Copy of Technology

Physics-Guided AI
for Seismic Interpretation

A physics-guided deep learning 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.

Seismic noise and imaging artefact classes for robust denoising and interpretation training.

Geomorphological building blocks of paleochannel systems, fills, levees and erosional edges.

Normal, reverse, thrust, listric, relay-linked and other structural families.

Depositional environments represented across marine, continental and transitional systems.

Physics-consistent training patterns with exact seismic / label pairing.

Dataset

65 settings

25 components

15 fault types

1 000 150

Core IP around generation, interpretation workflows and deployment logic.

Technology and physics-generator R&D behind the production stack.

Performance proven in distinct geological and operational contexts.

Already using the platform in interpretation and seismic QC workflows.

Synthetic coverage built for transparent training

Computer-vision metrics translated into interpretation value

Deployment evidence from operating assets and basins

Real projects worldwide across producing basins.

Field

500 companies

5 patents

12 yr

24 regions

300+ projects

Seismic facies handled inside one interpreted volume.

Enough reference sections for transfer-learning adaptation.

Basin adaptation with transfer learning for a new geological setting.

Minimum traceable reflector thickness after enhancement and structural cleanup.

30 sources

Maximum signal-to-noise ratio improvement with structural continuity preserved.

Average fault detection on representative interpretation workflows.

Model

5.2×

12 classes

2 sections

~20 min

5 m

80%

Latin America /
Brazil Margin
loU: 0.66
Precision: 0.88
Dice: 0.80
Recall: 0.81

Asia / Se Asia
loU: 062
Осе: 077
Precision: 097 Recall: 0.76

North Sea
loU: 0.63
Precision: 0.86
Осе: 7
Recall: 0.79

Middle East
loU: 0.68
Precision: 0.89
Осе: 0.81
Recall: 0.83

Latin America /
Northern Margin
IoU: 0.59
Precision: 0.85
Dice: 0.74
Recall: 0.72

East Siberia
loU: 0.57
Pecision: 0.82
Осе: 0.72
Recall: 0.74

West Siberia
loU: 0.61
Precision: 0.84
Осе: 0.76
Recall: 0.78

Validated worldwide across producing basins

Hover regional diamonds to inspect representative segmentation metrics

3D Residual U-Net — chosen
for geophysical reasons
Skip-connections inside both encoder and decoder blocks preserve high-frequency seismic components — the ones that carry thin-bed resolution and fault micro-throws — while aggressively suppressing coherent noise. SNR improvement: 1.4× to 5.2× with full kinematic and dynamic signal preservation.

Architecture-to-Geology Alignment

Synthetic Data at Scale

1M+ physically consistent training samples
Ground truth simply doesn't exist in exploration geophysics. Our Physically Consistent Seismic Generator closes this gap by combining structural modelling, rock-property simulation, wavefield modelling and imaging into a single controlled pipeline. We generate not only synthetics, but explainable training conditions with known assumptions, exact labels and systematic coverage of geological regimes.

Transparent training — physics
and data generation are inspectable
We avoid opaque AI rhetoric by making the training pipeline explicit. Geological scenarios, petrophysical contrasts, wave-propagation assumptions, imaging steps and labels are generated in-house and audited end-to-end. Because the data genesis is known, model behaviour can be interpreted in geological and wave-physics terms rather than treated as an opaque statistical correlator.

One-Shot Transfer Learning

Physics Guided AI

Adapt to any basin in tens
of minutes, not months
Early encoder weights — capturing universal seismic features — are frozen. Only the final decoder layers fine-tune to your reference sections. The result: basin adaptation in about 20 minutes for complex sub-salt, deltaic or structurally deformed settings without rebuilding the training stack from scratch.

Four foundational principles behind a transparent, physics-grounded workflow that answers the objections geophysicists usually raise.

Why Geoplat AI is different

Html code will be here

Step 01 — Structural scenario generation

objects → [faults, relay_ramps, channels, reefs, salt_bodies, fracture_corridors]
settings → [extensional, compressional, strike_slip, deltaic, sub_salt, carbonate]
control → explicit parameter ranges, not ad-hoc augmentation

The pipeline starts from geology, not from image augmentation. We generate large ensembles of 3D structural scenarios: listric and antithetic faults, horst-graben systems, wrench zones, rollover anticlines, salt walls and diapirs, channel-levee complexes, reefs and fracture corridors. Parameter ranges are controlled explicitly, so we know which tectonic styles are represented and where the training envelope begins and ends.

Rock properties & elastic models

Structural scenario generation

Step 02 — Rock properties and elastic earth-model population

earth_model → [Vp, Vs, ρ, impedance, Q, anisotropy]
linkage → geometry × facies × petrophysical priors
result → physically interpretable amplitude behaviour

Each structural realization is converted into a physically usable earth model. We populate layers and objects with Vp, Vs, density, impedance contrasts, attenuation and, where required, anisotropy parameters. This is the critical bridge between geometry and wave physics: without a plausible elastic model, synthetic seismic will look seismic-like but will not teach the network the right amplitude and interference behaviour.

Rock properties & elastic models

Structural scenario generation