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Applied Sciences
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Machine Learning Applications in Seismology: 2nd Edition
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Machine Learning Applications in Seismology: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 6693

Special Issue Editors

Dr. Ke Jia

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Interests: statistical seismology; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Wenhuan Kuang

E-Mail Website
Guest Editor
College of Marine Geosciences, Ocean University of China, Qingdao 266100, China
Interests: reinforcement learning; location and focal mechanism
Dr. Kai Deng

E-Mail Website
Guest Editor
College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
Interests: machine learning in induced seismicity
Prof. Dr. Shiyong Zhou

E-Mail Website
Guest Editor
School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: seismology; seismicity; machine learning; processing of seismic data
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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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Related Special Issue

Published Papers (4 papers)

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Research

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17 pages, 5164 KiB  
Article
A Microseismic Phase Picking and Polarity Determination Model Based on the Earthquake Transformer
by Ling Peng, Lei Li and Xiaobao Zeng
Appl. Sci. 2025, 15(7), 3424; https://doi.org/10.3390/app15073424 - 21 Mar 2025
Viewed by 405
Abstract
Phase arrival times and polarities provide essential kinematic constraints for and dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking and polarity determination are even more challenging but [...] Read more.
Phase arrival times and polarities provide essential kinematic constraints for and dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking and polarity determination are even more challenging but play a crucial role in source location and focal mechanism inversion. This study innovatively proposes a deep learning model suitable for simultaneous phase picking and polarity determination with continuous microseismic waveforms. Building upon the Earthquake Transformer (EQT) model, we implemented structural improvements through four distinct decoders specifically designed for three tasks of P-wave picking, S-wave picking, and P-wave first-motion polarity determination and named the model EQT-Plus (EQTP). Notably, the polarity determination task was decomposed into two independent decoders to enhance the learning of polarity characteristics. Through training on a northern California dataset and testing on microseismic events (Md < 3) in the Geysers region, the results demonstrate that the EQTP model achieves superior performance in both phase picking and polarity determination compared to the PhaseNet+ model. It not only provides accurate phase picking but also shows higher consistency with manual picking results in polarity determination. We further validated the good generalization ability of the model with the DiTing dataset from China. This study not only advances the adaptation of the Transformer model in seismology but also reliably delivers fundamental information essential for refined microseismic inversion, offering an alternative and advanced tool for the seismological community. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)
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21 pages, 2354 KiB  
Article
Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
by Jingye Zhang, Ke Sun, Xiaoming Han and Ning Mao
Appl. Sci. 2024, 14(24), 11854; https://doi.org/10.3390/app142411854 - 18 Dec 2024
Viewed by 1163
Abstract
Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° [...] Read more.
Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 < ML ≤ 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model’s potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)
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31 pages, 4839 KiB  
Article
Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis
by Cosmina-Mihaela Rosca and Adrian Stancu
Appl. Sci. 2024, 14(22), 10169; https://doi.org/10.3390/app142210169 - 6 Nov 2024
Cited by 4 | Viewed by 3708
Abstract
Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that [...] Read more.
Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)
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Other

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33 pages, 5228 KiB  
Systematic Review
Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review
by Andrés Navarro-Rodríguez, Oscar Alberto Castro-Artola, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Ulises Jesús Tamayo-Pérez, César Alberto López-Mercado and Everardo Inzunza-Gonzalez
Appl. Sci. 2025, 15(7), 3492; https://doi.org/10.3390/app15073492 - 22 Mar 2025
Cited by 1 | Viewed by 829
Abstract
Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection and characterization of seismic parameters, including location and magnitude, are crucial for real-time seismological applications, including Earthquake [...] Read more.
Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection and characterization of seismic parameters, including location and magnitude, are crucial for real-time seismological applications, including Earthquake Early Warning (EEW) systems. Machine learning (ML) has emerged as a powerful tool to enhance the accuracy of these applications, enabling more efficient responses to seismic events of different magnitudes. This systematic review aims to provide researchers and professionals with a summary of the current state of ML applications in seismology, particularly on early earthquake magnitude estimations and related topics such as earthquake detection and seismic phase identification. A systematic search was conducted in Scopus, ScienceDirect, IEEE Xplore, and Web of Science databases, covering the period from early 2014 to 7 March 2025. The search terms included the following: (“earthquake magnitude” OR “earthquake early warning”) AND (prediction OR forecasting OR estimation OR forecast OR classification) AND (“machine learning” OR “deep learning” OR “artificial intelligence”). Out of the 472 articles initially identified, 28 were selected based on pre-defined inclusion criteria. The described methods and algorithms illustrate the strong performance of ML in earthquake magnitude estimation despite limited implementation in real-time systems. This highlights the need to develop standardized benchmark datasets to promote future progress in this field. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)
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