Computational Geosciences最新文献

{"title":"3D Gaussian Splatting in Geosciences: A Novel High-Fidelity Approach for Digitizing Geoheritage from Minerals to Immersive Virtual Tours","authors":"Andrei Ionuț Apopei","doi":"10.3390/geosciences15100373","DOIUrl":"https://doi.org/10.3390/geosciences15100373","url":null,"abstract":"The digitization of geological heritage is essential for geoconservation, research, and education, yet traditional 3D methods like photogrammetry struggle to accurately capture specimens with complex optical properties. This paper evaluates 3D Gaussian Splatting (3DGS) as a high-fidelity alternative. This study presents a multi-scale comparative study, digitizing landscape-scale outcrops with UAVs, architectural-scale museum interiors with smartphones, and specimen-level minerals with complex lusters and transparency. The results demonstrate that 3DGS provides unprecedented realism, successfully capturing view-dependent phenomena such as the labradorescence of feldspar and the translucency of fluorite, which are poorly represented by photogrammetric textured meshes. Furthermore, the 3DGS workflow is significantly faster and eliminates the need for manual post-processing and texture painting. By enabling the creation of authentic digital twins and immersive virtual tours, 3DGS represents a transformative technology for the field. It offers powerful new avenues for enhancing public engagement and creating accessible, high-fidelity digital archives for geoeducation and geotourism.","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"15 10","pages":"373-373"},"PeriodicalIF":0.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2076-3263/15/10/373/pdf?version=1758719410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147893401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Isotopes in Archeology: Perspectives on Post-Mortem Alteration and Climate Change","authors":"Antonio Simonetti, Michele R. Buzon","doi":"10.3390/geosciences15080307","DOIUrl":"https://doi.org/10.3390/geosciences15080307","url":null,"abstract":"Isotopic investigations focused on determining the mobility and provenance of ancient human civilizations and sourcing of archeological artifacts continue to gain prominence in archeology. Most studies focus on the premise that the geographic variation in isotope systems of interest (e.g., Sr, Pb, Nd, O) in the natural environment is recorded in both human hard tissues of local individuals and raw materials sourced for artifacts within the same region. The introduction of multi-collection–inductively coupled plasma mass spectrometry (MC-ICP-MS) and laser ablation systems are techniques that consume smaller sample sizes compared to previous mass spectrometric approaches due to their higher ionization efficiency and increased sensitivity. This development has facilitated the isotopic measurement of trace elements present at low abundances (e.g., Pb, Nd, <1-to-low ppm range) particularly in human tooth enamel. Accurate interpretation of any isotope ratio measurement for the proveniencing of such low-abundance samples requires the adequate evaluation of post-mortem diagenetic alteration. A synopsis of practices currently in use for identifying post-mortem alteration in human archeological samples is discussed here. Post-mortem shifts in radiogenic isotope signatures resulting from secondary alteration are distinct from those potentially related to the impact of climate change on the bioavailable budgets for these elements. This topic is of interest to the archeological community and discussed here in the context of Holocene-aged samples from burial sites within the Nile River Valley System, and preferred dust source areas from the neighboring Sahara Desert.","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"15 8","pages":"307-307"},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2076-3263/15/8/307/pdf?version=1754563596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147903036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Data from Minor Mountainous Lakes as High-Resolution Geological Archives of the Northern Apennines, Italy: Lake Moo","authors":"Yago Nestola, Stefano Segadelli","doi":"10.3390/geosciences15060217","DOIUrl":"https://doi.org/10.3390/geosciences15060217","url":null,"abstract":"Sedimentary basins developed in mountain belts are natural traps of catchment erosion products and can produce comprehensive palaeoflood records that extend beyond instrumental or historical data. This study investigates the Lake Moo plain (1120 m a.s.l.), located in the Mt. Ragola (1712 m a.s.l.) ophiolitic massif in the Northern Apennines (Italy), which serves as an excellent case study for inferring the chronology of past flood events due to its position relative to the dominant atmospheric flow and its favorable geological and geomorphological characteristics. The Northern Apennines is a relatively understudied region regarding the reconstruction of past Holocene flood activity through the analysis of lake sediments and peat bogs, compared with areas like the Alps. The main objective of this research was to analyze sediment cores taken from a lake situated in a catchment area dominated by ultramafic rock lithologies and associated residual weathering cover deposits. This allowed us to detect and characterize past flood events in the Ligurian–Emilian Apennines. A multidisciplinary approach, integrated with reference data on geology, geomorphology, pedology, and petrography, enabled a more detailed description of the changes in the hydrologic cycle. Collectively, these data suggest that periods of increased past flood activity were closely linked to phases of rapid climate change at the scale of the Ligurian–Emilian Apennines. The preliminary results suggest that floods occurring during periods of temperature drops have distinct characteristics compared with those during temperature rises.","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"15 6","pages":"217-217"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2076-3263/15/6/217/pdf?version=1749644024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147896255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using a Hydro-Morphic Classification of Catchments to Characterise and Explain High Flow and Overbank Flood Behaviour","authors":"Amir Mohammad Arash, Kirstie Fryirs, Timothy J. Ralph","doi":"10.3390/geosciences15040141","DOIUrl":"https://doi.org/10.3390/geosciences15040141","url":null,"abstract":"The morphological characteristics of catchments are key controls on how flow is routed through catchments and the spatial and temporal dynamics of floods, therefore influencing the shape of hydrographs at any location. Here, we developed a hydro-morphic catchment classification to understand the extent to which various catchment characteristics act as controls on flood behaviour. The catchment characteristics include: size (as measured by gauge position in catchment and valley confinement at the gauge site), shape (elongation ratio and form factor), topography (catchment relief and longitudinal slope), and drainage network structure (drainage density). A total of 2452 high flow (near bankfull) and overbank flood hydrographs from rivers in 17 coastal catchments of New South Wales (NSW), Australia were used. Cluster analysis on hydrograph shape metrics of kurtosis, skewness, and rate-of-rise was performed to identify classes of hydrographs and their median shape. Three statistically distinct clusters were delineated for both high flows and overbank floods, and categorised as flashy, intermediate, and broad. Topographic characteristics of catchments (i.e., relief and longitudinal slope) were commonly among the dominant controls for all high flow and overbank flood hydrographs, excluding broad overbank floods. Drainage network structure (i.e., drainage density) also controlled flashy and intermediate high flows, and intermediate and broad overbank floods, while catchment size (i.e., gauge position in the network) influenced broad high flows. Catchment shape (i.e., elongation ratio) influenced broad overbank floods, and is a dominant control on flashy high flows, and intermediate and broad overbank floods. Overall, topographic controls were more useful for differentiating the hydrological behaviour of high flows relative to overbank floods. Understanding the relative control of different catchment morphometric characteristics on flow and flood behaviour can be used to identify the aspects of flood behaviour that are set by imposed controls and cannot therefore be realistically manipulated in management. A hydro-morphic classification can also be used in the design and calibration of hydrological models, tailoring their use to hydro-morphic catchment class.","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"15 4","pages":"141-141"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2076-3263/15/4/141/pdf?version=1744194728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147893640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-order exponential integration for seismic wave modeling","authors":"Fernando V. Ravelo, Martin Schreiber, Pedro S. Peixoto","doi":"10.1007/s10596-024-10319-5","DOIUrl":"https://doi.org/10.1007/s10596-024-10319-5","url":null,"abstract":"<p>Seismic imaging is a major challenge in geophysics with broad applications. It involves solving wave propagation equations with absorbing boundary conditions (ABC) multiple times. This drives the need for accurate and efficient numerical methods. This study examines a collection of exponential integration methods, known for their good numerical properties on wave representation, to investigate their efficacy in solving the wave equation with ABC. The purpose of this research is to assess the performance of these methods. We compare a recently proposed Exponential Integration based on Faber polynomials with well-established Krylov exponential methods alongside a high-order Runge-Kutta scheme and low-order classical methods. Through our analysis, we found that the exponential integrator based on the Krylov subspace exhibits the best convergence results among the high-order methods. We also discovered that high-order methods can achieve computational efficiency similar to low-order methods while allowing for considerably larger time steps. Most importantly, the possibility of undertaking large time steps could be used for important memory savings in full waveform inversion imaging problems.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"23 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating spatial variability in surface runoff modeling with new DEM-based distributed approaches","authors":"Dário Macedo Lima, Adriano Rolim da Paz, Yunqing Xuan, Daniel Gustavo Allasia Piccilli","doi":"10.1007/s10596-024-10321-x","DOIUrl":"https://doi.org/10.1007/s10596-024-10321-x","url":null,"abstract":"<p>This study introduces two novel DEM-based distributed rainfall-runoff models derived from the existing Hidropixel model: Hidropixel_TUH+ and Hidropixel_DLR. These models account for spatial variations in direct runoff generation, translation, and storage within a watershed, considering spatial variability in rainfall and basin characteristics. In Hidropixel_TUH+, a Triangular Unit Hydrograph (TUH) is determined for each Digital Elevation Model (DEM) pixel and lagged to the watershed outlet based on the travel time from the pixel to the outlet. In Hidropixel_DLR, a hydrograph is estimated for each pixel based on the travel time, which takes translation effects into account. To represent the storage effects, this hydrograph is attenuated by a linear reservoir at each pixel. Both approaches were applied to the Upper Medway catchment (250 km²) in southeastern England, using rainfall data from a rain gauge network. The outcomes revealed that the proposed approaches provided a reasonably accurate prediction of the hydrographs and exhibited notably superior performance compared to the original version of Hidropixel, which has limited capabilities in capturing translation effects. Hidropixel_TUH+ and Hidropixel_DLR predicted peak flows with an average absolute error of 11% and 10%, respectively. The Hidropixel_DLR achieved a more accurate time-to-peak estimation, with an average absolute error of 1 h, compared to the 1.5-h error from Hidropixel_TUH+. Additionally, the Hidropixel_DLR predicted the full direct runoff hydrograph more accurately, achieving an average Nash–Sutcliffe coefficient (<i>NSE</i>) of 0.89, while the Hidropixel_TUH+ had an <i>NSE</i> of approximately 0.84.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"38 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards practical artificial intelligence in Earth sciences","authors":"Ziheng Sun, Talya ten Brink, Wendy Carande, Gerbrand Koren, Nicoleta Cristea, Corin Jorgenson, Bhargavi Janga, Gokul Prathin Asamani, Sanjana Achan, Mike Mahoney, Qian Huang, Armin Mehrabian, Thilanka Munasinghe, Zhong Liu, Aaron Margolis, Peter Webley, Bing Gong, Yuhan Rao, Annie Burgess, Andrew Huang, Laura Sandoval, Brianna R. Pagán, Sebnem Duzgun","doi":"10.1007/s10596-024-10317-7","DOIUrl":"https://doi.org/10.1007/s10596-024-10317-7","url":null,"abstract":"<p>Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"10 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of deep learning reduced-order modeling for single-phase flow in faulted porous media","authors":"Enrico Ballini, Luca Formaggia, Alessio Fumagalli, Anna Scotti, Paolo Zunino","doi":"10.1007/s10596-024-10320-y","DOIUrl":"https://doi.org/10.1007/s10596-024-10320-y","url":null,"abstract":"<p>Our research is positioned within the framework of subsurface resource utilization for sustainable economies. We concentrate on modeling the underground single-phase fluid flow affected by geological faults using numerical simulations. The study of such flows is characterized by strong uncertainites in the data defing the problem due to the difficulty of taking precise measurements in the subsoil. We aim to demonstrate the feasibility of a reduced order model that is both reliable and computationally efficient, thereby facilitating the incorporation of uncertainties. We account for the uncertainities of the properties of the rock and the geometry of the fault. The latter is achieved by using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework to model the rock matrix and faults as <i>n</i> and <span>({n-1})</span> dimensional domains, allowing for non-conforming meshes. Our primary focus is on a reduced-order model capable of reproducing flow variables across the entire domain. We utilize the Deep Learning Reduced Order Model (DL-ROM), a nonintrusive neural network-based technique, and we compare it against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios. The most relevant contributions of this work are: the proof of concept of the use of neural network for reduced order models for subsoil flow, dealing with non-affine problems and mixed dimensional domain. Additionally, we generalize an existing mesh deformation method for discontinuous deformation maps. Our analysis highlights the capability of reduced order model, highlighting DL-ROM’s capacity to expedite complex analyses with promising accuracy and efficiency, making multi-query analyses with various quantities of interest affordable.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"59 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of supervised machine learning to assess and manage fluid-injection-induced seismicity hazards based on the Montney region of northeastern British Columbia","authors":"Afshin Amini, Erik Eberhardt, Ali Mehrabifard","doi":"10.1007/s10596-024-10318-6","DOIUrl":"https://doi.org/10.1007/s10596-024-10318-6","url":null,"abstract":"<p>One of the key challenges in assessing, managing and mitigating induced-seismicity hazards related to hydraulic fracturing and fluid injection activities is understanding how geological and operational features influence the likelihood and severity of an event. Geological features point to the pre-existing conditions that affect a well’s susceptibility to generating induced seismicity. In contrast, operational features are controllable and can be engineered to mitigate and minimize potential hazards. In recent years, with increased data availability and the rapid development of machine learning techniques, the application of these statistical tools has been proposed to investigate induced seismicity. However, this raises the question of the performance and interpretability of these methods, which requires thorough investigation. This paper presents the results of a detailed study utilizing data for the Montney region of northeastern British Columbia that investigates the robustness of several machine learning algorithms in predicting induced seismicity likelihood and severity and compares the importance of geological and operational features on the triggering and maximum magnitude of these events. The analyses include seismic monitoring, regional geology and well completions data, and the novel use of geophysical well log data to provide a more comprehensive database of geological features.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"1 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general approach to computing derivatives for Hessian-based seismic inversion","authors":"Bruno S. Silva, Jessé C. Costa, Jörg Schleicher","doi":"10.1007/s10596-024-10316-8","DOIUrl":"https://doi.org/10.1007/s10596-024-10316-8","url":null,"abstract":"<p>Full waveform inversion (FWI), a powerful geophysical technique for subsurface imaging through seismic velocity-model construction, relies on numerical optimization, thus requiring the computation of derivatives for an objective function. This paper proposes a discrete development for accurate computation of the gradient and Hessian-vector product, providing second-order optimization benefits like higher convergence rates and improved resolution. The approach is a promising alternative for computing the gradient and Hessian action in time-domain FWI, applicable to various geophysical problems. Computational costs and memory requirements are comparable to the Adjoint-State Method and more avorable than Automatic Differentiation. While efficient automatic differentiation algorithms have transformed gradient computation in applications like FWI, challenges may arise in 3D due to unforeseen memory allocations. Our approach addresses this by exploring the reverse mode differentiation algorithm, mapping temporary memory allocations and computational complexity. By means of introducing auxiliary fields all involved wavefield evolutions can be carried out with the very same evolution scheme, in this way simplifying the implementation and focusing the performance improvement effort in a single routine thus reducing the maintenance cost of these algorithms, especially when using GPU implementations.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"53 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142214185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}