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Higher-order thin layer method as an efficient forward model for calculating dispersion curves of surface and Lamb waves in layered media Comput. Geosci. (IF 4.4) Pub Date : 2024-03-02 Mrinal Bhaumik, Tarun Naskar
The thin layer method (TLM) is an effective semi-discrete numerical tool for analyzing wave motion in stratified media. The TLM can calculate the and for both propagating and decaying waves, which is essential to simulate waves near the source. The are particularly useful in various applications, such as estimating modal contributions, calculating synthetic seismograms, determining Green’s function
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Stochastic reconstruction of geological reservoir models based on a concurrent multi-stage U-Net generative adversarial network Comput. Geosci. (IF 4.4) Pub Date : 2024-02-23 Wenyao Fan, Gang Liu, Qiyu Chen, Zhesi Cui, Xuechao Wu, Zhiting Zhang
For complex geological reservoir modeling, some numerical-simulation-based methods, such as traditional multiple-point statistics (MPS), cannot extract nonstationary patterns of training images (TIs) effectively. CPU-intensive calculations require that variables information only be stored in RAM instead of other storage mediums to avoid huge time consumption if many simulations are performed successively
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Efficient optimization of fracturing parameters with consideration of fracture propagation and heterogeneity in tight gas reservoirs Comput. Geosci. (IF 4.4) Pub Date : 2024-02-20 Shangui Luo, Huiying Tang, Liehui Zhang, Tao Wang, Yulong Zhao, Weihua Chen
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Spatial bagging to integrate spatial correlation into ensemble machine learning Comput. Geosci. (IF 4.4) Pub Date : 2024-02-13 Fehmi Özbayrak, John T. Foster, Michael J. Pyrcz
We propose a novel spatial bagging workflow for predictive ensemble machine learning that improves on standard bagging models. Our proposed method integrates spatial bootstrap for bagging with the number of effective sample size, , for integration of the spatial context of the dataset. We benchmark the improved performance over standard machine learning bagging models with a large number of two-dimensional
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RockS2Net: Rock image classification via a spatial localization siamese network Comput. Geosci. (IF 4.4) Pub Date : 2024-02-12 Zhu Qiqi, Wang Sai, Tong Shun, Yin Liangbin, Qi Kunlun, Guan Qingfeng
The acquisition of rock property information is at the core of regional geological survey and mineral exploration, but hand-crafted feature-based methods are heavily influenced by human prior knowledge and have limited transferability. End-to-end deep learning techniques, exemplified by convolutional neural networks (CNNs), have attained significant accomplishments in the domain of image classification
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Corrigendum to “An adaptive-size vector tile pyramid construction method considering spatial data distribution density characteristics” [Comput. Geosci. 184 (2024) 105537] Comput. Geosci. (IF 4.4) Pub Date : 2024-02-12 Guowen Li, Jingzhong Li
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Evaluating geophysical monitoring strategies for a CO2 storage project Comput. Geosci. (IF 4.4) Pub Date : 2024-02-10 Susan Anyosa, Jo Eidsvik, Dario Grana
Geophysical monitoring of CO storage projects enables informed decision making of injection strategies. When monitoring projects are designed, decisions should be made related to which geophysical data should be collected, for example seismic or electromagnetic data, and when surveys should be collected. In this work, we conduct value of information analysis to assess when to perform monitoring and
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Efficient geo-electromagnetic modeling techniques for complex geological structures: A karst MT example Comput. Geosci. (IF 4.4) Pub Date : 2024-02-07 Minghong Liu, Huaifeng Sun, Rui Liu, Liqiang Hu, Ruijin Kong, Shangbin Liu
In the field of geo-electromagnetic modeling, the unstructured tetrahedral mesh technique holds great potential for realistically simulating various complex geological structures. However, its practical application is limited by the difficulty in building complex geological models and performing tetrahedral meshing. To solve these problems, we propose a novel workflow for geological modeling and tetrahedral
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Image-guided structure-constrained inversion of electrical resistivity data for improving contaminations characterization Comput. Geosci. (IF 4.4) Pub Date : 2024-01-30 Xinmin Ma, Jieyi Zhou, Jing Li, Jiaming Zhang, Chunmei Han, Lili Guo, Shupeng Li, Deqiang Mao
Electrical resistivity tomography often has high uncertainty in contamination characterization due to the complex subsurface structure. Utilizing available prior information is crucial for enhancing geological plausibility. We propose an improved structure-constrained method that updates the smooth weights of all eight elements surrounding a boundary element using three different magnitudes. The methodology
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Micromagnetic tomography: Numerical libraries Comput. Geosci. (IF 4.4) Pub Date : 2024-02-03 David Cortés-Ortuño, Frenk Out, Martha E. Kosters, Karl Fabian, Lennart V. de Groot
Micromagnetic tomography (MMT) is an emerging technique in rock and paleomagnetism to determine individual magnetic moments of tomographically defined magnetic source regions within a natural sample by means of surface scans of the magnetic field above the sample. MMT relies on combining large high-resolution data sets from X-ray tomography and magnetic scanning devices, like quantum diamond magnetometers
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PyRefra – Refraction seismic data treatment and inversion Comput. Geosci. (IF 4.4) Pub Date : 2024-02-03 Hermann Zeyen, Emmanuel Léger
Open-source software in the geophysical community has been increasingly taking importance since more than a decade. Following this spirit, this study presents an open-source Python software for display, processing, picking of near-surface refraction seismic data and tomographic modeling using some of the main packages already developed for geosciences. The PyRefra package allows the display of different
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A simple and fast method to calculate dispersion curves of Rayleigh waves due to models with a low-velocity half-space Comput. Geosci. (IF 4.4) Pub Date : 2024-01-28 Kai Zhang, Kai Wang, Xiaojiang Wang
For layered models with a low-velocity half-space, Rayleigh waves can exhibit inversely dispersive patterns and thus are advantageous for providing useful information on the seismic properties of such structures. However, the calculation of dispersion curves usually encounters some difficulties such as root skipping or numerical instability due to the presence of leaky modes. The full-wavefield method
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An automatic arrival time picking algorithm of P-wave based on adaptive characteristic function Comput. Geosci. (IF 4.4) Pub Date : 2024-01-29 Aiping Cheng, Enjie Xu, Pengfei Yao, Yafeng Zhou, Shibing Huang, Zuyang Ye
The accuracy of arrival time and picking speed are significant issues in terms of micro-seismic identification, location, travel-time tomography, as well as source mechanism. In order to detect small changes in signal amplitude and frequency with different signal-to-noise ratios (SNRs), the Allen's characteristic function (Allen's CF) was modified to an adaptive characteristic function (ACF) by relative
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A fast and robust method for detecting trend turning points in InSAR displacement time series Comput. Geosci. (IF 4.4) Pub Date : 2024-01-29 Ebrahim Ghaderpour, Benedetta Antonielli, Francesca Bozzano, Gabriele ScarasciaMugnozza, Paolo Mazzanti
Ground deformation monitoring is a crucial task in geohazard management to ensure the safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced technique for measuring small displacements on the Earth’s surface. Estimated PS-InSAR time series acquired by Sentinel-1 satellites provide a great opportunity for effective monitoring of ground
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An adaptive-size vector tile pyramid construction method considering spatial data distribution density characteristics Comput. Geosci. (IF 4.4) Pub Date : 2024-01-20 Guowen Li, Jingzhong Li
Vector tile pyramid is a technology that provides a compact representation of geospatial data. It enables efficient transmission and rendering by storing geographic information in a tile-based format on the server side. Traditional vector tile construction methods divide vector data into a series of tiles of fixed size, such as 256*256 pixels, which leads to numerous empty tiles, imbalanced data distribution
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One-dimensional parallel forward modeling for geophysical electromagnetic fields excited by sagging overhead transmission lines Comput. Geosci. (IF 4.4) Pub Date : 2024-01-23 Siwei Zhu, Jianhui Li, Xiangyun Hu, Yukai Yi
Geophysicists often regard the 50/60 Hz electromagnetic (EM) waves and their harmonics, excited by overhead transmission lines, as noise that interferes with geophysical prospecting. However, some researchers view them as effective signals for geophysical exploration. Forward modeling can be used to study the characteristics of EM fields generated by overhead transmission lines. It can help in either
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Improving the quality evaluation process of machine learning algorithms applied to landslide time series analysis Comput. Geosci. (IF 4.4) Pub Date : 2024-01-17 Marco Conciatori, Alessandro Valletta, Andrea Segalini
The introduction of Machine Learning (ML) in the geotechnical community has led to numerous applications for monitoring data elaboration. These techniques demonstrate promising performance in comparison to conventional methods aimed at determining the future behavior of a landslide. In this context, it is fundamental to have access to reliable methodologies and procedures to assess the quality of algorithms'
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Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting Comput. Geosci. (IF 4.4) Pub Date : 2024-01-17 Lei Xu, Xihao Zhang, Hongchu Yu, Zeqiang Chen, Wenying Du, Nengcheng Chen
Hourly precipitation forecasting is considered a spatiotemporal sequence forecasting problem that plays an increasingly important role in early warning of rainfall-induced floods and secondary disasters. Current popular precipitation forecasting methods usually ignore the spatial autocorrelation features, resulting in limited spatial information representations and extractions. In this work, a deformable
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Mineral prediction based on prototype learning Comput. Geosci. (IF 4.4) Pub Date : 2024-01-17 Liang Ding, Bainian Chen, Yuelong Zhu, Hai Dong, Pengcheng Zhang
In the field of mineral resource prediction, acquiring labeled data and bearing high annotation costs pose significant challenges. Moreover, distinct characteristics are present in different types of data, including geophysical, geochemical, and geological data. However, conventional deep learning methods often treat all data uniformly, neglecting the specificities inherent in various domains of knowledge
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Quality control in deep learning and confidence quantification: Seismic velocity regression through classification Comput. Geosci. (IF 4.4) Pub Date : 2024-01-20 Jérome Simon, Gabriel Fabien-Ouellet, Erwan Gloaguen
Deep learning methods are increasingly used in seismic, but the black-box nature of neural networks hinders the confidence users may have in their outputs. Moreover, conventional neural networks are not probabilistic. In velocity model building, neural networks predict a velocity value whether they are confident in the result or not. The absence of confidence information is problematic when there are
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Commutative encryption and watermarking algorithm based on compound chaotic systems and zero-watermarking for vector map Comput. Geosci. (IF 4.4) Pub Date : 2024-01-16 Tao Tan, Liming Zhang, Mingwang Zhang, Shuai Wang, Lei Wang, Ziyi Zhang, Shuaikang Liu, Pengbin Wang
Commutative encryption and watermarking (CEW) is an efficient and safe protection technology combining cryptography and digital watermarking. It features the dual functions of secure transmission and copyright safeguarding. In traditional CEW algorithms for vector map, the watermark is embedded by modifying the host data, which cannot meet the needs of high precision vector map, especially for vector
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On the numerical resolution of the second-order viscoacoustic and viscoelastic anisotropic wave equations using the recursive convolution method Comput. Geosci. (IF 4.4) Pub Date : 2024-01-19 Chao Jin, Bing Zhou, Mohamed Kamel Riahi, Mohamed Jamal Zemerly, Danping Cao
Accurate and efficient seismic wave modeling is fundamental for the high-resolution seismic full-waveform inversion and correct interpretation of seismic data. Based on the viscoelastic mechanism of the Generalized Standard Linear Solid and the constitutive relationship between stress and strain, we demonstrate a generalized recursive formula using the Taylor series expansion to directly compute the
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A multi-condition denoising diffusion probabilistic model controls the reconstruction of 3D digital rocks Comput. Geosci. (IF 4.4) Pub Date : 2024-01-19 Xin Luo, Jianmeng Sun, Ran Zhang, Peng Chi, Ruikang Cui
The integration of deep learning techniques from the field of image generation into digital rock analysis has resulted in substantial advancements. However, generating high-quality and highly controllable heterogeneous digital rocks still presents challenges and necessities. This paper reports a digital rock reconstruction method using a multi-condition denoising diffusion probabilistic model (MCDDPM)
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Thermotopes-COH—A software for carbon isotope modeling and speciation of COH fluids Comput. Geosci. (IF 4.4) Pub Date : 2024-01-17 Antoine Boutier, Isabelle Martinez, Isabelle Daniel, Simone Tumiati, Guillaume Siron, Alberto Vitale Brovarone
Carbon-bearing fluids and condensed carbon are common on and inside planetary bodies. Understanding the mechanisms capable of transferring carbon from fluids into solids and vice-versa is central in many fundamental and applied research targets within the Earth and Planetary Sciences. A broad range of applications can benefit from the thermodynamic properties of carbon-oxygen-hydrogen (COH) systems
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Interpretable spatial-temporal attention convolutional network for rainfall forecasting Comput. Geosci. (IF 4.4) Pub Date : 2024-01-15 Pingping Shao, Jun Feng, Pengcheng Zhang, Jiamin Lu
The interpretability of rainfall forecasting models is a major challenge in the field of artificial intelligence. Its importance is equal to the evaluation of model accuracy. Owing to the uncertainty and nonlinearity of the rainfall forecasting process, existing hydrological rainfall forecasting models often have low prediction robustness, and the machine learning method ignores the influence of physical
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An autocorrelated conditioned Latin hypercube method for temporal or spatial sampling and predictions Comput. Geosci. (IF 4.4) Pub Date : 2024-01-15 Van Huong Le, Rodrigo Vargas
A data-driven method is presented for improving sampling designs from times series (1D approach) or spatial arrays (2D approach) of digital information. We present the autocorrelated conditioned Latin Hypercube Sampling (acLHS). This method combines a conditioned Latin Hypercube (cLHS) to obtain a representative sample of the joint probability distribution function and an autocorrelation model to reproduce
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Hybrid CPU-GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion problems Comput. Geosci. (IF 4.4) Pub Date : 2024-01-13 Hao Dong, Kai Sun, Gary Egbert, Anna Kelbert, Naser Meqbel
The Curl-Curl equation is the foundation of time-harmonic electromagnetic (EM) problems in geophysics. The efficiency of its solution is key to EM simulations, accounting for over 95% of the computation cost in geophysical inversions for magnetotelluric or controlled-source EM problems. However, most published EM inversion codes are still central processing unit (CPU)-based and cannot utilize recent
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Improvements in deep learning-based precipitation nowcasting using major atmospheric factors with radar rain rate Comput. Geosci. (IF 4.4) Pub Date : 2024-01-11 Wonsu Kim, Chang-Hoo Jeong, Seongchan Kim
Recently, deep learning-based precipitation nowcasting has been investigated and its usefulness has been recognized. However, existing approaches have treated precipitation nowcasting as a spatiotemporal sequence prediction problem and have mainly used only radar images. Radar images show the distribution of water or ice droplets, but are limited in providing information about the dynamic or thermodynamic
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SwinMin: A mineral recognition model incorporating convolution and multi-scale contexts into swin transformer Comput. Geosci. (IF 4.4) Pub Date : 2024-01-11 Liqin Jia, Feng Chen, Mei Yang, Fang Meng, Mingyue He, Hongmin Liu
Mineral recognition plays a pivotal role in advancing geological survey methodologies and exploration techniques, serving as a cornerstone of contemporary geoscience research. Recently, Transformer-based neural networks have outperformed ConvNets and have become increasingly prominent in vision models. However, adapting Transformer models to mineral photograph recognition presents two significant challenges
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First national inventory of high-elevation mass movements in the Italian Alps Comput. Geosci. (IF 4.4) Pub Date : 2024-01-07 Guido Nigrelli, Roberta Paranunzio, Laura Turconi, Fabio Luino, Giovanni Mortara, Michele Guerini, Marco Giardino, Marta Chiarle
Climate change in the European Alps, in particular in the high-elevation environments, is causing an increase in mass movements and hazards. To learn more about relationships between mass movements and climate drivers, the location of the starting zone and date of the instability events need to be known. Nevertheless, not all existing inventories of mass movements are suitable for the purpose. For
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A kinematic method for generating earthquake sequences Comput. Geosci. (IF 4.4) Pub Date : 2024-01-06 Brendan J. Meade
Computational earthquake sequence models provide generative estimates of the time, location, and size of synthetic seismic events that can be compared with observed earthquake histories and assessed as rupture forecasts. Here we describe a three-dimensional probabilistic earthquake sequence model that produces slip event time series constrained across geometrically complex non-planar fault systems
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Denoising sparker seismic data with Deep BiLSTM in fractional Fourier transform Comput. Geosci. (IF 4.4) Pub Date : 2024-01-06 Dawoon Lee, Sung Ryul Shin, Eun-Min Yeo, Wookeen Chung
Sparkers are widely-used sources of high-resolution images for shallow sub-bottom seismic monitoring. Sparker seismic data contain high random noise levels caused by the high voltage and electrical current required to power the sparker source. Random noise is distributed over the entire frequency and wavenumber, and is thereby difficult to suppress. Numerous studies have been conducted to develop random
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Geology-constrained dynamic graph convolutional networks for seismic facies classification Comput. Geosci. (IF 4.4) Pub Date : 2024-01-03 Ziyad Alswaidan, Motaz Alfarraj, Hamzah Luqman
Knowing a land’s facies type before drilling is an essential step in oil exploration. In seismic surveying, subsurface images are analyzed to segment and classify the facies in that volume. With the recent developments in deep learning, multiple works have utilized deep neural networks to classify facies from subsurface images. Unlike natural images, seismic data have different patterns and structures
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Determination of earthquake focal mechanism via multi-task learning Comput. Geosci. (IF 4.4) Pub Date : 2024-01-02 Pengyu Wang, Tao Ren, Rong Shen, Hongfeng Chen, Xinliang Liu, Fanchun Meng
A multi-task learning-based focal mechanism network (MTFMN) is proposed for calculating parameters of the focal mechanism of earthquakes by regression with incorporating expert prior knowledge. The model automatically learns feature representations of seismic waveforms and transforms the inversion task of the focal mechanism into multi-task learning. Experimental results suggest that MTFMN outperforms
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A hybrid domain-based watermarking for vector maps utilizing a complementary advantage of discrete fourier transform and singular value decomposition Comput. Geosci. (IF 4.4) Pub Date : 2023-12-29 Chengyi Qu, Jinglong Du, Xu Xi, Huimin Tian, Jie Zhang
Digital watermarking plays a crucial role in the copyright protecting of vector maps. Due to its solid theoretical foundation, Discrete Fourier transform (DFT) is frequently used in the construction of watermarking scheme for diverse electronic data. However, when applied to vector maps, DFT is particularly vulnerable to local changes in coordinate points, posing challenges in surviving coordinate
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Comparative analysis of computational schemes for FEM modeling of 3D time-domain geoelectromagnetic fields excited by a horizontal grounded-wire source Comput. Geosci. (IF 4.4) Pub Date : 2023-12-27 Yuri G. Soloveichik, Marina G. Persova, Denis V. Vagin, Anastasia P. Sivenkova, Dmitry S. Kiselev, Yulia I. Koshkina
Two mathematical formulations are considered for calculating the time-domain electromagnetic fields excited by a horizontal grounded-wire source in a 3D geological medium. The first of them is formulated for a vector potential (A-formulation), the second one is formulated for the electric field strength (E-formulation). Both formulations are also considered when using the primary-secondary field approach
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An encoder–decoder deep neural network for binary segmentation of seismic facies Comput. Geosci. (IF 4.4) Pub Date : 2023-12-21 Gefersom Lima, Felipe André Zeiser, Ariane da Silveira, Sandro Rigo, Gabriel de Oliveira Ramos
To explore hydrocarbons, it is necessary to interpret seismic data to identify facies and geological features. Traditionally, this work is performed by visually choosing points representing the limits of seismic features and using a tool to infer the other limit points. This process requires much manual work and may leave some features aside, making the work less accurate. Recently, deep learning has
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A hybrid method of combination probability and machine learning for Chinese geological text segmentation Comput. Geosci. (IF 4.4) Pub Date : 2023-12-23 Zhiyong Guo, Jiqiu Deng, Yu Zou, Yu Tang
To address the issues surrounding incomplete coverage of core dictionaries, limited training corpora, and low precision in Chinese geological text segmentation, a knowledge- and data-driven word segmentation method by combining combination probability and machine learning was proposed in this paper. We extracted mathematical feature information from terms in Chinese geological text to construct a Term
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Explainable deep learning for automatic rock classification Comput. Geosci. (IF 4.4) Pub Date : 2023-12-21 Dongyu Zheng, Hanting Zhong, Gustau Camps-Valls, Zhisong Cao, Xiaogang Ma, Benjamin Mills, Xiumian Hu, Mingcai Hou, Chao Ma
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Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites Comput. Geosci. (IF 4.4) Pub Date : 2023-12-14 Aurelien Meray, Lijing Wang, Takuya Kurihana, Ilijana Mastilovic, Satyarth Praveen, Zexuan Xu, Milad Memarzadeh, Alexander Lavin, Haruko Wainwright
Contamination of soil and groundwater presents a widespread global problem, significantly impacting both human well-being and environmental stability. Conventional models employed for estimating pollutant concentrations under varying climatic conditions demand extensive computational power and high-performance computing resources. In response to this issue, we have devised an innovative method utilizing
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Swin Transformer for simultaneous denoising and interpolation of seismic data Comput. Geosci. (IF 4.4) Pub Date : 2023-12-13 Lei Gao, Housen Shen, Fan Min
Seismic data are often characterized by low quality due to noise contamination or missing traces. Convolutional neural networks are popular in dealing with denoising and interpolation. However, fixed-size convolution kernels have limited feature extraction range, and popular networks aim at either denoising or interpolating. In this paper, we propose a Swin Transformer convolutional residual network
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Surrogate models of heat transfer in fractured rock and their use in parameter estimation Comput. Geosci. (IF 4.4) Pub Date : 2023-12-12 Guofeng Song, Delphine Roubinet, Xiaoguang Wang, Gensheng Li, Xianzhi Song, Daniel M. Tartakovsky
Fracture distribution plays a significant role in the behavior of subsurface environments, affecting such activities as geothermal production, exploitation and management of groundwater resources, and long-term storage of nuclear waste and carbon dioxide. A key challenge in these and other applications is to estimate the fracture network properties from sparse and noisy observations. We evaluate the
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A 3D TLM code for the study of the ELF electromagnetic wave propagation in the Earth's atmosphere Comput. Geosci. (IF 4.4) Pub Date : 2023-12-05 Alfonso Salinas, Jorge Portí, Enrique A. Navarro, Sergio Toledo-Redondo, Inmaculada Albert, Aida Castilla, Víctor Montagud-Camps
The interest in the study of electromagnetic propagation through planetary atmospheres is briefly discussed. Special attention is devoted to extremely-low-frequency fields in the Earth's atmosphere for its global nature and possible applications to climate monitoring studies among others. In the Earth's case, the system can be considered as a spherical electromagnetic shell resonator in which two concentric
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Total water storage anomalies reconstruction using noise-augmented u-shaped network: A case study in the Yangtze River Basin Comput. Geosci. (IF 4.4) Pub Date : 2023-12-03 Jielong Wang, Ling Yang, Yunzhong Shen, Qiujie Chen
The satellite mission of Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) have characterized global total water storage anomalies (TWSA) with unprecedented accuracy. However, the data gap between GRACE and GRACE-FO from July 2017 to May 2018 represents challenges for interpreting long-term water storage changes. In this study, we present a deep learning model, a
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A novel algorithm for seismic events multiplets search Comput. Geosci. (IF 4.4) Pub Date : 2023-11-28 Roberto Carluccio
A common practice of seismology is to analyze earthquake occurrence in terms of events catalogues, with the aim to either find useful correlations between internal mechanisms under study and their outcome in the spatial/temporal series of the events or, more directly, to assess some statistical rules from observations. With this approach, catalogues are often searched for some recognizable patterns
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Seimic impedance inversion based on semi-supervised learning Comput. Geosci. (IF 4.4) Pub Date : 2023-11-25 Suzhen Shi, Mingxuan Li, Jianhua Wang, Weiming Chang, Li Li, Dongshan Xie
In coalfield seismic exploration, seismic impedance inversion can infer subsurface physical properties through seismic data, allowing the identification of subsurface strata. Deep learning has shown great potential in the field of seismic impedance inversion. Typically, the quantity of labeled data during training significantly impacts model validity. To reduce model dependence on labels, we propose
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GROWTH-23: An integrated code for inversion of complete Bouguer gravity anomaly or temporal gravity changes Comput. Geosci. (IF 4.4) Pub Date : 2023-11-23 Antonio G. Camacho, Peter Vajda, José Fernández
A new code for gravity inversion is freely available. It can be used to work with data on complete Bouguer anomaly (CBA) and temporal gravity changes (dg), and is derived from several previous independent codes for CBA data inversion (GROWTH gravity inversion) and for gravity inversion of dg data (GROWTH-dg). This methodology enables the recovery of general 3D structures for anomalous density as a
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Study on InSAR image fusion for improved visualization of active landslides in alpine valley areas: A case in the Batang Region, China Comput. Geosci. (IF 4.4) Pub Date : 2023-11-21 Yang Liu, Xin Yao, Zhenkui Gu, Zhenkai Zhou, Xinghong Liu, Shangfei Wei
InSAR observation with a wide range and high sensitivity is one of the main techniques for active landslide identification, but due to the influence of the SAR satellite side-view imaging method and observation angle, single-track images in alpine valley areas facing geometric distortion, such as shadowing, layover, and foreshortening, which leads to ineffective observation in some areas. Combining
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Advection-based tracking and analysis of salinity movement in the Indian Ocean Comput. Geosci. (IF 4.4) Pub Date : 2023-11-14 Upkar Singh, P.N. Vinayachandran, Vijay Natarajan
The Bay of Bengal (BoB) has maintained its salinity distribution over the years despite a continuous flow of fresh water entering it through rivers on the northern coast, which is capable of diluting the salinity. This can be attributed to the cyclic flow of high salinity water (≥35 psu), coming from Arabian sea and entering BoB from the south, which moves northward and mixes with this fresh water
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Physics-Informed Neural Networks for solving transient unconfined groundwater flow Comput. Geosci. (IF 4.4) Pub Date : 2023-11-14 Daniele Secci, Vanessa A. Godoy, J. Jaime Gómez-Hernández
Neural networks excel in various machine learning applications; however, they lack the physical interpretability and constraints crucial for numerous scientific and engineering problems. This limitation hinders their ability to accurately capture and predict complex physical systems’ behavior, potentially yielding inaccurate or unreliable results. Physics-Informed Neural Networks (PINNs) are a class
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An advanced median filter for improving the signal-to-noise ratio of seismological datasets Comput. Geosci. (IF 4.4) Pub Date : 2023-11-10 Yapo Abolé Serge Innocent Oboué, Yunfeng Chen, Sergey Fomel, Wei Zhong, Yangkang Chen
Strong noise usually disturbs the recorded seismic waves, resulting in low-quality seismic data sometimes with an extremely low signal-to-noise ratio (S/N), which negatively impacts the subsequent seismological processes, e.g., imaging, inversion, and interpretation. Suppressing undesirable noise is a meaningful procedure to increase the S/N of seismological datasets. This issue can be partially addressed
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Intelligent terrain generation considering global information and terrain patterns Comput. Geosci. (IF 4.4) Pub Date : 2023-10-29 Zhanlong Chen, Xiaochuan Ma, Houpu Li, Xuwei Xu, Xiaoyi Han
Simulated terrains can provide rich information for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. Quickly generating an accurate simulated terrain for target areas is of great importance. However, for existing data-driven terrain generation methods, it is difficult to balance modeling accuracy and the amount of data required. To overcome this
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A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration Comput. Geosci. (IF 4.4) Pub Date : 2023-10-28 Renguang Zuo, Ying Xu
Geochemical survey data provide rich information on geochemical elemental concentrations and their spatial patterns in relation to mineralization or pollution. A geochemical data cube can be generated using geochemical raster maps which can be obtained by interpolating geochemical samples into raster maps. In these maps, each pixel contains a geochemical spectrum that records the characteristics of
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QuadMap: Variable resolution maps to better represent spatial uncertainty Comput. Geosci. (IF 4.4) Pub Date : 2023-10-20 J. Padarian, A.B. McBratney
Uncertainty assessment is an integral component of spatial modelling not only from a analytical point of view but also as a communication tool. However, end users find uncertainty maps difficult to perceive alongside the prediction map. A common misconception is that finer resolution maps necessarily have higher precision. Here, we present an approach to take advantage of users’ perceptions of the
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GeoFault: A well-founded fault ontology for interoperability in geological modeling Comput. Geosci. (IF 4.4) Pub Date : 2023-10-14 Yuanwei Qu, Michel Perrin, Anita Torabi, Mara Abel, Martin Giese
Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general
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A novel finer soil strength mapping framework based on machine learning and remote sensing images Comput. Geosci. (IF 4.4) Pub Date : 2023-10-13 Ruizhen Wang, Siyang Wan, Weitao Chen, Xuwen Qin, Guo Zhang, Lizhe Wang
Soil strength is an important factor for assessing the vehicle trafficability in the wilds and making reliable off-road path planning. Rating Cone index (RCI) has been widely used as an indicator of soil strength for mobility assessment. Currently, regional RCI are mainly obtained by using Soil Moisture Strength Prediction (SMSP) Model based on the Unified Soil Classification System (USCS) soil types
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Physics-constrained neural networks for half-space seismic wave modeling Comput. Geosci. (IF 4.4) Pub Date : 2023-10-10 Yi Ding, Su Chen, Xiaojun Li, Liguo Jin, Shaokai Luan, Hao Sun
Forward modeling of seismic waves using physics-informed neural networks (PINNs) has attracted much attention. However, a notable challenge arises when modeling seismic wave propagation in large domains (i.e., a half-space), PINNs may encounter the issue of "soft constraint failure". To address this problem, we propose a novel framework called physics-constrained neural networks (PCNNs) specifically