Despiking Acoustic Doppler Velocimeter Data

Abstract

A new method for detecting spikes in acoustic Doppler velocimeter data sequences is suggested. The method combines three concepts: ͑1͒ that differentiation enhances the high frequency portion of a signal, ͑2͒ that the expected maximum of a random series is given by the Universal threshold, and ͑3͒ that good data cluster in a dense cloud in phase space or Poincaré maps. These concepts are used to construct an ellipsoid in three-dimensional phase space, then points lying outside the ellipsoid are designated as spikes. The new method is shown to have superior performance to various other methods and it has the added advantage that it requires no parameters. Several methods for replacing sequences of spurious data are presented. A polynomial fitted to good data on either side of the spike event, then interpolated across the event, is preferred by the authors.

Space–time measurements of oceanic sea states

“…A solution accounting for the sun position with respect to the stereo system could alleviate this drawback, but it is beyond the goal of this study. To remove unwanted spikes (outlier) from disparities we used the algorithm described in Goring and Nikora (2002). We found that WASS estimates of wave parameters fairly agree with those from standard wave gauge instruments available at Acqua Alta (see Benetazzo et al 2012).…”

Measurement of turbulent flow in a narrow open channel

“…During the experiment, the beds were immobile as no movement of the bed materials was observed. The raw Vectrino data were despiked by the acceleration threshold method (Goring and Nikora, 2002), which was capable in identifying and substituting spikes. The threshold values were chosen in the ranges between 1-1.5 by trial and error (Dey and Das, 2012).…”

“…Before using the captured data to analyze for turbulent characteristics they should be free from spikes. Goring and Nikora (2002) is probably the most popular paper that provides the details of despiking the ADV data where several despiking algorithms are available. Later, some of other algorithms have been developed by Cea et al (2007), Parsheh et al (2010), Wright andBaas (2013), andMacVicar et al (2014) etc.…”

Effective energy controls on flocculation under various wave-current regimes

“…The instrument recorded current velocity and pressure at 16 Hz during 20-minutes each hour at the same times as the LISST allowing simultaneous measurements of both instruments. The noise in ADV data was removed using a despiking algorithm based on a three dimensional phase space method by Mori et al (2007) which is based on the method by Goring and Nikora (2002). We apply time-averaging of the 20 minute sampling period in order to obtain hourly values of turbulence statistics.…”