Marchenko multiple elimination schemes are able to attenuate all internal multiple reflections in acoustic reflection data. These can be implemented with and without compensation for two-way transmission effects in the resulting primary reflection data set. The methods are fully automated and run without human intervention, but they require the data to be properly sampled and preprocessed. Even when several primary reflections are invisible in the data because they are masked by overlapping multiples, such as in the resonant wedge model, all missing primary reflections are restored and recovered with the proper amplitudes. Investigating the amplitudes in the primary reflections after multiple elimination with and without compensation for transmission effects shows that transmission effects are properly accounted for in a constant-velocity model. When the layer thickness is one quarter of the wavelength at the dominant frequency of the source wavelet, the methods cease to work properly. Full-wavefield migration relies on a velocity model and runs a nonlinear inversion to obtain a reflectivity model, which results in the migration image. The primary reflections that are masked by interference with multiples in the resonant wedge model are not recovered. In this case, minimizing the data misfit function leads to the incorrect reflector model even though the data fit is optimal. This method has much lower demands on data sampling than the multiple elimination schemes, but it is prone to getting stuck in a local minimum even when the correct velocity model is available. A hybrid method that exploits the strengths of each of these methods could be worth investigating.

中文翻译：

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谐振尖灭模型中的马尔琴科多重消除和全波场偏移

Marchenko 多重消除方案能够衰减声学反射数据中的所有内部多重反射。这些可以在补偿或不补偿得到的初级反射数据集中的双向传输效应的情况下实现。这些方法是完全自动化的，无需人工干预即可运行，但它们需要对数据进行适当的采样和预处理。即使当几个主要反射在数据中不可见时，因为它们被重叠的多次波掩蔽，例如在谐振楔模型中，所有丢失的主要反射都会以适当的幅度恢复和恢复。在对传输效应进行补偿和不进行补偿的情况下，对多次消除后初级反射的幅度进行研究表明，在恒速模型中可以适当地考虑传输效应。当层厚度是源小波主频率处波长的四分之一时，这些方法将停止正常工作。全波场偏移依赖于速度模型并运行非线性反演以获得反射率模型，从而产生偏移图像。在谐振楔形模型中被多次干扰掩盖的初级反射不会被恢复。在这种情况下，即使数据拟合是最佳的，最小化数据错配函数也会导致不正确的反射器模型。这种方法对数据采样的要求比多重消除方案低得多，但即使有正确的速度模型，也容易陷入局部最小值。利用每种方法的优势的混合方法可能值得研究。