DOI · DOI-0121 · record R2637

[Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks

Department of the Interior · USGS

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely-recorded seismic sources like earthquakes and explosions. This project aims to develop a classification scheme to differentiate between seismic signals generated by different sources, especially for those generated by vertical processes ("fall") versus those generated by horizontal processes ("slides").

Expected benefits

By automatically discriminating the different sources that generate observed seismic signals, we can more accurately catalog the events and respond to them in more timely, appropriate ways.

System outputs

Events will be automatically classified as seismogenic (e.g., eathquakes or explosions) surficial mass motions (e.g., falls or slides) using statistical metrics extracted from real-time seismic waveform 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: No
Public source code: Not reported

Sourcing

How it was built: Not reported
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: Not reported
Federal Data Catalog: Not reported

Governance

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

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationReported in progressSource: e) In-progress

Potential impacts and how they were identified

Not reported