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Access analytics-ready data packages from Earth Science Analytics and partners

Multi-Client Products

Access analytics-ready data packages from
Earth Science Analytics and partners

Our multi-client products

Access analytics-ready subsurface data packages that help you screen opportunities faster, reduce uncertainty, and make more confident decisions.

Delivered by Earth Science Analytics and partners, our cleaned, curated, and contextualized datasets integrate directly into existing geoscience workflows or through EarthNET Data Lake for faster visualization, analysis, and insight generation.

Access analytics-ready data packages from Earth Science Analytics and partners

Data delivery

Data is delivered through our data platform, EarthNET Data lake, and as a zip file with las and csv data. Delivery through EarthNET enables ingestion to the OSDU™ Data Platform and allows you to utilise our data visualisation and insight tools.

With EarthNET Viewer and EarthNET Insights you can visualise, query, filter and review the data to reveal insights relevant to your business. We also offers a separate subscription to the complete EarthNET platform which includes AI-applications for automated data interpretation.

Why subscribe to our data packages?

Our curated and analytics-ready data provide the foundation for turning data into insights and action. With our packages, you can access cleaned, indexed, and contextualised data that can be plugged directly into your geoscience workflows, saving you time and resources.

Analytics-ready data

Get access to cleaned, curated, indexed, and contextualised data that you can plug directly into your geoscience workflows

Data insights

Review outcomes, pull together predictions and scale up the insights behind your decision-making processes

Data visualisation

Explore and visualise your data through a holistic digital replica of the subsurface, combining all relevant data.

Continuous improvements

Benefit from the continuous growth in data volume and the continuous improvements of data quality and diversity.