Machine Learning and Seismic Hazard: A Combination of Probabilistic Approaches for Probabilistic Seismic Hazard Analysis

Abstract

Probabilistic seismic hazard analysis (PSHA) integrates seismology with invitation of civil engineering. Allin Cornell’s 1968 work, developed with Dr. Emilio Rosenblueth and Dr. Luis Esteban Maraboto, revolutionized earthquake engineering by making seismology practical for construction. Cornell’s deterministic equations, once valued for their elegance and simplicity, can now be enhanced with modern tools. Today, probabilistic seismic hazard analysis (PSHA) is evolving by integrating both deterministic and nondeterministic models, leveraging machine learning (ML) techniques such as Random Forests, Support Vector Machines, Neural Networks, Reinforcement Learning, and Bayesian Inferences. This chapter explores the future of PSHA through these advanced methods. While ML offers powerful solutions, it is crucial to recognize that it is not a one-size-fits-all answer. The optimal approach involves using a hybrid ensemble of systems, each designed to address specific challenges in detail.