Geophysical Prospecting最新文献

{"title":"Topography-dependent eikonal solver in tilted transverse isotropic media with anelliptical factorization","authors":"Xiaole Zhou,&nbsp;Serge Sambolian,&nbsp;Haiqiang Lan,&nbsp;Stéphane Operto","doi":"10.1111/1365-2478.13656","DOIUrl":"https://doi.org/10.1111/1365-2478.13656","url":null,"abstract":"<p>Incorporating anisotropy and complex topography is necessary to perform traveltime tomography in complex land environments while being a computational challenge when traveltimes are computed with finite-difference eikonal solvers. Previous studies have taken this challenge by computing traveltimes in transverse isotropic media involving complex topography with a finite-difference eikonal equation solver on a curvilinear grid. In this approach, the source singularity, which is a major issue in eikonal solvers, is managed with the elliptical multiplicative factorization method, where the total traveltime field is decomposed into an elliptical base traveltime map, which has a known analytical expression and an unknown perturbation field. However, the group velocity curve can deviate significantly from an ellipse in anellipitically anisotropic media. In this case, the elliptical base traveltime field differs significantly from the anelliptical counterpart, leading to potentially suboptimal traveltime solutions, even though it helps to mitigate the detrimental effects of the source singularity. To overcome this issue, we develop a more accurate topography-dependent eikonal solver in transverse isotropic media that relies on anelliptical factorization. To achieve this, we first define the coordinate transform from the Cartesian to the curvilinear coordinate system, which provides the necessary framework to implement the topography-dependent transverse isotropic finite-difference eikonal solver with arbitrary source and receiver positioning. Then, we develop a semi-analytical method for the computation of the topography-dependent anelliptical base traveltime field. Finally, we efficiently solve the resulting quadratic elliptical equation using the fast sweeping method and a quartic anelliptical source term through fixed-point iteration. We assess the computational efficiency, stability and accuracy of the new eikonal solver against the solver based on elliptical factorization using several transverse isotropic numerical examples. We conclude that this new solver provides a versatile and accurate forward engine for traveltime tomography in complex geological environments such as foothills and thrust belts. It can also be used in marine environments involving complex bathymetry when tomography is applied to redatumed data on the sea bottom.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"595-610"},"PeriodicalIF":1.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116021","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":"Compressive sensing principles applied in time and space for three-dimensional land seismic data acquisition and processing","authors":"Woodon Jeong,&nbsp;Constantinos Tsingas,&nbsp;Mohammed S. Almubarak,&nbsp;Yue Ma","doi":"10.1111/1365-2478.13654","DOIUrl":"https://doi.org/10.1111/1365-2478.13654","url":null,"abstract":"<p>Compressive sensing introduces novel perspectives on non-uniform sampling, leading to substantial reductions in acquisition cost and cycle time compared to current seismic exploration practices. Non-uniform spatial sampling, achieved through source and/or receiver areal distributions, and non-uniform temporal sampling, facilitated by simultaneous-source acquisition schemes, enable compression and/or reduction of seismic data acquisition time and cost. However, acquiring seismic data using compressive sensing may encounter challenges such as an extremely low signal-to-noise ratio and the generation of interference noise from adjacent sources. A significant challenge to this innovative approach is to demonstrate the translation of theoretical gains in sampling efficiency into operational efficiency in the field. In this study, we propose a spatial compression scheme based on compressive sensing theory, aiming to obtain an undersampled survey geometry by minimizing the mutual coherence of a spatial sampling operator. Building upon an optimised spatial compression geometry, we subsequently consider temporal compression through a simultaneous-source acquisition scheme. To address challenges arising from the recorded compressed seismic data in the non-uniform temporal and spatial domains, such as missing traces and crosstalk noise, we present a joint deblending and reconstruction algorithm. Our proposed algorithm employs the iterative shrinkage-thresholding method to solve an <i>ℓ</i>₂–<i>ℓ</i>₁ optimization problem in the frequency–wavenumber–wavenumber (<i>ω</i>–<i>k_x</i>–<i>k_y</i>) domain. Numerical experiments demonstrate that the proposed algorithm produces excellent deblending and reconstruction results, preserving data quality and reliability. These results are compared with non-blended and uniformly acquired data from the same location illustrating the robustness of the application. This study exemplifies how the theoretical improvements based on compressive sensing principles can significantly impact seismic data acquisition in terms of spatial and temporal sampling efficiency.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"3-18"},"PeriodicalIF":1.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114469","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":"Hydrocarbon detection via quantum mechanics–based highlight volumes extraction","authors":"Ya-juan Xue,&nbsp;Hong Zhang,&nbsp;Jin-Qiang Zhang,&nbsp;Xing-jian Wang,&nbsp;Jun-xing Cao,&nbsp;Zhe-ge Liu,&nbsp;Wu Wen,&nbsp;Jia Yang,&nbsp;Dong-Fang Li","doi":"10.1111/1365-2478.13653","DOIUrl":"https://doi.org/10.1111/1365-2478.13653","url":null,"abstract":"<p>Spectral decomposition, aiding for direct hydrocarbon detection, generally employs time–frequency analysis methods to characterize the time-varying frequency contents of the subsurface layers. However, ongoing efforts to improve time–frequency analysis resolution still face limitations, leading to inaccurate spectral decomposition. In this study, a quantum mechanics–based highlight volumes extraction method, which includes the quantum peak amplitude above average volume and the quantum peak frequency volume, is proposed as a novel spectral decomposition method for hydrocarbon detection. Seismic data are transformed into the time–frequency domain using continuous wavelet transform, and then each sample's amplitude spectrum of each trace is projected on a specific basis constructed by the wave functions using the Schröedinger equation. This yields the corresponding projection coefficient for each sample's amplitude spectrum. For each projection coefficient, the quantum peak amplitude above average volume is calculated by subtracting the average amplitude from the maximum amplitude. The quantum peak frequency volume consists of the local frequency points where the quantum peak amplitude is the maximum. Our approach stands out for its ability to indicate strong amplitude anomalies typically associated with the hydrocarbons and precise gas reservoir locations and has also been validated to handle seismic data with low signal-to-noise ratio well. Model tests and field data applications show the effectiveness and the advantages of the proposed quantum mechanics–based highlight volumes extraction method. The comparison with the conventional methods illustrates that the proposed quantum mechanics–based highlight volumes extraction method has higher temporal and spatial resolution and is more accurate in detecting the hydrocarbons in the gas reservoir. However, it may require longer computational times compared with the conventional methods. This work aims to complement the current spectral decomposition techniques with a new quantum mechanics–based highlight volumes extraction method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"19-37"},"PeriodicalIF":1.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114205","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":"Accelerating target-oriented multi-parameter elastic full-waveform uncertainty estimation by reciprocity","authors":"W. A. Mulder,&nbsp;B. N. Kuvshinov","doi":"10.1111/1365-2478.13650","DOIUrl":"https://doi.org/10.1111/1365-2478.13650","url":null,"abstract":"<p>The accuracy of a model obtained by multi-parameter full-waveform inversion can be estimated by analysing the sensitivity of the data to perturbations of the model parameters in selected subsurface points. Each perturbation requires the computation of the seismic response in the form of Born scattering data for a typically very large number of shots, making the method time consuming. The computational cost can be significantly reduced by placing sources of different types at the Born scatterer, the point where the subsurface parameters are perturbed. Instead of modelling each shot separately, reciprocity relations provide the wavefields from the shot positions to the scatter point in terms of wavefields from the scatterer to the shot positions. In this way, the Born scattering data from a single point in the isotropic elastic case for a marine acquisition with pressure sources and receivers can be expressed in terms of the wavefields for force and moment tensor sources located at the scatterer and only a small number of forward runs are required. A two-dimensional example illustrates how the result can be used to determine the Hessian and local relative covariance matrix for the model parameters at the scatterer at the cost of five forward simulations. In three dimensions, that would be nine.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"38-48"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114094","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":"Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks","authors":"Ravi Kant,&nbsp;Brijesh Kumar,&nbsp;Ajay P. Singh,&nbsp;G. Hema,&nbsp;S. P. Maurya,&nbsp;Raghav Singh,&nbsp;K. H. Singh,&nbsp;Piyush Sarkar","doi":"10.1111/1365-2478.13651","DOIUrl":"https://doi.org/10.1111/1365-2478.13651","url":null,"abstract":"<p>Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (&gt;20%) with a lower acoustic impedance of 6000–8500 m/s g cm³, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two-way travel time, provides high-resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high-resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"49-66"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114096","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":"Velocity model building via combining seismic slope tomography and supervised deep learning","authors":"Yao Huang,&nbsp;Huachen Yang,&nbsp;Jianzhong Zhang","doi":"10.1111/1365-2478.13652","DOIUrl":"https://doi.org/10.1111/1365-2478.13652","url":null,"abstract":"<p>Seismic slope tomography is an effective method to build macro velocity model. In order to improve the accuracy and resolution of the slope tomography, we proposed a novel approach that combines slope tomography with supervised deep learning. First, the slope tomography is used to obtain the macro velocity model and the positions of reflection points. Subsequently, the slope tomographic model, positions of reflection points and the corresponding observed traveltimes are used as inputs simultaneously for a neural network, whereas the actual velocity models are used as the labels. Through training the neural network with sufficient samples, the mapping from the inputs to the real velocity model is established. The neural network learns the background velocity of the real model from the smooth tomographic model, the velocity details from the traveltimes and the formation interface information from the positions of reflection points. Consequently, a high-accuracy and high-resolution velocity model is obtained on the basis of the slope tomographic model. Both tests on synthetic seismic data and applications to field seismic data demonstrate the effectiveness of the proposed method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"67-79"},"PeriodicalIF":1.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113394","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 novel strategy for simultaneous super-resolution reconstruction and denoising of post-stack seismic profile","authors":"Wenshuo Yu,&nbsp;Shiqi Dong,&nbsp;Shaoping Lu,&nbsp;Xintong Dong","doi":"10.1111/1365-2478.13646","DOIUrl":"https://doi.org/10.1111/1365-2478.13646","url":null,"abstract":"<p>Post-stack seismic profiles are images reflecting geological structures which provide a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post-stack profiles suffer from low resolution and strong noise issues, which severely affects subsequent seismic interpretation. To better enhance the spatial resolution and signal-to-noise ratio of post-seismic profiles, a multi-scale attention encoder–decoder network based on generative adversarial network is proposed. This method improves the resolution of post-stack profiles and effectively suppresses noises and recovers weak signals as well. A multi-scale residual module is proposed to extract geological features under different receptive fields. At the same time, an attention module is designed to further guide the network to focus on important feature information. Additionally, to better recover the global and local information of post-stack profiles, an adversarial network based on a Markov discriminator is proposed. Finally, by introducing an edge information preservation loss function, the conventional loss function of the Generative Adversarial Network is improved, which enables better recovery of the edge information of the original post-stack profiles. Experimental results on simulated and field post-stack profiles demonstrate that the proposed multi-scale attention encoder–decoder network based on generative adversarial network method outperforms two advanced convolutional neural network-based methods in noise suppression and weak signal recovery. Furthermore, the profiles reconstructed by the multi-scale attention encoder–decoder network based on generative adversarial network method preserve more geological structures.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"96-112"},"PeriodicalIF":1.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120248","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":"Broadband seismic data acquisition and processing of iron oxide deposits in Blötberget, Sweden","authors":"Lea Gyger,&nbsp;Alireza Malehmir,&nbsp;Musa Manzi,&nbsp;Lilas Vivin,&nbsp;Jean Lépine,&nbsp;Ayse Kaslilar,&nbsp;Oleg Valishin,&nbsp;Paul Marsden,&nbsp;Ronne Hamerslag","doi":"10.1111/1365-2478.13648","DOIUrl":"https://doi.org/10.1111/1365-2478.13648","url":null,"abstract":"<p>In June 2022, an innovative seismic survey was conducted in Blötberget, central Sweden, to evaluate the effectiveness of employing both a broadband seismic source and broadband receivers for mineral exploration in a challenging hardrock setting. The Blötberget mine hosts high-quality iron oxides, predominantly magnetite and hematite, sometimes enriched with apatite. These deposits comprise 10–50 m thick sheet-like horizons with a moderate eastward dip (<span></span><math>\u0000 <semantics>\u0000 <mo>∼</mo>\u0000 <annotation>$sim$</annotation>\u0000 </semantics></math>45°) along an NNE-trending zone. The survey employed a combination of co-located micro-electromechanical sensors, three-component recorders, surface and borehole distributed acoustic sensing, along with a 77-kN broadband seismic vibrator operating with 2–200 Hz linear sweeps. A tailored processing workflow was applied to preserve the broadband nature of the recorded data, and a one-dimensional velocity model was derived from the borehole distributed acoustic sensing data for migration and time-to-depth conversion purposes. Compared to the previous seismic surveys, the resulting seismic cross section reveals several well-defined reflections with improved resolution. Notably, a reflection intersecting the main deposits at a depth of approximately 1200 m exhibits a distinct polarity reversal relative to the reflection from the mineralization, providing further evidence for its interpretation as originating from a fault zone. Shallow reflections align with geological boundaries and partially coincide with weak magnetic anomalies. Additional reflections were revealed underneath the known mineralization on both sides of the fault zone and may suggest the presence of potential additional resources. The delineation of these reflections and the fault zone is critical for future mine planning and development in the region. This case study underscores the potential of broadband data in achieving high-resolution subsurface imaging in hardrock environment and its pivotal role in mineral resource assessment processes.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"80-95"},"PeriodicalIF":1.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120245","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":"Deep learning-based inversion with discrete cosine transform discretization for two-dimensional basement relief imaging of sedimentary basins from observed gravity anomalies","authors":"Arka Roy,&nbsp;Yunus Levent Ekinci,&nbsp;Çağlayan Balkaya,&nbsp;Hanbing Ai","doi":"10.1111/1365-2478.13647","DOIUrl":"https://doi.org/10.1111/1365-2478.13647","url":null,"abstract":"<p>Sedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two-dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one-dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non-Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization-based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"113-129"},"PeriodicalIF":1.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119200","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":"Analytical formulas for geometrical factor and sensitivity for long electrodes","authors":"S. L. Butler","doi":"10.1111/1365-2478.13644","DOIUrl":"https://doi.org/10.1111/1365-2478.13644","url":null,"abstract":"<p>In the electrical resistivity method, electrodes are usually modelled as point current sources and point voltage measurements. If the burial depth of the electrode is significant compared with the spacing between electrodes, this point approximation may not be accurate. Common situations employing long electrodes include the use of metal-cased boreholes as electrodes and small-scale, high-resolution environmental, engineering and archaeological surveys where electrode spacings may be very small. In this contribution, I present analytical expressions for the mutual resistance between long electrodes modelled as line current sources. Mutual resistances are then used to calculate geometrical factors. Additionally, I present an expression for the current density and use it to derive an analytical expression for the sensitivity of electrode arrays with long electrodes. The sensitivity is, in turn, used to calculate the mean depth and position which can be used as estimates of depth and position of investigation and as pseudosection plot points. Example calculations using the geometrical factor, sensitivity and mean depth are shown, and comparisons are made with simulations and lab-scale experiments.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"130-141"},"PeriodicalIF":1.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118151","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}