Geophysical Prospecting最新文献

{"title":"Cross-equalization for time-lapse sparker seismic data","authors":"Soojin Lee,&nbsp;Jongpil Won,&nbsp;Hyunggu Jun","doi":"10.1111/1365-2478.13600","DOIUrl":"https://doi.org/10.1111/1365-2478.13600","url":null,"abstract":"<p>Time-lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time-lapse seismic exploration has been conducted using a large-scale exploration system. However, for efficient monitoring of shallow subsurface, time-lapse monitoring based on the small-scale exploration system is required. Small-scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal-to-noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small-scale time-lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre-stack, post-stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre-stack data processing stage. In the post-stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high-resolution time-lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3258-3279"},"PeriodicalIF":1.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430245","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 Q model building for high-resolution imaging","authors":"Xin Ju,&nbsp;Jincheng Xu,&nbsp;Jianfeng Zhang","doi":"10.1111/1365-2478.13616","DOIUrl":"https://doi.org/10.1111/1365-2478.13616","url":null,"abstract":"<p>Building a macro <i>Q</i> model for deabsorption migration using surface reflection data is challenging owing to interferences of the reflections resulting from stacked thin layers. The effective <i>Q</i> approach gives an alternative way to overcome this difficulty. However, manual processing is involved for effective <i>Q</i> estimation. This restricts the use of denser grids in building an inhomogeneous <i>Q</i> model. We therefore incorporate deep learning into the effective <i>Q</i> approach, thus yielding a deep learning-based <i>Q</i> model building scheme. The resulting scheme improves the manual effective <i>Q</i> estimation by simultaneously accounting for the imaging resolution and induced noises using two networks. Moreover, most manual processing is reduced in spite of denser grids in building a 3D <i>Q</i> model. One of the networks used is a 1D convolutional neural network that determines the optimal upper cut-off frequency for a selected <i>Q</i> with an input of multi-channel amplitude spectra, and another is a residual neural network that determines the optimal <i>Q</i> for a series of <i>Q</i> values with an input of multi-channel imaging sections inside the selected small window filtered under the corresponding upper cut-off frequencies. As a result, a <i>Q</i> model that improves the imaging resolution in the absence of amplification of noises is gained. Transfer learning is used, thus reducing the training cost when applied to different geological targets. We test our scheme using 3D field data. Higher resolution images without induced noises are obtained by a deabsorption migration using the <i>Q</i> model built and compared to those obtained by the migration without absorption compensation.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"699-711"},"PeriodicalIF":1.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119752","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":"Prestack reservoir prediction method in ray parameter domain based on wide azimuth ocean bottom node seismic data","authors":"Huang Jiangbo,&nbsp;Ming Jun,&nbsp;Wang Jianli,&nbsp;Xia Tongxing,&nbsp;Liu Chuanqi","doi":"10.1111/1365-2478.13609","DOIUrl":"https://doi.org/10.1111/1365-2478.13609","url":null,"abstract":"<p>Wide azimuth seismic data play an important role in deep reservoir prediction. According to the Paleogene clastic rock reservoir prediction, the high-density and wide azimuth 3D seismic data acquisition of ocean bottom nodes was first carried out in a Chinese offshore oilfield in 2019. After high precision amplitude-preserving processing, we obtained the high-quality wide azimuth gathers. However, the research on anisotropy and reservoir prediction using wide azimuth seismic data mainly focuses on carbonate and bedrock intervals, which is not suitable for clastic rock reservoir prediction. Therefore, this paper innovatively proposes a clastic rock reservoir prediction method, which studies prestack reservoir prediction in the ray parameter domain based on wide azimuth ocean bottom node seismic data. Based on the azimuth gathers, we can obtain elastic parameters through prestack amplitude versus offset inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction results. In this paper, we develop an amplitude versus offset inversion method based on the Bayesian theory in ray parameter domain, the output of which is density, P-wave impedance and <i>V</i>_p/<i>V</i>_s. These elastic parameters have high precision, and density data are valuable input for reservoir characterization because they are sensitive to the lithology of clastic rock reservoir at different orientations. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation; thus, extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on the Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable. Based on the above high precision density inversion results, an innovative wide azimuth data reservoir prediction technology based on elliptical short-axis fitting was proposed. The actual prediction of the deep reservoir in the Bohai oilfield shows that sand thickness fitting prediction results in the short axis can best match the actual drilling sandstone thickness. The coincidence rate is 86% and the short-axis fitting results are more in agreement with geological laws. Theoretical research and practical applications have shown that this method is feasible and effective, with high prediction accuracy, computational efficiency and strong application value.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 3","pages":"942-959"},"PeriodicalIF":1.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513646","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":"Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines","authors":"Kamal Souadih,&nbsp;Anis Mohammedi,&nbsp;Sofia Chergui","doi":"10.1111/1365-2478.13603","DOIUrl":"10.1111/1365-2478.13603","url":null,"abstract":"<p>Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub-surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U-net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state-of-the-art techniques, our model demonstrates superior performance, achieving a stable and validated <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>96</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$96%$</annotation>\u0000 </semantics></math> intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>90</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$90%$</annotation>\u0000 </semantics></math> underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3280-3294"},"PeriodicalIF":1.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251929","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 improved affine mixed-grid method for frequency-domain finite-difference elastic modelling","authors":"Shu-Li Dong,&nbsp;Jing-Bo Chen","doi":"10.1111/1365-2478.13596","DOIUrl":"https://doi.org/10.1111/1365-2478.13596","url":null,"abstract":"<p>In seismic frequency-domain finite-difference modelling, the affine mixed-grid method effectively eliminates the spatial sampling restriction associated with square meshes of the rotated mixed-grid method. Nevertheless, the affine mixed-grid method makes a weighted average of the entire elastic wave equations, resulting in reduced accuracy compared to the average-derivative method in the case of rectangular meshes. It is worth noting, however, that the average-derivative method is presently inapplicable to free-surface scenarios, whereas the affine mixed-grid method is applicable. By performing weighted averages of the derivative terms instead of the entire elastic wave equations in Cartesian and affine rotated coordinate systems, we have developed an improved affine mixed-grid method for elastic-wave frequency-domain finite-difference modelling. The proposed improved affine mixed-grid method 9-point scheme overcomes the drawback that the accuracy of affine mixed-grid method is lower than that of average-derivative method for unequal directional grid intervals. Moreover, the improved affine mixed-grid method 6-point scheme provides much higher numerical accuracy than the affine mixed-grid method 6-point scheme at either equal or unequal directional grid intervals. On the other hand, the proposed improved affine mixed-grid method simplifies the coding complexity for implementing free-surface condition in elastic-wave frequency-domain finite-difference modelling by modifying the elastic parameters of the free-surface layer and thus constructing the impedance matrix containing the free-surface condition directly.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3295-3315"},"PeriodicalIF":1.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430094","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":"Estimation of rock mass permeability using relaxation time and P-wave velocity","authors":"Zhicheng Song,&nbsp;Lichao Nie,&nbsp;Zhiqiang Li,&nbsp;Shilei Zhang,&nbsp;Zhaoyang Deng,&nbsp;Yuancheng Li","doi":"10.1111/1365-2478.13602","DOIUrl":"10.1111/1365-2478.13602","url":null,"abstract":"<p>Due to the inherent unpredictability of geological conditions, tunnelling operations are often at risk of encountering water inrushes. Such incidents can lead to construction delays, impose financial strains and pose significant safety threats to the workers involved. Water-bearing geological formations are the main triggers for such incidents, with factors such as the positioning, water quantity and permeability distribution of these formations being key to predicting the occurrence and severity of water inrush disasters. By leveraging the complex interplay among relaxation time, P-wave velocity and permeability within the rock's physical properties, a series of indoor tests were conducted on 40 artificial reef limestone cores to extract the necessary parameters. Through the analysis of the data, the comprehensive permeability prediction model was established, and the correlation coefficient was 0.9420 between the model's predictions and actual measurements. At the same time, through theoretical and mechanism analysis, the relationship between permeability and relaxation time and the relationship between permeability and P-wave velocity were analysed. Finally, 10 natural reef limestone samples were used to verify the accuracy of the model. The prediction model enables an accurate evaluation of tunnel permeability, thus providing a scientific basis for the mitigation of tunnel water inrush hazards.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3371-3380"},"PeriodicalIF":1.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222277","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":"Laboratory measurements of water saturation effects on the acoustic velocity and attenuation of sand packs in the 1–20 kHz frequency range","authors":"Hanif S. Sutiyoso,&nbsp;Sourav K. Sahoo,&nbsp;Laurence J. North,&nbsp;Timothy A. Minshull,&nbsp;Ismael Himar Falcon-Suarez,&nbsp;Angus I. Best","doi":"10.1111/1365-2478.13607","DOIUrl":"10.1111/1365-2478.13607","url":null,"abstract":"<p>We present novel experimental measurements of acoustic velocity and attenuation in unconsolidated sand with water saturation within the sonic (well-log analogue) frequency range of 1–20 kHz. The measurements were conducted on jacketed sand packs with 0.5-m length and 0.069-m diameter using a bespoke acoustic pulse tube (a water-filled, stainless steel, thick-walled tube) under 10 MPa of hydrostatic confining pressure and 0.1 MPa of atmospheric pore pressure. We assess the fluid distribution effect on our measurements through an effective medium rock physics model, using uniform and patchy saturation approaches. Our velocity and attenuation (<i>Q</i>⁻¹) are accurate to ±2.4% and ±5.8%, respectively, based on comparisons with a theoretical transmission coefficient model. Velocity decreases with increasing water saturation up to ∼75% and then increases up to the maximum saturation. The velocity profiles across all four samples show similar values with small differences observed around 70%–90% water saturation, then converging again at maximum saturation. In contrast, the attenuation increases at low saturation, followed by a slight decrease towards maximum saturation. Velocity increases with frequency across all samples, which contrasts with the complex frequency-dependent pattern of attenuation. These results provide valuable insights into understanding elastic wave measurements over a broad frequency spectrum, particularly in the sonic range.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3316-3337"},"PeriodicalIF":1.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142222278","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":"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}