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Machine Learning Approaches for Geophysical Data Analysis

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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: closed (30 December 2024) | Viewed by 14971

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Special Issue Editors

Dr. José A. Peláez

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Guest Editor

Department of Physics, University of Jaén, 23071 Jaén, Spain
Interests: seismology; earthquakes; seismotectonics; earthquake geology; seismic hazard; seismic zonation; seismically induced landslides
Special Issues, Collections and Topics in MDPI journals

Dr. Peidong Shi

E-Mail Website
Guest Editor

Swiss Seismological Service (SED), 8092 Zürich, Switzerland
Interests: monitoring earthquakes for understanding earthquake physics and mitigating seismic hazard; seismology; geothermal energy exploitation; induced seismicity; real-time seismic monitoring

Prof. Dr. Sanyi Yuan

E-Mail Website
Guest Editor

State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, 102249, China
Interests: artificial intelligence in geophysical exploration; integration of seismic and geological engineering; high-resolution seismic data processing
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Special Issue Information

Dear Colleagues,

Geophysical data lie in the central part of geophysical studies and provide the basis for gaining insights into the underlying fundamental principles. However, geophysical data are often complex, noisy and difficult to evaluate, making their analysis and interpretation very challenging. In addition, with the growing availability of geophysical data from different instruments and sources, the need for innovative data analysis methods has become increasingly pressing.

The recent surge in machine learning studies and applications shows it is a powerful tool for extracting valuable information and insights from complex datasets. Due to the nature of big data, machine learning provides a promising avenue for enhancing the accuracy and efficiency of geophysical data analysis. The use of machine learning in geophysics has already led to significant advances in the fields of seismology, geodesy, environmental science, and geo-energy exploration, among others.

In this context, we are pleased to announce a Special Issue on "Machine Learning Approaches for Geophysical Data Analysis". This Special Issue aims to explore the growing role of machine learning techniques in geophysics and their potential to transform the way geophysical data are analyzed and interpreted. This Special Issue seeks to bring together researchers from both geophysics and machine learning communities to share their expertise, present their research findings, and promote collaborations in this exciting and rapidly evolving field.

This Special Issue invites original research articles, reviews, and case studies that demonstrate the application of machine learning techniques in geophysical data processing and analysis, such as in the area of earthquake seismology, exploration geophysics, geothermal and carbon sequestration, geological mapping, environmental monitoring, and more. We invite all researchers working in related areas to submit their manuscripts and contribute to this Special Issue. Topics of interest for this Special Issue include but are not limited to:

Machine learning for geophysical data interpretation;
Data-driven geophysical imaging and inversion techniques;
Feature extraction and dimensionality reduction for geophysical data;
Data fusion and integration of different geophysical data via machine learning;
Uncertainty quantification and data-driven modeling in geophysics;
Deep learning for seismic interpretation and reservoir characterization;
Machine learning for environmental monitoring and hazard assessment;
Hybrid approaches combining machine learning with physics-based modeling;
Machine learning for geospatial data analysis and integration;
Machine learning for geophysical survey optimization;
Machine learning for rock physics modeling;
Transfer learning for geophysical data analysis.

We are confident that this Special Issue will provide a valuable and timely platform for researchers to share their latest findings and insights on the application of machine learning in geophysics. We look forward to receiving high-quality contributions that will help advances in this relevant field.

Dr. José A. Peláez
Dr. Peidong Shi
Prof. Dr. Sanyi Yuan
Guest Editors

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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.

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Keywords

machine learning
geophysical data processing
deep learning
transfer learning
geophysical data analysis
seismic data
geophysical data imaging and inversion
seismology
geothermal
seismic hazard
exploration geophysics

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Published Papers (8 papers)

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Research

18 pages, 880 KiB

Open AccessArticle

Exploring Vision Transformers and Convolution Neural Networks for the Thermal Image Classification of Volcanic Activity

by Giuseppe Nunnari and Sonia Calvari

Appl. Sci. 2025, 15(5), 2604; https://doi.org/10.3390/app15052604 - 28 Feb 2025

Cited by 3 | Viewed by 595

Abstract

This paper addresses the classification of images depicting the eruptive activity of Mount Etna that were captured by a network of ground-based thermal cameras. This study aimed to evaluate the performance of Vision Transformers (ViTs), such as the Swin Transformer, compared with Convolutional Neural Networks (CNNs), including AlexNet and ShuffleNet. A dataset of 3000 images, evenly distributed across six classes, was utilized for training and testing. The results indicate that for this specific application, the performance advantage of Vision Transformers over CNNs was marginal, likely due to the nature of the classification task. While the Transformer-based models, like the Swin Transformer, demonstrated a slightly improved accuracy for certain complex classes, the CNN-based models, such as AlexNet and ShuffleNet, exhibited superior computational efficiency, particularly in terms of the classification speed. These findings highlight the suitability of CNNs for real-time volcanic activity monitoring. Additionally, this paper provides a comprehensive review of the various CNN and Vision Transformer architectures, offering insights into their strengths and limitations in the context of volcanic activity classification. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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15 pages, 7718 KiB

Open AccessArticle

Investigating the Effects of Ground-Transmitted Vibrations from Vehicles on Buildings and Their Occupants, with an Idea for Applying Machine Learning

by Marta Mikielewicz, Anna Jakubczyk-Gałczyńska and Robert Jankowski

Appl. Sci. 2025, 15(4), 1689; https://doi.org/10.3390/app15041689 - 7 Feb 2025

Viewed by 857

Abstract

Vibrations observed as a result of moving vehicles can potentially affect both buildings and the people inside them. The impacts of these vibrations are complex, affected by a number of parameters, like amplitude, frequency, and duration, as well as by the properties of the soil beneath. These factors together lead to various effects, from slight disruptions to significant structural damage. Occupants inside affected buildings may experience discomfort, disrupted sleep patterns, and increased stress levels due to the pervasive nature of vibrations. Low-frequency vibrations, typically ranging from 5 to 25 Hz, are of particular concern since they can exacerbate these effects by resonating with internal human organs. To effectively mitigate these issues, a comprehensive approach is required, starting with some interventions at the source. This may involve strategic choices in road construction materials and advancements in vehicle design to reduce the transmission of vibrations through the ground to the surrounding environment. Understanding the complexities of vibration dynamics is essential in urban planning, serving as a fundamental consideration in the development of modern infrastructure that prioritizes the well-being and safety of its inhabitants. Therefore, the aim of the present study is to consider artificial neural networks to assess the potential impact of traffic-induced vibrations on a building’s residents. The results of the study indicate that the proposed method of utilizing machine learning can be effectively applied for such purposes. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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14 pages, 4781 KiB

Open AccessArticle

A 1D Convolutional Neural Network (1D-CNN) Temporal Filter for Atmospheric Variability: Reducing the Sensitivity of Filtering Accuracy to Missing Data Points

by Dan Yu, Hoiio Kong, Jeremy Cheuk-Hin Leung, Pak Wai Chan, Clarence Fong, Yuchen Wang and Banglin Zhang

Appl. Sci. 2024, 14(14), 6289; https://doi.org/10.3390/app14146289 - 19 Jul 2024

Cited by 5 | Viewed by 1704

Abstract

The atmosphere exhibits variability across different time scales. Currently, in the field of atmospheric science, statistical filtering is one of the most widely used methods for extracting signals on certain time scales. However, signal extraction based on traditional statistical filters may be sensitive to missing data points, which are particularly common in meteorological data. To address this issue, this study applies a new type of temporal filters based on a one-dimensional convolution neural network (1D-CNN) and examines its performance on reducing such uncertainties. As an example, we investigate the advantages of a 1D-CNN bandpass filter in extracting quasi-biweekly-to-intraseasonal signals (10–60 days) from temperature data provided by the Hong Kong Observatory. The results show that the 1D-CNN achieves accuracies similar to a 121-point Lanczos filter. In addition, the 1D-CNN filter allows a maximum of 10 missing data points within the 60-point window length, while keeping its accuracy higher than 80% (R² > 0.8). This indicates that the 1D-CNN model works well even when missing data points exist in the time series. This study highlights another potential for applying machine learning algorithms in atmospheric and climate research, which will be useful for future research involving incomplete time series and real-time filtering. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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18 pages, 12447 KiB

Open AccessArticle

A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning

by Pan Xiong, Gang Bian, Qiang Liu, Shaohua Jin and Xiaodong Yin

Appl. Sci. 2024, 14(11), 4369; https://doi.org/10.3390/app14114369 - 22 May 2024

Cited by 1 | Viewed by 1160

Abstract

Geomagnetic diurnal variation significantly influences the precision of marine magnetic measurements. Precise estimation of this variation is crucial for enhancing the accuracy of offshore magnetic surveys. To address the challenges in achieving the desired accuracy with current estimation methods for geomagnetic diurnal variation, this study introduces a high-precision estimation model that integrates support vector machine (SVM) and random forest (RF) techniques. Initially, the data preprocessing phase includes an innovative extreme value adjustment method to rectify the temporal discrepancies across different stations, alongside employing the base period technique for daily baseline correction. Subsequently, we construct models to capture the daily variation trends at various times, facilitating an in-depth analysis of the diurnal variation patterns. The culmination of this process involves employing a fusion model algorithm to compute the diurnal variations across all stations comprehensively. Comparative analyses with conventional methods, such as distance weighting, bifactor weighting, and latitude weighting, reveal that our proposed model achieves a significant reduction in the root mean square error (RMSE) by an average of 31%, decreases the mean absolute error (MAE) by 35%, and enhances the Pearson correlation coefficient by 20% on average. These improvements underscore the superior accuracy of our geomagnetic diurnal variation estimation model. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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15 pages, 2158 KiB

Open AccessArticle

Predicting the Remaining Time before Earthquake Occurrence Based on Mel Spectrogram Features Extraction and Ensemble Learning

by Bo Zhang, Tao Xu, Wen Chen and Chongyang Zhang

Appl. Sci. 2023, 13(22), 12268; https://doi.org/10.3390/app132212268 - 13 Nov 2023

Viewed by 2022

Abstract

Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment. In this study, we employed a mel spectrogram and the mel frequency cepstral coefficient (MFCC) to extract relevant features from seismic signals. Furthermore, we proposed a deep learning model with a hierarchical structure. This model combines the characteristics of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D-CNN), and two-dimensional convolutional neural networks (2D-CNN). Additionally, we applied a stacking model fusion strategy, combining gradient boosting trees with deep learning models to achieve optimal performance. We compared the performance of the aforementioned feature extraction methods and related models for earthquake prediction. The results revealed a significant improvement in predictive performance when the mel spectrogram and stacking were introduced. Additionally, we found that the combination of 1D-CNN and 2D-CNN has unique advantages in handling time-series problems. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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16 pages, 17209 KiB

Open AccessArticle

Application of a Pre-Trained CNN Model for Fault Interpretation in the Structurally Complex Browse Basin, Australia

by Md Mahmodul Islam, Ismailalwali Babikir, Mohamed Elsaadany, Sami Elkurdy, Numair A. Siddiqui and Oluwaseun Daniel Akinyemi

Appl. Sci. 2023, 13(20), 11300; https://doi.org/10.3390/app132011300 - 14 Oct 2023

Cited by 4 | Viewed by 2099

Abstract

Fault detection is an important step in subsurface interpretation and reservoir characterization from 3D seismic images. Due to the numerous and complex fault structures in seismic images, manual seismic interpretation is time-consuming and requires intensive work. We applied a pre-trained CNN model to predict faults from the 3D seismic volume of the Poseidon field in the Browse Basin, Australia. This field is highly structured with complex normal faulting throughout the targeted Plover Formations. Our motivation for this work is to compare machine-learning-based fault prediction to user-interpreted fault identification supported by seismic variance attributes. We found reasonably satisfactory results using CNN with an improved fault probability volume that outperforms variance technology. Therefore, we propose that this workflow could reduce time and be able to predict faults quite accurately in most structurally complex areas. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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15 pages, 4761 KiB

Open AccessArticle

Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture

by Qingsheng Meng, Yuhong Chen, Fei Sha and Tao Liu

Appl. Sci. 2023, 13(18), 10194; https://doi.org/10.3390/app131810194 - 11 Sep 2023

Cited by 5 | Viewed by 2394

Abstract

The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (V_S) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. To address these challenges, we describe a six-layer deep neural network algorithm for the inversion of 1D V_S from dispersion curves of the fundamental mode Rayleigh surface waves. Our method encompasses several key advancements: (1) we use a finer layer to construct the 1-D V_S model of the subsurface, which can describe a more complex near-surface geology structure; (2) considering the ergodicity and orderliness of strata evolution, the constrained Markov Chain was employed to reconstruct the complex velocity model; (3) we build a practical and complete dispersion curve inversion process. Our model tested the performance using a random synthetic dataset and the influence of different factors, including the number of training samples, learning rate, and the selection of optimal artificial neural network architecture. Finally, the field test dispersion data were used to further verify the method’s effectiveness. Our synthetic dataset proved the diversity and rationality of the random V_S model. The results of training and predicting showed higher accuracy and could speed the inversion process (only ~15 s), and we proved the important effect of different factors. The outcomes derived from the application of this technique to the measured dispersion data in the Yellow River Delta exhibit a strong correlation with the outcomes obtained from the integration of the very fast simulated annealing method and the downhill simplex method, as well as the statistically derived shear wave velocity data of the sedimentary layers in the Yellow River Delta. From a long-term perspective, our method can provide an alternative for deriving V_S models for complex near-surface structures. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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21 pages, 22844 KiB

Open AccessArticle

Vector Decomposition of Elastic Seismic Wavefields Using Self-Attention Deep Convolutional Generative Adversarial Networks

by Wei Liu, Junxing Cao, Jiachun You and Haibo Wang

Appl. Sci. 2023, 13(16), 9440; https://doi.org/10.3390/app13169440 - 21 Aug 2023

Cited by 1 | Viewed by 1378

Abstract

Vector decomposition of P- and S-wave modes from elastic seismic wavefields is a key step in elastic reverse-time migration (ERTM) to effectively improve the multi-wave imaging accuracy. Most previously developed methods based on the apparent velocities or the polarization characteristics of different wave modes are unable to accurately achieve the vector decomposition of P- and S-wave modes. To effectively overcome the shortcomings of conventional methods, we develop a vector decomposition method of P- and S-wave modes using self-attention deep convolutional generative adversarial networks (SADCGANs) to effectively separate the horizontal and vertical components of P- and S-wave modes from elastic seismic wavefields and accurately preserve their amplitude and phase characteristics for isotropic elastic media. For an elastic model, we use many time slices for a given source position to train the neural network, and use other time slices not in this training dataset to test the neural network. Numerical examples of different models demonstrate the effectiveness and feasibility of our developed method and indicate that it provides an effective intelligent data-driven vector decomposition method of P- and S-wave modes. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Geophysical Data Analysis)

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