Maofa Wang
Guilin University of Electronic Technology
Professor
Expertise: I am mainly engaged in earthquake artificial intelligence research. At present, I have developed a set of medium and strong earthquake short-term and imminent aftershock prediction system and a set of strong robust analog seismometry vector system.
About Me
Publications
Wang Maofa, male, 40 years old, doctor, professor. He is mainly engaged in the research of earthquake artificial intelligence algorithm and system development, has published more than 20 high-level SCI papers, presided over 2 NSFC projects, 2 provincial and ministerial level projects, and participated in many other provincial and ministerial level projects. The main courses he teaches include: deep learning, meta learning, data mining, natural language processing, etc. At present, the scientific research direction is divided into three parts:
1. Deep meta learning theory. We mainly designs and develops a variety of mechanism enhanced meta learning algorithms and frameworks to solve the cross domain deep learning problems in the case of small samples, unbalanced samples and no samples.
2. Study on intelligent prediction of short-term and imminent aftershocks of moderate and strong earthquakes. We mainly studies how to deeply mine the complex, abstract and deep big data correlation between the main shock parameters (magnitude, radiant energy, apparent stress, seismic moment, seismic nodal plane, etc.) and the subsequent aftershocks (magnitude, time, location) based on the earthquake catalog and focal mechanism solution, and develop a high accuracy intelligent software for short-term and imminent aftershocks prediction of moderate and strong earthquakes based on the AI research and development platform, The application of short-term and imminent aftershock prediction of moderate and strong earthquakes is carried out in real time.
3. Research on strong robust vector algorithm for analog seismic waveform recording based on depth element learning.
(1)Maofa Wang*, Juan Shen, Zhian Pan, Dingliang Han, An improved supported vector regression algorithm with application to predict aftershocks, Journal of Seismology, 2019, 23:983–993.（JCR Q1 SCI）
(2)Maofa Wang*, Qigang Jiang, Qingjie Liu, Meng Huang, A new program on digitizing analog seismograms, Computers & Geosciences, 2016, 93,70-76. (JCR Q1 SCI)
(3)Weifeng Shan, Mingjie Zhang, Maofa Wang* , Huiling Chen, Ruilei Zhang , Guangze Yang , Yixiang Tang , Yuntian Teng, and Jun Chen, EPM–DCNN: Earthquake Prediction Models Using Deep Convolutional Neural Networks, BSSA, accepted,2022
(4)Maofa Wang*, Hongliang Huang, Guangda Gao, Weiyu Tang, Trend prediction of irrigation area using improved random forest regression, Irrig. and Drain. 2022, DOI: 10.1002/ird.2695. ( JCR Q3 SCI)
1. Deep meta learning theory. We mainly designs and develops a variety of mechanism enhanced meta learning algorithms and frameworks to solve the cross domain deep learning problems in the case of small samples, unbalanced samples and no samples.
2. Study on intelligent prediction of short-term and imminent aftershocks of moderate and strong earthquakes. We mainly studies how to deeply mine the complex, abstract and deep big data correlation between the main shock parameters (magnitude, radiant energy, apparent stress, seismic moment, seismic nodal plane, etc.) and the subsequent aftershocks (magnitude, time, location) based on the earthquake catalog and focal mechanism solution, and develop a high accuracy intelligent software for short-term and imminent aftershocks prediction of moderate and strong earthquakes based on the AI research and development platform, The application of short-term and imminent aftershock prediction of moderate and strong earthquakes is carried out in real time.
3. Research on strong robust vector algorithm for analog seismic waveform recording based on depth element learning.
(1)Maofa Wang*, Juan Shen, Zhian Pan, Dingliang Han, An improved supported vector regression algorithm with application to predict aftershocks, Journal of Seismology, 2019, 23:983–993.（JCR Q1 SCI）
(2)Maofa Wang*, Qigang Jiang, Qingjie Liu, Meng Huang, A new program on digitizing analog seismograms, Computers & Geosciences, 2016, 93,70-76. (JCR Q1 SCI)
(3)Weifeng Shan, Mingjie Zhang, Maofa Wang* , Huiling Chen, Ruilei Zhang , Guangze Yang , Yixiang Tang , Yuntian Teng, and Jun Chen, EPM–DCNN: Earthquake Prediction Models Using Deep Convolutional Neural Networks, BSSA, accepted,2022
(4)Maofa Wang*, Hongliang Huang, Guangda Gao, Weiyu Tang, Trend prediction of irrigation area using improved random forest regression, Irrig. and Drain. 2022, DOI: 10.1002/ird.2695. ( JCR Q3 SCI)
