Do you know how to really measure the quality of your data? And what to do to fix it?
The Seismic Data QC & Processing in PSPro training course will teach you new methods to quickly QC 3D datasets, identify processing problems, and solve them using Pre-Stack Pro’s rich Processing Toolkit. Learn techniques to improve event alignment, equalize bandwidth across partial stacks, and remove residual noise.
In 2 days you will:
Get an introduction to post-migration gather processing and QC – what ‘s stacked in your data, and can additional processing improve its quality?
Quantify data problems with Health Check routines.
Learn how to use Parabolic Radon Demultiple, 2D Random Noise Attenuation, and RMO to remove residual noise, improve event alignment, and generate more reliable data for quantitative interpretation.
Quantify data quality using QC attributes, spectral analysis, and generate frequency-matched volumes for inversion.
Who should attend:
Geoscientists who clean-up and condition gathers for sharper images and fluid discrimination.
Prospect generators interested in learning how pre-stack methods can identify problems in the data and improve them by processing.
PSPro Beginners (1 Day Getting Started Course recommended first)
Future users who are interested in learning about pre-stack geophysics, the importance of quality-assured data, and what you can do with it.
Our Getting Started course is a 1-Day introduction to Pre-Stack Pro and is offered before each 2-Day course as an optional add-on. For new users of Pre-Stack Pro, this Getting Started course provides the necessary knowledge for the 2-day courses.
The course is for new users and non-experts who are looking to broaden their understanding of the Pre-Stack Pro’s functionalities, or a refresher for current users. Participants create their own projects, import various data types, design and execute a basic post-migration data processing flow.
Following the course, participants should be able to generate their own Pre-Stack Pro projects and do prospect assessment on their own datasets.