Machine Learning for geophysical data inversion

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

R&D use case that is NOT being used to control or significantly influence a decision or outcome about individuals and does not have an approved agreement for transition into agency operations.

Start date

2025-09-26 (day precision)Source: 2025-09-26T00:00:00

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Energy and the Environment

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Leak detection.

Expected benefits

Faster/better leak detection.

System outputs

Synthetic seismic and gravity data.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Yes

Public source code

edx.netl.doe.gov ↗

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

No

Demographic features

k) None of the above

Training and evaluation data

Seismic and gravity data, potentially geological models and leak locations for training labels.

Federal Data Catalog

edx.netl.doe.gov ↗

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported