Towards Enhanced Earthquake Resilience: Linking Early Warning Systems, Ground Motion Prediction, and Stress Field Analysis

Earthquakes pose a significant threat to life and infrastructure, making it essential to improve both our understanding and preparedness for seismic events. This study investigates several interconnected aspects of earthquake science to enhance our ability to forecast and mitigate the impacts of these natural disasters. We explore the development of Earthquake Early Warning (EEW) systems, which provide real-time alerts to minimize damage and save lives. While EEW systems like ShakeAlert, the system in operation along the west coast of the US, show great promise, challenges remain, particularly in accurately predicting ground motion and event characteristics in complex tectonic settings. To address this, we examine how fault complexity affects ground motion predictions, emphasizing that realistic fault geometries and fault-zone properties can significantly influence seismic behavior. The study also analyzes the local stress field in tectonically active regions to better understand earthquake dynamics and faulting patterns. Using data from focal mechanism catalogs, we investigate stress orientations and their role in earthquake triggering and fault reactivation. The findings highlight the importance of accurately modeling stress conditions and fault structures to refine seismic hazard assessments. By combining advancements in early warning systems, ground motion prediction, and stress analysis, this research contributes to more effective earthquake preparedness strategies, improving resilience in earthquake-prone regions.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2024

Author dc:creator

• Chatterjee, Avigyan

Advisor dc:contributor.advisor

• Trugman, Daniel

Committee members dc:contributor.committeemember

• Seylabi, Elnaz Esmaeilzadeh
• Bogolub, Kyren
• Savran, William
• Scheingross, Joel

• Earthquake Early Warning
• Earthquake Source
• Ground Motion
• Machine Learning

Language dc:language.iso

en_US, English

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11265

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11265