Seismic Waveform Interpretation
AI platform for seismic and marine energy analysis, combining subsurface modelling, wave resource data delivery, capacity factor estimation, and coastal early-warning intelligence to support exploration, investment, and resilience decisions.
The Problem
“Fragmented seismic, wave, and coastal risk analysis slows energy decisions and raises project risk”
Organizations face these key challenges:
Seismic, metocean, bathymetry, and commercial data are stored in disconnected systems
Subsurface and investment teams use separate workflows with limited traceability
Wave resource analysis requires time-consuming data cleaning and harmonization
Capacity factor estimation is expensive when every scenario requires custom engineering work
Impact When Solved
The Shift
Human Does
- •Gather seismic, metocean, bathymetry, device, and coastal data from multiple sources
- •Clean and reconcile datasets across separate geoscience, engineering, and investment workflows
- •Build study-specific resource, subsurface, and performance assessments in specialist tools
- •Review reports, compare scenarios, and decide which projects or risks need action
Automation
- •Apply basic threshold alerts or rule-based monitoring for weather and shoreline conditions
- •Generate limited static calculations or simplified scenario outputs in desktop models
- •Provide ad hoc data exports or summaries from individual datasets when requested
Human Does
- •Set study objectives, decision criteria, and portfolio or public-safety priorities
- •Review AI-ranked prospects, capacity estimates, and coastal risk outputs before action
- •Approve investment, exploration, operational, or emergency-response decisions
AI Handles
- •Ingest, harmonize, and serve wave, seismic, bathymetry, weather, and coastal datasets through standardized analysis workflows
- •Estimate capacity factor and compare device-site combinations for rapid pre-feasibility screening
- •Rank subsurface and marine energy opportunities using integrated technical, commercial, and uncertainty signals
- •Monitor multivariate coastal conditions, forecast surge and erosion risk, and generate alert recommendations
Operating Intelligence
How Seismic Waveform Interpretation runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
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.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve exploration, investment, or public-safety actions without review by the accountable human decision maker [S3][S4].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Seismic Waveform Interpretation implementations:
Key Players
Companies actively working on Seismic Waveform Interpretation solutions:
Real-World Use Cases
Wave resource data access for energy site assessment
A developer can call the Wave API to fetch ocean wave conditions and model outputs instead of collecting all measurements themselves.
AI to reduce costs of new energy technologies
AI can help make newer clean-energy technologies cheaper by improving design, planning and operations.
Lens Subsurface Modelling for capital allocation decisions
AI combines underground technical data with business data so oil and gas teams can decide faster where to invest money.
Programmatic wave resource analysis in Python and MATLAB
Instead of clicking around a map, analysts can pull wave data directly into code and automate studies of how devices might perform at many sites.
Coastal storm surge and erosion early-warning system for São Paulo beaches (SARIC)
A public system watches weather, tides, and waves for each beach in São Paulo and warns authorities up to 4 days before dangerous sea conditions can cause flooding, erosion, or storm damage.