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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 (31 May 2023) | Viewed by 18079

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Dr. Luciano Zuccarello

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Guest Editor

Universidad de Granada, Granada, Spain
Interests: geophysics; seismology; volcanology; Artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Janire Prudencio

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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".

In recent years, interest has increased in time series and image processing analyses in a number of fields related to earth sciences. The improvements in data acquisition systems have increased the quantity and quality of data analysed, processed, and interpreted, and have shortened the time in which results can be produced. The large data volume acquired by the different acquisition systems requires suitable analysis tools that enhance traditional approaches by extracting and applying the latent knowledge embedded in the data. One of the key challenges is structuring and organising the huge amount of raw data; the type of information that could aid the scientific community must be determined to achieve a deeper knowledge of the complex dynamics that govern the geophysical and geochemical systems of 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 analyses of these high throughput data.

In this Special Issue, we welcome contributions concerning recent machine learning advances applied to earth sciences that improve our understanding of the complexity of our planet. We would also appreciate if you could forward this to your team members and colleagues who may also be interested in the topic. We look forward to hearing from you, and we remain at your disposal for more information.

Dr. Luciano Zuccarello
Dr. Janire Prudencio
Guest Editor

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
Soil-Cement
satellite observations
artificial intelligence
data mining
cloud computing

Published Papers (13 papers)

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Research

21 pages, 7696 KiB

Open AccessArticle

Non-Standard Address Parsing in Chinese Based on Integrated CHTopoNER Model and Dynamic Finite State Machine

by Mengwei Zhang, Xingui Liu, Jingzhen Ma, Zheng Zhang, Yue Qiu and Zhipeng Jiang

Appl. Sci. 2023, 13(17), 9855; https://0-doi-org.brum.beds.ac.uk/10.3390/app13179855 - 31 Aug 2023

Viewed by 666

Abstract

Information in non-standard address texts in Chinese is usually presented with rough content, complex and diverse presentation forms, and inconsistent hierarchical granularity, causing low accuracy in Chinese address parsing. Therefore, we propose a method for parsing non-standard address text in Chinese that integrates the Chinese Toponym Named Entity Recognition (CHTopoNER) model and a dynamic finite state machine (FSM). First, named entity recognition is performed by the CHTopoNER model. Sets of dynamic FSMs are then constructed based on the address hierarchical characteristics to sort and combine the Chinese address elements, thereby achieving address parsing on the Chinese internet. This method showed excellent accuracy in parsing both standard and non-standard placename addresses. In particular, this method performed better in address parsing for disordered or missing hierarchical elements than traditional methods using an FSM. Specifically, this method achieved accuracies of 96.6% and 96.8% for standard and non-standard placenames, respectively. These accuracies increased by 8.0% and 57.1%, respectively, compared with the integrated CHTopoNER model and traditional FSM, and by 7.4% and 19.8%, respectively, compared with the integrated CHTopoNER model and bidirectional FSM. After analysis, the address-parsing method showed good scalability and adaptability, which could be applied to various types of address-parsing tasks. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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13 pages, 5011 KiB

Open AccessArticle

Characteristics and Predictive Significance of Spatio-Temporal Space Images of M ≥ 4.0 Seismic Gaps on the Southeastern Margin of the Tibetan Plateau

by Xiaoyan Zhao, Youjin Su and Guangming Wang

Appl. Sci. 2023, 13(13), 7937; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137937 - 06 Jul 2023

Viewed by 660

Abstract

In the present study, seismic gaps were identified as periods with no occurrence of M ≥ 4.0 earthquake over dT ≥ 400 days. After examining all records in the Sichuan–Yunnan–Tibet–Qinghai junction area on the southeastern margin of the Tibetan Plateau in 1970–2022, a total of six M ≥ 4.0 seismic gaps were identified. Spatio-temporal images of the seismic gaps had similar characteristics and demonstrated spatial overlapping and statistical significance. The quiet periods of the six seismic gaps included 419–777 days (approximately 580 days on average). The semi-major-axis and semi-minor-axis lengths were in the 880–1050 km (approximately 987 km on average) and 500–570 km (about 533 km on average) ranges, respectively. Case analysis results revealed that the images of M ≥ 4.0 seismic gaps were of high significance in predicting M ≥ 6.7 strong earthquakes in the region, and they could be used as a predictive index on a time scale of about 1–0.5 years or less. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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13 pages, 5916 KiB

Open AccessArticle

An Optimized Hybrid Transformer for Enhanced Ultra-Fine-Grained Thin Sections Categorization via Integrated Region-to-Region and Token-to-Token Approaches

by Hongmei Zhang and Shuiqing Wang

Appl. Sci. 2023, 13(13), 7853; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137853 - 04 Jul 2023

Viewed by 771

Abstract

The analysis of thin sections for lithology identification is a staple technique in geology. Although recent strides in deep learning have catalyzed the development of models for thin section recognition leveraging varied deep neural networks, there remains a substantial gap in the identification of ultra-fine-grained thin section types. Visual Transformer models, superior to convolutional neural networks (CNN) in fine-grained classification tasks, are underexploited, especially when dealing with limited, highly similar sample sets. To address this, we incorporated a dynamic sparse attention mechanism and tailored the structure of the Swin Transformer network. We initially applied a region-to-region (R2R) approach to conserving key regions in coarse-grained areas, which minimized the global information loss instigated by the original model’s local window mechanism and bolstered training efficiency with scarce samples. This was then fused with deep convolution, and a token-to-token (T2T) attention mechanism was introduced to extract local features from these regions, facilitating fine-grained classification. In comparison experiments, our approach surpassed various sophisticated models, showcasing superior accuracy, precision, recall, and F1-score. Furthermore, our method demonstrated impressive generalizability in experiments external to the original dataset. Notwithstanding our significant progress, several unresolved issues warrant further exploration. An in-depth investigation of the adaptability of different rock types, along with their distribution under fluctuating sample sizes, is advisable. This line of inquiry is anticipated to yield more potent tools for future geological studies, thereby widening the scope and impact of our research. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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25 pages, 4532 KiB

Open AccessArticle

Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction

by M. M. Zaki, Shaojie Chen, Jicheng Zhang, Fan Feng, Liu Qi, Mohamed A. Mahdy and Linlin Jin

Appl. Sci. 2023, 13(13), 7622; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137622 - 28 Jun 2023

Cited by 2 | Viewed by 955

Abstract

The economic value of a mineral resource is highly dependent on the accuracy of grade estimations. Accurate predictions of mineral grades can help businesses decide whether to invest in a mining project and optimize mining operations to maximize the resource. Conventional methods of predicting gold resources are both costly and time-consuming. However, advances in machine learning and processing power are making it possible for mineral estimation to become more efficient and effective. This work introduces a novel approach for predicting the distribution of mineral grades within a deposit. The approach integrates machine learning and optimization techniques. Specifically, the authors propose an approach that integrates the random forest (RF) and k-nearest neighbor (kNN) algorithms with the marine predators optimization algorithm (MPA). The RFKNN_MPA approach uses log normalization to reduce the impact of extreme values and improve the accuracy of the machine learning models. Data segmentation and the MPA algorithm are used to create statistically equivalent subsets of the dataset for use in training and testing. Drill hole locations and rock types are used to create each model. The suggested technique’s performance indices are superior to the others, with a higher R-squared coefficient of 59.7%, a higher R-value of 77%, and lower MSE and RMSE values of 0.17 and 0.44, respectively. The RFKNN_MPA algorithm outperforms geostatistical and conventional machine-learning techniques for estimating mineral orebody grades. The introduced approach offers a novel solution to a problem with practical applications in the mining sector. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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15 pages, 10521 KiB

Open AccessArticle

Study on the Change in Vegetation Coverage in Desert Oasis and Its Driving Factors from 1990 to 2020 Based on Google Earth Engine

by Xu Li, Ziyan Shi, Jun Yu and Jiye Liang

Appl. Sci. 2023, 13(9), 5394; https://0-doi-org.brum.beds.ac.uk/10.3390/app13095394 - 26 Apr 2023

Cited by 2 | Viewed by 1074

Abstract

Fractional Vegetation Cover (FVC) is an important indicator to evaluate the quality of the regional ecological environment. Alar City is a typical desert oasis region. Investigating the spatial and temporal changes in its vegetation cover at different stages is a guide to the ecological balance and sustainable green development of the Tarim River basin. Based on the Google Earth Engine (GEE) cloud platform, this study analyzed the spatial and temporal characteristics and trends of vegetation cover changes in Alar City from 1990 to 2020 using the Hurst index and coefficient of variation. The results show that the spatial distribution of vegetation in the study area in the last 30 years shows a wave-like characteristic with an overall apparent upward trend. The vegetation cover in the study area is predominantly increasing and the spatial distribution shows a phased and regional character. Compared with 1990, there is a significant increase in the area of cultivated land in 2020. Among them, the areas of vegetation growth mainly occur in the basin around the Tarim River. Human activities have weakened the influence of natural factors on FVC. The results of the study suggest that the GEE platform can be an effective tool for permanently monitoring vegetation. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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19 pages, 2356 KiB

Open AccessArticle

Machine Learning Approaches for Slope Deformation Prediction Based on Monitored Time-Series Displacement Data: A Comparative Investigation

by Ning Xi, Qiang Yang, Yingjie Sun and Gang Mei

Appl. Sci. 2023, 13(8), 4677; https://0-doi-org.brum.beds.ac.uk/10.3390/app13084677 - 07 Apr 2023

Cited by 2 | Viewed by 1232

Abstract

Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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20 pages, 6187 KiB

Open AccessArticle

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

by Fakhar Elahi, Khan Muhammad, Shahab Ud Din, Muhammad Fawad Akbar Khan, Shahid Bashir and Muhammad Hanif

Appl. Sci. 2022, 12(23), 12147; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312147 - 28 Nov 2022

Cited by 3 | Viewed by 2061

Abstract

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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14 pages, 6941 KiB

Open AccessArticle

Recognition of Earthquake Surface Ruptures Using Deep Learning

by Xiaolin Chen, Guang Hu and Xiaoli Liu

Appl. Sci. 2022, 12(22), 11638; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211638 - 16 Nov 2022

Cited by 2 | Viewed by 1205

Abstract

Investigating post-earthquake surface ruptures is important for understanding the tectonics of seismogenic faults. The use of unmanned aerial vehicle (UAV) images to identify post-earthquake surface ruptures has the advantages of low cost, fast data acquisition, and high data processing efficiency. With the rapid development of deep learning in recent years, researchers have begun using it for image crack detection. However, due to the complex background and diverse characteristics of the surface ruptures, it remains challenging to quickly train an effective automatic earthquake surface rupture recognition model on a limited number of samples. This study proposes a workflow that applies an image segmentation algorithm based on convolutional neural networks (CNNs) to extract cracks from post-earthquake UAV images. We selected the 16-layer visual geometry group (VGG16) network as the primary network architecture. Then, we improved the VGG16 network and deleted several convolutional layers to reduce computation and memory consumption. Moreover, we added dilated convolution and atrous spatial pyramid pooling (ASPP) to make the network perform well in the surface crack identification of post-earthquake UAV images. We trained the proposed method using the data of the MS 7.4 Maduo earthquake and obtained a model that could automatically identify and draw small and irregular surface ruptures from high-resolution UAV images. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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12 pages, 3827 KiB

Open AccessArticle

Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine

by Yuanzhe Wu, Yunpeng Fan, Yan Liu, Kewen Li, Tingxiang Zeng, Yong Ma, Yongjing Tian, Yaohui Xu, Zhigang Wen, Xiaomin Xie and Juan Teng

Appl. Sci. 2022, 12(18), 9046; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189046 - 08 Sep 2022

Viewed by 1206

Abstract

Identifying organic matter in laminae is fundamental to petroleum geology; however, many factors restrict manual quantification. Therefore, computer recognition is an appropriate method for accurately identifying microscopic components. In this study, we used support vector machine (SVM) to classify the preprocessed photomicrographs into seven categories: pyrite, amorphous organic matter, mineral matter, alginite, sporinite, vitrinite, and inertinite. Then, we performed a statistical analysis of the classification results and highlighted spatial aggregation of some categories using the kernel density estimation method. The results showed that the SVM can satisfactorily identify the macerals and minerals of the laminae, and its overall accuracy, kappa, precision, recall, and F1 are 82.86%, 0.80, 85.15%, 82.86%, and 82.75%, respectively. Statistical analyses revealed that pyrite was abundantly distributed in bright laminae; vitrinite and sporinite were abundantly distributed in dark laminae; and alginite and inertinite were equally distributed. Finally, the kernel density maps showed that all classification results, except inertinite, were characterized by aggregated distributions: pyrite with the distribution of multi-core centers, alginite, and sporinite with dotted distribution, and vitrinite with stripe distribution, respectively. This study may provide a new method to quantify the organic matter in laminae. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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11 pages, 1569 KiB

Open AccessArticle

Prediction of Elastic Modulus for Fibre-Reinforced Soil-Cement Mixtures: A Machine Learning Approach

by Dominic Owusu-Ansah, Joaquim Tinoco, António A. S. Correia and Paulo J. Venda Oliveira

Appl. Sci. 2022, 12(17), 8540; https://0-doi-org.brum.beds.ac.uk/10.3390/app12178540 - 26 Aug 2022

Cited by 2 | Viewed by 1272

Abstract

Soil-cement mixtures reinforced with fibres are an alternative method of chemical soil stabilisation in which the inherent disadvantage of low or no tensile or flexural strength is overcome by incorporating fibres. These mixtures require a significant amount of time and resources for comprehensive laboratory characterisation, because a considerable number of parameters are involved. Therefore, the implementation of a Machine Learning (ML) approach provides an alternative way to predict the mechanical properties of soil-cement mixtures reinforced with fibres. In this study, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), and Multiple Regression (MR) algorithms were trained for predicting the elastic modulus of soil-cement mixtures reinforced with fibres. For ML algorithms training, a dataset of 121 records was used, comprising 16 properties of the composite material (soil, binder, and fibres). ANN and RF showed a promising determination coefficient (R² ≥ 0.93) on elastic modulus prediction. Moreover, the results of the proposed models are consistent with the findings that the fibre and binder content have a significant effect on the elastic modulus. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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25 pages, 3123 KiB

Open AccessArticle

Deep Transfer Learning Approach for Identifying Slope Surface Cracks

by Yuting Yang and Gang Mei

Appl. Sci. 2021, 11(23), 11193; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311193 - 25 Nov 2021

Cited by 7 | Viewed by 2006

Abstract

Geohazards such as landslides, which are often accompanied by surface cracks, have caused great harm to public safety and property. If these surface cracks could be identified in time, this would be of great significance for the monitoring and early warning of geohazards. Currently, the most common method for crack identification is manual detection, which has low efficiency and accuracy. In this paper, a deep transfer learning approach is proposed to effectively and efficiently identify slope surface cracks for the sake of fast monitoring and early warning of geohazards, such as landslides. The essential idea is to employ transfer learning by training (a) a large sample dataset of concrete cracks and (b) a small sample dataset of soil and rock masses’ cracks. In the proposed approach, (1) pretrained crack identification models are constructed based on a large sample dataset of concrete cracks; (2) refined crack identification models are further constructed based on a small sample dataset of soil and rock masses’ cracks. The proposed approach could be applied to conduct UAV surveys on high and steep slopes to provide monitoring and early warning of landslides to ensure the safety of people and property. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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17 pages, 3136 KiB

Open AccessArticle

Forecasting the Daily Maximal and Minimal Temperatures from Radiosonde Measurements Using Neural Networks

by Gregor Skok, Doruntina Hoxha and Žiga Zaplotnik

Appl. Sci. 2021, 11(22), 10852; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210852 - 17 Nov 2021

Viewed by 1482

Abstract

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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17 pages, 5070 KiB

Open AccessArticle

UVI Image Segmentation of Auroral Oval: Dual Level Set and Convolutional Neural Network Based Approach

by Chenjing Tian, Huadong Du, Pinglv Yang, Zeming Zhou and Libin Weng

Appl. Sci. 2020, 10(7), 2590; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072590 - 09 Apr 2020

Cited by 1 | Viewed by 2075

Abstract

The auroral ovals around the Earth’s magnetic poles are produced by the collisions between energetic particles precipitating from solar wind and atoms or molecules in the upper atmosphere. The morphology of auroral oval acts as an important mirror reflecting the solar wind-magnetosphere-ionosphere coupling process and its intrinsic mechanism. However, the classical level set based segmentation methods often fail to extract an accurate auroral oval from the ultraviolet imager (UVI) image with intensity inhomogeneity. The existing methods designed specifically for auroral oval extraction are extremely sensitive to the contour initializations. In this paper, a novel deep feature-based adaptive level set model (DFALS) is proposed to tackle these issues. First, we extract the deep feature from the UVI image with the newly designed convolutional neural network (CNN). Second, with the deep feature, the global energy term and the adaptive time-step are constructed and incorporated into the local information based dual level set auroral oval segmentation method (LIDLSM). Third, we extract the contour of the auroral oval through the minimization of the proposed energy functional. The experiments on the UVI image data set validate the strong robustness of DFALS to different contour initializations. In addition, with the help of deep feature-based global energy term, the proposed method also obtains higher segmentation accuracy in comparison with the state-of-the-art level set based methods. Full article

(This article belongs to the Special Issue Applications of Machine Learning on Earth Sciences)

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