Intelligent systems for seismic motion detection: a systematic review
DOI:
https://doi.org/10.63688/kfafxt40Keywords:
artificial intelligence, seismic sensors, deep learning, earthquake detection, early warning systems, systematic reviewAbstract
Seismic movements represent one of the natural phenomena with the greatest social impact due to their destructive potential and the difficulty of accurately predicting their occurrence. In response to this challenge, the development of intelligent systems based on physical sensors and artificial intelligence has emerged as a technological alternative to improve early detection, seismic signal analysis, and earthquake risk reduction. This study aimed to analyze recent scientific evidence regarding the application of intelligent systems for seismic monitoring through a systematic literature review. A qualitative descriptive-analytical approach was applied following the PRISMA 2020 guidelines, considering studies published between 2020 and 2026 from academic databases such as Scopus, Web of Science, ScienceDirect, IEEE Xplore, SpringerLink, and other scientific sources. From a total of 122 identified records, 24 studies were included after applying the established selection and eligibility criteria. The findings showed that the integration of physical sensors with machine learning and deep learning algorithms improves seismic pattern recognition, automated wave identification, and early warning generation. Additionally, the reviewed evidence revealed challenges related to data availability, adaptation to different geological conditions, model validation, and technological infrastructure requirements. It is concluded that intelligent systems represent a complementary tool to traditional seismic monitoring approaches, contributing to faster and more efficient strategies for earthquake risk prevention and management.
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Copyright (c) 2026 Marlon Jonathan Estrella Semblantes, David Sebastián Pozo Guapucal, Ángel Mesías Gualpa Mamarandi (Autor/a)
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