. OpportunityFinder® - first of its kind pattern based geoscience search. http://www.infosciencetechnologies.com

Research published this week on the effects of COVID-19 on search in the enterprise. People often use search engines to resolve some level of uncertainty. Patterns in enterprise search logs may therefore yield insights in extreme situations such as a pandemic and lockdowns. These data could be used by organisations to assess the effectiveness of... Continue Reading →

Example showing autoclassification output from GeoClassifier® from a selection of public domain geoscience documents. The proportion of topics are clustered in a Pearson dendrogram heatmap. Those above the mean are in red, below the mean in dark blue relative to the corpus/collection. Easy to see clusters of documents predominantly about certain topics and to spot... Continue Reading →

A new class has been added to the text GEOCLASSIFIER  Algorithm It can now predict sentences, paragraphs, pages or documents related to Palaeogeography and automatically tag them. _____________________________________________ Image Credit: Map of the Late Cretaceous https://creativecommons.org/licenses/by-sa/3.0/deed.en https://commons.m.wikimedia.org/wiki/File:LateCretaceousMap.jpg

To welcome in 2021 https://infosciencetechnologies.com/ is gifting GEOCLASSIFIER® – a geological machine learnt text classifier to not-for-profit organisations. This assists information searching, filtering and discovery of geoscience topics in text. Even documents predominantly about one topic, often reference other geoscience topics buried within their pages. Automatically surfacing these topics could lead to insights that may... Continue Reading →

Interviewed today by PhD candidate Suraj Ibrahim researching the information behaviour of digital entrepreneurs during ideation. Specifically, the motivations regarding information seeking, how it is being done and other related cognitive, emotive and affective behaviours. Ideation, in this context, pertains to the creation of start-ups or enhancing of an existing business. The goal of the... Continue Reading →