1. 4. Spectral Decomposition
AASPI
Overview of AASPI accomplishments and software development
Kurt J. Marfurt
Attribute-Assisted Seismic Processing and Interpretation

2. AASPI 2018 Sponsors AASPI Welcome 2012
Current AASPI sponsors. More details on our website mcee.ou.edu/aaspi

3. He’s not going anywhere
He now have more time for research (and documentation!)
He will work increasingly with students of fellow faculty
Heather Bedle has taken over his teaching duties
We will see more students from these two folks!

4. Postdocs Visiting Scholars
Current AASPI postdocs and visiting scholars. More details on our website mcee.ou.edu/aaspi

5. M.S. in machine learning
Current AASPI students. More details on our website mcee.ou.edu/aaspi
M.S. in data informatics

6. M.S. in data informatics
Current AASPI students. More details on our website mcee.ou.edu/aaspi

7. DISC2018 book:
This book written by Marfurt for his 2018 DISC tour is available for purchase from the SEG. AASPI users will recognize much of the mathematics (most of which are in the AASPI documentation) and many of the examples from former students and colleagues. If you don’t’ much about attributes, this is a good place to start.
Based on contributions from an average of 15 students/year over 20 years working ½ time – so built on 150 man/woman years of effort
Mathematical details appear in gray boxes

8. Recent graduates Xuan Qi (Ph.D.) to Petuum, Inc.
Lennon Infante (Ph.D.) to BP
Mohsen al-Ali (Ph.D.) to Saudi Aramco
Murphy Cassels (M.S.) to Haliburton
Lennon Infante (M.S.) to OU PhD program
Tobi Olorunsola (M.S.) to U. Arkansas PhD program
Kara Rohan (M.S) at Devon Energy
2018 AASPI theses and dissertations. You can download these publications from our website mcee.ou.edu/aaspi

9. 2018 Publications
2018 AASPI publications. You can download these publications from our website mcee.ou.edu/aaspi
Plus 11 expanded abstracts and 7 AAPG Explorer geophysical corner notes

10. New AASPI Software Capabilities
New AASPI Algorithms: June 2018
Application Name
Application Description
Location
Software Documentation
AASPI References
ica3d
Independent component analysis. Unlike principal component analysis, which constructs orthogonal components that best represent the data in a mathematical sense, ICA attempts to separate different patterns of signal from each other and from coherent noise such as acquisition footprint using a principal of negentropy.
under aaspi_util > Attribute Correlation Tools tab
rfc3d
Random forest classification is one of the more promising decision tree supervised learning techniques that uses training data to identify similar patterns in the seismic data volume.
under aaspi_util> Attrubute Correlation Tools tab
in progress
iterative_fault_enhancemet workflow
Using iteratively energy-weighted directional Laplacian of a Gaussian filtering for fault enhancement, which rejects lateral dicontinuity anomalies, smooths faults, and enhances low incoherent anomalies.
under aaspi_util > AASPI Workflows tab
nonparallelism
Computation of the variance of the dip vector within an analysis window. This measure is complementary to reflector convergence and has shown some value in machine learning and supervised classification of chaotic features such as mass transport deposits and salt
under aaspi_util> Geometric Attributes tab
documentation in progress
New 2018 AASPI capabilities. Go to our website mcee.ou.edu/aaspi and click on the software documentation and references links.
New AASPI Algorithms: December 2018
Application Name
Application Description
Location
Software Documentation
AASPI References
pnn3d
Probalistic neural network supervised learning
under aaspi_util > Facies analysis tab

11. Geochronostratigraphic horizon mapping
Lots of challenges:
Algorithmic (for low S/N horizons)
Graphical interaction
Graphical display
Horizon formatting
Goal: Release a version by 12/31/2018

12. Initial steps in computing “well-log attributes”
Well log utilities
las import
las export
Kuwahara estimate of upward fining slopes
Plot well log
API/ft
No wells, Poor resolution area
Kuwahara filtering
We have begun building well log import/export utilities as well as the computation of well log attributes. Import and export capabilities will be there by 12/31/2018. Saurabh Sinha has prototyped Kuwahara and Expectation Maximization algorithms to quantify upward-fining and upward-coarsening sequences. Please see his poster and give him some feedback!
(Sinha and Pires de Lima, 2018)

13. Enhanced AASPI Software Capabilities
Enhanced AASPI Algorithms: June 2018
Application Name
Application Description
Software Documentation
AASPI References
curvature3d
Vector aberrancy scaling now in world coordinates ;
dip3d
Signficantly improved the gradient structure tensor estimate of dip computation. For data with moderate dips, the results between this technique and the previous semblance search technique are comparable. The GST based algorithm is signficantly faster for data exhibiting steep dip. In addition to a measure of confidence and the moderately useful chaos estimate, the GST algorithm also outputs a measure of planarity, call gst_similarity. As of January 2018, the gst algorithm is set to be the default algorithm. Because steep dips (associated with noise, such as internal to salt domes and mass transport complexes) may occur in the data, one should alway run filter_dip_components after dip computation to remove non-physical artifacts in the data; otherwise, these artifacts will be amplified by subsequent curvature and aberrancy calculations.
all algorithms
We now support the Petrel ZGY-format when running under the Windows environment. To use this format internally, you must set an appropriate parameter under the aaspi_default_parameters tab and place a checkmark in front of "Output binary data as ZGY format". Because of scaling problems, at present, we only support 32-bit ZGY format. Attributes with well-known ranges (e.g. coherence, frequency, phase, azimuth, dip, will be saved as 8-bit volumes in the future)
spec_cwt
A significant rewrite of the documentation. Implementation of Matos' (2011) inverse continuous wavelet deconvolution bandwidth extension algorithm. Successful application requires (1) internally interpolating the data to a finer sample increment, sampling the spectrum by 5 or more frequencies per octave, and (3) selecting appropriate bandwidths for the initial decomposition and subsequent bandwidth extension. Good defaults are provided by the GUI.
filter_single_attribute
Modified to allow subtraction of a filtered volume from the original data. This allows us to "enhance" coherence anomalies subparallel to reflector dip using an alpha-trim mean or LUM filter and subtract it from the original data, leaving more steeply dipping faults in the resulting image.
fault_enhancement
Coherence or other edge attributes can now be weighted by the energy, envelope, or other amplitude measure, thereby more heavily weighting discontinuities that occur at the peak energy part of the wavelet. Such weighting further diminishes the stairstep artifacts seen on vertical slices through coherence. The 2nd moment tensor used in defining the orientation of a hypothesized fault is now computed about the center of mass within the analysis window, rather than about the center of the analysis window itself
kuwahara3d
Now (optionally) allows a time-variant adaptive window size that increases with decreasing average spectral content of the survey computed in time or depth.
most poststack algorithms
Now print most errors such that they appear in a pop-up window as well as in the *.out file
cigar_probe
Now allows multiple input volumes, simplifying the workflow
Enhanced 2018 AASPI capabilities. Go to our website mcee.ou.edu/aaspi and click on the software documentation and references links.

14. Spectral decomposition limited to zone between two picked surfaces
Release spec_cwt by 12/15/2018

15. Today’s program