I presented some text analytics work at a recent GeoScienceWorld (GSW) meeting in New Mexico, USA. GSW is a not-for-profit cooperation of Geological Societies, Associations & Institutes to disseminate geoscience information. First, some information on the trip, then the analytics!


The Geological Field Trip was to Santa Fe and Abiquiu areas approx. 7,000 Ft above sea level. To the west across the Rio Grande Rift Basin are the Jemez Mountains (a super volcano) and the town of Los Alamos (home of the Manhattan project). To the North is the Colorado Plateau and Ghost Ranch where over 100 articulated skeletons of the Triassic Theropod Dinosaur Coelophysis have been found (the state fossil of New Mexico). These would have stood about one metre tall at the hips and up to three metres long.

1 Coelophysis

The red cliffs at Chimney Rock contain Triassic deposits overlain unconformably by cross bedded Jurassic desert sandstones topped with white limestone and gypsum in places. The beautiful scenery of Chimney Rock can be seen in photo below:

2 Chimney Rock

The view from the top of Chimney Rock is even more breath-taking in the photo I took below.

 3 Top Chimney Rock


There has been a continuing shift from just Information Retrieval (IR) systems – a search box and ten blue links, to the search for patterns, through what is increasingly called ‘insight engines’ within the cognitive computing paradigm. After all, big data is about small patterns.

All of the work below is approximately five days work and shows what is possible using some of the techniques available today in a short space of time. I wrote scripts using Python and used OpenSource utilities, these included some new techniques not published before. For the analytic content, I used the Society of Economic Geology (SEG) text corpus (1905-2017) as an example focused on mining, mainly of heavy metals. This consists of over 6,800 articles, 4.3 million lines of text and 35 million words. Several examples of analytics techniques are shown below increasing in their sophistication.

  1. Statistical Word Co-occurrence

The results of counting the frequency of terms and their adjacent associations are called n-grams. The image below is a graph of nodes (unigrams) and edges (bigram associations) automatically generated from the SEG text corpus of journals.

Click here to view a video showing how the text graph can be explored

Terms that have high authority (many links) can clearly be seen, along with rarer terms with few associative links. This is one way to explore text in an easy and visual way which can be linked to queries to the documents or contexts in which those words or associations occur.

4 Graph

These displays can be complemented by word clouds, with the most frequent associations stripped down to reveal the ‘more interesting’. Previous research I performed with geoscientists indicated that more frequent associations were ‘relevant but not interesting’. So stripping away the most frequent may be desirable, hyperlinking every word so scientists can drill down into the articles and sentences in which the associations are mentioned. The example below is for the search query ‘precambrian’.

x Word cloud

Just as the Google n-gram viewer allows someone to explore word usage through time, similar approaches can be taken with journals. The image below shows the trends of some common words in the SEG text corpus over the past twenty years. The y-axis is normalized relative word frequency (compared to total number of words used in that journal in that year).

6 Word Frequency

For example, from the image above it is plain to see that the popularity of the terms ‘gold’ and ‘hydrothermal’ (frequency of occurrence) have increased over the past twenty years, whilst the terms ‘manganese’ and ‘metamorphic’ have decreased. The increase in popularity of ‘gold’ has been theorized as possibly related to gold price which has also been plotted on this chart!

  1. Simple Entity Extraction

Extracting entities from text and associating them to a spatial context and geological time period has been of increasing interest to both academia and practice. The NSF EarthCube GeoDeepDive Cyber Infrastructure is one such example with some fascinating findings related to stromatolite distribution and sea water chemistry for example driven by patterns in text.

The example below shows the frequency of mentions (histogram in green) in SEG journal articles of geological periods (including their constituent sub-divisions). A knowledge representation (taxonomy) has been applied to the text in order to surface a pattern. This would appear to support a proposition that over more than a hundred years, the focus for mining geologists has been the Pre-Cambrian and Tertiary (Neogene and Paleocene) periods (denoted by the acronyms ‘PC’ and ‘T’ respectively) on the y-axis. The Silurian period appears to have been of least interest.

7 Entity Extraction

Plotting the world-wide distribution of copper ore by Geological age (orange line) as a form of ‘control’, supports the theory that patterns in journal text may surface ‘real’ trends and phenomena of interest.

  1. Numerical Data Extraction

Another relatively common technique is to extract numerical integer and float data. The chart below shows the results of automatically extracting integer and float data in association with the mnemonic ‘ppm’ (parts per million), plotting where it can be associated to a chemical element. The ppm data is on the y-axis (logarithmic), with mentions on the x-axis (1,709 were found in total). This could be turned into a hyper-linkable user interface, taking the user to the sentence/paragraph in question for each data point. This type of extraction is quite trivial although potentially under-used by organizations despite much of these data not necessarily being stored in structured databases.

8 PPM extraction

Another common technique is entity-entity matrices, showing how common two entities occur together in the same sentence or equivalent semantic text unit. The example below shows lithology and minerals for the SEG corpus.


The associations are clustered using least squares to group similar lithology and mineral associations. You may just be able to pick out ‘Diamond’ on the middle right and its strongest association to Breccia and Conglomerate. These displays may reveal surprising associations worth of further exploration and are used extensively in biomedical for tasks such as gene discovery.

  1. Geo-Sentiment

Looking at individual geological formation names as they appear in text, it may be possible to derive ‘sentiment’ and ‘subjectivity’ of the formation. Using Part of Speech (POS) tagging, nouns that occur before the phrase ‘Formation’ or ‘Fm’ for example, can be extracted.

The cross-plot below shows some Formation names that appear around the search query term ‘leaching’. Polarity is on the x-axis, denoting how the Formation is perceived, negative (-1) versus a positive (+1) light. This is achieved by analysing the words (using Bayesian Statistical algorithms) that co-occur with the geological Formation mentions in text. Simplifying, terms such as ‘good’, ‘surprising’ and ‘abundance’ area deemed as ‘positive’ whereas terms such as ‘poor’, ‘error’ and ‘problem’ are deemed as negative. On the y-axis is subjectivity, from objective (0) to subjective (1). Terms such as ‘strongly suggest’ and ‘by far’ being indicative of subjective views. Standard sentiment algorithms cannot be used with accuracy on geoscience content, as the everyday terms ‘old’, ‘fault’ and ‘thick’ for example, which can be used in social media to denote negative views, are not negative in a geoscience sense!

10 sentiment

From these data, the Citronelle Formation appears in a negative light that may stimulate the scientist to investigate the sentences (context) which may lead to a learning event. Conversely, the high ‘subjectivity’ of the Popovich Formation may also trigger curiosity to understand the context which may lead to a re-interpretation.

  1. Automatic Geo-coding

Geo-referencing journal articles is not a new technique. However, in many cases it is the entire journal article (or just images within the article) that is referenced. In essence it is a summary of ‘aboutness’. The map below shows ‘mentions’ of the search query term concept ‘precambrian’ in the full text (body text) of all articles in the SEG corpus where they can be automatically geo-located. Comparing to techniques that only use keywords and/or abstracts of the journal article (the ‘information container’), there is a 200% enhancement (increase) of geo-locations. Clicking on the locations to show the ‘mentions’, the sentence or paragraphs in which the query term concept is mentioned, may yield insights that simply geo-locating entire journal articles cannot.

11 Geocoding

Instead of colour coding the frequency of occurrence by colour, bubble plots can be used, where the size of the bubble relates to the frequency of mention. This can also be combined with external data to the text corpus. An example is shown below integrating with the surface geology on the United States using US Geological Survey (USGS) WMS GIS spatial data.

12 Geocoding USGS

More granular geo-coding is simply a case of adding in more specific lookup lists for latitude and longitude of any entity.

13 Geocoding Mine

  1. Topics

Another common technique to ‘summarize’ the essence of what ‘lies beneath’ in text, relies on a range of techniques from complex word co-occurrence patterns, Principal Component Analysis (PCA) to Eigen Values and Vectors. The image below shows the clusters of topics for the search query ‘leaching’ in the SEG text corpus. These techniques can be applied at any level of granularity, on an abstract, a single article, a whole corpus or as a delta between journals or corpora. Topic modelling is typically applied longitudinally (through time) to surface changes in the intent behind text.

14 Topic Models


  1. Mathematical Word Vectors and Hypothesis Testing

The word co-occurrence patterns of any entity can be converted into a mathematical vector and the similarity compared with one another. From literature reviews, these techniques have been applied more sparsely/largely non-existent within the geoscience discipline compared to simple entity extraction and association.

The cross-plot below shows geological periods in the SEG corpus plotted to their similarity to the word vector of ‘volcanics’ on the x-axis and ‘limestone’ on the y-axis. In the bottom right, the Pre-Cambrian Archean period (2-4 Billion years ago largely before life on earth) is very similar to ‘volcanics’ and not ‘limestone’ which is what you would expect. Conversely, the Mississippian (top middle) is very similar to ‘limestone’ and not ‘volcanics’ which is what you would expect as sea level was very high with warm shallow seas. So again, this supports the theory that word vectors from text can surface real word patterns that make sense. Perhaps they can also reveal what we don’t yet know.

15 word vectors

A variation of this technique which it is believed may have never been tried before in the Geosciences, is combining data from a database, with word vector information. In the cross-plot below, US states (e.g. Florida, Wyoming, Oregon) are plotted by their annual rainfall on the y-axis (from the National Oceanic and Atmospheric Administration (NOAA) database) and their similarity to the word vector ‘Arsenic’ in the SEG corpus on the x-axis. A weak correlation (R2=0.26) is found, implying more similarity to the word vector ‘Arsenic’ with decreasing rainfall. Simplistically, this could be due to more arid environments (less rainfall) leading to higher Ph conditions with Arsenic more likely to mobilise from the underlying geology into groundwater and aquifers.

16 Word Vectors Arsenic

This could point to the potential value of combining word vector similarities from text with traditional measured data stored in structured databases. The whole may be greater than the sum of the parts.

A final example integrates data from the US National Cancer Database (CNC), Alzheimer’s Association and again, text vectors from the SEG corpus. The average cancer mortality rate per US state (per 100,000 people) is plotted on the x-axis, the average Alzheimer’s mortality rate per US state is plotted on the y-axis. The similarity of US state word vectors to the heavy metal ‘Cadmium’ word vector is shown by the colour and size of marker. The more similar, the larger the circle. Those above average similarity in the sample are coloured orange, below average are coloured blue. There is no statistically significant correlation and even if there was, correlation is of course not causation. There are many demographic and socio-economic factors at play in a complex system. However, these techniques may be useful in surfacing patterns that warrant further investigation or hypothesis testing.

17 Word Vectors Cadmium


  1. Automated Discovery

The final example compares the linkage between the word vector of every concept in the corpus, with the word vector of every other concept in the corpus and their similarity to the word vectors of a hypothesized theme. In the example below, the theme are elements typically associated with geogenesis (natural) contamination in groundwater (e.g. Aluminium, Iron, Copper, Mercury, Lead).

A new simple ratio has been developed (Cleverley 2017) by combining linear regression with a scaling factor to represent the individual similarity of the concept(s) to the theme, to surface potentially the ‘unusual’ associations which may warrant further discovery. In the run below, over 150Million word vector combinations were tested by an automated algorithm. This took four hours on a standard laptop.

 18 Automated discovery

19 Equation

For example, ‘Argon dating’ and ‘Feldspar Chlorite’ as individual concepts, do not have high similarity to the theme. However, as an association, they have a disproportionately higher correlation than one would expect, which may warrant further exploration to identify a causal mechanism.

Just as Swanson (1988) manually identified (inferred) a link between magnesium deficiency and migranes, that was not present in any single article, but they shared similar concepts, these automated techniques could highlight new associations. This could lead to new knowledge and ultimately, new scientific discoveries that are hidden amongst our text in plain sight.

Knowledge is socially constructed and different text copora will likely lead to different word vectors for the same concepts depending on the sub-discipline and nature of the text. These differences may also surface clues to new phenomena of interest.

Based on literature reviews, the use of word vector similarities of entities with external data is potentially under-utilized in the geosciences. Future work will most likely expand the research to apply to much larger quantities of journals and further develop automated approaches. Questions, comments and ideas are always welcome, feel free to contact me on the email below.

Paul Cleverley PhD


Robert Gordon University

Email: p.h.cleverley@rgu.ac.uk

Blog: www.paulhcleverley.com

A PDF of this article is available By Clicking <Here>


20 - References



Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

Website Powered by WordPress.com.

Up ↑

%d bloggers like this: