Biological Noise Modeling for Active and Passive Sonar System Performance Predictions
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P.F.J. Lermusiaux, P.J. Haley, Jr., C. Mirabito Massachusetts Institute of Technology
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Project Summary Ongoing MIT-MSEAS Research Additional Links MSEAS Project-supported Publications Background Information
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 Image credit: ADEON/UNH |
 Image credit: NOAA |
| This research is sponsored by Applied Ocean Sciences. | |||
Project Summary
To provide biological noise predictions for passive and active performance predictions, one would ideally need to know where the marine mammals are and then predict where they are going to be. The inputs to the marine mammal behaviors are the future environment, especially where the food and ideal living conditions are going to be in the future, the state of the mammal (in a group, alone, healthy, sick, gestation, etc.) and a probabilistic model of the mammal intelligence to forecast what the mammal(s) may do including their vocalization. In this first phase, we will focus on predicting the future environment, especially the biogeochemical conditions using our MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS).
The specific objectives of the MIT-MSEAS component of the research are described below.
Background information is also available below.
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Ongoing MIT-MSEAS Research
Specific Objectives:
- Attend a 2-day focused seminar – “Defining scientific needs for biological noise prediction systems”
- Set up and run our MIT-MSEAS ocean physical-biogeochemical modeling system for February 2019, encompassing the greater Atlantic Outer Continental Shelf (OCS) region and the Atlantic Deepwater Ecosystem Observatory Network (ADEON) sites, specifically Jacksonville (JAX), Blake Escarpment (BLE), Savannah Deep (SAV), and Charleston Bump (CHB). Provide our data-driven modeling outputs to feed the model for marine mammal distribution.
- Evaluate methodology for extrapolating to global coverage
- Write report sections related to the ocean physical-biogeochemical model
Publications
MSEAS Projects-supported Publications
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Additional Links
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Background Information
Our research builds on many years of experience in multidisciplinary research fields and ocean applications. Our MSEAS software (MSEAS, 2010; Haley et al., 2015) has been used for fundamental research and for simulations and forecasts of fields and uncertainties in many regions of the world’s oceans (Lermusiaux et al., 2006; Leslie et al., 2008; Onken et al., 2003, 2008; Haley et al., 2009; Ramp et al., 2011; Gangopadhyay et al., 2011; Colin et al., 2013; Kelly and Lermusiaux, 2016; Lermusiaux et al., 2011, 2017a,b; Subramani et al., 2017a,b; Kulkarni et al., 2018; Gupta et al., 2019; Lermusiaux et al., 2019, Haley et al., 2023). Modeling capabilities include implicit two-way nesting/tiling for multiscale hydrostatic PE dynamics with a nonlinear free surface (Haley and Lermusiaux, 2010) and a high-order finite element code on unstructured grids for non-hydrostatic processes (Ueckermann and Lermusiaux, 2010, 2016; Foucart et al., 2021). The MSEAS subsystems that are of interest to this proposal include: initialization schemes (Haley et al., 2015); nested data-assimilative tidal prediction and inversion (Logutov and Lermusiaux, 2008); fast-marching objective analysis around complex topography (Agarwal and Lermusiaux, 2011); subgrid-scale models (e.g., Lermusiaux, 2001, 2006); advanced data assimilation (Lermusiaux, 1999, 2007); biogeochemical-ecosystem models (Besiktepe et al, 2003; Lermusiaux et al, 2004; Lermusiaux et al., 2011; Haley et al, 2020); fish modeling and Bayesian learning (Gupta et al., 2019); planning for underwater vehicles (Schofield et al., 2010; Lolla et al., 2014a,b; Lermusiaux et al., 2016; Subramani et al., 2017a,b, 2018; Subramani and Lermusiaux, 2019; Kulkarni and Lermusiaux, 2020; Doshi et al., 2023); adaptive sampling (Lermusiaux, 2007, 2017a,b; Heaney et al., 2007, 2016); and possibly reduced-order modeling systems for forecasting onboard UUVs/SUVs (Heuss et al., 2020; Ryu et al., 2021). MSEAS has been validated in numerous real-time forecasting exercises (see MSEAS - Sea Exercises). The most recent ones include NASCar (Lermusiaux et al., 2017a,b), FLEAT (Pan et al., 2021; Johnston et al., 2019a,b; Haley et al., 2024c-prep), Lagrangian transport studies for NSF-ALPHA (Kulkarni and Lermusiaux, 2019; Haley et al., 2024-prep), modeling for deep-sea sediment plumes from mining (Muñoz-Royo et al., 2021), probabilistic ocean forecasting for 3D underwater positioning (DARPA-POINT, Lermusiaux et al., 2020a), and prediction of subduction dynamics in the Alboran Sea (CALYPSO; Garcia-Jove et al., 2022; Aravind et al., 2023; Mirabito et al., 2024-prep). Applications of our MSEAS-PE system include ocean monitoring (Lermusiaux et al., 2007), real-time acoustic predictions (Xu et al., 2008; Lam et al., 2009; Lermusiaux et al., 2010; Duda et al., 2011; Lermusiaux et al., 2020a,b), biogeochemical-ecosystem predictions and environmental management (Besiktepe et al., 2003; Cossarini et al., 2009; Coulin et al., 2017), and relocatable rapid response (e.g., Rixen et al., 2012; De Dominicis et al., 2014).
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