- Energy transition
- Safe and liveable delta
- Effects of mining
- Digital subsurface
The Geological Survey of the Netherlands (GDN) collects, manages, and provides access to large quantities of data on the deep and shallow subsurface. Our challenge is to further improve the ease and flexibility with which data can be supplied and accessed. To this end, we are constantly developing new models and software, which increasingly use data science and machine learning.
Data sets on the subsurface
One of GDN’s statutory tasks, set out in the Mining Act, is to manage data on the deep subsurface. We receive data from, for example, drillings for oil, gas, and geothermal energy, and from salt production projects. We subsequently also receive the production data. In addition, we manage non-statutory data sets, such as geo-electrical surveys, borehole descriptions, and analyses of, for example, grain sizes or groundwater compositions that are not (or cannot be) included in the National Key Registry of the Subsurface (BRO).
Together, these statutory and non-statutory data sets are extensive. Moreover, the data types relevant to research and geological modelling are evolving, meaning new types of data are often added. As our capacity to collect data from the subsurface increases, our subsurface data sets are becoming larger, more dynamic, and more accurate. They are available to everyone, from local authorities and businesses to scientists and citizens. Thanks to our data management and standardisation projects, we are able to give meaning to the data. How the data is subsequently interpreted depends on the user.
Research for the future
We want to flexibly use the data we manage and make it publicly accessible in a secure manner. That is why we are setting up the National Data Repository, an online database containing information on Dutch geology. The repository has web portals that enable users to access and download information for research.
Using available time series, it is possible to gain insight into changes in the subsurface at a particular location. Machine learning can be used to discover patterns and extract information from the enormous and at times confusing amount of data.
Our data experts
We support our clients with the geological data models and geo-software we build. Our data architect is responsible for the structuring of all GDN’s data. Data analysts are able to map out a project’s data flows at the right moment and create standards in coordination with their users. Data (geo)scientists establish the link between the data and their application, making the information even more useful. This is how GDN is able to make subsurface data accessible to everyone.
Do you have a question? Our Service Desk can help you. Contact us via the blue ‘mail directly’ button below.