Deep-Time Intelligence for Precision Discovery

We combine Deep-time reconstruction and Multi-Physics AI to transform your multi-source data into clear discovery vectors. Experience transparent, Explainable AI that empowers decision-making while maintaining total data security and sovereignty.

Applied AI

From raw data to discovery-grade intelligence

Three end-to-end field deployments that combine multi-source satellite and geophysical datasets, physics-informed feature engineering, and explainable machine learning — delivering actionable exploration vectors at scale.

Satellite lithological mapping — spectral classification output
01
Remote Sensing Machine Learning Spectral Analytics

Satellite-Based Lithological Mapping at Scale

Autonomous discrimination of ultramafic lithologies across inaccessible high-altitude terrain using multi-sensor spectral data. Sentinel-2, ASTER, and Hyperion fusion enables high-confidence geological classification — replacing weeks of field traverses with analysis deployable in hours.

3× Sensors fused
96% Classification accuracy
4,800 km² Terrain covered
Serpentinization mapping without field access
Ophiolite lithology boundaries at 15 m resolution
Explainable spectral AI — physics-validated outputs
Sentinel-2 ASTER Hyperion FLAASH SAM PCA XGBoost
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AI-driven porphyry copper prospectivity map
02
Machine Learning Deep-Time Modelling 4D Earth Intelligence

Hyperdimensional Porphyry Copper Prospectivity

Two-stream AI architecture that fuses present-day geophysical signals with reconstructed deep-time geodynamic trajectories. Positive–Unlabeled learning on incomplete deposit records, hyperdimensional integration via multiplicative fusion, and exploration-ranked outputs that outperform purely spatial models by 40% on Recall@5%.

+40% Recall@5% vs spatial-only
0.95 AUC-ROC
170 Ma Temporal history modelled
Prioritises top 5% area containing 72% of known deposits
Carbonate–redox engine encoded as temporal feature
Generalises across Tethyan, Pacific, and Andean arcs
XGBoost PU Bagging pyDTDM GPlates SHAP 4D Fusion
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Reduction-to-pole magnetic field of the Arabian Shield with known deposits
03
Geophysics Potential Fields Feature Engineering

Geophysical Preprocessing for Mineral Prospectivity

Raw gravity and magnetic grids hide the faults, contacts and intrusion margins that control ore. A suite of physics-based filters — reduction-to-pole, derivatives, tilt, analytic signal and upward continuation — turns those fields into the structural features an AI prospectivity model learns from. Demonstrated on the Arabian Shield against 875 known metallic deposits.

875 Deposits analysed
1.5× Gradient enrichment at deposits
33 Derived feature layers
Reframes raw fields into model-ready structural layers
Deposits sit on magnetic gradients 1.5× above background
Fully reproducible from open data with open filters
EMAG2_V3 WGM2012 RTP Tilt / THG harmonica Kalpa
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Every analysis is built on transparent, explainable AI — so your geoscience team remains in control of every discovery decision.

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Featured Software Solutions

Comprehensive suite of AI-powered geoscience tools

Kalpa

A comprehensive geospatial AI platform that unifies satellite data, geological information, and advanced analytics in one integrated environment.

Explore Kalpa

Vyom

Python-based plugin designed for highly efficient, flexible, and scalable access to satellite Earth observation data.

Explore Vyom
GeoMech well log analysis

GeoMech

Open-source geomechanical modeling software optimized for borehole stability analysis and drilling risk assessment.

Explore GeoMech View on GitHub

pyDTDM

Powerful open-source Python library for deep-time spatiotemporal geological data analysis and paleogeographic reconstruction.

Explore pyDTDM View on GitHub
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