Auswahl der wissenschaftlichen Literatur zum Thema „Multiphysical inversion“
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Zeitschriftenartikel zum Thema "Multiphysical inversion"
Zheng, Yi-kang, Chong Wang, Hao-hong Liang, Yi-bo Wang und Rong-shu Zeng. „3D seismic forward modeling from the multiphysical inversion at the Ketzin CO2 storage site“. Applied Geophysics 21, Nr. 3 (September 2024): 593–605. http://dx.doi.org/10.1007/s11770-024-1132-5.
Der volle Inhalt der QuelleAl-Yasiri, Zainab Riyadh Shaker, Hayder Majid Mutashar, Klaus Gürlebeck und Tom Lahmer. „Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves“. Infrastructures 7, Nr. 8 (12.08.2022): 104. http://dx.doi.org/10.3390/infrastructures7080104.
Der volle Inhalt der QuelleColombo, Daniele, Diego Rovetta und Ersan Turkoglu. „CSEM-regularized seismic velocity inversion: A multiscale, hierarchical workflow for subsalt imaging“. GEOPHYSICS 83, Nr. 5 (01.09.2018): B241—B252. http://dx.doi.org/10.1190/geo2017-0454.1.
Der volle Inhalt der QuelleSun, Jiajia, Daniele Colombo, Yaoguo Li und Jeffrey Shragge. „Geophysics introduces new section on multiphysics and joint inversion“. Leading Edge 39, Nr. 10 (Oktober 2020): 753–54. http://dx.doi.org/10.1190/tle39100753.1.
Der volle Inhalt der QuelleGao, Guozhong, Aria Abubakar und Tarek M. Habashy. „Joint petrophysical inversion of electromagnetic and full-waveform seismic data“. GEOPHYSICS 77, Nr. 3 (01.05.2012): WA3—WA18. http://dx.doi.org/10.1190/geo2011-0157.1.
Der volle Inhalt der QuelleLouboutin, Mathias, Ziyi Yin, Rafael Orozco, Thomas J. Grady, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman und Felix J. Herrmann. „Learned multiphysics inversion with differentiable programming and machine learning“. Leading Edge 42, Nr. 7 (Juli 2023): 474–86. http://dx.doi.org/10.1190/tle42070474.1.
Der volle Inhalt der QuelleTu, Xiaolei, und Michael S. Zhdanov. „Joint Gramian inversion of geophysical data with different resolution capabilities: case study in Yellowstone“. Geophysical Journal International 226, Nr. 2 (05.04.2021): 1058–85. http://dx.doi.org/10.1093/gji/ggab131.
Der volle Inhalt der QuelleColombo, Daniele, Diego Rovetta, Taqi Al-Yousuf, Ernesto Sandoval, Ersan Turkoglu und Gary McNeice. „Multiple joint wavefield inversions: Theory and field data implementations“. Leading Edge 39, Nr. 6 (Juni 2020): 411–21. http://dx.doi.org/10.1190/tle39060411.1.
Der volle Inhalt der QuelleZhdanov, Michael S., Michael Jorgensen und Leif Cox. „Advanced Methods of Joint Inversion of Multiphysics Data for Mineral Exploration“. Geosciences 11, Nr. 6 (21.06.2021): 262. http://dx.doi.org/10.3390/geosciences11060262.
Der volle Inhalt der QuelleWu, Pingping, Handong Tan, Changhong Lin, Miao Peng, Huan Ma und Zhengwen Yan. „Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints“. Geophysical Journal International 221, Nr. 2 (25.01.2020): 938–50. http://dx.doi.org/10.1093/gji/ggaa045.
Der volle Inhalt der QuelleDissertationen zum Thema "Multiphysical inversion"
Varignier, Geoffrey. „Construction de fonctions de sensibilité spatiales et prédictions rapides de diagraphies nucléaires en environnement de puits tubés“. Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALY026.
Der volle Inhalt der QuelleIn petroleum wells, many tools operating on different physical principles are commonly used for data acquisition. This thesis focuses on actives nuclear logging probes involving a neutron or a gamma source. They are used in the oil industry to characterize the well geology and have been initially developed to realize quantitative measurements in open hole conditions where the probe is directly in contact with the rock formation. Once the petroleum well is drilled, a steel casing is installed and cemented, the probes are then no longer in contact with the rock formation and the measurements are considered qualitative due to the complexity of the geometry and the signal attenuation.With the hydrocarbon resources rarefaction, the number of explorations projects decease each year. Petroleum companies have more and more mature wells whose production capacities must be maintained and others at the end of their life which must be abandoned. Those phases require systematically logging measurements. The quantity of logs in cased-hole configuration tends to increase a lot and it becomes necessary to improve their interpretation.The industrial problematic is to characterize quantitatively, in a filed with strong radial heterogeneity, all the components the well (e.g. the fluids, the casing, the cement) and not just the rock reservoir parameters. The approach developed in the thesis is based on the concept of sensitivity function of nuclear logging probes, which represents the 3D dependency of the measurement to the model elements and are obtained by Monte-Carlo simulation. Due to the large number of unknowns, a multiphysical inversion considering the all the measurements of the different nuclear probes (porosity by neutron diffusion, density by gamma diffusion, lithology by neutron-gamma activation) is essential.The first part of the thesis allowed to compare the Monte-Carlo particles transport codes GEANT4 and MCNP for Geosciences applications. Results show a very good agreement for the gamma-gamma physics and a good agreement for the neutron-neutron physics but significant discrepancies for the neutron-gamma physics where MCNP seems to be more relevant.The second part of the thesis allowed to experimental validate Monte-Carlo simulations and to design a sensitivity function computation method specific for the cased-hole configuration. The validation is a comparison between the experimental sensitivity functions measured in calibration center and the numerical sensitivity functions computed using two different methods, the first one based on spatial importances estimated with MCNP and the second one based on interaction locations obtained with GEANT4. The results show good experimental agreement between the measured and calculated radial and axial sensitivity profiles, which validates the concept of sensitivity function with a preference for the interaction locations method which presents greater radial contrast between the different components of the well.The third part of the thesis consisted of making the geological interpretation of a reservoir zone of a cased hole well with sensitivity functions. The neutron-gamma and porosity logs predicted using the sensitivity functions are compared to the measured logs. An optimal earth model is obtained by iteration, showing a good capacity of the fast forward modeling algorithums to quantitatively reproduce the logs in cased-hole configuration providing that a relevant calibration is apply
Bücher zum Thema "Multiphysical inversion"
Zhdanov, Michael S. Advanced Methods of Joint Inversion and Fusion of Multiphysics Data. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3.
Der volle Inhalt der QuelleZhdanov, Michael. Advanced Methods of Joint Inversion and Fusion of Multiphysics Data. Springer, 2023.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Multiphysical inversion"
Zhdanov, Michael S. „Joint Focusing Inversion of Multiphysics Data“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 193–213. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_10.
Der volle Inhalt der QuelleZhdanov, Michael S. „Machine Learning Inversion of Multiphysics Data“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 305–15. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_16.
Der volle Inhalt der QuelleZhdanov, Michael S. „Introduction to Inversion Theory“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 3–12. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_1.
Der volle Inhalt der QuelleZhdanov, Michael S. „Joint Minimum Entropy Inversion“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 215–24. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_11.
Der volle Inhalt der QuelleZhdanov, Michael S. „Probabilistic Approach to Gramian Inversion“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 245–58. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_13.
Der volle Inhalt der QuelleZhdanov, Michael S. „Joint Inversion Based on Structural Similarities“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 177–92. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_9.
Der volle Inhalt der QuelleZhdanov, Michael S. „Gradient-Type Methods of Nonlinear Inversion“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 129–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_7.
Der volle Inhalt der QuelleZhdanov, Michael S. „Gramian Method of Generalized Joint Inversion“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 225–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_12.
Der volle Inhalt der QuelleZhdanov, Michael S. „Simultaneous Processing and Fusion of Multiphysics Data and Images“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 259–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_14.
Der volle Inhalt der QuelleZhdanov, Michael. „Modeling and Inversion of Potential Field Data“. In Advanced Methods of Joint Inversion and Fusion of Multiphysics Data, 319–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6722-3_17.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Multiphysical inversion"
Feng, Shihang, Peng Jin, Xitong Zhang, Yinpeng Chen, David Alumbaugh, Michael Commer und Youzuo Lin. „Extremely weak supervision inversion of multiphysical properties“. In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3746487.1.
Der volle Inhalt der QuelleHallinan, Stephen, Wolfgang Soyer, Randall Mackie, Carsten Scholl und Federico Miorelli. „Geologically Consistent Multiphysics Inversion“. In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-21936-ea.
Der volle Inhalt der QuelleHu, Yanyan, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiefu Chen, Jiuping Chen und Yueqing Huang. „Deep learning-enhanced multiphysics joint inversion“. In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3583667.1.
Der volle Inhalt der QuelleMolodtsov, Dmitry, und Vladimir Troyan. „Multiphysics joint inversion through joint sparsity regularization“. In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17792589.1.
Der volle Inhalt der QuelleHu, Yanyan, Jiefu Chen, Xuqing Wu und Yueqin Huang. „Multiphysics Joint Inversion Using Successive Deep Perceptual Constraints“. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI). IEEE, 2022. http://dx.doi.org/10.1109/ap-s/usnc-ursi47032.2022.9887246.
Der volle Inhalt der QuelleCeci, F., und A. Battaglini. „Reducing geothermal exploration uncertainty via multiphysics joint inversion“. In 2nd Geoscience & Engineering in Energy Transition Conference. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202121025.
Der volle Inhalt der QuelleShahin, A., M. Myers und L. Hathon. „Deciphering Dual Porosity Carbonates Using Multiphysics Modeling and Inversion“. In Third EAGE WIPIC Workshop: Reservoir Management in Carbonates. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201903112.
Der volle Inhalt der QuelleHu, Yanyan, Jiefu Chen, Xuqing Wu und Yueqin Huang. „Deep Learning Enhanced Joint Inversion of Multiphysics Data with Nonconforming Discretization“. In 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI). IEEE, 2021. http://dx.doi.org/10.1109/aps/ursi47566.2021.9703802.
Der volle Inhalt der QuelleChikhaoui, Zeineb, Julien Gomand, François Malburet und Pierre-Jean Barre. „Complementary Use of BG and EMR Formalisms for Multiphysics Systems Analysis and Control“. In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82318.
Der volle Inhalt der QuelleDomenzain, Diego, John Bradford und Jodi Mead. „Multiphysics joint inversion of field FWI-GPR and ER surface acquired data“. In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3576479.1.
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