Computers & Geosciences最新文献

{"title":"A mixture learning strategy for predicting aquifer permeability coefficient K","authors":"Kouao Laurent Kouadio ,&nbsp;Jianxin Liu ,&nbsp;Wenxiang Liu ,&nbsp;Rong Liu","doi":"10.1016/j.cageo.2024.105819","DOIUrl":"10.1016/j.cageo.2024.105819","url":null,"abstract":"<div><div>Aquifers permeability coefficient (K) is critical for understanding, managing, and protecting groundwater resources. However, obtaining reliable K values directly from pumping tests is costly and time-consuming, often yielding suboptimal results that lead to significant financial losses. Recent advances in machine learning offer an alternative, cost-effective approach for estimating K. Yet, the primary challenge lies in the substantial proportion of missing K data, as K measurements can only be recorded in aquifer layers. Such sparse and incomplete data severely limit the effectiveness of classical supervised learning methods. To address this challenge, we propose a mixture learning strategy (MXS) that combines unsupervised and supervised techniques to improve K prediction. First, a K-Means clustering approach is applied to delineate a naïve group of aquifers (NGA), effectively generating proxy labels for layers where direct K measurements are unavailable. Next, these NGA labels are integrated with existing K values to form enhanced input features for supervised prediction. We then apply support vector machines (SVMs) and extreme gradient boosting (XGB) to predict K more accurately. Experimental results show that both SVMs and XGB achieve prediction accuracies exceeding 80% when evaluated using confusion matrices and micro- and macro-averaged precision-recall metrics. Testing the MXS approach on an independent borehole dataset confirms its robustness and effectiveness. By enabling accurate K predictions in the presence of significant data gaps, MXS supports more informed decision-making, reduces the likelihood of unsuccessful pumping tests, and aids in the sustainable planning and management of groundwater resources.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105819"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093170","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":"Interpreting Deepkriging for spatial interpolation in geostatistics","authors":"Fabian Leal-Villaseca ,&nbsp;Edward Cripps ,&nbsp;Mark Jessell ,&nbsp;Mark Lindsay","doi":"10.1016/j.cageo.2024.105833","DOIUrl":"10.1016/j.cageo.2024.105833","url":null,"abstract":"<div><div>In the current era marked by an unprecedented abundance of data, the usage of conventional methods such as kriging persists in some applications of geostatistics, despite their limitations in adequately capturing the intricate relationships found in contemporary, multivariate datasets. Although deep neural networks (DNNs) have demonstrated remarkable efficacy in capturing complex nonlinear feature relationships across various domains, their success in geostatistical applications has been limited. This can be partly attributed to two significant challenges. Firstly, the opaque nature of these black box models raises concerns about the dependability of their outputs for critical decision-making, as the inner workings of the model remain less interpretable. Secondly, DNNs do not explicitly capture spatial dependencies within data. To address these shortcomings, we employ a methodology to interpret the recently proposed spatial DNNs known as Deepkriging, and we apply it to dry bulk rock density estimation, an often-overlooked aspect in mineral resource estimation. Through our adaptation of Shapley values—Batched Shapley—we overcome significant computational challenges to quantify feature importance for Deepkriging. This approach takes into account feature interactions, which is crucial for DNNs, as they rely on high-order interactions, especially in a complex application like mineral resource estimation. Additionally, we demonstrate in the 3D case that Deepkriging outperforms ordinary kriging and regression kriging in terms of mean squared errors, in both the purely spatial case and in the presence of auxiliary variables. Our study produces the first methodology to interpret Deepkriging, which is suitable for any model with a large number of features; it reaffirms the efficacy of Deepkriging through several comparisons in a 3D application, and most importantly; it underscores the adaptability and broader potential of DNNs to cater to various challenges in geostatistics.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105833"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093414","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":"Edge-guided segmentation of digital rock images: Integrating a pretrained edge aware path with the main segmentation path","authors":"Ziqiang Wang ,&nbsp;Zhiyu Hou ,&nbsp;Danping Cao","doi":"10.1016/j.cageo.2025.105884","DOIUrl":"10.1016/j.cageo.2025.105884","url":null,"abstract":"<div><div>Accurate segmentation of digital rock images is pivotal in digital rock analysis, as it significantly influences the outcomes of subsequent numerical simulations and parameter calculations. Traditional deep learning models for semantic segmentation often require extensive datasets for effective training, but acquiring rock samples used to be costly, hindering dataset expansion. Typical single-path segmentation models primarily focus on extracting semantic features, which may limit segmentation accuracy, especially for fine-grained segmentation of minor features. Incorporating edge feature information relevant to matrix and pore segmentation can improve segmentation accuracy while optimizing limited data resources. Therefore, a dual-path deep learning segmentation model introducing an additional edge-aware pathway to improve segmentation accuracy, because the edge features obtained from the edge-aware pathway are not only utilized as prior information alongside the original image to guide more effective feature extraction but also integrated into the decoding module to offer boundary constraint support for the image information restoration process. As an example of SegNet, the improved SegNet has shown improvements of 9.58%, 16.44%, 10.98%, and 7.57% in Dice, IoU, Precision, and Recall metrics, respectively, and the relative errors of elastic properties in terms of bulk modulus, shear modulus, and P- and S- wave velocities decrease by 7.06%, 12.13%, 4.22%, and 6.71%, respectively, and its performance better than the powerful DeepLabv3+ model. The similar improvement is observed in ResSegNet, UNet and ResUNet as introducing edge information, which demonstrates excellent performance on small datasets and lower computational costs and dataset requirements.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"197 ","pages":"Article 105884"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347036","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":"Tar yield prediction of tar-rich coal based on geophysical logging data: Comparison between semi-supervised and supervised learning","authors":"Qingmin Shi ,&nbsp;Xuhu Geng ,&nbsp;Shuangming Wang ,&nbsp;Yue Cai ,&nbsp;Hongchao Zhao ,&nbsp;Ruijun Ji ,&nbsp;Luyu Xing ,&nbsp;Xinyu Miao","doi":"10.1016/j.cageo.2024.105848","DOIUrl":"10.1016/j.cageo.2024.105848","url":null,"abstract":"<div><div>Accurate and economical prediction of tar yield is essential for precise evaluation of tar-rich coal geological distribution. Through the correlation analysis of conventional logging parameters and tar yield, this identified the highly correlated logging parameters including high-definition deep investigate resistivity log (HLLD), density (DEN), caliper (CAL), acoustic (AC), natural gamma ray (GR), and spontaneous potential (SP). Based on them, the predictive models for tar yield have been built by semi-supervised learning and supervised learning methods, and a comparison was made. First, applying the self-training algorithm based on a semi-supervised learning framework, this research has built a Semi-Supervised Recurrent Neural Network (SSRNN) model for the prediction of tar yield. Second, based on supervised learning, this research has built tar yield prediction models, such as backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Random Forest (RF). Semi-supervised learning can effectively utilize unlabeled data to enhance model performance and address the problem of expensive access to labeled data. Supervised learning, based on mapping inputs directly to outputs, offers a clear and intuitive training process, making it ideal for tasks with well-defined input-output relationships. This paper builds a tar yield prediction model for multiple drilling wells in the Santanghu Basin, utilizing 121 labeled and 1952 unlabeled data sets, of which the labeled data were used for supervised learning, and the unlabeled data were employed for semi-supervised learning. In addition, the generalization abilities of different prediction models were evaluated by the use of 48 labeled data from the GM2 well. The results indicated that the SSRNN model, which demonstrates better generalization capability, is superior to the RNN, BPNN, SVR, and RF models in performance.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105848"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093107","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":"Anisotropic resistivity estimation and uncertainty quantification from borehole triaxial electromagnetic induction measurements: Gradient-based inversion and physics-informed neural network","authors":"Misael M. Morales,&nbsp;Ali Eghbali,&nbsp;Oriyomi Raheem,&nbsp;Michael J. Pyrcz,&nbsp;Carlos Torres-Verdín","doi":"10.1016/j.cageo.2024.105786","DOIUrl":"10.1016/j.cageo.2024.105786","url":null,"abstract":"<div><div>Rapid and accurate petrophysical reservoir description and quantification is important for subsurface energy resource modeling and engineering. Triaxial borehole resistivity measurements enable the estimation of key in-situ rock properties, such as the volumetric concentration of shale and sandstone resistivity. However, traditional approaches for estimating these properties from triaxial, or anisotropic, borehole resistivity measurements rely on analytical solutions that solve a system of equations simultaneously, which can lead to numerical instability and inefficiency. By reformulating the system of anisotropic resistivity equations as an inverse problem, we can achieve a more stable, accurate, and efficient estimation of key petrophysical properties. We propose two methods, namely nonlinear gradient-based and physics-informed neural network (PINN) inversion, to estimate the volumetric concentration of shale and sandstone resistivity from the parallel- and perpendicular-to-bedding-plane resistivity logs, posed as the solution of an inverse problem. Furthermore, we compare the PINN, nonlinear gradient-based inversion and analytical solution methods in terms of accuracy, computational efficiency, and uncertainty quantification using four different datasets of anisotropic resistivity logs, i.e., two synthetic and two field cases. The PINN inversion technique estimates the petrophysical properties within 0.5 CPU milliseconds with an accuracy between 91% and 99%, while nonlinear gradient-based inversion can estimate the petrophysical properties with an accuracy between 98% and 99% but requires several CPU minutes depending on the size of the dataset. The proposed PINN technique is therefore capable of providing fast and accurate estimation and uncertainty quantification of key petrophysical properties from triaxial resistivity logs, with comparable accuracy and approximately <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>6</mn></mrow></msup></mrow></math></span> speedup compared to nonlinear gradient-based inversion, and can be used for real-time applications such as automatic shale properties estimation, logging-while-drilling measurement interpretation, automated well geosteering, and time-lapse reservoir monitoring.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105786"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093166","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":"LSTM-based proxy model combined with wellbore-reservoir coupling simulations for predicting multi-dimensional state parameters in depleted gas reservoirs","authors":"Jinyong Zhang ,&nbsp;Yi Hong ,&nbsp;Lizhong Wang ,&nbsp;Xiaochun Li ,&nbsp;Hongwu Lei ,&nbsp;Fangfang Li ,&nbsp;Bo Gao ,&nbsp;Jia-nan Zheng","doi":"10.1016/j.cageo.2024.105824","DOIUrl":"10.1016/j.cageo.2024.105824","url":null,"abstract":"<div><div>Although most CO₂ geologic storage projects focus on deep saline aquifers, depleted gas reservoirs are a more economical option as a potential site. However, due to the extremely low initial pressure, the injection of high-pressure supercritical CO₂ into the reservoir can result in dramatic changes in CO₂ properties, which may affect the well head pressure and bottom hole pressure. Aside from the injection rate, the injection of supercritical CO₂ at different temperature and pressure has varying degrees of impact on reservoir pressure. In order to design the optimal injection condition, a deep learning proxy model, combining wellbore-reservoir coupling numerous simulations, is proposed to quickly interrogate status response of wellbores and reservoirs. Based on 567 simulation cases of supercritical CO₂ injection into a deep depleted gas reservoir, the model uses the T2Well/ECO2N software to capture the time evolution of the pressure, temperature, and rate fields of wellbore and reservoirs, and is trained to get the optimal LSTM-based proxy network. Compared with simulation results, the proxy model predicts in less than 0.1s while ensuring an overall coefficient of determination (R²) of up to 99.9%. The maximum prediction errors of pressure, temperature, and rates at all times are also not more than 0.04, 0.02, and 0.08 for a single case, respectively. The assessment findings of the ultimate reservoir pressure based on the model show that injecting supercritical CO₂ under low initial pressure and high temperature is beneficial to the long-term safety of CO₂ sequestration engineering in depleted gas reservoirs.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105824"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093178","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":"Directional adaptive texture for edge detection in wide-azimuth seismic data","authors":"Hanpeng Cai,&nbsp;Zhiwei Zhang,&nbsp;Yaojun Wang,&nbsp;Liyu Zhang,&nbsp;Wandi Ma","doi":"10.1016/j.cageo.2025.105860","DOIUrl":"10.1016/j.cageo.2025.105860","url":null,"abstract":"<div><div>Seismic texture pattern analysis is one of the effective methods for edge detection, however existing methods fail to consider the differences in edge information inherent in pre-stack wide-azimuth seismic data from different offsets. This paper proposes an edge detection method based on directional adaptive texture pattern analysis (DATPA), fully using directional information for more accurate edge detection. Firstly, dip and azimuth data are obtained from 3D seismic data through structural gradient tensor decomposition, which are then considered as geological constraints to guide the calculation of high-precision dip and azimuth data. Using the corresponding relationship between high-precision dip and azimuth data and the direction and tendency of edge structures such as faults or channels, we adaptively determine the statistical directions sensitive to edge detection in multiple statistical directions constructed by interpolation methods. Pre-stack seismic texture analysis elements (STAE) are constructed in the determined direction, and the gray-level co-occurrence matrix algorithm is employed to obtain the pre-stack directional adaptive seismic texture attribute set. Next, the obtained seismic texture data set is subjected to dimensionality reduction and clustering using self-organizing maps (SOM), yielding the spatial distribution of different pre-stack directional adaptive seismic texture patterns. Finally, utilizing prior information from drilling and logging, we calibrate and analyze pre-stack seismic texture patterns related to edge structures like faults and channels, ultimately achieving edge detection based on DATPA. Field data demonstrate that compared with traditional seismic texture analysis methods, the texture patterns obtained by DATPA can effectively highlight edges in all directions while reflecting both local and overall discontinuity characteristics. The edge detection results align better with drilling data and display structural patterns more consistent with geologists' understanding, providing a reliable new approach for the precise detection of edges.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105860"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093385","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":"Local conditioning in posterior sampling methods with example cases in subsurface inversion","authors":"Mina Spremić ,&nbsp;Jo Eidsvik ,&nbsp;Thomas Mejer Hansen","doi":"10.1016/j.cageo.2025.105863","DOIUrl":"10.1016/j.cageo.2025.105863","url":null,"abstract":"<div><div>Local approaches have gained interest because they can provide fast approximate solutions for inverse problems. Following the idea of split-and-conquer, one aims to effectively condition variables to data using only small parts of the big model. We study and compare local approaches for conditioning in the context of seismic amplitude data and tomography, with two datasets relevant to improved oil and gas recovery in the North Sea and groundwater characterization in Denmark. In our comparison we study a local variant of an extended rejection sampler, termed localized extended rejection sampler (LERS), and a local ensemble transform Kalman filter (LETKF). Using various output statistics, we investigate the performance of the methods at marginal (e.g. mean and variance) level and joint properties (e.g. volume uncertainty and connectivity) of the subsurface variables of interest. Computed posterior statistics are compared with a reference Markov chain Monte Carlo solution. The results highlight benefits of the methods, such as fast reliable performance on the marginal properties, while joint properties in the more difficult cases show potential challenges of applying these local methodologies. Based on the results in our two cases, we discuss the applicability of the methods. We conclude that the localization methods are efficient and useful for estimating marginal properties and associated uncertainty, and can be an inexpensive tool for evaluating the need for further data processing. Local assimilation as outlined here is not suitable for generating posterior realizations of the spatial process variables.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105863"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093413","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":"An optimized 2D/3D finite-difference seismic wave propagator using rotated staggered grid for complex elastic anisotropic structures","authors":"Oumeng Zhang ,&nbsp;Douglas R. Schmitt","doi":"10.1016/j.cageo.2024.105850","DOIUrl":"10.1016/j.cageo.2024.105850","url":null,"abstract":"<div><div>The synergy of computing power and physical simulation has enabled deeper insights into geological processes and properties. In geophysics, seismic anisotropy is one such crucial yet often oversimplified property, where wave propagation velocity varies with direction. To simulate the seismic wave propagation in complex anisotropic media, the rotated staggered grid (RSG) scheme was introduced decades ago. However, publicly available software for this purpose has been scarce. To address this gap, we present a newly implemented wave solver, integrated with the open-source finite-difference package Devito, that supports the simulation of seismic wave propagation in both 2D and 3D complex anisotropic media at variable spatial orders. Our implementation includes strategies to mitigate checkerboard artifacts and optimizations to reduce the number of derivative operations, thereby enhancing performance and efficiency. This wave solver aims to assist the geophysics community in more accurately modeling seismic wave propagation in intricate materials, ultimately improving our understanding of geological processes, physical properties of earth material, and subsurface structures.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105850"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143215310","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":"Numerical dispersion mitigation neural network with velocity model correction","authors":"Elena Gondyul,&nbsp;Vadim Lisitsa,&nbsp;Kirill Gadylshin,&nbsp;Dmitry Vishnevsky","doi":"10.1016/j.cageo.2024.105806","DOIUrl":"10.1016/j.cageo.2024.105806","url":null,"abstract":"<div><div>The paper presents the Numerical Dispersion Mitigation neural network (NDM-net) to speed up seismic modeling. The idea of the NDM-net is to simulate the common-shot gathers for the entire set of source positions using a coarse grid. This solution can be computed fast but inaccurately. In addition, a small number of seismograms are generated using a fine enough grid to get an accurate solution. After that, the NDM-net is trained to map numerically polluted solutions to the accurate one and applied to correct the entire dataset. Previously, it was shown that NDM-net allows to speed up seismic modeling up to six times without noticeable loss of accuracy if the velocity model is fixed. In this paper, we focus on the applicability of NDM-net to the case where both the velocity model discretization and computational grid are corrected. We apply the NDM-net to suppress two types of numerical error: the numerical dispersion and the interface error.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105806"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093177","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}