Acoustic noise measurements of MBARI's Ventana ROV

This technical memorandum reports on the noise measurement results performed on MBARI's Ventana ROV. The measurement procedure and the instrumentation for this experiment are also described. This report is organized as follows: Section 1 provides some introductory information. Section 2 describes the experiment and the instrumentation. Section 3 presents the results. Section 4 contains some concluding remarks. (PDF contains 16 pages.)

Noise levels and sources in the Stellwagen Bank National Marine Sanctuary and the St. Lawrence River Estuary

Although ambient (background) noise in the ocean is a topic that has been widely studied since pre-World War II, the effects of noise on marine organisms has only been a focus of concern for the last 25 years. The main point of concern has been the potential of noise to affect the health and behavior of marine mammals. The Stellwagen Bank National Marine Sanctuary (SBNMS) is a site where the degradation of habitat due to increasing noise levels is a concern because it is a feeding ground and summer haven for numerous species of marine mammals. Ambient noise in the ocean is defined as “the part of the total noise background observed with an omnidirectional hydrophone.” It is an inherent characteristic of the medium having no specific point source. Ambient noise is comprised of a number of components that contribute to the “noise level” in varying degrees depending on where the noise is being measured. This report describes the current understanding of ambient noise and existing levels in the Stellwagen Bank National Marine Sanctuary. (PDF contains 32 pages.)

Removing observational noise from fisheries-independent time series data using ARIMA models

Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.