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Artificial Intelligence Applications in Earthquake Science
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Artificial Intelligence Applications in Earthquake Science

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

Deadline for manuscript submissions: 20 June 2026 | Viewed by 5413

Special Issue Editor

Dr. Sergio Baselga

E-Mail Website
Guest Editor
Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: artificial intelligence applications in GNSS; deformation monitoring; GNSS algorithms; robust estimation and optimization problems in cartographic sciences

Special Issue Information

Dear Colleagues,

The flourishing of artificial intelligence in its various forms, from deep learning to generative artificial intelligence, has made it possible in recent years to perform tasks that previously seemed out of reach or totally impossible. Its immense potential for accelerating scientific discovery cannot be ignored in any branch of scientific research today.

Artificial intelligence is revolutionizing earthquake science, particularly by enhancing our ability to predict, assess, and mitigate the impacts of seismic events. This Special Issue delves into cutting-edge applications of artificial intelligence, exploring how to better understand earthquake mechanisms and how predictive models as well as early warning systems can significantly reduce risks and improve disaster responses.

From advanced sensing technologies that monitor changes in the Earth's surface, water, and atmosphere, to the integration of big data analysis for comprehensive damage assessments, the contributions must highlight innovative approaches that are reshaping earthquake science and disaster prevention strategies. By opening new avenues in the field, the ideas presented in these contributions are intended to promote a safer and more resilient future in the face of earthquake impacts.

Dr. Sergio Baselga
Guest Editor

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

  • earthquake mechanisms
  • disaster prevention
  • early warning and prediction
  • damage assessment
  • earthquake engineering
  • big data analysis
  • artificial intelligence
  • machine learning
  • deep learning
  • artificial neural networks (ANNs)
  • random forests
  • support vector machines (SVMs)
  • ionospheric anomalies
  • total electron content (TEC)
  • geoacoustic signals
  • crustal strain variations
  • geomagnetic data
  • historical seismicity

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Published Papers (4 papers)

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Research

18 pages, 5042 KB  
Article
Are Ionospheric Disturbances Spatiotemporally Invariant Earthquake Precursors? A Multi-Decadal 100-Station Study
by Evangelos Chaniadakis, Ioannis Contopoulos and Vasilis Tritakis
Appl. Sci. 2025, 15(24), 13218; https://doi.org/10.3390/app152413218 - 17 Dec 2025
Abstract
Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate [...] Read more.
Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate performance metrics for highly imbalanced seismic data, the reliance on geographically and temporally narrow data, and inclusion of inherent spatial or temporal features that artificially inflate model performance while preventing the discovery of genuine ionospheric precursors. To address these challenges, we introduce a global, temporally validated machine learning framework grounded in thirty-eight years of ionospheric observations from more than a hundred ionosonde stations. We eliminate lookahead bias through strict temporal partitioning, prevent overlapping precursor windows across samples to eliminate autocorrelation artifacts and apply sophisticated feature selection to exclude spatial and temporal identifiers, enabling prevention of data leakage and coincidence effects. We investigate whether spatiotemporally invariant ionospheric precursors exist across diverse seismic regions, addressing the field’s reliance on geographically isolated case studies. Cross-regional validation shows that our models yield modest classification skill above chance levels, with our best-performing model achieving a weighted F1 score of 71% though performance exhibits pronounced sensitivity to temporal validation configuration, suggesting these results represent an upper bound on operational accuracy. While multimodal fusion with complementary precursor channels could possibly improve performance, our focus remains on establishing whether ionospheric observations alone contain learnable, region-independent seismic signatures. These findings suggest that ionospheric precursors, if they exist as universal phenomena, exhibit weaker cross-regional consistency than previously reported in case studies, raising questions about their standalone utility for earthquake prediction while indicating potential value as one component within multimodal observation systems. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Earthquake Science)
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13 pages, 2077 KB  
Article
Forecasting Future Earthquakes with Machine Learning Models Based on Seismic Prediction Zoning
by Xiaolin Chen, Daicheng Peng and Li Li
Appl. Sci. 2025, 15(24), 13116; https://doi.org/10.3390/app152413116 - 12 Dec 2025
Viewed by 213
Abstract
Predicting future seismic trends and occurrence of earthquakes remains a long-standing challenge in seismology. Despite substantial efforts to unravel the physical mechanisms underlying earthquake occurrence, currently, no well-defined physical or statistical model is capable of reliably predicting major earthquakes. However, machine learning methods [...] Read more.
Predicting future seismic trends and occurrence of earthquakes remains a long-standing challenge in seismology. Despite substantial efforts to unravel the physical mechanisms underlying earthquake occurrence, currently, no well-defined physical or statistical model is capable of reliably predicting major earthquakes. However, machine learning methods have demonstrated exceptional proficiency in identifying patterns within large-scale datasets, offering a promising avenue for enhancing earthquake prediction performance. Within the framework of machine learning, this study has developed a feature extraction method based on seismic prediction zoning, improving the effectiveness of machine learning-based earthquake prediction. The research findings indicate that the ensemble learning Stacking method, which is based on seismic prediction zoning, exhibits superior performance and high robustness in predicting the annual maximum earthquake magnitude. Additionally, the long short-term memory (LSTM) method demonstrates commendable performance within specific tectonic zones (e.g., the southwestern Yunnan region), providing valuable guidance for analyzing seismic trends in these regions. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Earthquake Science)
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18 pages, 7101 KB  
Article
B-Value Spatiotemporal Changes and Aftershock Correlation Prior to the Mwg 7.1 Dingri Earthquake in Southern Tibet: Implications for Land Deformation and Seismic Risk
by Xiaojuan Wang, Yating Lu, Xinxin Yin, Run Cai, Liyuan Zhou, Shuwang Wang and Feng Liu
Appl. Sci. 2025, 15(21), 11685; https://doi.org/10.3390/app152111685 - 31 Oct 2025
Viewed by 330
Abstract
This study investigates spatiotemporal b value variations and seismic interaction networks preceding the Mwg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, [...] Read more.
This study investigates spatiotemporal b value variations and seismic interaction networks preceding the Mwg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Earthquake Science)
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12 pages, 2652 KB  
Article
Artificial Intelligence for Earthquake Prediction: A Preliminary System Based on Periodically Trained Neural Networks Using Ionospheric Anomalies
by Sergio Baselga
Appl. Sci. 2024, 14(23), 10859; https://doi.org/10.3390/app142310859 - 23 Nov 2024
Cited by 2 | Viewed by 4060
Abstract
There is increasing evidence that anomalies in the ionosphere could appear a few days before large earthquakes. Many significant successes with using anomalies for predictions have been reported, although they are usually limited, both in space, to a specific geographic area, and in [...] Read more.
There is increasing evidence that anomalies in the ionosphere could appear a few days before large earthquakes. Many significant successes with using anomalies for predictions have been reported, although they are usually limited, both in space, to a specific geographic area, and in time, to one or a few events. To date, no solution has been presented that consistently yields the location and magnitude of future earthquakes and thus can be used to develop a warning service. The purpose of this research is to improve on the possible use of Global Ionospheric Maps for earthquake prediction. The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. This network was trained on all large earthquakes occurring from the beginning of the year 2011 to the beginning of October 2024 but it is proposed that it be periodically retrained with new data. This network has reached an accuracy of around 60% in the validation data for a division into eight categories of different earthquake magnitudes. Nevertheless, this percentage increases considerably if the classification into neighboring categories is also accepted, something that could be clearly admissible for the purposes of a warning system. The author believes that success in this endeavor has to come from a collaborative effort. For this reason, the training and validation data with three-dimensional matrices (latitude/longitude/time) of total electron content values along with the subsequent earthquake magnitudes are provided in this paper along with the trained network. Researchers are strongly encouraged to improve on the current neural network with or without the inclusion of additional information. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Earthquake Science)
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