Surveys in Geophysics最新文献

{"title":"Correction to: Satellite Remote Sensing of Surface Winds, Waves, and Currents: Where are we Now?","authors":"D. Hauser, S. Abdalla, F. Ardhuin, J. Bidlot, M. Bourassa, D. Cotton, C. Gommenginger, Hayley Evers-King, H. Johnsen, J. Knaff, S. Lavender, A. Mouche, N. Reul, C. Sampson, E. Steele, A. Stoffelen","doi":"10.1007/s10712-023-09786-9","DOIUrl":"https://doi.org/10.1007/s10712-023-09786-9","url":null,"abstract":"","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"1 1","pages":"1-2"},"PeriodicalIF":4.6,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47657104","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":"Near-Surface Seismic Arrival Time Picking with Transfer and Semi-Supervised Learning","authors":"Ngo Nghi Truyen Huynh,&nbsp;Roland Martin,&nbsp;Thomas Oberlin,&nbsp;Bastien Plazolles","doi":"10.1007/s10712-023-09783-y","DOIUrl":"10.1007/s10712-023-09783-y","url":null,"abstract":"<div><p>The understanding of subsurface information on the Earth is crucial in numerous fields such as economics of oil and gas, geophysical exploration, archaeology and hydro-geophysics, particularly in a context of climate change. The methodology consists in reconstructing the seismic velocity model of the near surface, that contains information about the basement structure, by solving the inverse problem and resolving the related complex nonlinear systems with the data collected from seismic experiments and measurements. In the last few years, many deep neural networks have been proposed to simplify the seismic inversion problem based, for instance, on automatic differentiation of the adjoint operator, or on automatic arrival time picking. However, such approaches require a large amount of labeled training data, which are hardly available in real applications. We present here a deep learning approach for arrival time picking, aimed to deal with unlabeled data. The main building blocks are transfer learning, as well as a semi-supervised learning strategy where the pseudo-labels are greedily computed with robust regression, and classification algorithms. The hybrid method showcases very high scores when evaluating on synthetic data, and its application to a real dataset containing a limited amount of labeled data shows the computational efficiency and very accurate results.</p></div>","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"44 6","pages":"1837 - 1861"},"PeriodicalIF":4.6,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48159438","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":"Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets","authors":"S. Sandven, G. Spreen, G. Heygster, F. Girard-Ardhuin, S. Farrell, W. Dierking, R. Allard","doi":"10.1007/s10712-023-09781-0","DOIUrl":"https://doi.org/10.1007/s10712-023-09781-0","url":null,"abstract":"","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"1 1","pages":"1-37"},"PeriodicalIF":4.6,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47819921","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":"Ocean Circulation from Space","authors":"R. Morrow, L. Fu, M. Rio, R. Ray, P. Prandi, P. Le Traon, J. Benveniste","doi":"10.1007/s10712-023-09778-9","DOIUrl":"https://doi.org/10.1007/s10712-023-09778-9","url":null,"abstract":"","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"1 1","pages":"1-44"},"PeriodicalIF":4.6,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41622483","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":"Tools for Edge Detection of Gravity Data: Comparison and Application to Tectonic Boundary Mapping in the Molucca Sea","authors":"Jie Liu,&nbsp;Sanzhong Li,&nbsp;Suhua Jiang,&nbsp;Xu Wang,&nbsp;Jianli Zhang","doi":"10.1007/s10712-023-09784-x","DOIUrl":"10.1007/s10712-023-09784-x","url":null,"abstract":"<div><p>Edge detection techniques for potential field data are effective methods for identifying local and regional geological boundaries. Numerous edge detectors (e.g., derivative-, ratio- and statistic-based methods) have been successively proposed and applied to different scenarios. However, these edge detectors show diverse results, which can confuse interpreters in their filter selection and interpretation schemes. To better understand the capabilities of various edge detection methods and avoid over-interpretation of artifacts, it requires a unified evaluation of different edge detectors with the same test models. In this view, we first present a brief review of the previous edge detection methods. Then, using gravity data as an example, we build 2.5D and 3D models to examine the boundary recognition capabilities of 28 edge detectors. Based on the model test results, we classify the existing edge detectors and discuss the similarities and discrepancies of different detectors. These comparisons help us to infer the optimal edge interpretation by integrating multiple results and screening for false appearances. Finally, we apply edge detection techniques to the earthquake-prone Molucca Sea region and present a refined tectonic boundary division, assisted by the focal-mechanism solutions. Besides, we identified four deep boundaries that may be associated with plate subduction. These boundaries correspond well to the source location of earthquakes at different depths; hence, five depth-dependent earthquake zones are partitioned. In addition to subduction, we suggest that the fault system also contributes to the present-day tectonic configuration around the Molucca Sea. The relationship between the earthquake activity near the subduction zones or faults and the boundaries derived from edge detection provides new insights to study multi-plate convergence using multiple observations.</p></div>","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"44 6","pages":"1781 - 1810"},"PeriodicalIF":4.6,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43905039","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":"Satellite Remote Sensing of Surface Winds, Waves, and Currents: Where are we Now?","authors":"D. Hauser, S. Abdalla, F. Ardhuin, J. Bidlot, M. Bourassa, D. Cotton, C. Gommenginger, Hayley Evers-King, H. Johnsen, J. Knaff, S. Lavender, A. Mouche, N. Reul, C. Sampson, E. Steele, A. Stoffelen","doi":"10.1007/s10712-023-09771-2","DOIUrl":"https://doi.org/10.1007/s10712-023-09771-2","url":null,"abstract":"","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"1 1","pages":"1-90"},"PeriodicalIF":4.6,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43789792","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":"Analytical Solutions for Gravitational Potential up to Its Third-order Derivatives of a Tesseroid, Spherical Zonal Band, and Spherical Shell","authors":"Xiao-Le Deng,&nbsp;Nico Sneeuw","doi":"10.1007/s10712-023-09774-z","DOIUrl":"10.1007/s10712-023-09774-z","url":null,"abstract":"<div><p>The spherical shell and spherical zonal band are two elemental geometries that are often used as benchmarks for gravity field modeling. When applying the spherical shell and spherical zonal band discretized into tesseroids, the errors may be reduced or cancelled for the superposition of the tesseroids due to the spherical symmetry of the spherical shell and spherical zonal band. In previous studies, this superposition error elimination effect (SEEE) of the spherical shell and spherical zonal band has not been taken seriously, and it needs to be investigated carefully. In this contribution, the analytical formulas of the signal of derivatives of the gravitational potential up to third order (e.g., <i>V</i>, <span>(V_{z})</span>, <span>(V_{zz})</span>, <span>(V_{xx})</span>, <span>(V_{yy})</span>, <span>(V_{zzz})</span>, <span>(V_{xxz})</span>, and <span>(V_{yyz})</span>) of a tesseroid are derived when the computation point is situated on the polar axis. In comparison with prior research, simpler analytical expressions of the gravitational effects of a spherical zonal band are derived from these novel expressions of a tesseroid. In the numerical experiments, the relative errors of the gravitational effects of the individual tesseroid are compared to those of the spherical zonal band and spherical shell not only with different 3D Gauss–Legendre quadrature orders ranging from (1,1,1) to (7,7,7) but also with different grid sizes (i.e., <span>(5^{circ }times 5^{circ })</span>, <span>(2^{circ }times 2^{circ })</span>, <span>(1^{circ }times 1^{circ })</span>, <span>(30^{prime }times 30^{prime })</span>, and <span>(15^{prime }times 15^{prime })</span>) at a satellite altitude of 260 km. Numerical results reveal that the SEEE does not occur for the gravitational components <i>V</i>, <span>(V_{z})</span>, <span>(V_{zz})</span>, and <span>(V_{zzz})</span> of a spherical zonal band discretized into tesseroids. The SEEE can be found for the <span>(V_{xx})</span> and <span>(V_{yy})</span>, whereas the superposition error effect exists for the <span>(V_{xxz})</span> and <span>(V_{yyz})</span> of a spherical zonal band discretized into tesseroids on the overall average. In most instances, the SEEE occurs for a spherical shell discretized into tesseroids. In summary, numerical experiments demonstrate the existence of the SEEE of a spherical zonal band and a spherical shell, and the analytical solutions for a tesseroid can benefit the investigation of the SEEE. The single tesseroid benchmark can be proposed in comparison to the spherical shell and spherical zonal band benchmarks in gravity field modeling based on these new analytical formulas of a tesseroid.</p></div>","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"44 4","pages":"1125 - 1173"},"PeriodicalIF":4.6,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10712-023-09774-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4725464","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":"Characterization of Pore Electrical Conductivity in Porous Media by Weakly Conductive and Nonconductive Pores","authors":"Linqi Zhu,&nbsp;Shiguo Wu,&nbsp;Chaomo Zhang,&nbsp;Siddharth Misra,&nbsp;Xueqing Zhou,&nbsp;Jianchao Cai","doi":"10.1007/s10712-022-09761-w","DOIUrl":"10.1007/s10712-022-09761-w","url":null,"abstract":"<div><p>The formation factor, which reflects the electrical conductivity of porous sediments and rocks, is widely used in a range of research fields. Consequently, given the discovery of numerous porous reservoir rocks and sediments exhibiting complex conductivity characteristics, methods to quantitatively predict the formation factor have been actively pursued by many scholars. Nevertheless, the agreement between the theoretically calculated and measured formation factors remains unsatisfactory, partially because the distribution characteristics of the entire pore space affect the final formation factor. In this study, a new method for characterizing the formation factor is proposed that considers the impacts of different complex pore structures on the conductivity of pores at different positions in the pore space. With this method, the electrical transmission through a rock can be accurately and quantitatively estimated based on the conductivity and shape of pores, the tortuous conductivity, and the classification of the pore space into conductive, weakly conductive, and nonconductive pores. By evaluating 24 datasets encompassing 7 types of rocks and sediments, including marine hydrate-bearing sediments and shale, the proposed model achieves remarkable agreement with the experimental data. These excellent confirmation results are attributed to the ubiquitous presence of weakly conductive and nonconductive pores in almost all rocks and sediments. Through further research based on this paper, an increasing number of adaptation models and a comprehensive set of evaluation methods can be developed.</p></div>","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"44 3","pages":"877 - 923"},"PeriodicalIF":4.6,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4694858","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":"Deep Learning with Fully Convolutional and Dense Connection Framework for Ground Roll Attenuation","authors":"Liuqing Yang,&nbsp;Shoudong Wang,&nbsp;Xiaohong Chen,&nbsp;Omar M. Saad,&nbsp;Wanli Cheng,&nbsp;Yangkang Chen","doi":"10.1007/s10712-023-09779-8","DOIUrl":"10.1007/s10712-023-09779-8","url":null,"abstract":"<div><p>Ground roll could seriously mask the useful reflection signals and decrease the signal-to-noise ratio (S/N) of seismic data, thereby affecting the subsequent seismic data processing. It is challenging for traditional methods to effectively extract high-fidelity reflection signals when ground roll noise and low-frequency reflection signals overlap in the frequency domain. We propose a fully convolutional framework with dense connections to attenuate ground roll (GRDNet) in land seismic data. GRDNet mainly consists of four blocks, which are convolutional, dense, transition down, and transition up blocks. The dense block consists of several convolution blocks to extract the waveform features of the seismic data. The short-long connection in the dense block and the skip connection in the encoder-decoder not only reuses the features extracted by the previous layer but also adds constraints other than the loss function to each convolution block. The well-trained network is tested on one synthetic data and two real land seismic datasets containing strong ground roll with linear and hyperbolic moveouts, respectively. Three traditional and two state-of-the-art deep learning (DL) methods are used as benchmarks to compare denoising performance with GRDNet. The testing results show that the proposed method can effectively attenuate the ground roll in seismic data and preserve useful reflection signals.</p></div>","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"44 6","pages":"1919 - 1952"},"PeriodicalIF":4.6,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48353741","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":"Permafrost Monitoring from Space","authors":"A. Bartsch, T. Strozzi, Ingmar Nitze","doi":"10.1007/s10712-023-09770-3","DOIUrl":"https://doi.org/10.1007/s10712-023-09770-3","url":null,"abstract":"","PeriodicalId":49458,"journal":{"name":"Surveys in Geophysics","volume":"1 1","pages":"1-35"},"PeriodicalIF":4.6,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43126168","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}