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MethaneScan® AI
Low-Cost Methane Detection at Scale
Geofinancial Analytics Canada Corp.
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02 / 09
The Problem We Are Solving · Part 1
Methane Super-Emitters —
Rare Events, Outsized Impact
Most sites never have a super-emitter event
A small subset of high-emitting sites have multiple events per year — typically at unmanned locations
Share of Super Emitters
SITES SUPER EMITTERS 90% of sites 10% <10% >90% of events
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03 / 09
The Problem We Are Solving · Part 2
Early Detection Requires Wide-Area Monitoring
Although relatively rare, super emitter events collectively account for ~50% of fugitive emissions
Coverage Gap

Onsite continuous monitoring typically covers only 10% of sites — leaving the majority unobserved between scheduled inspections

Predictive Intelligence

AI-powered tools are needed to predict and target the most likely super-emitter sites before events occur

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The Solution
Coverage
Weekly Satellite Scans

Sub-weekly flyovers of every site using open-data from European Space Agency and NASA satellites.

Intelligence
Deep Learning AI

Compute-efficient model detects and quantifies methane plumes, attributing emissions to geo-located facilities.

Resolution
30 m · 150–200 kg/hr

High spatial resolution with a 150–200 kg/hr detection threshold. History back to 2015, near real-time today.

Economics
Order-of-Magnitude Savings

1/10th the cost of aerial flyovers. Designed for global scaling.

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05 / 09
AI-detected methane plume
Live Detection
AI-Simulated Methane Plume Detection

GEO's deep learning model processes satellite imagery to detect methane plumes in near real-time. Observed plumes are analyzed by GEO scientists and rated for confidence — similar to how radiologists assess medical imaging.

© 2026 GEO · AI-simulated methane plume

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Why MethaneScan® AI Wins
CapabilityMethaneScan AIAerial FlyoversFree SatellitesOnsite Monitors
Weekly site revisit
Low cost
Full geographic coverage~
Plume quantification
No onsite visit required
History to 2015~
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07 / 09
Sentinel-2
ESA Sentinel-2
How It Works
Technical Specifications
Spatial Res.
30 metres
Revisit
Weekly scans of every site
Coverage
Anywhere onshore (subject to cloud cover)
Detection
150–200 kg/hr or better
History
2015 to near real-time
AI Model
Compute-efficient deep learning — built for global scale
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Use Cases & Benefits
01

Boost sales to buyers of low methane natural gas

02

Strengthen your company brand & reputation

03

Retain and attract investors

04

Prioritize well abandonment sites by leak risk

05

Validate permanence claims for carbon credits

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Trusted. Independent. AI-Powered.

Innovation funded by U.S. National Science Foundation

NSF Funded
1-778-402-1850  ·  info@geofinancial.ca
geofinancial.ca