Following our launch at this year's SEG Expo, we would like to welcome you to the first newsletter since OpenCPS became Shearwater Reveal.
This is a periodic newsletter that aims to highlight recent changes and additions to Shearwater Reveal. In this issue we discuss new tools added, a few updates to tools, and we introduce a new team member.
Shearwater's Big Reveal at SEG Houston
The Shearwater Team Celebrates a Successful SEG Expo in Houston
The Reveal team had a very successful 2017 SEG, complete with two technical abstracts, booth demos and in-booth one-on-one sessions.
Thank you to everyone who helped make 2017 a successful one!
As one exhibition ends, another approaches... be sure to meet with us at AAPG SEG ICE, London UK, in October.
Welcome to the team!
Camilo graduated from the University of Texas at Austin with bachelors in Physics and Mathematics. He started out as a trainee geophysicist in Geotrace Technologies before joining Dolphin Geophysical, where he worked in the Houston processing center since its inception. As a experienced user of Reveal, he is now joining the team as a research geophysicist.
He is excited to use his experience with Reveal to help with the continuing development.
New Analysis Tools
A new tool for removing coherent ground roll from data. It fits simultaneously the signal and ground roll using user-provided velocities and creates a model of the coherent component of the ground roll. The model is adaptively subtracted from the data using SRMESubtract.
Left: original data, Middle: data with ground roll removed using GroundRollPredict and SRMESubtract, Right: ground roll component removed by SRMESubtract.
A new tool to compute the 3D linear forward and inverse Radon transform. The tool is high resolution even with highly aliased data. Applications include interpolation, denoising and deghosting.
The figures below show a y-slice of a 3D volume with two linear events, FK transforms of the data, and and Py slices of 3D Tau-Px-Py transforms of the data.
The data are aliased in the y direction (2x decimation), and have successive degrees of decimation in the x direction. The Radon transform displays confirm that the transform is high resolution even in the face of 3x and 4x decimation in the x direction (along with aliasing in the y direction).
Upper row: Y slices of the data with X decimation (left: none, and 2x, 3x, 4x decimations).
Lower row: The corresponding 2D F-K transforms of the Y slices. (left: no aliasing, and successively more aliasing wraparound to the right).
Py slice of Tau-Px-Py transforms of the above data. Each plot is the transform corresponding to the degree of decimation in the previous display.
A new tool that checks to see if a trace is inside of a polygon. It is faster than SetPoly and also has an option to check multiple polygon files at once.
A new option for picking first breaks has been added. It is an auto-picker that uses a thresholded FFT ratio to find the location of the first break. When running in batch mode, an option to automatically assign refractors to the picks using machine learning is available. These assigned refractors can then be used in refraction statics.
Above: Interactive auto-picked first breaks
Below: auto-picked first breaks with refractor assignment
Snapping for Copied Velocity Picks
A new velocity picking option has been added to enable the copying of picks (via F6) such that they snap to the maximum semblance location within a user-specified window.
This update adds SRC_X, SRC_Y, REC_X, REC_Y and AZIMUTH headers to interpolated traces created by FourierRegularizeND. It uses original traces within a user defined radius to compute the new headers and it also updates the original traces’ headers to bin center.
An enhanced version of the Mix tool. In addition to allowing 3d volumes to be run over multiple nodes, the smoothing options have been expanded to include median and Gaussian smoothing. The new tool also accepts any sort instead of being restricted to smoothing over CMP or ILINE/XLINE volumes
New functionaility has been added to VelocityMLPicker to use machine learning to auto-pick an eta volume throughout a survey. Using an input VRMS and eta guide table, an eta value is picked for each specified location.
A C++ version of the Python tool HeaderFiltering.