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Full waveform inversion method using envelope objective function without low frequency data

机译:使用包络目标函数的全波形反演方法,无低频数据

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Full waveform inversion (FWI) has been a successful tool to build high resolution velocitymodels, but it is affected by a local minima problem. The conventional multi-scale strategy to tackle this severe problem may not work for real seismic data without long offsets and low frequency data.We use an envelope-based objective function FWI method to provide the long wavelength components of the velocitymodel for the traditional FWI. The gradient can be computed efficiently with the adjoint state method without any additional computational cost. Simple models are used to prove that the envelope-based objective function is more convex than the traditional misfit function, thus the cycle-skipping problem can be mitigated. Due to the envelope demodulation effect, the adjoint source of the envelope-based FWI contains abundant low frequency information, therefore the gradient tends to sense the low wavenumber model update. A Marmousi synthetic data example illustrates that the envelope-based FWImethod can provide an adequately accurate initialmodel for the traditional FWI approach evenwhen the initial model is far from the true model and low-frequency data are missing.
机译:全波形反演(FWI)是建立高分辨率速度模型的成功工具,但受到局部极小问题的影响。没有长偏移和低频数据的常规多尺度策略可能不适用于真实的地震数据。我们使用基于包络的目标函数FWI方法为传统FWI提供速度模型的长波长分量。可以使用伴随状态方法有效地计算梯度,而无需任何额外的计算成本。使用简单的模型来证明基于包络的目标函数比传统的失配函数更凸,因此可以缓解周期跳跃问题。由于包络解调效应,基于包络的FWI的伴随源包含丰富的低频信息,因此,梯度倾向于感测低波数模型更新。一个Marmousi综合数据示例说明,即使初始模型与真实模型相距甚远且缺少低频数据,基于包络的FWI方法也可以为传统FWI方法提供足够准确的初始模型。

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