Article
Environmental Sciences
M. V. Ninu Krishnan, M. Prasanna, H. Vijith
Summary: The erosional status and geomorphic evolutional stages of the Limbang River Basin and its fifteen subwatersheds were investigated in this research through analysis of hypsometric curve characteristics, showing differential and varying proneness of the subwatersheds to erosion. Parameters such as concavity, slope inflection point, and normalized hypsometric curve height at various points revealed varying level of influence of fluvial and diffusive processes in terrain erosion and subwatershed characteristics. The spatial variation of lithology has induced the variation in erosional characteristics and controlled the geomorphic evolutional stages of the subwatersheds in LRB.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Paul A. Hwang
Summary: The article introduces a method to obtain L-band tilting ocean surface roughness and discusses several related issues. Topics include high-frequency wave spectrum, integration limits, swell contribution, and measurements in extreme wind conditions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geosciences, Multidisciplinary
Jon Mosar, Jeremiah Mauvilly, Kakhaber Koiava, Irakli Gamkrelidze, Nikolay Enna, Vladimir Lavrishev, Vera Kalberguenova
Summary: The Greater Caucasus doubly-vergent orogenic system originated from the collision between the Neotethys Ocean and the Eurasian continent, forming a bivergent orogenic wedge. The pro-wedge is located in the Transcaucasian Kartli foreland fold-and-thrust belt, while the retro-wedge is located in the North Caucasian Terek-Sunzha foreland fold-and-thrust belt.
MARINE AND PETROLEUM GEOLOGY
(2022)
Article
Geochemistry & Geophysics
Paul A. Hwang
Summary: This study investigates the spatial distribution of L-band lowpass mean square slope (LPMSS) inside tropical cyclones using wind and wave data. The results show that there is an azimuthal variation in the dependence of LPMSS on wind speed, with higher values in the back region compared to the front region, especially at higher wind speeds.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Geological
Mateusz Maslowski, Malgorzata Labus
Summary: This paper presents experiments on proppant embedment phenomenon on shale rock from the Baltic Basin, an unconventional gas deposit region. The results show that proper selection of proppant and fracturing fluid can reduce fracture width and increase conductivity. The novel laboratory imaging procedure introduced in the article is a valuable method for assessing the vulnerability of reservoir rocks to embedment.
ROCK MECHANICS AND ROCK ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Nirmal Kumar, Sudhir Kumar Singh
Summary: Soil erosion rate in Ghaghara river basin was estimated using different digital elevation models (DEMs), with RUSLE showing rates varying from 4.35 to 21.39 ton/ha/year. Water holding capacity varied between clay_loam soil and glacier areas. Hypsometry analysis indicated sub-basins transitioning from young to mature due to erosion, with prioritized conservation measures needed in upper and middle basin areas.
Article
Physics, Applied
Ryosuke Kizu, Ichiko Misumi, Akiko Hirai, Satoshi Gonda
Summary: This study presents a technique for measuring the sidewall of photoresist line patterns using atomic force microscopy (AFM). The AFM technique overcomes limitations of conventional scanning electron microscopy (SEM) techniques, such as pattern shrinkage and low resolution. It enables three-dimensional, high-resolution, low-noise, and nondestructive measurements.
JOURNAL OF APPLIED PHYSICS
(2023)
Article
Geochemistry & Geophysics
Aisling O'Kane, Alex Copley
Summary: Rapid urban growth has increased population density in foreland basins, leading to higher earthquake risk. Seismic wave propagation in these areas is mainly controlled by source depth and basin structure. Matching basin depth with dominant wavelength results in maximum ground motion.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Engineering, Civil
Shan-e-hyder Soomro, Caihong Hu, Muhammad Waseem Boota, Zubair Ahmed, Liu Chengshuai, Han Zhenyue, Li Xiang, Mairaj Hyder Alias Aamir Soomro
Summary: This article presents a study on the hydro-geomorphic characterization of the Kunhar river basin using remote sensing and GIS tools. The morphometric attributes of the basin and its dynamics are analyzed. The study also suggests prioritizing the Kunhar watershed for water resource management based on the outcomes of the morphometric and hydrological models.
WATER RESOURCES MANAGEMENT
(2022)
Article
Geochemistry & Geophysics
Itzhak Hamdani, Einat Aharonov, Jean-Arthur Olive, Stanislav Parez, Zohar Gvirtzman
Summary: The study investigates the spatial correlation and formation mechanism of normal faults and salt pinch-out in salt basins. By analyzing and numerically modeling the salt tectonics system in the Levant basin, it is found that the viscosities and thicknesses of salt and overburden layers control the deformation of the coupled layers. The model quantitatively explains the fault position and temporal evolution of brittle deformation, providing theoretical support for local geological deformation in salt basins.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Chemistry, Multidisciplinary
Guoliang Peng, Huidan Lu, Yongping Liu, Dayong Fan
Summary: A novel highly efficient photoanode was constructed through two hydrothermal reactions and an iodination reaction to form a SbSI/WO3 heterostructure; after optimizing the solvent, the SbSI/WO3 photoanode exhibited excellent photocurrent performance and improved photostability; the higher crystallinity of SbSI was found to have a positive effect on the photostability of the photoanodes constructed.
CHEMICAL COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Reza Teimouri, Sebastian Skoczypiec
Summary: An integrated algorithm is developed in this study to optimize the machining chain by simulating the surface roughness generation and modification caused by milling and burnishing. By adding mechanical attributes such as surface work hardening and springback effect to the predictive algorithm, the accuracy of roughness prediction improves by up to 50%.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Engineering, Mechanical
Vimal Edachery, V Swamybabu, D. Adarsh, Satish Kailas
Summary: The study discusses the influence of surface roughness frequencies and parameters on mixed wetting and its transitions. It was found that while all roughness frequencies contribute to net roughness parameters, not all of them affect wettability transitions. Parameters such as Rz and LSm were identified as predictors for spatial roughness frequencies that dictate transitions to different wettings states.
TRIBOLOGY INTERNATIONAL
(2022)
Article
Environmental Sciences
Ze Yang, Zhizhong Kang
Summary: This study proposes a weight balance-based approach to extract ice flow lines and redefine Antarctic basins. The results show that this method significantly improves the performance of the results and is beneficial for investigating the mechanism of ice shelf calving and mass balance in the Antarctic.
Article
Engineering, Marine
Douglas R. Krafft, Richard Styles, Mitchell E. Brown
Summary: Increasing societal pressures are driving land use change in coastal areas, potentially altering hydrodynamics and sediment transport. Deeper estuaries without extensive tidal flats tend to promote sediment import and exacerbate channel shoaling, while restricted intertidal areas at higher elevations reduce the likelihood of channel shoaling by bypassing deeper sections.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Kasra Rafiezadeh Shahi, Pedram Ghamisi, Behnood Rasti, Paul Scheunders, Richard Gloaguen
Summary: This article proposes a multisensor deep clustering (MDC) algorithm for joint processing of multisource imaging data. The algorithm combines spectral, spatial, and fusion networks to reconstruct image information through optimizing network parameters. Experimental results on two different applications of multisensor datasets show the superior performance of the proposed algorithm compared to state-of-the-art clustering algorithms.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Zijia Zhang, Yaoming Cai, Wenyin Gong, Pedram Ghamisi, Xiaobo Liu, Richard Gloaguen
Summary: This article introduces a method called hypergraph convolutional subspace clustering (HGCSC) for subspace clustering in the non-Euclidean domain. It represents intraclass relations as hyperedges in a hypergraph and recasts the classic self-expression as a hypergraph convolutional self-representation model. The method also introduces a multihop hypergraph convolution process to explore long-range neighboring relations. Experimental results demonstrate that HGCSC outperforms competitors in terms of clustering accuracy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Samuel T. Thiele, Sandra Lorenz, Moritz Kirsch, Richard Gloaguen
Summary: The widespread use of drones and miniaturized sensors has increased remote sensing applications with high spatial and spectral resolutions. The detailed geometry captured by drones can be utilized to correct illumination effects, enhancing the accuracy of spectral analysis.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Rene Booysen, Sandra Lorenz, Samuel T. Thiele, Warrick C. Fuchsloch, Timothy Marais, Paul A. M. Nex, Richard Gloaguen
Summary: This article introduces an innovative method that integrates multiple sensors and data acquisition scales to accurately explore and map complex geological deposits. The study demonstrates that this method has achieved good results in lithium and tin exploration and can be applied to other minerals.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Samuel T. Thiele, Zakaria Bnoulkacem, Sandra Lorenz, Aurelien Bordenave, Niccolo Menegoni, Yuleika Madriz, Emmanuel Dujoncquoy, Richard Gloaguen, Jeroen Kenter
Summary: Uncrewed aerial vehicles are commonly used for hyperspectral imaging, but mostly in nadir imaging orientations. Oblique hyperspectral imaging is hindered by the lack of robust registration and correction protocols. In this paper, a novel correction methodology and toolbox are proposed for accurate production of hyperspectral data acquired by UAVs, without any restrictions on view angles or target geometry. The importance of these corrections is demonstrated through the first fully corrected oblique SWIR drone-survey.
Article
Environmental Sciences
Roberto de la Rosa, Raimon Tolosana-Delgado, Moritz Kirsch, Richard Gloaguen
Summary: Hyperspectral drill-core scanning provides valuable mineralogical data for exploration campaigns, but it needs to be transformed into compatible lithological domains for geomodeling. Previous studies have addressed automated geological logging and multi-scale hierarchical domaining of drill-cores using univariate information. This study presents a method that utilizes wavelet transform and tessellation to transform multivariate mineralogical data into lithological domains for 3D geological modeling, improving boundary detection and geological domaining.
Article
Geochemistry & Geophysics
Hernan Ugalde, William Morris, Yuleika Madriz, Moritz Kirsch, Richard Gloaguen, Michael Schneider, Markus Schiffler, Bernhard Siemon, Tristan Freville, Marc Munschy
Summary: Magnetic data can be acquired from different platforms and sensors to detect magnetic anomalies associated with mineral content. Prior measurements and lab testing can provide information on physical contrast and magnetic properties. In this study, magnetic patterns of skarn bodies in Germany were analyzed.
GEOPHYSICAL PROSPECTING
(2022)
Review
Remote Sensing
Wilfried Yves Hamilton Adoni, Sandra Lorenz, Junaidh Shaik Fareedh, Richard Gloaguen, Michael Bussmann
Summary: Uncrewed aerial vehicles (UAVs), or drones, are widely used in various sectors such as agriculture, medicine, and transportation. The implementation of autonomous multi-UAV systems for collaborative decisions poses challenges in terms of hardware, software, communication, coordination, and environment. Existing research papers have focused on the applications of multi-UAV systems, but there is a lack of understanding of the difficulties involved in implementing these systems from scratch. This review article aims to address the communication issues and provide a comparative study of UAV types, communication architectures, and routing protocols. It also offers decision-making roadmaps and discusses open challenges and future directions for effectively utilizing autonomous swarms.
Article
Geochemistry & Geophysics
Lea Gering, Moritz Kirsch, Samuel Thiele, Andrea De Lima Ribeiro, Richard Gloaguen, Jens Gutzmer
Summary: The analysis of hydrothermal alteration in exploration drill cores using hyperspectral imaging techniques can help trace fluid-rock interaction processes, identify fluid flow paths, and determine vectors in mineral systems. This study on mineralised drill cores from the Spremberg-Graustein Kupferschiefer-type Cu-Ag deposit in Germany demonstrates the suitability of hyperspectral imaging for recognising and tracking hydrothermal alteration effects. The technique successfully identifies markers of alteration associated with or adjacent to Cu-Ag mineralisation and distinguishes different mineralogically distinct styles of alteration.
Proceedings Paper
Geosciences, Multidisciplinary
Behnood Rasti, Pedram Ghamisi, Richard Gloaguen
Summary: In this paper, we propose a deep learning-based method called DeepHyIn for hyperspectral inpainting. The proposed approach is unsupervised and uses the observed image to train the network. We introduce a novel model that represents the degraded hyperspectral image as a linear mixture of endmembers and degraded abundances. An optimization problem is formulated to estimate the unknown abundances using an image prior. DeepHyIn demonstrates significant improvements both quantitatively and qualitatively compared to state-of-the-art techniques.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Kasra Rafiezadeh Shahi, Pedram Ghamisi, Behnood Rasti, Paul Scheunders, Richard Gloaguen
Summary: Hyperspectral imaging is an important technology in geosciences and remote sensing. To address the challenges in processing hyperspectral images, researchers propose a deep multi-resolution clustering network (DMC-Net) that can analyze HSIs without requiring training samples. DMC-Net captures the non-linear intrinsic relation within data points in an HSI and analyzes the image at various resolutions. Experimental results demonstrate the superior performance of DMC-Net in terms of clustering accuracy compared to state-of-the-art deep learning-based clustering approaches.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Computer Science, Software Engineering
Andrea de Lima Ribeiro, Margret C. Fuchs, Sandra Lorenz, Christian Roeder, Yuleika Madriz, Erik Herrmann, Richard Gloaguen, Johannes Heitmann
Summary: Electronic waste, which contains a significant amount of plastics, requires proper identification of plastic constituents for effective recycling. In this study, we investigated the potential of optical spectroscopy-based methods for identifying plastic components in electronic waste using three promising sensor types. We successfully identified plastic materials for 60% of the samples using spectral fingerprint identification, but were unable to identify black plastics. We recommend using a combination of at least two sensors for recycling activities to improve accuracy.
OPTICS, PHOTONICS AND DIGITAL TECHNOLOGIES FOR IMAGING APPLICATIONS VII
(2022)
Article
Mineralogy
Christina Loidolt, Robert Zimmermann, Laura Tusa, Sandra Lorenz, Doreen Ebert, Richard Gloaguen, Sam Broom-Fendley
Summary: The Storkwitz carbonatite breccia in Germany is a rare earth elements deposit. Petrological investigation reveals REE enrichment in the breccia, possibly from late magmatic processes. Late-stage hydrothermal or supergene processes resulted in minor REE recrystallization and redistribution without significant enrichment in the upper part of the breccia. Cross-cutting faults, post-dating carbonatite intrusion, may play a role in recrystallization of the breccia matrix.
CANADIAN MINERALOGIST
(2022)
Proceedings Paper
Remote Sensing
Seema Chouhan, Behnood Rasti, Pedram Ghamisi, Sandra Lorenz, Margret Fuchs, Richard Gloaguen
Summary: Advancements in hyperspectral imaging systems have allowed for the identification and distinction of materials based on their unique spectral characteristics. This study presents a novel approach using convolutional autoencoders for hyperspectral unmixing to detect and distinguish copper and aluminum foils in shredded lithium-ion batteries. Experimental results show that the convolutional autoencoder outperforms competing unmixing methods in terms of performance. This work is the first to implement hyperspectral unmixing using autoencoders in LIB recycling, making it highly significant for automated sorting of valuable metals in the industry.
2022 12TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS)
(2022)
Proceedings Paper
Remote Sensing
Daniel Coquelin, Behnood Rasti, Markus Goetz, Pedram Ghamisi, Richard Gloaguen, Achim Streit
Summary: Hyperspectral cameras induce different types of noise in acquired data, making hyperspectral denoising a crucial step for analysis. Traditional computational methods lack efficiency and open-source availability, while GPU-accelerated deep learning-based methods are challenging for many researchers to apply in real-world scenarios. To address this, the authors propose HyDe, an open-source Python-based toolbox for hyperspectral image denoising. HyDe consists of various methods, from low-rank wavelet-based approaches to deep neural network (DNN) models, with an improved interface to enhance interoperability and performance while consuming significantly less energy. The authors also present a method for training DNNs to denoise hyperspectral images with different perspectives, and demonstrate an effective sliding window approach to denoise images without requiring excessive storage space. The package can be found at: https://github.com/Helmholtz-AI-Energy/HyDe.
2022 12TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS)
(2022)
Article
Computer Science, Interdisciplinary Applications
Yapo Abole Serge Innocent Oboue, Yunfeng Chen, Sergey Fomel, Wei Zhong, Yangkang Chen
Summary: Strong noise can disrupt the recorded seismic waves and negatively impact subsequent seismological processes. To improve the signal-to-noise ratio (S/N) of seismological data, we introduce MATamf, an open-source MATLAB code package based on an advanced median filter (AMF) that simultaneously attenuates various types of noise and improves S/N. Experimental results demonstrate the usefulness and advantages of the proposed AMF workflow in enhancing the S/N of a wide range of seismological applications.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Upkar Singh, P. N. Vinayachandran, Vijay Natarajan
Summary: The Bay of Bengal maintains its salinity distribution due to the cyclic flow of high salinity water and the mixing with freshwater. This paper introduces an advection-based feature definition and algorithms to track the movement of high salinity water, validated through comparison with observed data.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Bijal Chudasama, Nikolas Ovaskainen, Jonne Tamminen, Nicklas Nordback, Jon Engstro, Ismo Aaltonen
Summary: This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from aerial photographs acquired by unmanned aerial vehicles (UAV). The workflow includes training a U-Net CNN using a small subset of photographs with manually traced fractures, semantic segmentation of input images, pixel-wise identification of fracture traces, ridge detection algorithm and vectorization. The results show the effectiveness and accuracy of the workflow in automated mapping of bedrock fracture traces.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Ruizhen Wang, Siyang Wan, Weitao Chen, Xuwen Qin, Guo Zhang, Lizhe Wang
Summary: This paper proposes a novel framework to generate a finer soil strength map based on RCI, which uses ensemble learning models to obtain USCS soil classification and predict soil moisture, in order to improve the resolution and reliability of existing soil strength maps.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhanlong Chen, Xiaochuan Ma, Houpu Li, Xuwei Xu, Xiaoyi Han
Summary: Simulated terrains are important for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. This study proposes a deep learning method, IGPN, that integrates global information and pattern features of the local terrain to generate accurate simulated terrains quickly.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniele Secci, Vanessa A. Godoy, J. Jaime Gomez-Hernandez
Summary: Neural networks excel in various machine learning applications, but lack physical interpretability and constraints, limiting their accuracy and reliability in predicting complex physical systems' behavior. Physics-Informed Neural Networks (PINNs) integrate neural networks with physical laws, providing an effective tool for solving physical problems. This article explores recent developments in PINNs, emphasizing their application in solving unconfined groundwater flow, and discusses challenges and opportunities in this field.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Renguang Zuo, Ying Xu
Summary: This study proposes a hybrid deep learning model consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN) to extract joint spectrum-spatial features from geochemical survey data for mineral exploration. The physically constrained hybrid model performs better in geochemical anomaly recognition compared to other models.
COMPUTERS & GEOSCIENCES
(2024)
