GeoManager AI-Agent Beta: Join the Early Access Program
Help us shape the first truly useful AI orchestrator for geoscience projects. This short survey (5–7 minutes) will help us understand your team, your workflows, and whether GeoManager AI-Agent is a good fit for your seismic interpretation projects. By answering these questions, you’ll get a chance to join a small group of early adopters, influence our roadmap, and be among the first to try Geoplat’s new AI‑powered experience in real projects.
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Reduced operational time
Intuitive ML workflow building that significantly decreases processing time and delivers fast and accurate prediction results
Applying your knowledge
Intelligent ML workflow Customisation aimed at retaining your competences and experience
Advanced hybrid neural network
Pretrained Models on more than 1M data samples including multiple seismic attributes
Data model at a new level
Robust prediction of the most complex geological features based on varying seismic quality and complexity of geological strata
8
Years of ML research and development
15
Leading Academic Institutions participating in the R&D program
80
Successful consultancy projects
сompleted by the Geoplat team
сompleted by the Geoplat team
Up to 70 %
Productivity improvement
500
Geoscientists have already
applied Geoplat AI on their data
applied Geoplat AI on their data
1M
Data samples are included in the AI models
А physics-guided deep Iearning platform with transparent training logic: we generate, label and quality-control the data ourselves, so model behavior is traceable back to geology, wave physics and imaging assumptions — not hidden heuristics.
Physics-Guided AI
for Seismic Interpretation
for Seismic Interpretation
AI Webinar Series
Features
Our ML software Geoplat AI is here to significantly speed up the processes for your business challenges and to give you valuable data insights
Fault Interpretation
Fault interpretation is one of the most difficult tasks within a general structural interpretation workflow.
Geoplat developed the technology which can help to significantly reduce time and resources spent on building a geological model. The use of machine learning based on deep neural networks allows to calculate fault probability distribution, extract surfaces, and eliminate interpretation uncertainties.
More Geoplat developed the technology which can help to significantly reduce time and resources spent on building a geological model. The use of machine learning based on deep neural networks allows to calculate fault probability distribution, extract surfaces, and eliminate interpretation uncertainties.
Horizon Interpretation
Horizon interpretation is the core process in understanding the structural features of a geological cross section and conducting reliable dynamic seismic analysis.
Geoplat offers a new approach to address automatic horizon tracing. You can trace a single horizon or the whole set, preserving complex fault structures and regional geological features.
More Geoplat offers a new approach to address automatic horizon tracing. You can trace a single horizon or the whole set, preserving complex fault structures and regional geological features.
Structure-oriented Seismic Conditioning
Low quality seismic datasets often make it difficult to build a structural framework and predict pay zone properties.
Machine learning algorithms developed by Geoplat provide a powerful workflow for interactive and intelligent data conditioning of post stack seismic data. It enables getting instant results considerably saving time on defining functions and workflows.
More Machine learning algorithms developed by Geoplat provide a powerful workflow for interactive and intelligent data conditioning of post stack seismic data. It enables getting instant results considerably saving time on defining functions and workflows.
- Case Studies
Check out an effective application of the Geoplat technologies on real seismic data
Seismic data quality increasing with a help of seismic data AI-based cleaning functions
Salt geobody delineation outcome based on the AI capabilities
Results of automatic horizon interpretation, propagated over the entire survey area with a help of AI-driven tool
Detecting paleo channels on the entire survey area enabled by the AI geobody solution
Typical Opaque AI Platform
Geoplat Artificial Intelligence
Synthetic data often narrower, with weaker control of imaging realism
Requires spectral decomposition + attribute pre-computation
Aggressive filtering blurs high-frequency components
Requires full retraining for new basins (weeks to months)
Limited visibility into training data provenance and failure modes
SaaS / cloud-only — proprietary seismic data must be uploaded externally
Heavy infrastructure requirements, often impractical for secure corporate environments
12 years physics generator R&D — 1M+ paired training samples since 2019
Optimised runtime: ≤32 GB RAM at peak, 16 GB stable — no GPU cluster required
Transparent training pipeline with explicit geological and physical assumptions
Transfer Learning — any basin adapted in ~20 min
Full kinematic + dynamic signal preservation (AVO-safe)
Works directly on raw amplitude — no attribute pre-computation
On-premises deployment — seismic data never leaves your infrastructure
The differences that matter to a working geophysicist: training transparency, physics control, data provenance — and data sovereignty.
Geoplat AI vs. opaque AI platforms
Enjoy the Advantages of the Integrated AI Workflow Today!
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GeoManager AI-Agent Beta: Join the Early Access Program
Help us shape the first truly useful AI orchestrator for geoscience projects. This short survey (5–7 minutes) will help us understand your team, your workflows, and whether GeoManager AI-Agent is a good fit for your seismic interpretation projects. By answering these questions, you’ll get a chance to join a small group of early adopters, influence our roadmap, and be among the first to try Geoplat’s new AI‑powered experience in real projects.
You agree with our Terms and Conditions