Laboratory Observations of Artificial Sand and Oil Agglomerates Video and Velocity Data: Sea Floor Interaction Experiment Interpretive Video Previews

For experiment A, the movable sand bed was initially leveled. The aSOAs of diameter 0.5 cm, 1.0 cm, and 2.5 cm, were placed in order of class size in two rows. One row was placed proud (i.e. not partially buried) on the bottom, while the second row of aSOAs were placed partially buried with only the tops were exposed.
 The initial conditions of Experiment B were simply the final conditions of Experiment A. The bed was not leveled. The aSOAs ranging in diameter from 0.5 cm, 1 cm and 2.5cm were deployed sitting proud on the sand bed. A GoPro camera was also deployed in the trough of one bed ripple to provide a unique perspective of aSOA motion and sand bed evolution.
 The initial conditions of Experiment C utilized the final conditions of Experiment B. The bed was not re-leveled. The aSOAs ranging in diameter from 5 cm to 10 cm were deployed sitting proud on the sand bed. The GoPro camera was removed from the small-oscillatory flow tunnel.
 For experiment D, the movable sand bed was initially leveled. All previously deployed aSOA were removed. The aSOAs were deployed in two groupings which simulated recently broken up SOA mats. One grouping was arranged in a tight group, while the second was arranged loosely.

First, velocities were first passed through a MATLAB built-in one dimensional smoothing filter (filter.m) with a window size of ten.
Second, the difference was taken between the raw and smoothed time series and the standard deviation of that difference was calculated.
Third, points with a difference greater than three times the standard deviation were identified, and replaced with the smoothed signal value at the same time.
The first, second, and third, steps were then repeated three times, with the previously de-spiked signal replacing the input each time, which produced final, quality controlled, de-spiked and smoothed de-spiked time series of velocities.

A measure of signal quality, called Correlation, was recorded by the Vectrino profiler along 	with the raw data. Correlation is the measure of the data correlation as returned by each of the three acoustic beams of the profiler.
Time points with correlation less than 70% are replaced in the record with non-number (NaN) values.

A single JPEG from the video record is read using 'imread.'
The single JPEG is rotated 180 degrees, to correct the image orientation.
The overall figure is created using “figure.”
The single JPEG from the video record is plotted within the overall figure using “subplot,” and “imshow.”
The graph of velocity, and indicator of current time was plotted using “subplot,” and “plot.”
The current overall figure is collated as a video frame in a sequence of frames using MATLAB built-in function “getframe.”
The interpretive video product “.avi” file, has one frame added to it using MATLAB built-in function “WriteVideo” to add the frame created using “getframe.”