Relevant bibliographies by topics / Multiphysical inversion

Academic literature on the topic 'Multiphysical inversion'

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Published: 12 October 2024

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Journal articles on the topic "Multiphysical inversion"

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Zheng, Yi-kang, Chong Wang, Hao-hong Liang, Yi-bo Wang, and Rong-shu Zeng. "3D seismic forward modeling from the multiphysical inversion at the Ketzin CO2 storage site." Applied Geophysics 21, no. 3 (September 2024): 593–605. http://dx.doi.org/10.1007/s11770-024-1132-5.

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Al-Yasiri, Zainab Riyadh Shaker, Hayder Majid Mutashar, Klaus Gürlebeck, and Tom Lahmer. "Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves." Infrastructures 7, no. 8 (August 12, 2022): 104. http://dx.doi.org/10.3390/infrastructures7080104.

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One of the most important renewable energy technologies used nowadays are wind power turbines. In this paper, we are interested in identifying the operating status of wind turbines, especially rotor blades, by means of multiphysical models. It is a state-of-the-art technology to test mechanical structures with ultrasonic-based methods. However, due to the density and the required high resolution, the testing is performed with high-frequency waves, which cannot penetrate the structure in depth. Therefore, there is a need to adopt techniques in the fields of multiphysical model-based inversion schemes or data-driven structural health monitoring. Before investing effort in the development of such approaches, further insights and approaches are necessary to make the techniques applicable to structures such as wind power plants (blades). Among the expected developments, further accelerations of the so-called “forward codes” for a more efficient implementation of the wave equation could be envisaged. Here, we employ electromagnetic waves for the early detection of cracks. Because in many practical situations, it is not possible to apply techniques from tomography (characterized by multiple sources and sensor pairs), we focus here on the question of whether the existence of cracks can be determined by using only one source for the sent waves.
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Colombo, Daniele, Diego Rovetta, and Ersan Turkoglu. "CSEM-regularized seismic velocity inversion: A multiscale, hierarchical workflow for subsalt imaging." GEOPHYSICS 83, no. 5 (September 1, 2018): B241—B252. http://dx.doi.org/10.1190/geo2017-0454.1.

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Seismic imaging in salt geology is complicated by highly contrasted velocity fields and irregular salt geometries, which cause complex seismic wavefield scattering. Although the imaging challenges can be addressed by advanced imaging algorithms, a fundamental problem remains in the determination of robust velocity fields in high-noise conditions. Conventional migration velocity analysis is often ineffective, and even the most advanced methods for depth-domain velocity analysis, such as full-waveform inversion, require starting from a good initial estimate of the velocity model to converge to a correct result. Nonseismic methods, such as electromagnetics, can help guide the generation of robust velocity models to be used for further processing. Using the multiphysics data acquired in the deepwater section of the Red Sea, we apply a controlled-source electromagnetic (CSEM) resistivity-regularized seismic velocity inversion for enhancing the velocity model in a complex area dominated by nappe-style salt tectonics. The integration is achieved by a rigorous approach of multiscaled inversions looping over model dimensions (1D first, followed by 3D), variable offsets and increasing frequencies, data-driven and interpretation-supported approaches, leading to a hierarchical inversion guided by a parameter sensitivity analysis. The final step of the integration consists of the inversion of seismic traveltimes subject to CSEM model constraints in which a common-structure coupling mechanism is used. Minimization is performed over the seismic data residuals and cross-gradient objective functions without inverting for the resistivity model, which is used as a reference for the seismic inversion (hierarchical approach). Results are demonstrated through depth imaging in which the velocity model derived through CSEM-regularized hierarchical inversion outperforms the results of a seismic-only derived velocity model.
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Sun, Jiajia, Daniele Colombo, Yaoguo Li, and Jeffrey Shragge. "Geophysics introduces new section on multiphysics and joint inversion." Leading Edge 39, no. 10 (October 2020): 753–54. http://dx.doi.org/10.1190/tle39100753.1.

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Geophysicists seek to extract useful and potentially actionable information about the subsurface by interpreting various types of geophysical data together with prior geologic information. It is well recognized that reliable imaging, characterization, and monitoring of subsurface systems require integration of multiple sources of information from a multitude of geoscientific data sets. With increasing data volumes and computational power, new data types, constant development of inversion algorithms, and the advent of the big data era, Geophysics editors see multiphysics integration as an effective means of meeting some of the challenges arising from imaging subsurface systems with higher resolution and reliability as well as exploring geologically more complicated areas. To advance the field of multiphysics integration and to showcase its added value, Geophysics will introduce a new section “Multiphysics and Joint Inversion” in 2021. Submissions are accepted now.
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Gao, Guozhong, Aria Abubakar, and Tarek M. Habashy. "Joint petrophysical inversion of electromagnetic and full-waveform seismic data." GEOPHYSICS 77, no. 3 (May 1, 2012): WA3—WA18. http://dx.doi.org/10.1190/geo2011-0157.1.

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Accurate determination of reservoir petrophysical parameters is of great importance for reservoir monitoring and characterization. We developed a joint inversion approach for the direct estimation of in situ reservoir petrophysical parameters such as porosity and fluid saturations by jointly inverting electromagnetic and full-waveform seismic measurements. Full-waveform seismic inversions allow the exploitation of the full content of the data so that a more accurate geophysical model can be inferred. Electromagnetic data are linked to porosity and fluid saturations through Archie’s equations, whereas seismic data are linked to them through rock-physics fluid-substitution equations. For seismic modeling, we used an acoustic approximation. Sensitivity studies combined with inversion tests show that seismic data are mainly sensitive to porosity distribution, whereas electromagnetic data are more sensitive to fluid-saturation distribution. The separate inversion of electromagnetic or seismic data is highly nonunique and thus leads to great ambiguity in the determination of porosity and fluid saturations. In our approach, we used a Gauss-Newton algorithm equipped with the multiplicative regularization and proper data-weighting scheme. We tested the implemented joint petrophysical inversion method using various synthetic models for surface and crosswell measurements. We found that the joint inversion approach provides substantial advantage for an improved estimation of porosity and fluid-saturation distributions over the one obtained from the separate inversion of electromagnetic and seismic data. This advantage is achieved by significantly reducing the ambiguity on the determination of porosity and fluid saturations using multiphysics measurements. We also carried out a study on the effects of using inaccurate petrophysical transform parameters on the inversion results. Our study demonstrated that up to 20% errors in the saturation and porosity exponents in Archie’s equations do not cause significant errors in the inversion results. On the other hand, if the bulk modulus and density of the rock matrix have a large percentage of errors (i.e., more than 5%), the inversion results will be significantly degraded. However, if the density of the rock matrix has an error of less than 2%, the joint inversion can tolerate a large percentage of errors in the bulk modulus of the rock matrix.
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Louboutin, Mathias, Ziyi Yin, Rafael Orozco, Thomas J. Grady, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, and Felix J. Herrmann. "Learned multiphysics inversion with differentiable programming and machine learning." Leading Edge 42, no. 7 (July 2023): 474–86. http://dx.doi.org/10.1190/tle42070474.1.

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We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning.
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Tu, Xiaolei, and Michael S. Zhdanov. "Joint Gramian inversion of geophysical data with different resolution capabilities: case study in Yellowstone." Geophysical Journal International 226, no. 2 (April 5, 2021): 1058–85. http://dx.doi.org/10.1093/gji/ggab131.

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SUMMARY Joint inversion of multiphysics data is a practical approach to the integration of geophysical data, which produces models of reduced uncertainty and improved resolution. The development of effective methods of joint inversion requires considering different resolutions of different geophysical methods. This paper presents a new framework of joint inversion of multiphysics data, which is based on a novel formulation of Gramian constraints and mitigates the difference in resolution capabilities of different geophysical methods. Our approach enforces structural similarity between different model parameters through minimizing a structural Gramian term, and it also balances the different resolutions of geophysical methods using a multiscale resampling strategy. The effectiveness of the proposed method is demonstrated by synthetic model study of joint inversion of the P-wave traveltime and gravity data. We apply a novel method based on Gramian constraints and multiscale resampling to jointly invert the gravity and seismic data collected in Yellowstone national Park to image the crustal magmatic system of the Yellowstone. Our results helped to produce a consistent image of the crustal magmatic system of the Yellowstone expressed both in low-density and low-velocity anomaly just beneath the Yellowstone caldera.
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Colombo, Daniele, Diego Rovetta, Taqi Al-Yousuf, Ernesto Sandoval, Ersan Turkoglu, and Gary McNeice. "Multiple joint wavefield inversions: Theory and field data implementations." Leading Edge 39, no. 6 (June 2020): 411–21. http://dx.doi.org/10.1190/tle39060411.1.

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Accurate velocity models for the near surface and overburden are needed for seismic processing and reliable depth imaging. Seismic with multiphysics data, well logs, and geology information need to be quantitatively integrated to obtain high-resolution velocity models. We detail our development and application of the joint wavefield inversion software platform, which enables flexible algorithmic schemes for the integration of multiparameter data and constraints. Inversion is performed in cascade or simultaneously using a variety of input data to constrain the velocity field reconstruction at multiple scales. Coupling mechanisms based on structure similarity together with rock-physics relations are optimally combined to boost resolution and enhance accuracy of the inverted velocity models. Ill-posed inversion problems are then solved using extensive geologic and rock-physics regularization instead of relying on smoothness constraints alone. We detail workflows and algorithms to guide the application of multiparameter joint inversion for velocity model building whether the input data are seismic traveltimes, electromagnetics (time/frequency domains), gravity, and/or surface waves. Extensive applications of multiparameter joint inversion are presented for a variety of complex geologic scenarios in which various multiparameter coupling strategies are illustrated. Robust velocity modeling and enhanced seismic imaging in time and depth domains are obtained as a result, proving the importance of multiphysics integration for reliable earth model parameter estimation.
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Zhdanov, Michael S., Michael Jorgensen, and Leif Cox. "Advanced Methods of Joint Inversion of Multiphysics Data for Mineral Exploration." Geosciences 11, no. 6 (June 21, 2021): 262. http://dx.doi.org/10.3390/geosciences11060262.

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Different geophysical methods provide information about various physical properties of rock formations and mineralization. In many cases, this information is mutually complementary. At the same time, inversion of the data for a particular survey is subject to considerable uncertainty and ambiguity as to causative body geometry and intrinsic physical property contrast. One productive approach to reducing uncertainty is to jointly invert several types of data. Non-uniqueness can also be reduced by incorporating additional information derived from available geological and/or geophysical data in the survey area to reduce the searching space for the solution. This additional information can be incorporated in the form of a joint inversion of multiphysics data. This paper presents an overview of the main ideas and principles of novel methods of joint inversion, developed over the last decade, which do not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. These approaches are designated as follows: (1) Gramian constraints; (2) Gramian-based structural constraints; (3) localized Gramian constraints; and (4) joint focusing constraints. We provide a short description of the mathematical foundations of each of these approaches and discuss the practical aspects of their applications in mineral exploration.
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Wu, Pingping, Handong Tan, Changhong Lin, Miao Peng, Huan Ma, and Zhengwen Yan. "Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints." Geophysical Journal International 221, no. 2 (January 25, 2020): 938–50. http://dx.doi.org/10.1093/gji/ggaa045.

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SUMMARY Multiphysics imaging for data inversion is of growing importance in many branches of science and engineering. Cross-gradient constraint has been considered as a feasible way to reduce the non-uniqueness problem inherent in inversion process by finding geometrically consistent images from multigeophysical data. Based on OCCAM inversion algorithm, a direct inversion method of 2-D profile velocity structure with surface wave dispersion data is proposed. Then we jointly invert the profiles of magnetotelluric and surface wave dispersion data with cross-gradient constraints. Three synthetic models, including block homogeneous or heterogeneous models with consistent or inconsistent discontinuities in velocity and resistivity, are presented to gauge the performance of the joint inversion scheme. We find that owning to the complementary advantages of the two geophysical data sets, the models recovered with structure coupling constraints exhibit higher resolution in the classification of complex geologic units and settle some imaging problems caused by the separate inversion methods. Finally, a realistic velocity model from the NE Tibetan Plateau and its corresponding resistivity model calculated by empirical law are used to test the effectiveness of the joint inversion scheme in the real geological environment.
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Dissertations / Theses on the topic "Multiphysical inversion"

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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.

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Dans les puits pétroliers, de nombreux outils fonctionnant sur différents principes physiques sont couramment utilisés pour l’acquisition de données. Cette thèse se concentre sur les sondes de diagraphies nucléaires actives, faisant intervenir une source neutronique ou gamma. Elles sont utilisées dans l'industrie pétrolière pour caractériser la géologie des puits et ont été initialement développées pour réaliser des mesures quantitatives en conditions puits ouvert où la sonde est en contact direct avec la formation rocheuse. Une fois le puits pétrolier foré, un tube en acier est installé puis cimenté, les sondes ne sont alors plus en contact avec la formation rocheuse et les mesures sont considérées comme qualitatives en raison de la complexité de la géométrie et de l'atténuation du signal.Avec la raréfaction des ressources en hydrocarbures, le nombre de projets d’explorations diminue chaque année. Les compagnies pétrolières ont de plus en plus de puits dont il faut maintenir les capacités de production et d’autres en fin de vie qu’il faut abandonner, ce qui passe systématiquement par des mesures. La quantité de diagraphies en configuration puits tubé tend donc fortement à augmenter et il devient nécessaire d’améliorer leur interprétation.La problématique industrielle est de pouvoir caractériser de manière quantitative, dans un domaine à forte hétérogénéité radiale, l’ensemble de tous les éléments du puits (e.g. les fluides, le tubage, le ciment) et pas uniquement les paramètres du réservoir rocheux. L’approche développée dans la thèse se base sur le concept des fonctions de sensibilité des sondes diagraphiques nucléaires, qui représentent la dépendance en 3D de la mesure aux éléments du modèle et sont obtenues par simulation Monte-Carlo. Du fait du nombre important de variables, une inversion multiphysique prenant en compte l’ensemble des mesures des différentes sondes nucléaires (de porosité par diffusion neutronique, de densité par diffusion gamma, de lithologie par activation neutron-gamma) est indispensable.La première étape de la thèse a permis de comparer les codes Monte-Carlo de transport de particules GEANT4 et MCNP pour des applications de Géosciences. Les résultats montrent un très bon accord pour la physique gamma-gamma, un bon accord pour la physique neutron-neutron mais des écarts significatifs pour la physique neutron-gamma pour laquelle MCNP semble plus pertinent.La deuxième étape de la thèse a permis de valider expérimentalement les simulations Monte Carlo et de concevoir une méthode de calcul des fonctions de sensibilité numériques spécifique au domaine des puits tubés. La validation se traduit par une comparaison entre les fonctions de sensibilité expérimentales mesurées en centre d’étalonnage et les fonctions de sensibilité numériques calculées avec deux méthodes différentes, l’une basée sur des importances spatiales estimées par MCNP, l’autre sur les lieux d’interaction obtenus avec GEANT4. Les résultats montrent un bon accord expérimental entre les profils de sensibilité radial et axial mesurés et calculés, ce qui valide le concept de fonction de sensibilité avec une préférence pour la méthode des lieux d’interaction qui présente un contraste radial plus importante entre les différents constituants du puits.La troisième étape de la thèse a consisté à faire l’interprétation géologique d’une zone réservoir d’un puits tubé avec les fonctions de sensibilité. Les diagraphies neutron-gamma et de porosité prédites grâce aux fonctions de sensibilité sont comparées à celles mesurées en puits. Un modèle de terrain optimal est obtenu par itération, montrant une bonne capacité des algorithmes de prédiction à reproduire quantitativement en configuration puits tubé ce type de diagraphies à condition de choisir un étalonnage pertinent
In 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
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Books on the topic "Multiphysical inversion"

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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.

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Zhdanov, Michael. Advanced Methods of Joint Inversion and Fusion of Multiphysics Data. Springer, 2023.

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Book chapters on the topic "Multiphysical inversion"

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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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Zhdanov, 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.

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Conference papers on the topic "Multiphysical inversion"

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Feng, Shihang, Peng Jin, Xitong Zhang, Yinpeng Chen, David Alumbaugh, Michael Commer, and 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.

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Hallinan, Stephen, Wolfgang Soyer, Randall Mackie, Carsten Scholl, and Federico Miorelli. "Geologically Consistent Multiphysics Inversion." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-21936-ea.

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Abstract A subsurface volume that can be reliably interpreted in terms of geologically relevant attributes is a desirable objective for products from geophysics inversion workflows. Geophysics data contain uncertainties, however, and the solution of the inverse problem is non-unique. Some form of constraint is then required to obtain geologically reasonable outputs. Our inversions may include a priori geological information quantitatively in the regularization, e.g. to test two or more structural models for consistency with the observed geophysics data sets, updating the models accordingly. We have implemented a cross-gradient constraint for diverse geophysical data types at several geological settings for the O&G, geothermal, mining E&P community (e.g. Scholl et al 2015, 2016, Soyer et al 2018, 2021 and Mackie et al 2020). The basic application covers the familiar structural similarity objective introduced by Gallardo and Meju 2003 and 2011 - comparing the gradient fields of property volumes, e.g. velocity, resistivity, density, derived from different geophysical domains - but a distinct advantage comes when gradient control from geology (e.g. surface dip and strike data, subsurface interpreted structural models, etc.) or an ancillary geoscience property (e.g. porosity volumes as a proxy for geological structure, structural tensors extracted from PSDM, etc.) is included during single or joint domain inversions of geophysical data. In reverse, the cross gradient application may be used to de-risk competing geological or structural models, testing the consistency of the structure in each model variation – as defined by the 3D gradient field – against the observed geophysical dataset(s) (e.g. Miorelli et al 2019). A range of applications is summarized in Figure 1. The method behind our implementation and representative practical examples are discussed in the sections below.
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Hu, Yanyan, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiefu Chen, Jiuping Chen, and 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.

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Molodtsov, Dmitry, and 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.

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Hu, Yanyan, Jiefu Chen, Xuqing Wu, and 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.

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Ceci, F., and 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.

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Shahin, A., M. Myers, and 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.

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Hu, Yanyan, Jiefu Chen, Xuqing Wu, and 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.

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Chikhaoui, Zeineb, Julien Gomand, François Malburet, and 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.

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In this paper, a complex multiphysics system is modeled using two different energy-based graphical techniques: Bond Graph and Energetic Macroscopic Representation. These formalisms can be used together to analyze, model and control a system. The BG is used to support physical, lumped-parameter modeling and analysis processes, and then EMR is used to facilitate definition of a control structure through inversion-based methodology. This complementarity between both of these tools is set out through a helicopter flight control subsystem.
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Domenzain, Diego, John Bradford, and 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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