Singular value decomposition filtering for enhanced signal extraction from two-dimensional beam emission spectroscopy measurements

The signal-to-noise ratio (SNR) of extracted turbulence features from beam emission spectroscopy (BES) data is significantly enhanced via application of singular value decomposition (SVD) methods. BES measures two-dimensional localized density fluctuations in DIII-D. The SNR of core turbulence characteristics is typically limited by noise arising from electronic noise, photon noise, and fluctuations in the observed neutral beam. SVD filtering has led to a significant enhancement in the SNR, reducing errors in time-resolved measurements of core turbulence characteristics, including correlation lengths, decorrelation rates, and group velocities. The SVD filtration technique is applied to BES data by combining multiple physically adjacent sampling locations to extract spatially correlated signals while partially removing unwanted incoherent noise. Using approximately half of the singular value weighted modes to reconstruct turbulence signals is found to improve SNR by up to a factor of 4, while maintaining the spatial structure of the turbulence. Unique aspects of application of SVD to broadband turbulence data are discussed.