The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted pa...The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.展开更多
The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in...The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.展开更多
基金financially supported by the Important National Science and Technology Specific Project of China (Grant No. 2016ZX05047-002)
文摘The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.
基金supported by the National Science and Technology Major Project of China(No.2017ZX05018-005)National Natural Science Foundation of China(No.41474110)
文摘The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.
