Seabed classification and source localization with Gaussian processes and machine learning | Zendy

Christina Frederick | Zendy; Zoi-Heleni Michalopoulou | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Seabed classification and source localization with Gaussian processes and machine learning

Author(s) -

Christina Frederick,

Zoi-Heleni Michalopoulou

Publication year - 2022

Publication title -

jasa express letters

Language(s) - English

Resource type - Journals

ISSN - 2691-1191

DOI - 10.1121/10.0013365

Subject(s) - seabed , gaussian , range (aeronautics) , sampling (signal processing) , computer science , noise reduction , gaussian process , aperture (computer memory) , field (mathematics) , pattern recognition (psychology) , artificial intelligence , geology , remote sensing , machine learning , acoustics , computer vision , engineering , mathematics , oceanography , physics , filter (signal processing) , quantum mechanics , aerospace engineering , pure mathematics

Workshop '97 data are employed for seabed classification and source range estimation. The data are acoustic fields computed at vertically separated receivers for various ranges and different environments. Gaussian processes are applied for denoising the data and predicting the field at virtual receivers, sampling the water column densely within the array aperture. The enhanced fields are used in combination with machine learning to map the signals to one of 15 sediment-range classes (corresponding to three environments and five ranges). The classification results after using Gaussian processes for denoising are superior to those when noisy workshop data are employed.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research