Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Deep Learning Technology in Earth Environment
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Deep Learning Technology in Earth Environment

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 5613

Special Issue Editors

Dr. Joongmoo Byun

E-Mail Website
Guest Editor
Department of Georesources and Environmental Engineering, Hanyang University, Seoul 04763, Korea
Interests: seismic data processing; quantitative seismic interpretation; electromagnetic survey; microseismic monitoring
Dr. Daeung Yoon

E-Mail Website
Guest Editor
Department of Energy & Resource Engineering, Chonnam National University, Gwangju 500-757, Korea
Interests: machine Learning; geophysical data processing

Special Issue Information

Dear Colleagues,

Understanding the behavior of the Earth’s near surface through the diverse fields of geoscience is an increasingly important task for the Earth environment. As the availability of data in various fields, such as geology, geophysics, geochemistry, and remote sensing, increases significantly, more information can be obtained to understand the Earth’s environment. However, there still remain challenges in processing and interpreting data due to the complexity, interdependence, and multiscale nature of the data. In these efforts, machine learning or deep learning technologies can play a key role.

In this Special Issue, we are inviting submissions on new discoveries in applications of machine learning or deep learning technologies in the Earth environment. The application areas include but are not limited to contaminated land investigations, explorations for ground water and for minerals and other economic resources, location of buried pipes, cables, and cavities, and carbon capture and storage (CCS). Survey papers and reviews are also welcomed.

Dr. Joongmoo Byun
Dr. Daeung Yoon
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

  • deep learning
  • machine learning
  • earth environment
  • geophysics
  • geology
  • geochemistry
  • contaminated land investigation
  • resource exploration
  • near-surface exploration
  • carbon capture and storage (CCS)

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 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

22 pages, 10659 KiB  
Article
Crossline Reconstruction of 3D Seismic Data Using 3D cWGAN: A Comparative Study on Sleipner Seismic Survey Data
by Jiyun Yu and Daeung Yoon
Appl. Sci. 2023, 13(10), 5999; https://doi.org/10.3390/app13105999 - 13 May 2023
Cited by 4 | Viewed by 1683
Abstract
In seismic data acquisition, data loss can occur, particularly with the use of streamer systems in marine seismic exploration. These systems often cause spatial aliasing problems by having close inline intervals and wide crossline intervals to maximize the exploration range. To improve the [...] Read more.
In seismic data acquisition, data loss can occur, particularly with the use of streamer systems in marine seismic exploration. These systems often cause spatial aliasing problems by having close inline intervals and wide crossline intervals to maximize the exploration range. To improve the resolution of seismic data in the crossline direction, various machine learning techniques have been employed for crossline data reconstruction. In this study, we introduce a 3D cWGAN (conditional Wasserstein generative adversarial network) for interpolating 3D seismic data. We evaluate the model’s performance by comparing it with 2D cWGAN and 3D U-Net. In this study, two interpolation strategies are employed to reconstruct missing data in the crossline direction. The first strategy uses a 2D network, which trains a model using inline data and applies the trained model to the crossline direction via 2D cWGAN. The second strategy employs a 3D network, which uses the 3D volume of the seismic data directly via 3D cWGAN and 3D U-Net. We demonstrate the effectiveness of the proposed method using the Sleipner CO2 4D seismic survey dataset. Our results show that the 3D cWGAN is more efficient in enhancing resolution and computation compared to the 2D cWGAN or 3D U-Net. Full article
(This article belongs to the Special Issue Deep Learning Technology in Earth Environment)
►▼ Show Figures

Figure 1

13 pages, 5192 KiB  
Article
Analysis of Airglow Image Classification Based on Feature Map Visualization
by Zhishuang Lin, Qianyu Wang and Chang Lai
Appl. Sci. 2023, 13(6), 3671; https://doi.org/10.3390/app13063671 - 13 Mar 2023
Viewed by 1369
Abstract
All-sky airglow imagers (ASAIs) are used in the Meridian Project to observe the airglow in the middle and upper atmosphere to study the atmospheric perturbation. However, the ripples of airglow caused by the perturbation are only visible in the airglow images taken on [...] Read more.
All-sky airglow imagers (ASAIs) are used in the Meridian Project to observe the airglow in the middle and upper atmosphere to study the atmospheric perturbation. However, the ripples of airglow caused by the perturbation are only visible in the airglow images taken on a clear night. It is a problem to effectively select images suitable for scientific analysis from the enormous amount of airglow images captured under various environments due to the low efficiency and subjectivity of traditional manual classification. We trained a classification model based on convolutional neural network to distinguish between airglow images from clear nights and unclear nights. The data base contains 1688 images selected from the airglow images captured at Xinglong station (40.4° N, 30.5° E). The entire training process was tracked by feature maps which visualized every resulting classification model. The classification models with the clearest feature maps were saved for future use. We cropped the central part of the airglow images to avoid disturbance from the artificial lights at the edge of the vision field according to the feature maps of our first training. The accuracy of the saved model is 99%. The feature maps of five categories also indicate the reliability of the classification model. Full article
(This article belongs to the Special Issue Deep Learning Technology in Earth Environment)
►▼ Show Figures

Figure 1

20 pages, 2825 KiB  
Article
Optimization of Aquifer Monitoring through Time-Lapse Electrical Resistivity Tomography Integrated with Machine-Learning and Predictive Algorithms
by Valeria Giampaolo, Paolo Dell’Aversana, Luigi Capozzoli, Gregory De Martino and Enzo Rizzo
Appl. Sci. 2022, 12(18), 9121; https://doi.org/10.3390/app12189121 - 11 Sep 2022
Viewed by 1943
Abstract
In this paper, an integrated workflow aimed at optimizing aquifer monitoring and management through time-lapse Electric Resistivity Tomography (TL-ERT) combined with a suite of predictive algorithms is discussed. First, the theoretical background of this approach is described. Then, the proposed approach is applied [...] Read more.
In this paper, an integrated workflow aimed at optimizing aquifer monitoring and management through time-lapse Electric Resistivity Tomography (TL-ERT) combined with a suite of predictive algorithms is discussed. First, the theoretical background of this approach is described. Then, the proposed approach is applied to real geoelectric datasets recorded through experiments at different spatial and temporal scales. These include a sequence of cross-hole resistivity surveys aimed at monitoring a tracer diffusion in a real aquifer as well as in a laboratory experimental set. Multiple predictive methods were applied to both datasets, including Vector Autoregressive (VAR) and Recurrent Neural Network (RNN) algorithms, over the entire sequence of ERT monitor surveys. In both field and lab experiments, the goal was to retrieve a determined number of “predicted” pseudo sections of apparent resistivity values. By inverting both real and predicted datasets, it is possible to define a dynamic model of time-space evolution of the water plume contaminated by a tracer injected into the aquifer system(s). This approach allowed for describing the complex fluid displacement over time conditioned by the hydraulic properties of the aquifer itself. Full article
(This article belongs to the Special Issue Deep Learning Technology in Earth Environment)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop