Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Machine Learning Approaches for Geophysical Data Analysis
share announcement

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 4561

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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 910
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)
►▼ Show Figures

Figure 1

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 982
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)
►▼ Show Figures

Figure 1

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 2 | Viewed by 978
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)
►▼ Show Figures

Figure 1

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 735
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop