Artificial intelligence in seismology: Advent, performance and future trends
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-05-01
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| Series: | Geoscience Frontiers |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987119301987 |
| _version_ | 1797763919425568768 |
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| author | Pengcheng Jiao Amir H. Alavi |
| author_facet | Pengcheng Jiao Amir H. Alavi |
| author_sort | Pengcheng Jiao |
| collection | DOAJ |
| description | 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. Keywords: Seismology, Artificial intelligence, Machine learning, Deep learning, Internet-of-Things |
| first_indexed | 2024-03-12T19:49:13Z |
| format | Article |
| id | doaj.art-8502e6ed699c4eee8d5d33d31821db11 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T19:49:13Z |
| publishDate | 2020-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-8502e6ed699c4eee8d5d33d31821db112023-08-02T03:20:20ZengElsevierGeoscience Frontiers1674-98712020-05-01113739744Artificial intelligence in seismology: Advent, performance and future trendsPengcheng Jiao0Amir H. Alavi1Ocean College, Zhejiang University, Zhoushan, Zhejiang 316021, ChinaDepartment of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA; Corresponding author.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. Keywords: Seismology, Artificial intelligence, Machine learning, Deep learning, Internet-of-Thingshttp://www.sciencedirect.com/science/article/pii/S1674987119301987 |
| spellingShingle | Pengcheng Jiao Amir H. Alavi Artificial intelligence in seismology: Advent, performance and future trends Geoscience Frontiers |
| title | Artificial intelligence in seismology: Advent, performance and future trends |
| title_full | Artificial intelligence in seismology: Advent, performance and future trends |
| title_fullStr | Artificial intelligence in seismology: Advent, performance and future trends |
| title_full_unstemmed | Artificial intelligence in seismology: Advent, performance and future trends |
| title_short | Artificial intelligence in seismology: Advent, performance and future trends |
| title_sort | artificial intelligence in seismology advent performance and future trends |
| url | http://www.sciencedirect.com/science/article/pii/S1674987119301987 |
| work_keys_str_mv | AT pengchengjiao artificialintelligenceinseismologyadventperformanceandfuturetrends AT amirhalavi artificialintelligenceinseismologyadventperformanceandfuturetrends |