Toward an automatic full-wave inversion: Synthetic study cases

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

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Toward an automatic full-wave inversion: Synthetic study cases. / Kormann, Jean; Rodriguez, Juan Esteban; Gutierrez, Natalia; Ferrer, Miguel; Rojas, Otilio; de la Puente, Josep; Hanzich, Mauricio; Cela, Jose Maria.

in: Leading Edge, Jahrgang 35, Nr. 12, 10.1190/tle35121047.1, 2016, S. 1047.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

Harvard

Kormann, J, Rodriguez, JE, Gutierrez, N, Ferrer, M, Rojas, O, de la Puente, J, Hanzich, M & Cela, JM 2016, 'Toward an automatic full-wave inversion: Synthetic study cases', Leading Edge, Jg. 35, Nr. 12, 10.1190/tle35121047.1, S. 1047.

APA

Kormann, J., Rodriguez, J. E., Gutierrez, N., Ferrer, M., Rojas, O., de la Puente, J., Hanzich, M., & Cela, J. M. (2016). Toward an automatic full-wave inversion: Synthetic study cases. Leading Edge, 35(12), 1047. [10.1190/tle35121047.1].

Vancouver

Kormann J, Rodriguez JE, Gutierrez N, Ferrer M, Rojas O, de la Puente J et al. Toward an automatic full-wave inversion: Synthetic study cases. Leading Edge. 2016;35(12):1047. 10.1190/tle35121047.1.

Author

Kormann, Jean ; Rodriguez, Juan Esteban ; Gutierrez, Natalia ; Ferrer, Miguel ; Rojas, Otilio ; de la Puente, Josep ; Hanzich, Mauricio ; Cela, Jose Maria. / Toward an automatic full-wave inversion: Synthetic study cases. in: Leading Edge. 2016 ; Jahrgang 35, Nr. 12. S. 1047.

Bibtex - Download

@article{da768c680afc412a8cc87847c98adf0a,
title = "Toward an automatic full-wave inversion: Synthetic study cases",
abstract = "Full-waveform inversion (FWI) in seismic scenarios continues to be a complex procedure for subsurface imaging that might require extensive human interaction in terms of model setup, constraints, and data preconditioning. The underlying reason is the strong nonlinearity of the problem that forces the addition of a priori knowledge (or bias) in order to obtain geologically sound results. In particular, when the use of a long-offset receiver is not possible or may not favor the reconstruction of the fine structure of the model, one needs to rely on reflection data. As a consequence, the inversion process is more prone to becoming stuck in local minima. Nevertheless, misfit functionals can be devised that can either cope with missing long-wavenumber features of initial models (e.g., cross-correlation-based misfit) or invert reflection-dominated data whenever the models are sufficiently good (e.g., normalized offset-limited least-squares misfit). By combining both, high-frequency data content with poor initial models can be successfully inverted. If one can figure out simple parameterizations for such functionals, the amount of uncertainty and manual work related to tuning FWI would be substantially reduced. Thus, FWI might become a semiautomatized imaging tool.",
author = "Jean Kormann and Rodriguez, {Juan Esteban} and Natalia Gutierrez and Miguel Ferrer and Otilio Rojas and {de la Puente}, Josep and Mauricio Hanzich and Cela, {Jose Maria}",
year = "2016",
language = "English",
volume = "35",
pages = "1047",
journal = "Leading Edge",
issn = "1070-485X",
publisher = "Society of Exploration Geophysicists",
number = "12",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Toward an automatic full-wave inversion: Synthetic study cases

AU - Kormann, Jean

AU - Rodriguez, Juan Esteban

AU - Gutierrez, Natalia

AU - Ferrer, Miguel

AU - Rojas, Otilio

AU - de la Puente, Josep

AU - Hanzich, Mauricio

AU - Cela, Jose Maria

PY - 2016

Y1 - 2016

N2 - Full-waveform inversion (FWI) in seismic scenarios continues to be a complex procedure for subsurface imaging that might require extensive human interaction in terms of model setup, constraints, and data preconditioning. The underlying reason is the strong nonlinearity of the problem that forces the addition of a priori knowledge (or bias) in order to obtain geologically sound results. In particular, when the use of a long-offset receiver is not possible or may not favor the reconstruction of the fine structure of the model, one needs to rely on reflection data. As a consequence, the inversion process is more prone to becoming stuck in local minima. Nevertheless, misfit functionals can be devised that can either cope with missing long-wavenumber features of initial models (e.g., cross-correlation-based misfit) or invert reflection-dominated data whenever the models are sufficiently good (e.g., normalized offset-limited least-squares misfit). By combining both, high-frequency data content with poor initial models can be successfully inverted. If one can figure out simple parameterizations for such functionals, the amount of uncertainty and manual work related to tuning FWI would be substantially reduced. Thus, FWI might become a semiautomatized imaging tool.

AB - Full-waveform inversion (FWI) in seismic scenarios continues to be a complex procedure for subsurface imaging that might require extensive human interaction in terms of model setup, constraints, and data preconditioning. The underlying reason is the strong nonlinearity of the problem that forces the addition of a priori knowledge (or bias) in order to obtain geologically sound results. In particular, when the use of a long-offset receiver is not possible or may not favor the reconstruction of the fine structure of the model, one needs to rely on reflection data. As a consequence, the inversion process is more prone to becoming stuck in local minima. Nevertheless, misfit functionals can be devised that can either cope with missing long-wavenumber features of initial models (e.g., cross-correlation-based misfit) or invert reflection-dominated data whenever the models are sufficiently good (e.g., normalized offset-limited least-squares misfit). By combining both, high-frequency data content with poor initial models can be successfully inverted. If one can figure out simple parameterizations for such functionals, the amount of uncertainty and manual work related to tuning FWI would be substantially reduced. Thus, FWI might become a semiautomatized imaging tool.

M3 - Article

VL - 35

SP - 1047

JO - Leading Edge

JF - Leading Edge

SN - 1070-485X

IS - 12

M1 - 10.1190/tle35121047.1

ER -