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Passive seismic event estimation using multiscattering waveform inversion

机译:使用多刻波形反转的被动地震事件估计

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Passive seismic monitoring has become an effective method to understand underground processes. Time-reversal-based methods are often used to locate passive seismic events directly. However, these kinds of methods are strongly dependent on the accuracy of the velocity model. Full-waveform inversion (FWI) has been used on passive seismic data to invert the velocity model and source image, simultaneously. However, waveform inversion of passive seismic data uses mainly the transmission energy, which results in poor illumination and low resolution. We developed a waveform inversion using multiscattered energy for passive seismic to extract more information from the data than conventional FWI. Using transmission wavepath information from single- and double-scattering, computed from a predicted scatterer field acting as secondary sources, our method provides better illumination of the velocity model than conventional FWI. Using a new objective function, we optimized the source image and velocity model, including multiscattered energy, simultaneously. Because we conducted our method in the frequency domain with a complex source function including spatial and wavelet information, we mitigate the uncertainties of the source wavelet and source origin time. Inversion results from the Marmousi model indicate that by taking advantage of multiscattered energy and starting from a reasonably acceptable frequency (a single source at 3 Hz and multiple sources at 5 Hz), our method yields better inverted velocity models and source images compared with conventional FWI.
机译:被动地震监测已成为理解地下过程的有效方法。基于时间反转的方法通常用于直接定位被动地震事件。然而,这些方法强烈依赖于速度模型的准确性。全波形反转(FWI)已用于被动地震数据,同时倒置速度模型和源图像。然而,被动地震数据的波形反转主要是传输能量,导致照明差和低分辨率。我们开发了使用多尺寸能量的波形反演,用于被动地震,从数据中提取比传统的FWI更多信息。使用从单次和双散射的传输波段信息,从作为二次来源的预测散射字段计算,我们的方法提供比传统FWI更好地照明速度模型。使用新的客观函数,我们优化了源图像和速度模型,包括多尺寸能量。由于我们在具有复杂源功能的频域中进行了我们的方法,包括包括空间和小波信息的复杂源功能,所以我们减轻了源小波和源原点时间的不确定性。 Marmousi模型的反演结果表明,通过利用多彩色能量并从合理可接受的频率开始(在3 Hz的单个源和5 Hz的多个源),与传统的FWI相比,我们的方法产生更好的倒速模型和源图像。

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