DOI · DOI-0120 · record R2640

Modeling Rupture Directivity Effects on Ground Motion Using Neural Networks

Department of the Interior · USGS

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2024-06-01 (day precision)Source: 6/1/2024
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

We use Artificial Neural Networks (ANNs) to more accurately modeling ground motion amplification effects caused by rupture directivity during earthquakes. These effects are complex and challenging to capture using traditional methods, which can impact the accuracy of seismic hazard assessments.

Expected benefits

computationally efficient method to improve seismic hazard modeling by incorporating rupture directivity effects

System outputs

The outputs here are adjustments to the median and standard deviation of ground-motion model predictions. The AI system predicts the amplified ground motion effects due to rupture directivity and are used to refine seismic hazard assessments in a computationally lightweight manner.

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: 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: Yes
Demographic features: k) None of the above
Training and evaluation data: The training data was derived from a publicly available rupture directivity model.
Federal Data Catalog: Not reported

Governance

0 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 reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported