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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-seismic-data-processing/
课程评论：没有评论
**课程名称：高级地震数据处理 (Advanced Seismic Data Processing)** **课程概述：** 本课程将提供一个从现场数据到解释者使用的叠加数据的一步步的详细讲解。课程涵盖信号处理、采样和重采样，以确保最佳数据质量。重点介绍地震数据处理的数学基础，如傅里叶变换。随后，将深入探讨地震数据处理的关键步骤，并详细讨论各种去卷积技术，包括逆Q滤波。课程还将详细介绍折射静校正的步骤、质量控制示例以及三维案例。此外，还将讲解视横向速度随时间变化（Apparent an-isotropic move-out）以及多种多次波压制方法，包括新技术、Radon变换和高分辨率Radon变换。课程将讨论Kirchhoff叠加和有限差分偏移方法。学员将学会如何构建最优的处理序列，以运用最新技术获得最佳数据质量。属性分析也将被纳入课程，以帮助解释者优化处理序列。成功的处理依赖于为特定数据集选择合适的程序和参数。课程还将讨论多种诊断程序，如速度和频率分析，以及自相关，这些方法有助于选择数据增强程序及其参数。 **核心内容（未列出详细大纲）：** * 信号处理、采样与重采样 * 地震数据处理的数学基础：傅里叶变换 * 关键的数据处理步骤 * 去卷积技术（含逆Q滤波） * 折射静校正（含质量控制和三维案例） * 视横向速度随时间变化 * 多次波压制技术（含Radon变换和高分辨率Radon变换） * Kirchhoff 叠加和有限差分偏移 * 构建最优处理序列 * 属性分析 * 速度分析、频率分析和自相关
Programs are usually used in a processing sequence and are selected from a library of several hundred programs.The seismic data processing course provided a step-by-step breakdown ranging from field data to the stacked data used by the interpreter.This course will cover signal processing, sampling, and resampling in order to ensure optimum data quality. The mathematics of seismic data processing, Fourier Transformation will be presented. This will be followed by the most important steps details of seismic data processing, then deconvolution types will be discussed in detail including inverse Q filtering. Refraction statics steps with full details and quality control examples will be presented in detail with 3D examples. Apparent an-isotropic move-out will be presented, several methods of multiple attenuation will be covered including the new techniques, radon, and high-resolution radon. The Kirchhoff summation and finite-difference migration methods will be discussed. The participants will then learn how to build an optimum processing sequence, in order to obtain the best data quality using the latest techniques. Attribute analysis will be also included in this course to help the interpreters optimize the processing sequence.Successful processing requires selecting the appropriate programs and parameters for a given set of data. Several diagnostic programs can be used to reveal details - velocity and frequency content, for example, which help in the choice of data enhancement programs and their parameters. Velocity analysis, frequency analysis, and aut-ocorrelation are frequently used and will be discussed.
