Computers & Geosciences最新文献

{"title":"A high-efficiency parallel fast marching method for large-scale seismic tomography in three-dimensional spherical coordinates","authors":"Junyi Xia ,&nbsp;Dinghui Yang ,&nbsp;Ping Tong","doi":"10.1016/j.cageo.2024.105841","DOIUrl":"10.1016/j.cageo.2024.105841","url":null,"abstract":"<div><div>The fast marching method is an essential step in the level set method, widely applied in seismic tomography. However, there are two key challenges in large-scale seismic tomography: significant time consumption and storage issues related to large-scale matrices. Therefore, it is crucial to develop a high-efficiency and high-accuracy parallel fast marching method. Although previous scholars have developed parallel fast marching algorithms based on various parallel strategies in Cartesian coordinates, these algorithms ignore the influence of the Earth’s curvature and generally achieve only first-order accuracy. To address these problems, this study introduces a distributed-memory parallel fast marching method based on domain decomposition to solve the eikonal equation in 3D spherical coordinates. By introducing spherical coordinates, the method naturally accounts for the Earth’s curvature. Additionally, this study designs a parallel strategy based on a second-order scheme and uses the multiplicative factorization technique to handle point source singularities. The parallel strategy ensures the global causality condition of the traveltime field and maintains global second-order accuracy. Numerical experiments show that the parallel algorithm can solve the factor eikonal equation for 8.5 billion grid points or greater. It distributes the over 200 GB memory requirement per node in sequential FMM across multiple nodes, significantly reducing computation time and memory needs while maintaining second-order accuracy. Furthermore, the algorithm proves to be suitable for earthquake location applications. This highly efficient and accurate parallel algorithm is applicable for large-scale seismic tomography and other related research.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105841"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LABQ3: Bayesian method for quantification of mineral compositions and nano-scale elemental mapping of 3D synchrotron XCT data","authors":"Conghao Yi ,&nbsp;Catherine A. Peters ,&nbsp;Viktor Nikitin ,&nbsp;Sang Soo Lee ,&nbsp;Paul Fenter","doi":"10.1016/j.cageo.2025.105858","DOIUrl":"10.1016/j.cageo.2025.105858","url":null,"abstract":"<div><div>Quantitative analysis of mineral compositions is essential in understanding geochemical, mineralogical and environmental processes. Fine-resolution 3D imaging is widely done using synchrotron X-ray computed tomography (XCT), but existing analyses are limited to visualization and segmentation. This paper presents a new method, <strong>L</strong>inear <strong>A</strong>ttenuation <strong>B</strong>ayesian <strong>Q</strong>uantitative <strong>3</strong>D-mapper (<strong>LABQ3</strong>), based on the linearity of X-ray attenuation with respect to elemental concentrations. To address the random variability in attenuation measurements, LABQ3 employs Bayesian decision theory to minimize classification error, using reference attenuation distributions from scans of pure mineral standards. To demonstrate LABQ3 and test its performance, we studied precipitated carbonate samples. XCT scans were done at multiple energies using the transmission X-ray microscope (TXM) at beamline 32-ID-C of the Advanced Photon Source at Argonne National Laboratory. The reconstructed 3D images have a voxel size of 20 nm. Analyses revealed rich nano-scale compositional heterogeneity within individual particles. A mixture of calcium and cadmium produced an overall stoichiometric composition of (Ca_0.78,Cd_0.22)CO₃, with some voxels containing nearly pure CdCO₃. The addition of zinc led to an overall stoichiometric composition of 33% Ca, 28% Cd, 39% Zn, with a nearly pure CaCO₃ core and compositional zonation through the rim. These compositional gradients are related to temporal sequences of carbonate mineral formation where Cd precipitated at the beginning in (Ca,Cd)CO₃, while Cd and Zn precipitated at the end in (Ca,Cd,Zn)CO₃. Results differ from bulk analyses using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), showing that LABQ3 provides particle-specific insights. LABQ3 distinguishes itself by quantifying chemical compositions along a continuum, making it different from XCT analyses based on segmentation. LABQ3 allows simultaneous acquisition of morphology and chemical composition in 3D, facilitating the interpretation of chemical gradients of trace elements, quantification of solid solution compositions, inferences about temporal sequences of mineral precipitation, and addressing other concerns about solid-phase chemistry.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105858"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Numerical simulations of time-domain seismic wave propagation for a point source in 2-D onshore and offshore geological models","authors":"Moosoo Won ,&nbsp;Bing Zhou ,&nbsp;Mohamed Jamal Zemerly ,&nbsp;Mohammad Al-Khaleel ,&nbsp;Mohamed Kamel Riahi ,&nbsp;Xu Liu","doi":"10.1016/j.cageo.2024.105846","DOIUrl":"10.1016/j.cageo.2024.105846","url":null,"abstract":"<div><div>This research demonstrates an innovative numerical technique to simulate seismic wave propagation of a practical point source in complex 2-D geological models, which encompass a free surface topography, an undulating seafloor, and acoustic, elastic isotropic, viscoacoustic and viscoelastic anisotropic rocks. This technique is particularly beneficial in scenarios where 3-D wave modeling is resource-intensive and may efficiently offer the 3-D wavefields from arbitrary 2-D geological models often encountered in practice. Based on the point-source viscoelastic wave equations in a 2-D heterogeneous tilted transversely isotropic (TTI) medium, representative of subsurface igneous and sedimentary rocks, we tailor the wave equations valid for different rocks and the boundary conditions of the free-surface topography and seafloor and adapt the conventional memory variable method and the newly developed Taylor-series recursive convolution method to solve such point-source comprehensive wave equations. To overcome the inherent computational intensity of the methods, we convert the complex domain into a real domain and implement a fully parallelized computing strategy to ensure that the runtime of the numerical simulation remains on par with that of common 2-D wave modeling. Our experimental validations confirm the accuracy of the Taylor-series recursive method to offer the 3-D wavefields in an arbitrary heterogeneous 2-D geological model having a free-surface topography or an undulating seafloor. Moreover, our applications of this technique to two benchmark practical 2-D geological models demonstrate its capability to replicate 3-D wavefields in arbitrary viscoelastic anisotropic media, and greatly help in interpreting offshore and onshore seismic data and generating an accurate image of the subsurface.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105846"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prestack three-parameter simultaneous inversion under well-control AVO","authors":"Ji-an Wu ,&nbsp;Fanchang Zhang ,&nbsp;Nanying Lan ,&nbsp;Xingyao Yin","doi":"10.1016/j.cageo.2024.105816","DOIUrl":"10.1016/j.cageo.2024.105816","url":null,"abstract":"<div><div>Poisson's ratio is a crucial parameter for reservoir identification and the evaluation of hydrocarbon-bearing characteristics. However, due to various assumptions that are hard to satisfy, the AVO approximation formula containing Poisson's ratio is inaccurate and struggles to meet the demand for higher accuracy of inversion results. To address this issue, the inversion method based on well-control AVO is proposed. Firstly, the well-control AVO operator is obtained through well-seismic data itself, not directly using the theoretical forward operator. Then, to ensure the feasibility and stability of obtaining the well-control AVO operator, the simplified forward framework is proposed based on the physical significance indicated by prestack seismic data. Furthermore, a comprehensive well-control AVO operator can be extracted by all well data, thereby ensuring its universality. Finally, the objective function of prestack three-parameter simultaneous inversion is constructed by the well-control AVO operator, and parameter decorrelation is introduced to eliminate mutual interference between parameters. Additionally, to obtain inversion results efficiently, the regularization factors in the objective function are optimized by orthogonal experiment. Numerical test results show that the well-control AVO can obtain more accurate forward record. Moreover, inversion using the method in this paper can yield more accurate Poisson's ratio and enable better identification of hydrocarbon-bearing reservoirs.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105816"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing class imbalance in micro-CT image segmentation: A modified U-Net model with pixel-level class weighting","authors":"Shahin Mahmoudi ,&nbsp;Omid Asghari ,&nbsp;Jeff Boisvert","doi":"10.1016/j.cageo.2025.105853","DOIUrl":"10.1016/j.cageo.2025.105853","url":null,"abstract":"<div><div>Micro X-ray Computed Tomography (micro-CT) segmentation is a cornerstone in Digital Rock Physics (DRP), enabling detailed analysis of pore structures and mineral distributions. However, class imbalance remains a critical challenge, often resulting in biased segmentation outcomes. To address this, a methodology combining a modified U-Net architecture with a Pixel-Level Class Weighting (PCW) strategy is introduced. Unlike traditional class-level weighting, PCW assigns weights at the pixel level, offering finer control over segmentation by prioritizing minority classes and challenging pixels. This approach leverages modern deep learning frameworks, where input, label, and weight maps are jointly fed into the network, facilitating dynamic adjustments to emphasize task-specific regions. The modified U-Net incorporates dynamic dropout layers, L2 regularization, and optimized convolutional filters, enhancing computational efficiency and generalization. A dataset of only 40 micro-CT slices from two unique Bentheimer sandstone core samples is used for training and validation. When testing blindly on a third unique core sample, the modified U-Net with PCW increased the F1 score from 0.88 to 0.95. The model maintains the F1 score of majority classes 'pore' and 'quartz', while increasing the F1 score of minority classes 'clay' and 'feldspar' by 31% and 4.2%, respectively. Accurate segmentation of micro-CT images directly impacts downstream computational modeling in petroleum fields, improving permeability and porosity predictions essential for reservoir characterization and fluid flow simulations. The proposed framework represents an efficient, robust solution for imbalanced segmentation tasks, with potential applications in geosciences, such as mineral prospectivity mapping and geochemical anomaly detection.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105853"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IsoMapGen: Framework for early prediction of peak ground acceleration using tripartite feature extraction and gated attention model","authors":"Anushka Joshi,&nbsp;Pradeep Singh,&nbsp;Balasubramanian Raman","doi":"10.1016/j.cageo.2024.105849","DOIUrl":"10.1016/j.cageo.2024.105849","url":null,"abstract":"<div><div>Time series data associated with seismic activities pose significant challenges in disaster preparedness. These challenges underscore the need for reliable and timely damage assessments, critical for developing effective response strategies. The computation of Peak Ground Acceleration (PGA) is central to these assessments, serving as a crucial element in generating dynamic damage maps essential for managing rescue operations. Traditional approaches usually derive PGA from full-length accelerograms after an event, a process that is often complicated and prone to delays. In this work, Isoseismal Map Generator (IsoMapGen) is an end-to-end deep-learning framework engineered to predict early PGA using the initial few seconds of the primary waveform. This model integrates a novel spatio-temporal learning approach with gated component-wise attention mechanisms to enhance PGA and magnitude predictions for real-time damage mapping. It employs a chained prediction methodology that dynamically updates damage maps in response to incoming seismic data. The waveform, as well as tabular features extracted from the waveform, are passed in the model. The data imbalance in high-magnitude earthquake records of the tabular datasets has been addressed through synthetic data using a Conditional Tabular Generative Adversarial Network (CTGAN). CTGAN’s application in generating synthetic earthquake indicator data is largely unexplored. A detailed comparative analysis of IsoMapGen has been designed against established baseline models, highlighting its strong performance in real-time applications. The models’ efficacy was demonstrated by successfully predicting site-specific PGA from early three seconds of ground motion related to three recent earthquakes of magnitude 7.6, 6.1, and 5.8 <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>J</mi><mi>M</mi><mi>A</mi></mrow></msub></math></span>, that occurred on January 01, 2024. This represents notable progress in earthquake damage mitigation using early PGA prediction. Furthermore, this work could be utilized for other short-length time series characterization problems.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105849"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 VTI medium prestack migration method based on the De Wolf approximation","authors":"Huachao Sun,&nbsp;Jianguo Sun","doi":"10.1016/j.cageo.2024.105835","DOIUrl":"10.1016/j.cageo.2024.105835","url":null,"abstract":"<div><div>Anisotropy of velocity is an inherent characteristic of subsurface rock layers, and neglecting its effects can lead to errors in imaging positioning. The present study assumes that subsurface anisotropy follows the VTI (vertically transversely isotropic) medium model and the De Wolf approximation is employed for wavefield computation to enhance imaging accuracy. Drawing on scattering theory, the medium parameters are divided into background parameters (background velocity and anisotropy) and disturbance parameters (velocity and anisotropy disturbances). The mathematical formulation of the De Wolf approximation integral equation in a VTI medium is derived, and a generalized screen approximation (VTI-GS) operator is developed for this medium. The VTI-GS operator is applied to prestack migration. An amplitude attenuation factor is introduced through algorithm implementation and programming to mitigate spatial aliasing and improve migration image accuracy. Error analysis and pulse response test demonstrate that the VTI-GS operator is well-suited for the VTI medium. Migration images for the concave model and the Hess model confirm that the VTI-GS operator yielded higher imaging accuracy than conventional isotropic imaging methods.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105835"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminator-based stratigraphic sequence semantic augmentation seismic facies analysis","authors":"Suibao Wang ,&nbsp;Baiquan Yan ,&nbsp;Yu Sun ,&nbsp;Zhenghao Tang","doi":"10.1016/j.cageo.2024.105828","DOIUrl":"10.1016/j.cageo.2024.105828","url":null,"abstract":"<div><div>With the rapid development of deep learning technologies, the seismic facies analysis technique using image classification and image segmentation models has made three-dimensional dense interpretation of seismic facies feasible. However, the application of deep learning models in seismic facies analysis is currently confronted with several challenges. These include difficulties in segmenting seismic facies in structurally complex areas, lower segmentation accuracy for rare categories within seismic facies, and the presence of significant “intra-facies noise” in the segmentation results. To address these issues, we propose a Discriminator-based Stratigraphic Sequence Semantic Augmentation Seismic Facies Analysis model (DSFA). Specifically, the model employs three primary strategies: firstly, the use of the Focal Loss function to enhance the model's learning capability for challenging segmentation samples and rare seismic facies categories; secondly, the utilization of the discriminator to output a Markov Random Field for learning stratigraphic sequence information; and lastly, the adoption of Skip connections between the model's Encoder and Decoder to integrate multi-scale seismic profile information. Experimental findings reveal that the DSFA model effectively addresses prevalent issues in seismic facies analysis, achieving optimal performance across comprehensive evaluation metrics. Additionally, the model is applicable to research in seismic geomorphology.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105828"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing managed artificial recharge backwash using a multi-objective particle swarm optimization coupled with a clogging simulation model","authors":"Tianjiao Zhang,&nbsp;Qi Zhu,&nbsp;Zhang Wen","doi":"10.1016/j.cageo.2025.105869","DOIUrl":"10.1016/j.cageo.2025.105869","url":null,"abstract":"<div><div>Artificial recharge (AR) plays an important role in the management of groundwater resources and the mitigation of hydrogeological problems. However, challenges related to clogging inevitably arise during groundwater recharge. Although the clogging mechanism during groundwater recharge has been intensively studied in the past decades, there is a relative scarcity of studies focused on strategies for preventing clogging through artificial interventions. This study introduces an optimization framework that integrates a clogging model with two objective functions to obtain an optimized backwashing strategy aimed at minimizing clogging during groundwater recharge. The proposed clogging model for the groundwater recharge process considers both physical clogging (attachment of suspended solids) and chemical clogging (iron oxide clogging) by coupling COMSOL and PHREEQC models. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was used to obtain the Pareto-optimal solutions, by evaluating the clogging conditions and recharge efficiencies of different strategies, which enables stakeholders to determine suitable backwashing frequency and duration among various groundwater backwashing strategies. The results indicate that optimized backwashing strategy can significantly reduce clogging in groundwater recharge projects. With the highest backwashing frequency and duration, clogging near the recharge wells would be reduced to 90% of that observed during normal recharge without strategies, and the time spent on backwashing would only constitute 4.8% of the recharge time.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105869"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated neighborhood and scale information network for open-pit mine change detection in high-resolution remote sensing images","authors":"Zilin Xie ,&nbsp;Kangning Li ,&nbsp;Jinbao Jiang ,&nbsp;Jinzhong Yang ,&nbsp;Xiaojun Qiao ,&nbsp;Deshuai Yuan ,&nbsp;Cheng Nie","doi":"10.1016/j.cageo.2025.105880","DOIUrl":"10.1016/j.cageo.2025.105880","url":null,"abstract":"<div><div>The open-pit mine change detection (CD) in high-resolution remote sensing images plays a crucial role in mineral development and environmental protection. Recent advancements in deep learning have significantly promoted the open-pit mine CD. However, the existing deep-learning-based CD methods encounter challenges in effectively integrating neighborhood and scale information from high-resolution remote sensing images, resulting in insufficient performance. Therefore, according to exploration of the influence patterns of neighborhood and scale information, this paper proposed an integrated neighborhood and scale information network (INSINet) dedicated to open-pit mine CD in high-resolution remote sensing images. Specifically, INSINet introduces 8-neighborhood-image information to expend the receptive field, which improves the recognition of boundary regions in center images. Drawing on techniques of skip connection, deep supervision, and attention mechanism, the multi-path deep supervised attention module is designed to enhance multi-scale information for fusion and change feature extraction. Experimental results demonstrate that incorporating neighborhood and scale information increases the F1 score of INSINet by 6.40%, with improvements of 3.08% and 3.32% respectively. INSINet outperforms existing methods, achieving an overall accuracy of 97.69%, an intersection over union of 71.26%, and an F1-score of 83.22%. INSINet shows significance for open-pit mine CD in high-resolution remote sensing images.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105880"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}