DOI: 10.6084/m9.figshare.12722540
title: ADEON AR040 Fine Scale Acoustic Survey (FSAS) binned into cells of 100 m horizontal and 5 m vertical.

description: At each survey site, a fine-scale acoustic grid was conducted at a speed of 8 kn. Survey lines were adjusted for the direction of the sea state. At a few sites, the survey grid was run multiple times, either during the day and then the night, or separated by several days or weeks. <br>A total of thirteen fine scale acoustic surveys (FSAS) were conducted on the AR040 cruise. Each FSAS was composed of 3 to 9 approximately 10km long transects, running across an approximately 10km<sup>2 </sup>area centered on the lander at seven of the ADEON sites. All FSAS data from the AR040 cruise have been processed using Echoview software, as described below. Data for all frequencies were collected using an EK80 echosounder system. For each site (VAC, HAT, etc.), water column sound speed, temperature, and salinity were edited to reflect the mean midwater environmental parameters derived from conductivity, temperature, and depth (CTD) profile data. Transducer depth was set to 3.96 m (13 feet) below the surface, the depth of the R/V Armstrong’s hull. A surface exclusion line was placed at 5 to 10m depth for all files and subsequently adjusted to ensure any backscatter from surface bubble intrusion were excluded. For all files, bottom-detection lines were automatically generated using the lowest-frequency data (18 kHz). These bottom lines were visually examined and edited for errors and then applied to data from all other frequencies. A bottom offset exclusion line was generated at one meter above the bottom line, to help ensure no backscatter from the seafloor was included in the water column data. The removal of ambient, background, and self-noise was a multistep process conducted in Echoview. Once areas above the surface line and below the bottom line were excluded, time-varied gain (TVG) noise was removed using the data generator operand to virtually generate TVG noise stripes for each frequency based on the S<sub>V</sub> value at 1 m. The S<sub>V</sub> value at 1 m was determined by scrutiny of the TVG evident on passive noise files, with adjustments based on the data files. For the AR040 data, the S<sub>V</sub> values at 1 m were: -130 dB for 18kHz data, -138 dB for 38 kHz, -132 for 70 kHz, -125 dB for 120 kHz, and -121 dB for 200 kHz. This variable allowed much of the TVG noise to be subtracted from the data via a linear minus and a transient noise removal operator. Once the noise spikes were removed, a background noise removal algorithm was applied with a maximum noise threshold of -125 dB and a minimum SNR of 10, as described in De Robertis & Higginbottom (2007)⁠. A final 5x5 median filter was applied to remove any remaining background noise sources. Before removing noise for each wideband frequency, the data were converted to base Sv using a type conversion operator, so that the background noise removal algorithms could be applied. In some files, the above background noise removal process was unable to remove certain sources of “bad data,” such as engine noise. In such cases, the noise was removed manually by defining regions of “bad data” that were then excluded from export. The acoustic data were binned into cells of 100 m horizontally and 5 m vertically for final exportation to .csv files.<br>This dataset has a readme file.<br>See https://adeon.unh.edu/cruise for more information about the project