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Machine Learning Applications in Seismology: 2nd Edition

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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 25719

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Special Issue Editors

Dr. Ke Jia

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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

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Guest Editor

College of Marine Geosciences, Ocean University of China, Qingdao 266100, China
Interests: reinforcement learning; location and focal mechanism

Dr. Kai Deng

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Guest Editor

College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
Interests: machine learning in induced seismicity

Prof. Dr. Shiyong Zhou

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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

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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

Machine Learning Applications in Seismology in Applied Sciences (16 articles)

Published Papers (8 papers)

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13 pages, 2140 KB

Open AccessArticle

Estimating Urban Travel Intensity from Ambient Seismic Signals via a Hybrid CatBoost–LSTM Framework

by Kai Guo and Jianmin Hou

Appl. Sci. 2026, 16(7), 3407; https://doi.org/10.3390/app16073407 - 1 Apr 2026

Viewed by 236

Abstract

Urban travel intensity is a practical proxy for human mobility, but direct mobility data are often costly, geographically restricted, and privacy sensitive. UTScan uses continuous ambient seismic data to estimate urban travel intensity in a passive, non-intrusive manner. Model development used 10 cities in Hubei Province during January–April 2020, and external validation used 84 non-Hubei cities that satisfied the study’s data-quality criteria. From each hourly power spectral density (PSD) curve, we extracted 13 features in the 2–20 Hz anthropogenic band, applied a station-wise low-activity baseline subtraction, and then modeled daily travel intensity with a CatBoost–LSTM framework. Under the calendar-based forward-validation protocol, the final UTScan implementation (FusionB) achieved a mean RMSE of 0.537 ± 0.214 and a mean Pearson correlation of 0.768 ± 0.076 across the internal Hubei folds and a mean RMSE of 0.789 ± 0.229 and a mean Pearson correlation of 0.605 ± 0.370 across the 84-city external validation set. Additional sensitivity analyses using alternative validation windows and light-touch outlier handling indicated that the main conclusions were stable, while single-station representativeness remained the principal limitation. Ambient seismic noise is therefore a useful passive proxy for estimating city-scale mobility dynamics, especially for abrupt mobility disruptions, but its interpretation remains conditional on station siting, source mixture, and the proxy nature of the Baidu travel-intensity target. Full article

(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)

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16 pages, 4695 KB

Open AccessArticle

A Principal Component Analysis Framework for Evaluating Mining-Induced Risk: A Case Study of a Chilean Underground Mine

by Felipe Muñoz, Rodrigo Estay, Claudia Pavez-Orrego and Gonzalo Nelis

Appl. Sci. 2026, 16(3), 1211; https://doi.org/10.3390/app16031211 - 24 Jan 2026

Viewed by 406

Abstract

Mining-induced seismicity presents significant challenges to the safety and operational continuity of underground mines, particularly in deep and highly stressed environments. This study proposes a methodological framework for seismic risk evaluation inspired by predictive-maintenance principles and applied to a high-resolution microseismic catalog from a Chilean underground mine. Using a combination of data filtering and correlation analyses, we identify the seismic parameters that control the most variability in the dataset: moment magnitude, frequency corner, and both dynamic and static stresses. Based on this, we perform a Principal Component Analysis (PCA), which clearly demonstrates the physical interconnection between the selected parameters, thereby helping to better characterize the seismic events and the mining environment. Using these results, a PCA-based risk map is constructed, enabling the delineation of zones with different levels of seismic risk. Additionally, a temporal tracking of potentially hazardous seismicity is included. The proposed methodology demonstrates that microseismic behavior can be effectively represented in a reduced-dimension space, offering a promising foundation for predictive and data-driven risk-assessment tools capable of supporting real-time decision-making in underground mining operations. Full article

(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)

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18 pages, 19605 KB

Open AccessArticle

A Semi-Supervised Approach to Microseismic Source Localization with Masked Pre-Training and Residual Convolutional Autoencoder

by Zhe Wang, Xiangbo Gong, Qiao Cheng, Zhuo Xu, Zhiyu Cao and Xiaolong Li

Appl. Sci. 2026, 16(2), 683; https://doi.org/10.3390/app16020683 - 8 Jan 2026

Viewed by 468

Abstract

Microseismic monitoring is extensively applied in hydraulic fracturing and mineral extraction, with accurate event localization being a critical component. Recently, deep learning approaches have shown promise for microseismic event localization; however, most of these supervised methods depend on large, labeled datasets, which are costly and challenging to acquire. To mitigate this issue, we propose a semi-supervised approach based on a residual convolutional autoencoder (RCAE) for automated microseismic localization, designed to leverage limited labeled data effectively and improve source localization accuracy even with small sample sizes. Our method employs pre-training by masking and reconstructing unlabeled seismic records, while integrating residual connections within the encoder to enhance feature extraction from seismic signals. This enables high localization accuracy with minimal labeled data, resulting in significant cost savings. Experimental results indicate that our method surpasses purely supervised approaches on both a 2D salt dome model and a 3D homogeneous half-space model, validating its effectiveness in microseismic localization. Further comparisons with baseline models highlight the method’s advantages, providing an innovative solution for improving cost-efficiency in practical applications. Full article

(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)

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21 pages, 23094 KB

Open AccessArticle

Deep Learning-Based Seismic Time-Domain Velocity Modeling

by Zhijun Ma, Xiangbo Gong, Xiaofeng Yi, Zhe Wang and Guangshuai Peng

Appl. Sci. 2025, 15(22), 12123; https://doi.org/10.3390/app152212123 - 14 Nov 2025

Viewed by 1302

Abstract

Accurate subsurface velocity modeling is of fundamental scientific and practical significance for seismic data processing and interpretation. However, conventional depth-domain methods still face limitations in physical consistency and inversion accuracy. To overcome these challenges, this study proposes a deep learning-based seismic velocity modeling approach in the time domain. The method establishes an end-to-end mapping between seismic records and velocity models directly in the time domain, reducing the nonlinear complexity of mapping time-domain data to depth-domain models and improving prediction stability and accuracy. Synthetic aquifer velocity models were constructed from representative stratigraphic features, and multi-shot seismic records were generated through forward modeling. A U-Net network was employed, taking multi-shot seismic records as input and time-domain velocity fields as output, with training guided by a mean squared error (MSE) loss function. Experimental results show that the proposed strategy outperforms conventional depth-domain approaches in aquifer structure identification, velocity recovery, and interlayer contrast depiction. Quantitatively, significant improvements in MSE, peak signal-to-noise ratio, and structural similarity index indicate higher reconstruction reliability. Overall, the results confirm the effectiveness and potential of the proposed time-domain framework for aquifer velocity inversion and its promise for intelligent seismic velocity modeling. Full article

(This article belongs to the Special Issue Machine Learning Applications in Seismology: 2nd Edition)

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17 pages, 5164 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2315

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 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 3028

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° 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 KB

Open AccessArticle

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 20 | Viewed by 9755

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 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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33 pages, 5228 KB

Open AccessEditor’s ChoiceSystematic 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 10 | Viewed by 6937

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 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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