Geophysical Prospecting最新文献

{"title":"Temperature effects on the electrical conductivity of K-feldspar","authors":"Supti Sadhukhan,&nbsp;Tapati Dutta","doi":"10.1111/1365-2478.13605","DOIUrl":"10.1111/1365-2478.13605","url":null,"abstract":"<p>K-feldspar, which constitutes about 60<span></span><math>\u0000 <semantics>\u0000 <mo>%</mo>\u0000 <annotation>$%$</annotation>\u0000 </semantics></math> of the Earth's crust, is crucial for understanding electrical conductivity in porous rocks. Its electrical properties are vital for applications in ceramics, electrical insulation and conductive polymers. In this work, we study the time evolution of electrical conductivity of K-feldspar-rich rocks with varying temperatures, at high and low pH, which has been studied through simulation using time domain random walk. Random walkers, mimicking ions in transport, move in accordance with appropriate hydrodynamic equations, dissolution and precipitation kinetics. Electrical conductivity has been calculated considering variations in the parameters of temperature, fluid pH and the abundance of K-feldspar in rocks. Electrical conductivity is found to increase with temperature up to a critical value, after which it decreases. The sharpness of the rise and fall in electrical conductivity is quantified through a measure defined as the conductivity quality factor <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>σ</mi>\u0000 </msub>\u0000 <annotation>$Q_{sigma }$</annotation>\u0000 </semantics></math>. We find that <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>σ</mi>\u0000 </msub>\u0000 <annotation>$Q_{sigma }$</annotation>\u0000 </semantics></math> increases with a decrease in the availability of K-feldspar mineral. Our simulated results of electrical conductivity show a good match with the experimental trends reported.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3338-3349"},"PeriodicalIF":1.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222279","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":"An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking","authors":"Bowen Deng,&nbsp;Guangui Zou,&nbsp;Suping Peng,&nbsp;Jiasheng She,&nbsp;Chengyang Han,&nbsp;Yanhai Liu","doi":"10.1111/1365-2478.13606","DOIUrl":"10.1111/1365-2478.13606","url":null,"abstract":"<p>This article introduces a novel method for geological fault interpretation utilizing a 2D convolutional neural network approach with a focus on coalbed horizons, dealing with the problem as an image classification task. At the beginning, linear annotations, reflecting geological fault features, are applied to multiple seismic sections. By considering texture differences between fault and non-fault areas, we construct samples that represent these distinct zones for training deep neural networks. Initially, fault annotations are transformed into single dots to facilitate pixel-based processing. To depict a specific dot's geological structure, we employ a matrix clipped around the point, determined by a combination of range and step parameters. Convolutional layers generate filters equivalent to seismic data transformation, streamlining the need for analysis and selection of seismic attributes. The article discusses enhancing the efficiency of 2D convolutional neural network–based fault interpretation by optimizing sample selection, data extraction and model construction procedures. Through the incorporation of data from two mining areas (totalling 27.09 km²) in sample creation, the overall accuracy exceeds 0.99. Recognition extends seamlessly to unlabelled sections, showcasing the innovative technical route and methodology of fault interpretation with linear annotation and pixel-based thinking. This study presents a method that integrates planar and raster thinking, transitioning from vision-oriented geological structure annotation to algorithm-oriented pixel location. The proposed 2D convolutional neural network–based matrix-oriented fault/non-fault binary classification demonstrates feasibility and reproducibility, offering a new automated approach for fault detection in coalbeds through convolutional neural network algorithms.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3350-3370"},"PeriodicalIF":1.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222348","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":"Bayesian time-lapse full waveform inversion using Hamiltonian Monte Carlo","authors":"P. D. S. de Lima,&nbsp;M. S. Ferreira,&nbsp;G. Corso,&nbsp;J. M. de Araújo","doi":"10.1111/1365-2478.13604","DOIUrl":"10.1111/1365-2478.13604","url":null,"abstract":"<p>Time-lapse images carry out important information about dynamic changes in Earth's interior, which can be inferred using different full waveform inversion schemes. The estimation process is performed by manipulating more than one seismic dataset, associated with the baseline and monitors surveys. The time-lapse variations can be so minute and localized that quantifying the uncertainties becomes fundamental to assessing the reliability of the results. The Bayesian formulation of the full waveform inversion problem naturally provides confidence levels in the solution, but evaluating the uncertainty of time-lapse seismic inversion remains a challenge due to the ill-posedness and high dimensionality of the problem. The Hamiltonian Monte Carlo can effectively sample over high-dimensional distributions with affordable computational efforts. In this context, we explore the sequential approach in a Bayesian fashion for time-lapse full waveform inversion using the Hamiltonian Monte Carlo method. The idea relies on integrating the baseline survey information as prior knowledge to the monitor estimation. We compare this methodology with a parallel scheme in perfect and a simple perturbed acquisition geometry scenario considering the Marmousi and a typical Brazilian pre-salt velocity model. We also investigate the correlation effect between baseline and monitor samples on the propagated uncertainties. The results show that samples between different surveys are weakly correlated in the sequential case, while the parallel strategy provides time-lapse images with lower dispersion. Our findings demonstrate that both methodologies are robust in providing uncertainties even in non-repeatable scenarios.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3381-3398"},"PeriodicalIF":1.8,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222291","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":"Imaging of train noise with heavy traffic events recorded by distributed acoustic sensing","authors":"Hanyu Zhang,&nbsp;Lei Xing,&nbsp;Xingpeng Zheng,&nbsp;Tuanwei Xu,&nbsp;Dimin Deng,&nbsp;Mingbo Sun,&nbsp;Huaishan Liu,&nbsp;Shiguo Wu","doi":"10.1111/1365-2478.13597","DOIUrl":"10.1111/1365-2478.13597","url":null,"abstract":"<p>Train noise is a kind of green, non-destructive and strong-energy artificial seismic sources, which is widely used in railway safety monitoring, near-surface imaging and urban underground space exploration. Distributed acoustic sensing is a new seismic acquisition technology, which has the advantages of dense sampling, simple deployment and strong anti-electromagnetic interference ability. In recent years, distributed acoustic sensing has been gradually applied in the fields of urban traffic microseism monitoring, crack detection and underground space imaging. However, previous studies mainly focused on microseism interferometry using train event coda noise, and there is limited research on the workflow of interferometry imaging using distributed acoustic sensing–based heavy train events noise (with short coda windows), which produces an abundant of near-source interference. Aiming at proving the effectiveness of this idea, we investigated a process workflow to get underground shear-velocity structure based on distributed acoustic sensing recorded heavy traffic noise near Qinhuangdao train station. A weighted sliding absolute average method is used to weaken the strong amplitude to the coda wave level and reduce the near-source influence. We demonstrated that the cross-coherence interferometry method, after spectral whitening, has the best effect on sidelobe suppression in the virtual source surface wave shot gathers, through a comparative analysis of cross-correlation and cross-coherence results. For obtaining concentrated energy and strong continuity in phase velocity spectra, we selected the time windows with high spatial coherence and signal-to-noise ratio not less than 1.2 for stacking from 720 time windows in <i>F</i>–<i>K</i> domain. When dividing subarrays to extract pseudo-two-dimensional profile, we set the overlap rate between adjacent time windows to 80% to increase stacking times, enhancing the precision of phase velocity spectra and reducing the errors of picking dispersion curve. Our results show that heavy traffic train events noise (non-pure coda) can be used to detect underground velocity structure with clear dispersion and high inversion reliability. This research provides a new processing flow for distributed acoustic sensing train noise imaging and can be applied in future urban underground space exploration.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3399-3413"},"PeriodicalIF":1.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222280","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":"Thomsen, L., 2023, A logical error in Gassmann poroelasticity: Geophysical Prospecting, 71, 649–663. by Leon Thomsen, University of Houston","authors":"","doi":"10.1111/1365-2478.13567","DOIUrl":"https://doi.org/10.1111/1365-2478.13567","url":null,"abstract":"<p>Two figure captions in this paper were in error, confusing compressibility and incompressibility (the figures themselves were correct). The proper figure captions are</p><p>FIGURE 2. Comparison of Berea sandstone data from Hart and Wang (2010) for <i>K</i>_ud − <i>K</i>_fm (as functions of differential pressure, <i>p_d</i> = <i>p</i> − <i>p_F</i>) with predictions from Gassmann theory (Equation 1, using data for <i>K_𝑆</i> (from Equation 14; see also the unnumbered equation from B&amp;K following Equation 17), or from VRH theory), and from B&amp;K theory (Equation 19, using data for <i><span>K</span>_𝑆</i> and for <i>κ_M</i> (from Equation 21)). The Fluid (water) incompressibility <i>K_F</i> is taken as 2.3 GPa.</p><p>FIGURE 4. Comparison of Indiana limestone data from Hart and Wang (2010) for <i>K</i>_ud − <i>K</i>_fm (as functions of differential pressure, <i>p_d</i> = <i>p</i> − <i>p_F</i>) with predictions from Gassmann theory (Equation 1, using data for <i>K_S</i> (from Equation 14; see also the unnumbered equation from B&amp;K following Equation 17), or from VRH theory), and from B&amp;K theory (Equation 19, using data for <i><span>K</span>_𝑆</i> and <i>κ_M</i> (from Equation 21)). The Fluid (water) incompressibility <i>K_F</i> is taken as 2.3 GPa.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 7","pages":"2857"},"PeriodicalIF":1.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045334","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":"Centralized feature pyramid-based supervised deep learning for object detection model from GPR data","authors":"Kun Yan,&nbsp;Xianlei Xu,&nbsp;Pengqiao Zhu,&nbsp;Zhaoyang Zhang","doi":"10.1111/1365-2478.13590","DOIUrl":"10.1111/1365-2478.13590","url":null,"abstract":"<p>To address low detection accuracy and speed due to the multisolvability of the ground-penetrating radar signal, we proposed a novel centralized feature pyramid-YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra-layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground-penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three-dimensional imaging. Our model is suitable for object detection in ground-penetrating radar images, thereby providing technical support for road damage and underground pipeline detection.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3414-3435"},"PeriodicalIF":1.8,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222299","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":"Blind spectral inversion of seismic data","authors":"Yaoguang Sun,&nbsp;Siyuan Cao,&nbsp;Siyuan Chen,&nbsp;Yuxin Su","doi":"10.1111/1365-2478.13594","DOIUrl":"10.1111/1365-2478.13594","url":null,"abstract":"<p>Reflectivity inversion is a key step in reservoir prediction. Conventional sparse-spike deconvolution assumes that the reflectivity (reflection coefficient series) is sparse and solves for the reflection coefficients by an <i>L</i>1-norm inversion process. Spectral inversion is an alternative to sparse-spike deconvolution, which is based on the odd–even decomposition algorithm and can accurately identify thin layers and reduce the wavelet tuning effect without using constraints from logging data, from horizon interpretations or from an initial model of the reflectivity. In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above, which comprises seismic wavelet and reflectivity sequence, assuming that the wavelets that affect some subsets of the seismic data are approximately the same. Therefore, we combined blind deconvolution with spectral inversion to propose blind spectral inversion. Given an initial wavelet, we can calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. During the update processing, we add the smoothness of the wavelet amplitude spectrum as a regularization term, thus reducing the wavelet oscillation in the time domain, increasing the similarity between inverted and initial wavelets, and improving the stability of the solution. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even when there is an error in wavelet extraction.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3436-3447"},"PeriodicalIF":1.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222289","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 probabilistic full waveform inversion of surface waves","authors":"Sean Berti,&nbsp;Mattia Aleardi,&nbsp;Eusebio Stucchi","doi":"10.1111/1365-2478.13595","DOIUrl":"10.1111/1365-2478.13595","url":null,"abstract":"<p>Over the past decades, surface wave methods have been routinely employed to retrieve the physical characteristics of the first tens of meters of the subsurface, particularly the shear wave velocity profiles. Traditional methods rely on the application of the multichannel analysis of surface waves to invert the fundamental and higher modes of Rayleigh waves. However, the limitations affecting this approach, such as the 1D model assumption and the high degree of subjectivity when extracting the dispersion curve, motivate us to apply the elastic full-waveform inversion, which, despite its higher computational cost, enables leveraging the complete information embedded in the recorded seismograms. Standard approaches solve the full-waveform inversion using gradient-based algorithms minimizing an error function, commonly measuring the misfit between observed and predicted waveforms. However, these deterministic approaches lack proper uncertainty quantification and are susceptible to get trapped in some local minima of the error function. An alternative lies in a probabilistic framework, but, in this case, we need to deal with the huge computational effort characterizing the Bayesian approach when applied to non-linear problems associated with expensive forward modelling and large model spaces. In this work, we present a gradient-based Markov chain Monte Carlo full-waveform inversion where we accelerate the sampling of the posterior distribution by compressing data and model spaces through the discrete cosine transform. Additionally, a proposal is defined as a local, Gaussian approximation of the target density, constructed using the local Hessian and gradient information of the log posterior. We first validate our method through a synthetic test where the velocity model features lateral and vertical velocity variations. Then we invert a real dataset from the InterPACIFIC project. The obtained results prove the efficiency of our proposed algorithm, which demonstrates to be robust against cycle-skipping issues and able to provide reasonable uncertainty evaluations with an affordable computational cost.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3448-3473"},"PeriodicalIF":1.8,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222288","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":"Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada","authors":"Limin Xu,&nbsp;E. C. R. Green,&nbsp;C. Kelly","doi":"10.1111/1365-2478.13589","DOIUrl":"10.1111/1365-2478.13589","url":null,"abstract":"<p>We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km² study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low-cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden-bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine <i>K</i>-nearest neighbours and weighted <i>K</i>-nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3474-3494"},"PeriodicalIF":1.8,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13589","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222290","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":"An efficient illumination compensation method for reverse time migration","authors":"Yang Zhou","doi":"10.1111/1365-2478.13581","DOIUrl":"10.1111/1365-2478.13581","url":null,"abstract":"<p>By directly solving the full two-way wave equation, reverse time migration has superiority over other imaging algorithms in handling steeply dipping structures and other complicated geological models. Moreover, by incorporating the asymptotic inversion operator into reverse time migration imaging condition, the imaging algorithm is able to give a quantitative estimation of parameter perturbation in high-frequency approximation sense. However, because conventional asymptotic inversion only accounts for geometrical spreading, uneven illumination due to irregular acquisition geometry and inhomogeneous subsurface at each image point is neglected. The omit of illumination compensation significantly affects the imaging quality. Wave-equation-based illumination compensation methods have been extensively studied in the past. However, the traditional wave-equation-based illumination compensation methods usually require high computational cost and huge storage. In this paper, we propose an efficient wave-equation-based illumination compensation method. Under high-frequency approximation, we first define a Jacobian determinant to measure the regularity of subsurface illumination, and then illumination compensation operators are proposed based on the Jacobian. Through boundary integration, we further express the illumination compensation operators through extrapolated wavefields; the explicit computation of asymptotic Green's functions is thus avoided, and an efficient illumination compensation implementation for reverse time migration is achieved. Numerical results with both synthetic and field data validate the effectiveness and efficiency of the presented method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 8","pages":"3140-3156"},"PeriodicalIF":1.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222293","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}