Investigation of the acoustic properties of underwater rainfall noise measured by a bottom-mounted hydrophone and its application to deep learning to classify and estimate rainfall

Abstract

Although the ocean covers more than 70 % of the Earth's surface, it is difficult to measure precipitation over the ocean due to the technical limitations of platforms such as surface buoys and satellites. Recently, the estimation of precipitation has been tried using the underwater sound generated by rainfall. In this talk, we present the results of comparing underwater noise and rainfall with and without precipitation using the underwater noise and rainfall data provided by OOI (Ocean Observatories Initiative, USA). The acoustic data was acquired by a bottom-moored hydrophone at a depth of 80 m off the coast of Oregon, and the rainfall data was measured from a rain gauge installed on a surface buoy. The 1-minute averaged power spectral densities for ambient noise were computed for 26 days from 5 - 30 April 2018, which were compared with rain rate and wind speed. Based on the comparison results, the presence or absence of rainfall was determined through a pattern recognition neural network, and finally, the amount of precipitation was estimated through multiple linear regression analysis.