Sign in

AI Seismic Interpretation

Machine learning for automated seismic data interpretation and analysis

The Problem

“Automate seismic interpretation to accelerate subsurface understanding”

Organizations face these key challenges:

1

Manual horizon and fault picking is slow and labor-intensive

2

Interpretation quality varies by interpreter experience and workflow

3

Subtle events such as diffractions and small faults are difficult to isolate

4

Large 3D seismic volumes overwhelm conventional manual workflows

5

Labeled seismic training data is scarce, expensive, and basin-specific

6

Generalizing models across surveys, vintages, and geological settings is difficult

7

Integration with existing geoscience interpretation platforms can be complex

Impact When Solved

Cuts manual interpretation time for large offshore and regional seismic datasetsImproves consistency of horizon and fault interpretation across interpretersDetects subtle diffraction and heterogeneity signatures missed in conventional workflowsEnables scalable screening of seismic volumes for exploration and CCS site characterizationReduces dependence on large labeled datasets through self-supervised learningCreates reusable seismic representations for multiple downstream interpretation tasks

The Shift

Before AI~85% Manual

Human Does

  • •Review seismic volumes and manually pick key horizons, faults, and geobodies
  • •Cross-check interpretations against attributes, time slices, and well ties
  • •Iterate maps and structural frameworks through peer review and rework cycles
  • •Update interpretations when new seismic reprocessing or well information arrives

Automation

  • •Provide basic rule-based auto-tracking suggestions for horizons or faults
  • •Generate standard seismic attribute views to support manual interpretation
  • •Flag obvious discontinuities or amplitude anomalies for interpreter review
With AI~75% Automated

Human Does

  • •Set interpretation objectives, confidence thresholds, and priority zones for review
  • •Validate AI-generated faults, horizons, salt boundaries, and other geobodies
  • •Resolve low-confidence or geologically ambiguous areas and approve final interpretations

AI Handles

  • •Scan 2D and 3D seismic volumes to identify and segment faults, horizons, salt, channels, and stratigraphic features
  • •Produce preliminary interpretation surfaces and geobodies across the full survey
  • •Score confidence, highlight uncertain or anomalous areas, and prioritize QC effort
  • •Refresh interpretations quickly when new seismic, reprocessing results, or well control are introduced

Operating Intelligence

How AI Seismic Interpretation runs once it is live

AI surfaces what is hidden in the data.

Humans do the substantive investigation.

Closed cases sharpen future detection.

Confidence93%
ArchetypeDetect & Investigate
Shape6-step funnel
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapefunnel

Step 1

Scan

Step 2

Detect

Step 3

Assemble Evidence

Step 4

Investigate

Step 5

Act

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in AI Seismic Interpretation implementations:

High-performance computing (HPC) platformOther
4 mentions
Pre-trained CNNsOther
4 mentions
User-trained CNNsOther
4 mentions
Convolutional neural networks (CNNs)Other
3 mentions
Survey-tailored training label datasetsOther
3 mentions
+3 more technologies(sign up to see all)

Key Players

Companies actively working on AI Seismic Interpretation solutions:

CGGCGG geoscience ML researchEmerging geoscience foundation model effortsPGSSLB
+1 more companies(sign up to see all)

Real-World Use Cases

Diffraction event detection for small-scale subsurface heterogeneity imaging

AI searches seismic volumes for faint diffraction patterns that can reveal tiny underground features that normal imaging may blur.

event detection and segmentationproposed/early application with strong technical promise rather than clearly stated broad production deployment.
10.0

Self-supervised seismic foundation model for feature categorization and downstream tasks

Pretrain a big 3D seismic model on unlabeled subsurface cubes so it learns general patterns, then adapt it to jobs like filling missing data or finding salt.

Self-supervised representation learning for 3D volumetric datain development, with demonstrated fine-tuning on interpolation and salt segmentation tasks.
10.0

ML-assisted seismic horizon interpretation in the Kwanza Basin

Machine learning helps geoscientists trace important underground layers in seismic data much faster than doing it all by hand, so they can evaluate exploration prospects sooner.

Computer vision-style pattern recognition and boundary prediction on seismic volumesproduction-oriented specialized deployment: cnn-based workflows were integrated into an hpc platform and used across multiple horizon types and study areas in the kwanza basin.
9.5

Unsupervised seismic interpretation using Seisnetics AI Engine

An AI system analyzes seismic data to find subsurface patterns without needing pre-labeled examples.

unsupervised pattern discovery / clustering in geoscience imagerypilot / applied research
9.5

Free access to this report