OOI Sonar for Ecosystem Monitoring

Wu-Jung Lee edited this page Jan 8, 2017 · 5 revisions


The temporal and spatial occurrence of predator-prey interactions, and the associated biomass change across the food chain, are of central importance in the marine ecosystem. Compared to net-based sampling methods, sonar systems (echosounders) offer promising potentials for quantifying such interactions, by delivering synoptic observation of the whole water column at each ensonification (echogram; Fig. 1). The Ocean Observatories Initiative (OOI) recently deployed numerous such systems, with an ambitious goal of cross-trophic observation at a significantly longer time scale than previously possible. However, there are imminent challenges, since the traditional subjective and non-adaptive echo analysis methods are not effective for analyzing the continuous ocean observatories sonar data flow with limited biological ground truth information (e.g., species composition) at the majority of locations.

To overcome these challenges, we will develop a suite of data-driven machine learning and inverse methods for objective segmentation and interpretation of OOI sonar echo time series. This is a crucial step toward delivering foundational biological information to understand the marine ecosystems under the changing climate – a highlight of the OOI program. We will use multi-dimensional echo features, including the mean echo strengths, distribution of echo fluctuation, and their joint variation across frequency as data descriptors. Since these features are strong functions of the size and identity of marine organism aggregations, by learning patterns based on echo features, we parse the incoming sonar data stream into biologically meaningful groups (e.g., fish-zooplankton foraging assemblage) useful for ecological research. The analysis framework will be structured such that the data parsing rules are updated adaptively based on the temporal evolution of echoes in the data stream.


The overall goal of this project is to develop a suite of objective, automated processing methods to interpret water-column echosounder data for biological information. The specific goal during the Incubator is a proof-of-concept study using a more confined set of data (e.g., one season) and use the results as basis of future development and continuing collaboration. The key objectives are:

  1. Construct a pipeline to query and download data from the [OOI data repository] (http://oceanobservatories.org/data-portal/). We will contact the OOI data management team and see if this may be of use to them. The current target data set is from the [OOI Cabled Array nodes off Oregon Coast] (http://oceanobservatories.org/wp-content/uploads/2014/12/Cabled_Array_Map_2014-12-021.jpg).

  2. Convert existing sonar echo feature extraction code from Matlab into Python and use them to process the target data set. The features include the absolute values and the statistical distribution of echo amplitudes received from the same ensonified water volume at different frequencies.

  3. Identify and test a few library methods that may be useful for parsing echogram, compare performance, and identify areas that require algorithm improvements or implementation of new algorithms.

  4. Formulate the problem using structured deconvolution with dynamic constraints, make necessary changes and apply existing algorithms with help from Prof. Sasha Aravkin, and test and compare with results from library methods.

The longer term goals after the Incubator include:

  1. Interpret the parsed echogram into compositions of marine organisms via a combination of physics-based acoustic scattering models and inversion methods. This will be pursued in collaboration with scientists from the [NOAA Northwest Fisheries Science Center (NWFSC)] (https://www.nwfsc.noaa.gov/) who are familiar with the local fauna.

  2. Enable cloud computing for established processing algorithms and make the code available to a wider user base in the fisheries and biological oceanography community.

Success Criteria

We will use the following methods to evaluate the success of the developed algorithms:

  1. Apply the developed algorithms to a set of simulated data with patches of varying composition of organisms at different depths (known ground-truth) and examine the algorithm performance.

  2. Apply the developed algorithms to a set of real data which were "ground-truthed" using net-tows. I will contact scientists at NOAA NWFSC scientists to determine relevant data sections.

  3. Compare the outputs of the developed algorithms to those from conventional methods that employ empirically determined hard threshold.

Deliverable Schedule

  • Now-Jan 27: Getting the data cube ready for method development. The data cube is a 3D array in which the dimensions are 1) depth (echo time series from a single sonar ping), 2) time (across multiple pings), and 3) multi-frequency echo features.

  • Jan 27-Midterm: Explore and apply library methods for segmenting echogram based on the data cube.

  • Midterm-Final: Continue using library methods and develop new formulation using structured deconvolution for segmenting echogram. Investigate inversion methods for interpreting organism composition in the segmented echogram.

You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.
Press h to open a hovercard with more details.