3-D seismic survey design by Gijs J. O. Vermeer

By Gijs J. O. Vermeer

An important component for winning 3-D seismic survey layout is a easy figuring out of the spatial homes of the seismic wavefield. those homes have been defined for 2-D seismic information in Seismic Wavefield Sampling by means of an analogous writer. This booklet extends the outline into the even more complicated box of three-D seismic facts. A bankruptcy on instructions for survey layout interprets conception into perform. a few case histories illustrate the thoughts. Noise suppression, solution, and imaging are mentioned intimately. Converted-wave survey layout is roofed in a separate bankruptcy. This publication presents crucial wisdom for any acquisition or processing geophysicist and is suggested to everybody facing 3D seismic info. An integrated CD-ROM additionally comprises an Acquisition layout Wizard and survey optimization software program.

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4 Density-Based Clustering Density-based clustering attempts to partition similar density-connected points into clusters of irregular shapes. The most commonly employed algorithms include density-based spatial clustering of applications with noise (DBSCAN), ordering points to identify the clustering structure (OPTICS), density-based clustering (DENCLUE), and CLIQUE. We illustrate this category of clustering by presenting the DBSCAN algorithm as follows: The DBSCAN algorithm (Ester et al. 1996) groups data points into ­clusters that have a higher density than a threshold value (MinPts) within a window of a specified size defined by the distance to the data point (Eps).

4) where wi and ci are the weight and center vector for neuron i, and n is the ­number of neurons in the hidden layer. Typically, the center vectors can be found by using the k-means or k-nearest neighbor (KNN) method. The norm function can be Euclidean distance, and the transfer function Tf can be the Gaussian function. The clustering method is SOM ANN. 4. The ANN methods have advantages in classifying or predicting latent variables that are difficult to measure, in solving nonlinear classification problems, and in solving problems that are insensitive to outliers.

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