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Deep Learning Technology in Earth Environment

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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 (20 February 2023) | Viewed by 4782

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Special Issue Editors

Dr. Joongmoo Byun

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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

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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

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Keywords

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

Published Papers (3 papers)

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Research

22 pages, 10659 KiB

Open AccessArticle

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 2 | Viewed by 1447

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 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)

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13 pages, 5192 KiB

Open AccessArticle

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 1144

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 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)

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20 pages, 2825 KiB

Open AccessArticle

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 1615

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 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)

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