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Frequency-domain reflection-based full waveform inversion for short-offset seismic data

机译:基于频域反射的全波形反演,用于短偏移地震数据

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摘要

The full waveform inversion (FWI) is a high-resolution algorithm used to invert accurate subsurface velocity models. However, inverting accurate velocity models from field data using the conventional FWI without an accurate long-wavelength starting velocity model is difficult. This difficulty occurs because of the band-limited frequency of the field seismic data and the acquisition geometry of the field seismic exploration. The low-frequency components and long-offset seismic data are essential for the inversion of the long-wavelength velocity model using the FWI. However, low-frequency signals are difficult to record from the field seismic exploration, and the maximum offset of the streamer is usually not sufficient in length. Therefore, the conventional FWI cannot easily invert the long-wavelength velocity from field seismic data but can invert the migration-like short-wavelength velocity, and it is subject to the problem of severe local minima. To invert the long-wavelength velocity from reflection-dominant, short-offset field seismic data, reflection-based full waveform inversion (RFWI) which decomposes the FWI gradient into high- and low-wavenumber components, is suggested. However, the conventional RFWI also contains high-wavenumber components, which obstruct long-wavelength velocity updates in the deep part of the model. Moreover, true amplitude migration and preprocessing to extract reflection signals from observed data are necessary for the conventional RFWI. In this study, a new frequency-domain RFWI algorithm, which uses wavefield separation and a two-step approach, is proposed. The wavefield separation divides the wavefield into up/down-going waves to remove the high-wavenumber component of the gradient and the two-step approach alternately updates the short- and long-wavelength velocities to reduce the computational cost. The effectiveness of the proposed algorithm is verified using the reflection-dominant, short-offset Marmousi synthetic seismic data a
机译:全波形反转(FWI)是用于反转准确的地下速度模型的高分辨率算法。然而,难以使用传统的FWI从现场数据反转精确的速度模型而没有精确的长波长开始速度模型。由于场地震数据的带状频率和场地震勘探的采集几何形状,发生这种困难。低频分量和长偏移地震数据对于使用FWI的长波长速度模型的反转是必不可少的。然而,从场地震勘探中难以记录低频信号,并且拖缆的最大偏移通常不足以长度。因此,传统的FWI不能容易地颠倒来自场地震数据的长波长速度,而是可以反转迁移类似的短波长速度,并且它受到严重局部最小值的问题。为了颠倒从反射主导,短偏移场地震数据,基于反射的完全波形反转(RFWI),将FWI梯度分解成高和低波数组件的反射速度。然而,传统的RFWI还包含高波纹组件,其在模型的深部中阻碍了长波长速度更新。此外,对于传统的RFWI,需要真正幅度迁移和预处理以从观察到的数据中提取来自观察数据的反射信号。在该研究中,提出了一种新的频域RFWI算法,其使用波场分离和两步方法。波场分离将波场分成向上/向下波以移除梯度的高波纹组件,两步方法交替地更新短波和长波长速度以降低计算成本。使用反射显性,短偏移Marmous I合成地震数据验证了所提出的算法的有效性

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