Espresso: An Open-Source Software Tool for Visualizing and Analysing Multibeam Water-Column Data

There is currently a lack of tools for the rapid visualisation and analysis of multibeam water-column data. To address this gap, a software tool named Espresso has been developed at NIWA. Its main feature is the capability to echo-integrate the water-column data vertically and display the result in the manner of an “aerial shot”, allowing for rapid broadscale visualisation of georeferenced acoustic anomalies in the water-column across multiple files. Espresso is now open-source, licensed under MIT, maintained internationally, and available on GitHub. The software is coded in MATLAB and a compiled version is available for Windows.

Espresso is a lightweight tool with a focused set of features. It can read water-column data in the Kongsberg formats (.all/.wcd, and .kmall/.kmwcd) and Teledyne Reson format (.s7k). Data from a single ping can be visualised in the traditional “wedge” display, while multiple pings can be visualised stacked in range, depth, and vertically echo-integrated. It allows the parameterizable masking of data to be ignored, such as samples within a set distance from the seabed, from the outer beams, or within the innermost or outermost range. Espresso incorporates the “slant-range signal normalisation” algorithm (Schimel et al. 2020, doi:10.3390/rs12091371) to filter out specular artefacts. Echo-integration can be referenced to the water surface or to the seabed, with parameterizable limitations in depth or height above the seabed. The software also includes geo-picking tools for interpreters to record the location of acoustic anomalies of interest and export their information.

Espresso implements strategies to manage the typical high-volume of water-column data including memory-mapping the converted data, and parallel processing on machines disposing of a GPU. As a research software, Espresso still has some limitations, including the need for data conversion into its internal format and limited data capacity (depending on the available RAM), and thus is best seen as a complement, rather than a replacement, to commercial software for the analysis of water-column data. Despite these limitations, Espresso has already been used for several research projects, including detecting gas seeps and extracting water-column features for supervised classification approaches to habitat mapping.