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In conventional seismic processing, the classical algorithm of Hubral and Krey is routinely applied to extract an initial macrovelocity model that consists of a stack of homogeneous layers bounded by curved interfaces. Input for the algorithm are identified primary reflections together with normal moveout (NMO) velocities, as derived from a previous velocity analysis conducted on common midpoint (CMP) data. This work presents a modified version of the Hubral and Krey algorithm that is designed to extend the original version in two ways, namely (a) it makes an advantageous use of previously obtained common-reflection-surface (CRS) attributes as its input and (b) it also allows for gradient layer velocities in depth. A new strategy to recover interfaces as optimized cubic splines is also proposed. Some synthetic examples are provided to illustrate and explain the implementation of the method. 相似文献
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When smoothing a function with high‐frequency noise by means of optimal cubic splines, it is often not clear how to choose the number of nodes. The more nodes are used, the closer the smoothed function will follow the noisy one. In this work, we show that more nodes mean a better approximation of Fourier coefficients for higher frequencies. Thus, the number of nodes can be determined by specifying a frequency up to which all Fourier coefficients must be preserved and increasing the number of nodes until this criterion is met. A comparison of the corresponding smoothing results with those obtained by filtering using moving average and moving median filters of corresponding length and a low pass with corresponding high‐cut frequency shows that optimal cubic splines yield better results as they preserve not only the desired low‐frequency band but also important high‐frequency characteristics. 相似文献
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