Seismic Resilience in the Age of AI and Advanced Remote Sensing

Prof. Dr. Cecilia Van Cauwenberghe, PhD, MSc, BS, MBA, is Research Director at Everest Group. Here, she explores seismic resilience in the age of artificial intelligence and advanced remote sensing, focusing on how science and technology transform earthquake preparedness.

Introduction: From detection to prediction

Earthquakes remain among the most devastating and unpredictable natural disasters, causing catastrophic loss of life and infrastructure damage. However, recent breakthroughs in artificial intelligence (AI), machine learning (ML), remote sensing, and cost-benefit modeling are transforming our ability to detect, predict, and mitigate seismic hazards. Historically, earthquake preparedness has relied on reactive detection systems and post-event damage assessments. However, cutting-edge research is shifting the paradigm toward proactive forecasting and real-time reconnaissance, enhancing both emergency response efficiency and long-term resilience planning.

AI-driven forecasting

Between 2019 and 2023, the ShakeAlert® Earthquake Early Warning System issued 95 alerts for earthquakes of 4.5 or higher, with 94 confirmed events. While this marks a high detection accuracy, seven false alerts and four missed earthquakes highlight network coverage and precision gaps, particularly in edge regions. AI-driven approaches are being developed to move from detection to prediction to address these limitations. Anbazhagu et al. (2025) highlight how deep learning models, seismic anomaly detection, and neural networks can improve forecasting accuracy by analyzing large-scale geophysical datasets.

Enhancing post-earthquake damage assessment with synergistic innovations

Once an earthquake strikes, rapid and precise damage assessment is critical for directing emergency response efforts. Traditionally, building damage evaluations have relied on manual inspections, which are time-consuming, hazardous, and inefficient for large-scale disasters. Singh et al. (2024) developed a metadata-enriched transformer-based model to improve efficiency and accuracy. The researchers synergistically integrated high-resolution optical and SAR satellite imagery, ground motion intensity data, and soil property analysis.

The economics of earthquake resilience: Evaluating cost vs. benefit

Zhang et al. (2024) conducted a comprehensive benefit-cost analysis (BCA) to evaluate the financial trade-offs of modern building codes, above-code structural designs, and seismic retrofitting. Future research will explore BCA integration with environmental benefits, mainly how earthquake-resistant infrastructure reduces carbon footprints, and assess policy-driven incentives to encourage resilience investments.

Policy and research roadmap: NEHRP and NSHM

The National Earthquake Hazards Reduction Program (NEHRP) has guided U.S. seismic risk reduction since its inception, undergoing multiple reauthorizations. Meanwhile, the 2023 U.S. National Seismic Hazard Model (NSHM) represents a landmark advancement in hazard assessment.

A future defined by AI, remote sensing, and resilience

Emerging technologies can minimize uncertainties, improve forecasting capabilities, and accelerate post-disaster response. To fully realize this vision, continued investment in AI-powered earthquake forecasting, real-time reconnaissance technologies, and optimized benefit–cost strategies for resilience infrastructure is crucial. A global collaborative effort among governments, researchers, and technology leaders will be key in scaling these innovations for widespread adoption.