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Machine Learning Applications in Seismology: 2nd Edition

This special issue belongs to the section “Earth Sciences“.

Special Issue Information

Dear Colleagues,

In recent years, machine-learning-based artificial intelligence technology has been rapidly applied to digital seismic data processing and developing a structured seismic catalog. Artificial intelligence (AI) methods hold significant promise for solving fundamental scientific problems in seismology. AI technology can carry out multiple geophysical observations, so as to identify signals or patterns that cannot be captured by traditional methods unable to easily generate information about strong earthquakes. AI can help us further understand the physical process of earthquakes.

In the last two years, we gathered 15 excellent papers and published them in the Special Issue “Machine Learning Applications in Seismology”. Following on from the success of this Special Issue, in 2024, we will once again collect papers for a Special Issue entitled “Machine Learning Applications in Seismology: 2nd Edition”. The 2nd Special Issue will present innovative ideas and the latest findings in earthquake monitoring and early warning and forecasting systems, as developed through different machine-learning-related methods, theories and applications. The scope of this Special Issue includes, but is not limited to, the following: seismic data processing, event location and discrimination, early warning, forecasting, machine learning, deep learning, and other applications in seismology.

Topics include, but are not limited to, the following:

  • Artificial intelligence;
  • Machine learning;
  • Deep learning;
  • Processing of seismic data;
  • Phase picking;
  • Denoising of seismic data;
  • Earthquake location;
  • Earthquake detection;
  • Focal mechanism;
  • Earthquake early warning;
  • Earthquake forecast and prediction.

Dr. Ke Jia
Dr. Wenhuan Kuang
Dr. Kai Deng
Prof. Dr. Shiyong Zhou
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • processing of seismic data
  • phase picking
  • denoising of seismic data
  • earthquake location
  • earthquake detection
  • focal mechanism
  • earthquake early warning
  • earthquake forecast and prediction

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Appl. Sci. - ISSN 2076-3417