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| == Reverse-time demigration using the extended-imaging condition == | == Reverse-time demigration using the extended-imaging condition == | ||
| Most classical seismic data processing methods in the data domain are based on simplified assumptions about the subsurface structure, such as horizontal layering and mild lateral variations in mechanical properties. Over such media, reflection data can be described by simple equations such as hyperbolas. However, complex geologic media will cause complicated waveforms as seismic waves propagate through them. As the medium deviates from the simple models, the complexity of the reflection data increases and the classical seismic data processing methods start to fail. This calls for special treatment of complex data, which substantially complicates seismic data processing. On the other hand, the image domain allows unified treatment of data acquired over simple and complex media because the effects of the medium on the kinematics of wave propagation are largely removed by the process of backpropagation, | Most classical seismic data processing methods in the data domain are based on simplified assumptions about the subsurface structure, such as horizontal layering and mild lateral variations in mechanical properties. Over such media, reflection data can be described by simple equations such as hyperbolas. However, complex geologic media will cause complicated waveforms as seismic waves propagate through them. As the medium deviates from the simple models, the complexity of the reflection data increases and the classical seismic data processing methods start to fail. This calls for special treatment of complex data, which substantially complicates seismic data processing. On the other hand, the image domain allows unified treatment of data acquired over simple and complex media because the effects of the medium on the kinematics of wave propagation are largely removed by the process of backpropagation, | ||
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| == Time-lapse full-waveform inversion of limited-offset seismic data using a local migration regularization == | == Time-lapse full-waveform inversion of limited-offset seismic data using a local migration regularization == | ||
| Conventional methods for quantifying time-lapse seismic effects rely on a linear assumption that is easily violated. Therefore, more sophisticated methods are necessary. The full-waveform inversion (FWI) method is an inverse method that is able to reveal time-lapse changes in the image domain, in which the conventional methods break down. We investigated the behavior of FWI using different approaches for applying FWI on limited-offset time-lapse data. We compared acoustic and elastic inversion schemes. We introduced a method for constraining the model update for the monitor model to remove time-lapse artifacts. This method was based on migration of the residuals in the time-lapse data, which, in combination with a local contrast estimation algorithm, formed the update constraint. We found that for limited-offset data, elastic theory was necessary for the success of FWI and that FWI was able to quantify the time-lapse changes in the parameter models. The local migration regularization approach was able to remove time-lapse artifacts. | Conventional methods for quantifying time-lapse seismic effects rely on a linear assumption that is easily violated. Therefore, more sophisticated methods are necessary. The full-waveform inversion (FWI) method is an inverse method that is able to reveal time-lapse changes in the image domain, in which the conventional methods break down. We investigated the behavior of FWI using different approaches for applying FWI on limited-offset time-lapse data. We compared acoustic and elastic inversion schemes. We introduced a method for constraining the model update for the monitor model to remove time-lapse artifacts. This method was based on migration of the residuals in the time-lapse data, which, in combination with a local contrast estimation algorithm, formed the update constraint. We found that for limited-offset data, elastic theory was necessary for the success of FWI and that FWI was able to quantify the time-lapse changes in the parameter models. The local migration regularization approach was able to remove time-lapse artifacts. | ||
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| - | **Read more:** {{:|PDF}}, [[http://dx.doi.org/ | + | **Read more:** {{:raknes2014.pdf|PDF}}, [[http://library.seg.org/doi/abs/10.1190/geo2013-0369.1|link]]. |
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| **Read more:** {{arntsen2013.pdf|pdf}} | **Read more:** {{arntsen2013.pdf|pdf}} | ||
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