The geological properties ontology

Definitions

Ontology: An ontology is the definition and classification of concepts and entities and the relationships between them.
Semantic data: Semantic data is data that is organised so it can be understood by machines.
Exploration: The process by which geological information is collected and analysed to identify mineral or energy resources as well as determining the economic feasibility of their extraction.

Attribution: The following content is derived from work undertaken for the Geological Survey of Queensland and is gratefully reproduced in-part under a Creative Commons Attribution 4.0 International Licence.

Why is an ontology needed?

An ontological approach can help both humans and computers to understand, integrate, and analyse exploration data across a challenging variety of data. Change factors include:

• The accelerating volume and variety of data types, data formats, and data sources.
• The use of big data, machine learning and AI to find new discoveries. Computers need semantic data.
• The incorporation of data from other domains, such as life sciences data, into exploration data holdings.
• The expectation of real-time data streaming, processing, and analysis.
• The move away from relational databases to NoSQL databases, document databases, and object storage.

Using the ontology in exploration

Explorers undertake a range of activities to understand the geological properties of a geological or administrative feature. The process typically goes like this:

1. We undertake a survey on the feature at a site.
2. The site may comprise of the whole feature, part of the feature, or may encompass and extend beyond the feature.
3. The survey yields samples that may be physical, such as a drillcore, or non-physical proxies such as photographs.
4. We conduct observations on the samples using various procedures.
5. The observation yields results as measured values or qualitative descriptions.
6. The results are interpreted to understand the geological properties of the feature, e.g. mineralogy or presence of hydrocarbons.

Examples of applying the ontology

The benefit of the ontology is that it can be applied across a wide variety of exploration methods. For example:

* Array data such as LAS files, grids, and images may theoretically have atomised results, but practically may be stored in native format as data objects.

The concepts in the ontology

Each concept in the ontology has their own ontological model to describe the elements and attributes of that concept.

Geological property

• The observable or measurable properties of a geological or administrative feature.
• Examples: mineralogy, hydrocarbon properties, water properties, stratigraphy, engineering data.

Geological or administrative feature (the Ultimate Feature of Interest)

• Geological features have properties that are of interest for commercial, environmental and societal reasons.
• Administrative features are spatial features that are defined and managed by regulatory agencies.
• Ultimate features of interest are entities that are discrete, complete, and internally coherent.
• Ultimate features may be components of larger features as part of a set, where each is an independent discrete entity e.g. formations within a basin.
• Examples: basin, province, trough, craton, orogen, formation, permit, sub-block, resource accumulation.

Site (the Proximate Feature of Interest)

• An entity or location within, or encompassing, a feature that acts as a proxy to represent a complete (ultimate) feature.
• A Feature of Interest is proximate when it represents a larger feature, as opposed to being a discrete component of a larger feature. e.g. an outcrop can be examined as a representative of a formation, whereas a formation does not represent a whole basin but is a component of it.
• Where a sampling is undertaken, but the sampling geometry and site geometry do not necessarily have to be equivalent.
• A site may be a component of a larger site.
• Examples: outcrop, borehole, stream, mine, alluvial site.

Survey

• The one-off event examining a geological or administrative feature.
• The type of exploration, assessment, or processing work that produces samples or observations.
• Survey is synonymous with the term Observation Collection, and with the term Project in the geochemistry dataset.
• Examples: seismic survey, geochemical survey, geophysical survey, petrophysical survey.

Sample

• An enduring artefact produced by a survey.
• Synonymous with specimen for physical artefacts.
• The sample is a representative part of a feature of interest.
• Samples may be original samples, subsamples where a new sample is split into smaller samples, processed samples where a sample content is retained but is processed to have altered properties, or duplicates — identical samples.
• A sample may be surveyed to produce a new sample or sub-sample.
• Examples: drill core, drill cuttings, soil sample, hand specimens, water, photograph, LAS file.

Observation

• An act of carrying out an observation using a procedure to measure, estimate, calculate a value of, or describe a feature, site or sample.
• Observations differ from sampling in that sampling yields an artefact, whereas an observation yields a qualitative or quantitative result.
• Observations may be the observation of the physical limits of an interval.
• Examples: physical properties, hyperspectral scanning, gravity, stratigraphic interval, inductively coupled plasma spectrometry, mineralogical components.

Result

• The result of the observation performed on a sample, stored as a description or as a value and unit of measure.
• Examples:
  • Physical properties, e.g. concentration, mass, temperature
  • Petrographic descriptions
  • Geophysical measurements e.g. gravity, magnetic field strength
  • Petrophysical log measurements e.g. gamma, density, resistivity.

What are the business advantages of an ontology?

Ontologies, along with taxonomies and vocabularies, provide these business advantages:

• The ontology provides the business with a shared understanding of business concepts, entities, and relationships.
• People across business divisions, as well as customers and suppliers, can use the ontology as the basis for seamless information exchange.
• Ontologies and their vocabularies allow multi-language taxonomies, and synonyms, regional, historical and colloquial terms for concepts. This enables semantic master data management.
• Teaching the ontology to new recruits during induction will help them to understand the business.

How does AI understand the ontology?

The ontology is made available to the AI in the Web Ontology Language (OWL) as an RDF (Resource Description Framework) file.

The AI can understand this semantic description of the data entities, their attributes, and their relationships.

We use RDF-based controlled vocabularies to feed the AI the taxonomy — a list of words related to each other. The AI uses SPARQL query language to query the vocabulary API to understand the words and their meaning.

A borehole example of AI reasoning

This example demonstrates the use of semantic data techniques that enable the AI to reason (understand) without needing a human to tell it what to do.

1. We feed the AI data for borehole CARINYA SOUTH 3 using the Persistent Identifier (PID) BH063772.
2. The AI can reason that a borehole is a type of geological site by querying the Geological Properties Ontology.
3. The geoproperties ontology directs the AI to the borehole ontology at https://linked.data.gov.au/def/borehole.
4. The borehole ontology tells the AI that an attribute of the borehole is a borehole purpose (see image below).
5. Using Linked Data, the AI finds the Borehole concept in the geological sites vocabulary.
6. The AI can reason that a Borehole has alternative labels of Core Hole, Corehole, Drillhole, and Well by querying the borehole concept in the geological sites vocabulary.
7. The AI can reason from the borehole purpose that it will process this borehole as a Coal Seam Gas Well.

Sure, we could have told the AI straight up that this was a Coal Seam Gas Well.

However, hopefully you can see the power of enabling the AI to reason across Features, Sites, Surveys, Samples, Observations, and Results, and all of their attributes, relationships, ontologies and vocabularies.

The Borehole Ontology from https://linked.data.gov.au/def/borehole.

What about the existing data models for exploration and appraisal data?

Standards such as GeoSciML (Geoscience Markup Language) for minerals and PPDM for petroleum and gas are detailed data models that fit within the ontology model and inform it.

Many of the geoscience data models exist as relational data models, and lack the semantic information required by AI for activities such as machine learning.

This ontology is not about replacing, but instead complements, extends and integrates these data models.

The ontology enables integration of data across these different models. For example, if we integrate GeoSciML mineral data with PPDM petroleum and gas data, the AI will know that a borehole and a well are synonymous.

Where does my database fit in?

This ontology can be represented in a relational database. However, if you're starting afresh, you should consider databases that support key-value data or document (JSON-based) data structures. Graph databases are perfect for OWL and RDF data.

Here are some things you can do to make your existing relational database more semantic:

• Map your existing reference data tables to ontologies and vocabularies.
• Create and store persistent identifiers for both data and metadata.
• Create semantic data views so machine consumers can query these views.

Can I get my database data into a semantic data store?

You can set up an Extract-Load-Transform (ELT) process to ingest your database into a knowledge graph.

More information

References

Geological Properties Database. Geological Survey of Queensland. Material was copied from this source, which is licensed under a Creative Commons Attribution 4.0 International License.

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