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Applied Sciences
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Applications of Machine Learning in Earth Sciences—2nd Edition
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Applications of Machine Learning in Earth Sciences—2nd Edition

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

Deadline for manuscript submissions: 10 November 2024 | Viewed by 2758

Special Issue Editors

Dr. Luciano Zuccarello

E-Mail Website
Guest Editor
Etna Observatory, National Institute of Geophysics and Volcanology, Rome, Italy
Interests: geophysics; seismology; applied geophysics; earthquake; seismic tomography data processing; volcano
Special Issues, Collections and Topics in MDPI journals
Dr. Janire Prudencio

E-Mail Website
Guest Editor
Department of Theoretical Physics and Cosmic - Physical Area of ​​the Earth, University of Granada, Campus of Fuentenueva, E-18071 Granada, Spain
Interests: seismology; seismics; scattering; inversion; geology-volcanology; tectonics; earthquake seismology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to inform you that Applied Sciences is currently running a Special Issue entitled "Applications of Machine Learning on Earth Sciences—2nd Edition".

In recent years, it has been possible to witness increasing interest in time series and image processing analyses in a number of fields related to earth sciences.

The improvements in data acquisition systems have brought increases in the quantity and quality of data analysed, processed and interpreted in the shortest time possible. The high data volume acquired via the different acquisition systems requires suitable analysis tools which can enhance traditional approaches by extracting and making use of the latent knowledge embedded in the data. One of the key challenges is structuring and organising the huge amount of raw data; it is crucial to determine the type of information which could aid the scientific community in achieving deeper knowledge of the complex dynamics which govern the geophysical and geochemical systems in our planet.

Upcoming methodologies need to address the long-term challenges of data management and accessibility. Data mining, cloud computing and machine learning are the most appropriate disciplines for the analysis of such high-throughput data.

In this Special Issue, we welcome contributions concerning recent machine learning advances applied to earth sciences to improve our understanding of the complexity of our planet.

We would also appreciate it if you could forward this call for papers to your team members and colleagues who may also be interested in the topic.

Dr. Luciano Zuccarello
Dr. Janire Prudencio
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
  • geophysics
  • geochemistry
  • remote sensing
  • seismology
  • volcanology
  • satellite observations
  • artificial intelligence
  • data mining
  • cloud computing

Published Papers (4 papers)

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18 pages, 5214 KiB  
Article
Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method
by Jianting Zhang, Ruifei Wang, Ailin Jia and Naichao Feng
Appl. Sci. 2024, 14(10), 3956; https://doi.org/10.3390/app14103956 - 7 May 2024
Viewed by 321
Abstract
The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is [...] Read more.
The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is necessary to identify the most effective approaches. Therefore, based on the extreme gradient boosting (XGBoost) algorithm, laboratory test data of the porosity and permeability of cores from the southern margin of the Ordos Basin were selected as the target labels, conventional logging curves were used as the input feature variables, and the mean absolute error (MAE) and the coefficient of determination (R2) were used as the evaluation indicators. Following the selection of the optimal feature variables and optimization of the hyper-parameters, an XGBoost porosity and permeability prediction model was established. Subsequently, the innovative application of homogeneous clustering (K-means) data preprocessing was applied to enhance the XGBoost model’s performance. The results show that logarithmically preprocessed (LOG(PERM)) target labels enhanced the performance of the XGBoost permeability prediction model, with an increase of 0.26 in its test set R2. Furthermore, the application of K-means improved the performance of the XGBoost prediction model, with an increase of 0.15 in the R2 of the model and a decrease of 0.017 in the MAE. Finally, the POR_0/POR_1 grouped porosity model was selected as the final predictive model for porosity in the study area, and the Arctan(PERM)_0/Arctan(PER0M)_1 grouped model was selected as the final predictive model for permeability, which has better prediction accuracy than logging curves. The combination of K-means and the XGBoost modeling method provides a new approach and reference for the efficient and relatively accurate evaluation of porosity and permeability in the study area. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Earth Sciences—2nd Edition)
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18 pages, 9951 KiB  
Article
The Short Time Prediction of the Dst Index Based on the Long-Short Time Memory and Empirical Mode Decomposition–Long-Short Time Memory Models
by Jinyuan Zhang, Yan Feng, Jiaxuan Zhang and Yijun Li
Appl. Sci. 2023, 13(21), 11824; https://doi.org/10.3390/app132111824 - 29 Oct 2023
Viewed by 908
Abstract
The Dst index is the geomagnetic storm index used to measure the energy level of geomagnetic storms, and the prediction of this index is of great significance for geomagnetic storm studies and solar activities. In contrast to traditional numerical modeling techniques, machine learning, [...] Read more.
The Dst index is the geomagnetic storm index used to measure the energy level of geomagnetic storms, and the prediction of this index is of great significance for geomagnetic storm studies and solar activities. In contrast to traditional numerical modeling techniques, machine learning, which emerged decades ago based on rapidly developing computer hardware and software and artificial intelligence methods, has been unprecedentedly developed in geophysics, especially solar–terrestrial space physics. This study uses two machine learning models, the LSTM (Long-Short Time Memory, LSTM) and EMD-LSTM models (Empirical Mode Decomposition, EMD), to model and predict the Dst index. By building the Dst index data series from 2018 to 2023, two models were built to fit and predict the data. Firstly, we evaluated the influences of the learning rate and the amount of training data on the prediction accuracy of the LSTM model, and finally, 10−3 was thought to be the optimal learning rate. Secondly, the two models were used to predict the Dst index in the solar active and quiet periods, respectively, and the RMSE (Root Mean Square Error) of the LSTM model in the active period was 7.34 nT and the CC (correlation coefficient) was 0.96, and those of the quiet period were 2.64 nT and 0.97. The RMSE and CC of the EMD-LSTM model were 8.87 nT and 0.93 in the active period and 3.29 nT and 0.95 in the quiet period. Finally, the prediction accuracy of the LSTM model in the short time period was slightly better than the EMD-LSTM model. However, there will be a problem of prediction lag, which the EMD-LSTM model can solve and better predict the geomagnetic storm. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Earth Sciences—2nd Edition)
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22 pages, 4471 KiB  
Article
Permafrost Probability Mapping at a 30 m Resolution in Arxan Based on Multiple Characteristic Variables and Maximum Entropy Classifier
by Ying Guo, Shuai Liu, Lisha Qiu, Yan Wang, Chengcheng Zhang and Wei Shan
Appl. Sci. 2023, 13(19), 10692; https://doi.org/10.3390/app131910692 - 26 Sep 2023
Cited by 1 | Viewed by 771
Abstract
High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy [...] Read more.
High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy classifier (MaxEnt) is used for permafrost mapping using the land surface temperature (LST) of different seasons, deviation from mean elevation (DEV), solar radiation (SR), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) as the characteristic variables. The prior data of permafrost distribution were primarily based on 201 borehole data and field investigation data. A permafrost probability (PP) distribution map with a resolution of 30 m was obtained. The receiver operating characteristic (ROC) curve was used to test the distribution results, with an area under the curve (AUC) value of 0.986. The results characterize the distribution of permafrost at a high resolution. Permafrost is mainly distributed in the Greater Khingan Mountains (GKM) in the research area, which run from the northeast to the southwest, followed by low-altitude area in the northwest. According to topographic distribution, permafrost is primarily found on slope surfaces, with minor amounts present in peaks, ridges, and valleys. The employed PP distribution mapping method offers a suggestion for high-resolution permafrost mapping in permafrost degradation areas. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Earth Sciences—2nd Edition)
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Other

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20 pages, 19157 KiB  
Case Report
Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks
by Dawei Yang, Jiahui Zhao and Ping Xu
Appl. Sci. 2024, 14(9), 3845; https://doi.org/10.3390/app14093845 - 30 Apr 2024
Viewed by 314
Abstract
Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. By leveraging machine learning, this study used a backpropagation (BP) neural network to optimize commercial spaces in Weinan City’s central urban area. The results indicate an increased number of commercial facilities [...] Read more.
Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. By leveraging machine learning, this study used a backpropagation (BP) neural network to optimize commercial spaces in Weinan City’s central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development that emphasizes aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small- and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Earth Sciences—2nd Edition)
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