Name: Out of the field, into the model: Turning data into drill targets
Start: 2026-10-01T10:00:00-04:00
End: 2026-10-01T11:00:00-04:00
Location: Virtual

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Events

July

9th

@

10am

PDT

Out of the field, into the model: Turning data into drill targets

You've collected new field data and drill results. Now what?

See how Leapfrog Geo's latest modelling and drill planning tools, alongside Imago's core imagery and AI-assisted insights, help you quickly update your geological model, sharpen your interpretations, and prioritize your next drill targets with greater confidence.

Registration closed

Out of the field, into the model: Turning data into drill targets

Join us for a webinar exploring how Leapfrog Geo and Imago can help geoscience teams turn newly collected field data into informed drilling decisions.

We'll showcase a practical post-fieldwork workflow, from integrating new observations and refining geological interpretations to updating your 3D geological model and identifying your next drill targets.

See how Leapfrog Geo's dynamic modelling workflow and latest drill planning capabilities can help you quickly incorporate new information, evaluate targets, and plan what's next. We'll also demonstrate how Imago's core imagery, visualization tools, and AI-assisted insights can support interpretation and provide additional context for your geological model.

Whether you're planning drillholes for next week or developing a strategy for the next field season, you'll leave with practical techniques for moving from new data to prioritized drill targets with greater confidence.

This free, 60-minute webinar is open to the public.

3 Key Takeaways

Integrate new field data into a geological model to ensure the latest observations and drilling results inform your interpretation.

Leverage Imago's core imagery, visualization capabilities, and AI-assisted insights to validate and refine geological interpretations.

Identify and prioritize the best next drill targets by utilizing new drillhole planning features.

Date/Time

July

9

,

2026

10am

-

11am

PDT

Resources

Learn more about Imago and Leapfrog Geo.

Registration closed

Presenters information

Sarah Furney

Technical Solutions Specialist, Geologist

Seequent

+

Stephanie Schweighardt

Product Marketing Manager

SEEQUENT

+

What is Driver?

Driver is a cloud-based rapid analysis tool that supports spatial exploratory data analysis of drilling datasets. Integrated machine learning automates analysis of numerical and categorical data so teams can quickly understand the structure of their deposit, understand domains, and make better downstream models – with less manual effort and no coding.

Key Benefits

Build better models with data-driven structural insights from Driver

Discover complex lithological and grade relationships

Integrated machine learning automatically detects 3D spatial continuity within numerical and categorical data, revealing key deposit features and patterns without coding for efficient geological insight.

Reveal mineralisation trends and structural deposit features

“Driver uses local ellipsoid drilling data analysis to accurately reveal structural trends influencing mineralisation, offering insights specific to your deposit.

Rapid implicit domain and vein geometry modelling

Driver outputs connect directly to Leapfrog Geo and Leapfrog Edge, guiding form interpolants and structural trends for more accurate and continuous geological models.

Remove manual processes

Driver automates deposit interpretation, providing confidence scores and metadata so geologists can review, refine, and integrate results confidently into Leapfrog models.

Drilling data into structural domains

Driver clusters numeric data into continuity-based domains, aiding identification of features, vein zones, and enabling data-driven modelling and interpretation comparisons across projects.

Discover new deposit features

Explore spatial structure in dozens of attributes, not just Au or Cu, and uncover supporting trends and secondary features that might otherwise be overlooked.