Geophysical Prospecting最新文献

{"title":"Seismic Random Noise Attenuation Using Non-Local Reference-Guided Deep Image Prior","authors":"Naihao Liu,&nbsp;Zhuo Wang,&nbsp;Ganlin Hua,&nbsp;Wen Deng,&nbsp;Jinghuai Gao","doi":"10.1111/1365-2478.70177","DOIUrl":"https://doi.org/10.1111/1365-2478.70177","url":null,"abstract":"<div>\u0000 \u0000 <p>Supervised deep learning methods have been widely applied for seismic random noise attenuation, but their dependence on large volumes of clean training data limits their practicality. Deep image prior (DIP) provides an unsupervised alternative by exploiting the structural bias of convolutional neural networks. However, its performance is sensitive to the choice of stopping iteration and does not explicitly incorporate structural prior information inherent in seismic data. In this study, we propose a non-local reference-guided deep image prior framework for seismic random noise attenuation. Non-local self-similarity (NSS) is extended from the patch level to the pixel level to improve noise level estimation accuracy and to generate structurally consistent reference data. Based on the estimated global noise level, a noise-driven early stopping criterion is introduced to determine the termination point of DIP optimization in a fully unsupervised manner. The NSS-refined reference is used as the network input, allowing structural information to be incorporated into the reconstruction process. In addition, selective weight decay applied to the decoder layers further enhances the separation between signal and high-frequency noise. Experiments on synthetic and field seismic data indicate that the proposed method effectively attenuates random noise while preserving structural continuity and reflection characteristics. Compared with existing unsupervised approaches, the method provides more stable optimization behaviour and improved reconstruction quality without requiring clean training data.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696290","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":"Physics-Informed Neural Networks for Super-Resolution of Geospatial Data: A Case Study From the Aeromagnetic Data of Central Iranian Plateau","authors":"Vahid Teknik","doi":"10.1111/1365-2478.70175","DOIUrl":"https://doi.org/10.1111/1365-2478.70175","url":null,"abstract":"<div>\u0000 \u0000 <p>High-resolution imaging of geospatial variables is typically achievable through costly and time-consuming dense spatial sampling. To optimise the cost of the geospatial surveys, line-based spatial data sampling is widely used for achieving high-resolution measurements along predefined survey lines with wider line spacings. The line-based surveying offers a relatively high-resolution signal along survey lines. However, applying conventional bidirectional raster grid methods on the line-based geospatial data reduces the resolution of the measured signals along the lines, resulting in grids with spatial resolution roughly one to two times the line spacing. This study introduces a supervised physics-informed neural network (PINN) framework to generate a super-resolution grid from line-based geospatial surveys. The method is applied to an aeromagnetic dataset in the central Iranian Plateau. The survey is characterised by ∼50 m sampling along flight lines and ∼7.5 km line spacing. Incorporating spatial coordinates and calculated gradients as training features enabled spatially aware prediction and enhanced the sensitivity of the trained model to the high-frequency signals. A physics-informed Laplacian constraint is imposed to enforce the harmonic nature of the potential field anomalies in the prediction. The trained PINN model predicted a high-resolution (∼2 km) magnetic grid from the low-resolution (∼15 km) bidirectional grid. Different statistical, spectral and spatial approaches are used to evaluate the accuracy of the prediction. This approach offers a robust and cost-effective solution for enhancing the resolution of the old geophysical, geological and environmental surveys, eliminating the need for additional field acquisition. The PINN-predicted magnetic grid facilitates a detailed identification of magmatic intrusions and a network of lineaments, which are closely correlated with the spatial occurrence of known ore deposits. This insight suggests prospective zones of natural resources and hazards of the lesser-known lineaments.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696298","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":"Enhancing Seismic Data Quality With Gabor-Kernel Unsupervised Denoising of Near-Surface Scattering Noise","authors":"Ziyan Zhou,&nbsp;Omar M. Saad,&nbsp;Sixiu Liu,&nbsp;Yang Liu,&nbsp;Tariq Alkhalifah","doi":"10.1111/1365-2478.70179","DOIUrl":"https://doi.org/10.1111/1365-2478.70179","url":null,"abstract":"<div>\u0000 \u0000 <p>Complex heterogeneities in the Earth's shallow subsurface can generate significant scattering noise. This noise obscures and distorts seismic reflections, reducing the signal-to-noise ratio (SNR) and further complicating subsequent processing steps such as static correction, velocity analysis and multiple attenuation. The geological assumption of small-scale, low-velocity bodies in the weathered layer provides a basis for constructing models that represent much of the land data. Using Gaussian random fields, we constructed models containing randomly distributed small-scale heterogeneities in the near-surface layer, which provided data similar to those we encounter in land, with low SNR. To reconstruct the distorted reflections from shallow scattering-based noisy data, we propose an unsupervised scheme based on the Gabor Dictionary Learning Network (GDLNet). GDLNet is an unrolled optimization model for natural image denoising that offers strong performance and structural interpretability. Its design enables flexible control over the physical characteristics of the convolution kernels through parameter tuning, enhancing adaptability to diverse noise patterns. We analyse the challenges of applying GDLNet to scattering-based noisy seismic data, including anisotropic resolution causing signal leakage and limited lateral continuity. To address these issues, we enhance the network with resampled Gabor kernels and directional convolution layers, and adopt a decaying learning rate strategy to prevent overfitting to noise. The improved network directly predicts the reflection signals from noisy input, and the scattering noise is subsequently obtained by subtracting the predicted reflections from the original data. For each shot gather, the proposed method requires only a few seconds of training, achieving computational efficiency comparable to conventional approaches. The proposed method is tested on both synthetic and field data, demonstrating the scheme's effectiveness in reducing scattering noise.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696297","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":"Physics-Guided Deep Learning for Adaptive Surface-Related Multiple Subtraction","authors":"Dong Zhang,&nbsp;Eric Verschuur","doi":"10.1111/1365-2478.70180","DOIUrl":"https://doi.org/10.1111/1365-2478.70180","url":null,"abstract":"<div>\u0000 \u0000 <p>Surface-related multiple elimination is a fundamental step in seismic data processing, typically relying on a two-stage procedure: multiple prediction followed by adaptive subtraction. While the prediction step is physically robust, the adaptive subtraction stage often struggles to resolve complex non-stationary discrepancies and overlapping primary-multiple events using conventional energy minimization criteria. In this paper, we propose a physics-guided deep learning (PGDL) framework to address these limitations by treating adaptive subtraction as a non-linear, physics-constrained mapping task. We utilize a U-Net architecture with a specialized dual-channel input: the original recorded full wavefield and the globally estimated multiples derived from the wave equation–based multi-dimensional convolution. By explicitly incorporating the multiple models, we inject robust kinematic constraints (i.e., physics) into the network, allowing the learning process to focus on the non-linear residual mapping required to correct amplitude and phase errors rather than learning wave propagation from scratch. We validate the proposed framework through three comprehensive scenarios: (1) synthetic-to-synthetic generalization, (2) field-to-field application using pseudo-labels and (3) a cross-data-distribution test training on synthetic data and applying it to field data. Our results demonstrate that the PGDL framework effectively suppresses surface-related multiples while preserving weak primary energy that is often damaged by traditional methods. Furthermore, we show that a transfer learning strategy using minimal field data effectively bridges the data distribution gap between synthetic training sets and real-world field acquisition, offering a scalable and computationally efficient way for industrial deployment.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696299","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":"Seismic Event Classification With a Lightweight Fourier Neural Operator Model","authors":"Ayrat Abdullin,&nbsp;Umair Bin Waheed,&nbsp;Leo Eisner,&nbsp;Abdullatif Al-Shuhail","doi":"10.1111/1365-2478.70176","DOIUrl":"https://doi.org/10.1111/1365-2478.70176","url":null,"abstract":"<div>\u0000 \u0000 <p>Real-time monitoring of induced seismicity is critical to mitigate operational risks, relying on the rapid and accurate classification of triggered data from continuous data streams. Deep learning models are effective for this purpose but require substantial computational resources, making real-time processing difficult. To address this limitation, a lightweight model based on the Fourier neural operator (FNO) is proposed for the classification of microseismic events, leveraging its inherent resolution-invariance and computational efficiency for waveform processing. In the STanford EArthquake Dataset (STEAD), a global and large-scale database of seismic waveforms, the FNO-based model demonstrates high effectiveness for trigger classification, with an F1 score of 95% even in the scenario of data sparsity in training. The new FNO model greatly decreases the computer power needed relative to current deep learning models without sacrificing the classification success rate measured by the F1 score. A test on a real microseismic dataset shows a classification success rate with an F1 score of 98%, outperforming many traditional deep-learning techniques. The reduced computational cost makes the proposed FNO model well-suited for deployment in resource-constrained, near-real-time seismic monitoring workflows, including traffic-light implementations. The source code for the proposed FNO classifier is available at https://github.com/ayratabd/FNOclass.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696319","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 Decade of Airborne Electromagnetic Surveying Lake Menindee (Australia) Under Varying Water Levels","authors":"A. Ray,&nbsp;A. McPherson,&nbsp;R. C. Brodie,&nbsp;A. Y. Ley-Cooper,&nbsp;K. P. Tan,&nbsp;M. Hatch,&nbsp;R. N. Deo,&nbsp;S. Wong,&nbsp;F. Dauti,&nbsp;W. Cook,&nbsp;T. Scarr,&nbsp;Y. Sun","doi":"10.1111/1365-2478.70171","DOIUrl":"https://doi.org/10.1111/1365-2478.70171","url":null,"abstract":"<p>Time domain airborne electromagnetic (AEM) surveying is a mature geophysical tool for imaging the Earth's shallow subsurface. It produces images of the electromagnetic conductivity structure of the earth, down to depths of a few hundred metres. The AEM method is fast, with rotary-wing or fixed-wing aircraft acquiring data at speeds of 100–300 km/h, making it an ideal near-surface reconnaissance tool. The physics of the AEM method is sensitive primarily to the subsurface conductivity, which is influenced by a range of geological factors such as mineral content, porosity, and water content and chemistry. In addition, the inferred subsurface conductivity depends on the accurate measurement and modelling of airborne transmitter and receiver geometries – a challenging task given the speed of acquisition and variability of wind conditions during an acquisition flight. In this work, we present inferences of the subsurface conductivity over Lake Menindee, New South Wales, Australia, using data from test flights and various AEM systems over a 10-year period (2014–2024). The lake storage has varied dramatically over this time, and the test flights have coincided with both high and low water levels. While this difference in storage volume undoubtedly influences the near-surface conductivity, a remarkably consistent interpretation of the regional geology is possible regardless of the hydrologic conditions. While the upper 10 m of the modelled depth sections exhibit the greatest time-variability in inferred electromagnetic conductivity, the hypothesis that lakebed near-surface conductivity is significantly correlated with the lake water volume cannot robustly be established. We also provide some information-theoretic calculations for each inversion result to aid in their quantitative comparison. The implications of our study are that subtle, shallow, hydrogeological changes are difficult to image with repeat AEM overflights from different systems. Conversely, we establish that different AEM systems with minimal extra processing robustly image the regional geo-electric structure of the near surface, validated by known stratigraphy and associated geological information, as well as borehole conductivity logs.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696351","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":"Acoustic Approximation for Viscoelastic Transversely Isotropic Models: Comparison Between Three Scenarios of Parameterizations","authors":"Qi Hao,&nbsp;Stewart Greenhalgh,&nbsp;Xingguo Huang","doi":"10.1111/1365-2478.70162","DOIUrl":"https://doi.org/10.1111/1365-2478.70162","url":null,"abstract":"<div>\u0000 \u0000 <p>The acoustic approximation is widely used in modelling and inverting P-wave data in transversely isotropic elastic media. However, no unique parameterization can be found in the literature to describe the acoustic approximation for viscoelastic transversely isotropic media. We examine three scenarios of acoustic parameterization, which have different complex stiffness coefficients for viscoacoustic transversely isotropic media. We analyse the Thomsen attenuation parameters corresponding to the three scenarios. We also compare the normalized attenuation coefficients from these scenarios with the rigorous solution that does not use the acoustic approximation. The results show that only one scenario can produce the phase and group attenuation coefficients close to those without using the acoustic approximation.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696384","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":"Magnetotelluric Exploration of Zn Pb Mineralization in Stonepark, Irish Midlands","authors":"K. Tietze,&nbsp;A. Platz,&nbsp;O. Ritter,&nbsp;U. Weckmann,&nbsp;M. Holdstock,&nbsp;P. Rieger,&nbsp;V. Susin,&nbsp;A. Melo,&nbsp;K. Torremans,&nbsp;D. Kiyan","doi":"10.1111/1365-2478.70170","DOIUrl":"https://doi.org/10.1111/1365-2478.70170","url":null,"abstract":"<p>To meet the increasing demand for raw materials and to foster the energy transition, advances in exploration techniques are required that extend capabilities to deeper and/or more densely populated areas while having a low environmental and societal impact. Although active electromagnetic (EM) geophysical techniques have a long tradition in mineral exploration, magnetotellurics (MT) as a passive EM method has recently attracted more attention in this context, as it naturally offers a wider range of survey depths while being even less invasive. Here, we present the results of an MT study conducted at the Stonepark Zn–Pb deposit, an exploration project hosted in Mississippian carbonates and volcanics in the Irish Orefield. The survey was carried out in late 2022, using a novel experimental layout of 33 five-component broadband MT stations deployed in concert with 75 two-component electric field only stations. Resulting data exhibit strong EM noise but reasonable MT transfer functions could be obtained in the frequency range of 10⁴–10⁻² Hz by using a combination of the robust remote reference (RR) technique, notch filtering and physical and statistical pre-selection thresholds. Quality of full MT and E-field stations is the same demonstrating the applicability of the hybrid surveying approach also in noisy environments. The electrical conductivity structure at Stonepark revealed by 2D and 3D inversion shows the host rocks as high resistivity material (500–5000 Ωm) typical of limestones with little lateral variations in resistivity across the survey area. Although the MT models do not provide a direct image of economically relevant mineralized zones, they are in excellent agreement with resistivity measurements on rock samples from nearby drill holes. This is remarkable because laboratory measurements are made on centimetre-sized samples, whereas MT soundings sample resistivities over volumes of 10–100 s of meters. Integration of the MT results with the drill hole data, a 3D geological model of the Stonepark mineral system and a reprocessed seismic profile allowed the continuation of the horizons to the south where they are sparsely documented by drill holes. In particular, the MT data revealed alteration of volcanic material in the upper 200–800 m as well as a subvertical conductive feature that is spatially coincident with a hidden fault zone inferred from the seismic data, suggesting that the fault zone may be fluid enriched.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696329","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":"Target-Oriented Time-Lapse Full Waveform Inversion Using Marchenko Redatumed Data","authors":"Yinghe Wu,&nbsp;Wei Zhang,&nbsp;Mauricio D. Sacchi,&nbsp;Shulin Pan","doi":"10.1111/1365-2478.70163","DOIUrl":"https://doi.org/10.1111/1365-2478.70163","url":null,"abstract":"<div>\u0000 \u0000 <p>Time-lapse full waveform inversion (TLFWI) is a powerful tool for monitoring spatial and temporal variations in the physical properties of Earth, offering valuable insights into the dynamic evolution of reservoirs. However, the influence of complex overburden (such as scattering responses and multiple reflections) and limited illumination of the target area can reduce the spatial resolution of interest targets. To alleviate this problem, we propose a target-oriented inversion method. It uses Marchenko redatuming to mimic the acquisition of sources and receivers close to the reservoir, after which the redatumed datasets are utilized for the joint time-lapse inversion. One benefit of this method is that it only requires a kinematically accurate overburden velocity model. A secondary advantage is that TLFWI becomes computationally more efficient because of the reduced dimension of model parameters. This method has two key points: First, the normalized zero-lag cross-correlation objective function is employed in TLFWI to mitigate issues related to amplitude distortions in the redatumed data, particularly at far offsets; second, we introduce a joint inversion strategy and the total variation regularization to stabilize the inverted solution. Three numerical experiments involving a Chevron 2014 model, a Marmousi model and a complex salt dome model demonstrate that the proposed method can achieve higher-resolution inversion results compared to traditional methods while reducing the computational cost of standard TLFWI. Specifically, the method demonstrates excellent performance in the normal overburden model, whereas in the complex salt dome model, despite the challenges posed by geological complexity that limit its application, it still achieves meaningful monitoring results. Overall, it provides a valuable and effective tool for time-lapse monitoring.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696259","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":"Implicit Neural Representation for Elastic Full-Waveform Inversion","authors":"Sean Berti,&nbsp;Mattia Aleardi,&nbsp;Eusebio Stucchi","doi":"10.1111/1365-2478.70168","DOIUrl":"https://doi.org/10.1111/1365-2478.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>Full-waveform inversion (FWI) has become a cornerstone for high-resolution seismic imaging, yet it remains computationally demanding and sensitive to initial model assumptions and noise. Recent advances have shown that representing the subsurface model, using implicit neural representations (INRs), can provide compact, continuous and differentiable parameterizations that improve convergence and reduce overfitting. In this study, we extend the INR-based FWI framework to the elastic regime, with a focus on near-surface applications and the inversion of surface waves. In particular, we performed the inversion of both synthetic and field surface wave datasets. Our method leverages Deepwave for elastic wave simulation and gradient computation via automatic differentiation. In the synthetic test, we compare the performance obtained using different INR architectures to find the optimal configuration. For the field dataset inversion instead, we compare our results with those obtained using a standard deterministic FWI approach, highlighting its superior robustness with respect to initialization.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696267","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}