Self-evolving artificial intelligence framework to better decipher short-term large earthquakes
Year: 2024
Type: article
Abstract: Large earthquakes (EQs) occur at surprising loci and timing, and their descriptions remain a long-standing enigma. Finding answers by traditional approaches or recently emerging machine learning (ML)-driven approaches is formidably difficult due to data scarcity, interwoven multiple physics, and absent first principles. This paper develops a novel artificial intelligence (AI) framework that can transform raw observational EQ data into ML-friendly new features via basic physics and mathematics and that can self-evolve in a direction to better reproduce short-term large EQs. An advanced reinforcement learning (RL) architecture is placed at the highest level to achieve self-evolution. It incorporates transparent ML models to reproduce magnitude and spatial location of large EQs ([Formula: see text] 6.5) weeks before of the failure. Verifications with 40-year EQs in the western U.S. and comparisons against a popular EQ forecasting method are promising. This work will add a new dimension of AI technologies to large EQ research. The developed AI framework will help establish a new database of all EQs in terms of ML-friendly new features and continue to self-evolve in a direction of better reproducing large EQs.
Source: Scientific Reports
Authors: In Ho Cho, Ashish Chapagain
Institution: Iowa State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,590
Awards: Single-Step, Rapidly Reconfigurable Grayscale Nanoprinting by Light-Controlled Nanocapillary Effect, Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions, MRI: Acquisition of a Shared High-Performance Computing System for Cyber-Enabled System Design
OpenAlex ID: W4402694879