Artificial intelligence in seismology: Advent, performance and future trends
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Abstract
Realistically predicting earthquake is critical for seismic risk assessment, prevention and safe design of major
structures. Due to the complex nature of seismic events, it is challengeable to efficiently identify the earthquake
response and extract indicative features from the continuously detected seismic data. These challenges severely
impact the performance of traditional seismic prediction models and obstacle the development of seismology in
general. Taking their advantages in data analysis, artificial intelligence (AI) techniques have been utilized as
powerful statistical tools to tackle these issues. This typically involves processing massive detected data with severe
noise to enhance the seismic performance of structures. From extracting meaningful sensing data to unveiling seismic
events that are below the detection level, AI assists in identifying unknown features to more accurately predicting the
earthquake activities. In this focus paper, we provide an overview of the recent AI studies in seismology and evaluate
the performance of the major AI techniques including machine learning and deep learning in seismic data analysis.
Furthermore, we envision the future direction of the AI methods in earthquake engineering which will involve deep
learning-enhanced seismology in an internet-of-things (IoT) platform.
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