Towards advancing the earthquake forecasting by machine learning of satellite data

Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen*, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.

Original languageEnglish
Article number145256
Number of pages16
JournalScience of the Total Environment
Early online date28 Jan 2021
Publication statusPublished - 01 Jun 2021

Bibliographical note

Funding Information:
This work is supported in part by the National Key Research and Development Program of China under Grant No. 2018YFC1503505 , and in part by the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration under Grant 2020IEF0510 and Grant 2020IEF0705 .

Publisher Copyright:
© 2021 Elsevier B.V.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Earthquake forecasting
  • Earthquake precursors
  • Infrared and hyperspectral parameters
  • Machine learning

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution


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