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.
What you'll learn
understand the mathematics behind seismic data processing
learn various methods of seismic data enhancement
develop skills in building an optimal processing sequence
apply attribute analysis techniques to optimize data processing
Course objectives
to equip learners with knowledge of advanced seismic data processing techniques
to enable students to apply various diagnostic methods for data quality improvement
to prepare participants to select appropriate software for different data sets