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40 RPT transfer velocity model made from velocity cube/property. 33 Trace AGC (iterative) and RMS (iterative) attributes. 20 Convert to multi-Z interpretation: inline/crossline increment. 20 Contextual tab for editable triangle mesh object.
#Structure oriented filter seismic rokdoc free
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#Structure oriented filter seismic rokdoc software
Similarly, you are advised that the software should be operated in a secure environment whether such software is operated across a network, on a single system and/or on a plurality of systems. You are advised that such minimum specifications are merely recommendations and not intended to be limiting to configurations that may be used to operate the software.
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The software described herein is configured to operate with at least the minimum specifications set out by Schlumberger. An asterisk (*) is used throughout this document to designate a mark of Schlumberger. Other company, product, and service names are the properties of their respective owners. In addition, covers, page headers, custom graphics, icons, and other design elements may be service marks, trademarks, and/or trade dress of Schlumberger, and may not be copied, imitated, or used, in whole or in part, without the express prior written permission of Schlumberger.
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These marks may not be copied, imitated or used, in whole or in part, without the express prior written permission of Schlumberger. Trademarks & Service Marks Schlumberger, the Schlumberger logotype, and other words or symbols used to identify the products and services described herein are either trademarks, trade names or service marks of Schlumberger and its licensors, or are the property of their respective owners. The method, called spectral SOF (SSOF), allows us to enhance the signal structures in the f - x - y domain by running a 1D edge-preserving filter along curvilinear self-adaptive trajectories. This work contains the confidential and proprietary trade secrets of Schlumberger and may not be copied or stored in an information retrieval system, transferred, used, distributed, translated or retransmitted in any form or by any means, electronic or mechanical, in whole or in part, without the express written permission of the copyright owner. We have developed an algorithm to perform structure-oriented filtering (SOF) in 3D seismic data by learning the data structure in the frequency domain. The results indicate that random noise, footprints, and other artifacts can be successfully suppressed, enhancing the delineation of geologic structures and seismic horizons and preserving the original signal bandwidth.Copyright Notice Copyright © 2016 Schlumberger. Finally, we analyze the Teapot stacked and depth-migrated subsets to show random and coherent noise removal, leading to an improvement of the volume structural details and overall lateral continuity. Then, we use the Penobscot subset to illustrate random noise and footprint signature attenuation, as well as to show how faults and fractures are improved. We use the Waipuku subset to indicate the signal preservation of the method in good-quality data when mostly background random noise is present. We determine the performance of SSOF using three public domain field data sets, which are subsets of the well-known Waipuku, Penobscot, and Teapot surveys.
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In contrast to other SOF techniques, such as anisotropic diffusion, anisotropic smoothing, and plane-wave prediction, SSOF does not require any iterative process to reach the denoised result. It is able to process a 3D data volume with a 2D strategy using basic 1D edge-preserving filters. SSOF relies on a few parameters that are easily tuned and on simple 1D convolutions for tensor calculation and smoothing. These self-adaptive paths are given by the eigenvectors of the smoothed structure tensor, which are easily computed using closed-form expressions. The method, called spectral SOF (SSOF), allows us to enhance the signal structures in the f- x- y domain by running a 1D edge-preserving filter along curvilinear self-adaptive trajectories that connect points of similar characteristics. We have developed an algorithm to perform structure-oriented filtering (SOF) in 3D seismic data by learning the data structure in the frequency domain.