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Machine Learning Approaches for Geophysical Data Analysis
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Machine Learning Approaches for Geophysical Data Analysis

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 9598

Special Issue Editors

Dr. José A. Peláez

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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

  • 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 (6 papers)

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Research

14 pages, 4781 KiB  
Article
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
Viewed by 1056
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 [...] Read more.
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  
Article
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
Viewed by 751
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, [...] Read more.
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  
Article
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 1515
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 [...] Read more.
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  
Article
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 1 | Viewed by 1524
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 [...] Read more.
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  
Article
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 3 | Viewed by 1805
Abstract
The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (VS) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. [...] Read more.
The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (VS) 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 VS 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 VS 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 VS 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 VS 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  
Article
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 1079
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 [...] Read more.
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Metaheuristic Strategies for Advancing Process Management in Building Foundations Using Soil Data
Authors: Anna Jakubczyk-Galczynska; Agata Siemaszko; Maryna Poltavets
Affiliation: Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
Abstract: This article focuses on the foundation of buildings, emphasizing the importance of considering geophysical parameters. It presents the development of a metaheuristic approach to create an algorithm that provides guidelines for planning and executing foundation works. The algorithm integrates critical geophysical data and principles of sustainable development, ensuring efficient and environmentally responsible construction practices.

Title: Exploring ViT Transformers for the Thermal Image Classification of Volcanic Activity
Authors: Giuseppe Nunnari
Affiliation: Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Università degli Studi di Catania, Viale A. Doria 6, 95122 Catania, Italy
Abstract: This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach employs Vision Transformers (ViT), focusing on evaluating their performance compared to Convolutional Neural Networks (CNNs) for this specific task. A dataset of 3000 images, evenly distributed across six classes, was used for training and testing. Contrary to the initial expectation that ViT would outperform CNNs, the experimental results indicated inferior performance when compared to networks such as SqueezeNet. This finding underscores the challenges of applying transformer models to smaller datasets and emphasizes the ongoing effectiveness of CNNs for this application.

Title: Investigating the Effects of Ground-Transmitted Vibrations from Vehicles on Buildings and Their Occupants with an Idea for Applying Machine Learning
Authors: Anna Jakubczyk-Gałczyńska; Marta Mikielewicz; Robert Jankowski
Affiliation: Gdańsk University of Technology, Faculty of Civil and Environmental Engineering, 80-233 Gdańsk, Poland
Abstract: Ground-transmitted vibrations caused by passing vehicles can have significant impacts on buildings and their occupants. The effects on buildings can range from minor damage to serious structural issues depending on factors such as vibration amplitude, frequency and duration as well as the building’s design and the type of soil it rest on. For occupants these vibrations can lead to discomfort, sleep disturbances and increase stress levels. Lowfrequency range (5 - 25 Hz) are particularly problematic as they can resonate with the human internal organs. Mitigating these vibrations at the source, such as optimizing vehicle design and road surface materials. Understanding the dynamic of these vibrations is crucial for urban planning and it is important for modern infrastructure.

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