Multistep autoregressive reconstruction of seismic records
Multistep autoregressive reconstruction of seismic records
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DOI：
10.1190/1.2771685
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发表时间：
2007-09
期刊：
影响因子：
3.3
通讯作者：
M. Naghizadeh;M. Sacchi
中科院分区：
文献类型：
--
作者：
M. Naghizadeh;M. Sacchi
F-x域中的线性预测滤波器被广泛用于对规则采样的数据进行内插。我们研究了利用线性预测滤波器在规则网格上重建不规则缺失数据的问题。我们提出了一种两阶段算法。首先，我们使用傅立叶方法(最小加权范数内插)重建数据频谱的未混叠部分。然后，从重构的低频中提取所有频率的预测滤波器。后者是通过多步自回归(MSAR)算法实现的。最后，使用这些预测滤波器在f-x域中重建完整的数据。用合成数据和野外数据算例验证了该方法的适用性。
Linear prediction filters in the f-x domain are widely used to interpolate regularly sampled data. We study the problem of reconstructing irregularly missing data on a regular grid using linear prediction filters. We propose a two-stage algorithm. First, we reconstruct the unaliased part of the data spectrum using a Fourier method (minimum-weighted norm interpolation). Then, prediction filters for all the frequencies are extracted from the reconstructed low frequencies. The latter is implemented via a multistep autoregressive (MSAR) algorithm. Finally, these prediction filters are used to reconstruct the complete data in the f-x domain. The applicability of the proposed method is examined using synthetic and field data examples.