Global seismic activity and the vulnerability of urbanized areas and complex supply chains are driving demand for sophisticated predictive analytics in disaster management. Governments and corporations worldwide are investing heavily in resilience, early warning systems, and digital transformation (DX) for disaster prevention. This technology aligns perfectly with these trends, offering a novel, satellite-based approach to enhance preparedness and mitigate the devastating economic and human costs of earthquakes.
Achieves High-Precision Earthquake Prediction: Demonstrates over 90% accuracy for regional-level predictions within 90 days for 97 past earthquakes, with 82% accuracy for magnitude within 0.7.
Enables Automated Detection Independent of Human Expertise: AI analyzes meteorological satellite infrared images, automating the identification of earthquake clouds that previously relied on skilled human judgment, ensuring objective and stable predictions.
Provides Broad-Area, Continuous Monitoring Capability: Utilizes meteorological satellite infrared images for continuous, wide-area monitoring, enabling early detection of widespread anomalies difficult for ground-based observation networks.
This patent protects a program, information processing apparatus, and method for high-precision earthquake prediction using AI analysis of meteorological satellite infrared images. It covers the generation of training data, reception of target images, and determination of earthquake clouds. The patent was granted after overcoming two office actions, resulting in a robust and clearly defined scope across 12 claims, demonstrating strong stability against invalidation.
This patent focuses on earthquake prediction using satellite imagery and AI. White space exists in developing advanced mitigation technologies, integrating predictive data into autonomous emergency response systems, or leveraging the core AI for other atmospheric anomaly detection beyond seismic precursors.
In civil engineering and infrastructure management, earthquake-related business interruptions, equipment damage, and recovery costs can reach hundreds of millions of dollars annually. By enabling early warning, this technology could reduce disaster-related costs by ~2% for companies with ~$66.5M (AI est.) in annual disaster expenses, leading to ~$1.5M/year (AI est.) in savings. This is based on projected benefits from planned equipment maintenance and damage reduction through early evacuation.
X: Prediction Accuracy and Reliability
Y: Ease of Implementation and Cost-Effectiveness