{"id":1038,"date":"2009-09-06T05:54:09","date_gmt":"2009-09-06T09:54:09","guid":{"rendered":"http:\/\/mseas.net16.net\/?p=1038"},"modified":"2021-08-16T20:20:25","modified_gmt":"2021-08-17T00:20:25","slug":"acoustically-focused-adaptive-sampling-and-on-board-routing-for-marine-rapid-environmental-assessment","status":"publish","type":"post","link":"https:\/\/mseas.mit.edu\/?p=1038","title":{"rendered":"Acoustically Focused Adaptive Sampling and On-board Routing for Marine Rapid Environmental Assessment"},"content":{"rendered":"Variabilities in the coastal ocean environment span a wide range of spatial and temporal scales. From an\nacoustic viewpoint, the limited oceanographic measurements and today&#8217;s ocean computational capabilities\nare not always able to provide oceanic-acoustic predictions in high-resolution and with enough accuracy.\nAdaptive Rapid Environmental Assessment (AREA) is an adaptive sampling concept being developed in\nconnection with the emergence of Autonomous Ocean Sampling Networks and interdisciplinary ensemble\npredictions and adaptive sampling via Error Subspace Statistical Estimation (ESSE). By adaptively and\noptimally deploying in situ sampling resources and assimilating these data into coupled nested ocean and\nacoustic models, AREA can dramatically improve the estimation of ocean fields that matter for acoustic\npredictions. These concepts are outlined and a methodology is developed and illustrated based on the\nFocused Acoustic Forecasting-05 (FAF05) exercise in the northern Tyrrhenian sea. The methodology first\ncouples the data-assimilative environmental and acoustic propagation ensemble modeling. An adaptive\nsampling plan is then predicted, using the uncertainty of the acoustic predictions as input to an optimization\nscheme which finds the parameter values of autonomous sampling behaviors that optimally reduce this\nforecast of the acoustic uncertainty. To compute this reduction, the expected statistics of unknown data to be\nsampled by different candidate sampling behaviors are assimilated. The predicted-optimal parameter values\nare then fed to the sampling vehicles. A second adaptation of these parameters is ultimately carried out in the\nwater by the sampling vehicles using onboard routing, in response to the real ocean data that they acquire.\nThe autonomy architecture and algorithms used to implement this methodology are also described. Results\nfrom a number of real-time AREA simulations using data collected during the Focused Acoustic Forecasting\n(FAF05) exercise are presented and discussed for the case of a single Autonomous Underwater Vehicle (AUV).\nFor FAF05, the main AREA-ESSE application was the optimal tracking of the ocean thermocline based on\nocean-acoustic ensemble prediction, adaptive sampling plans for vertical Yo-Yo behaviors and subsequent\nonboard Yo-Yo routing.","protected":false},"excerpt":{"rendered":"<p>Variabilities in the coastal ocean environment span a wide range of spatial and temporal scales. From an acoustic viewpoint, the limited oceanographic measurements and today&#8217;s ocean computational capabilities are not always able to provide oceanic-acoustic predictions in high-resolution and with enough accuracy. Adaptive Rapid Environmental Assessment (AREA) is an adaptive sampling concept being developed in [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31,32,182,183,37,33,34,35,40,5,185,186,62,194,63,64,57,187],"tags":[],"class_list":["post-1038","post","type-post","status-publish","format-standard","hentry","category-uncertainty-quantification-and-reduced-order-modeling","category-numerical-ocean-modeling","category-learning-and-data-assimilation","category-science-of-autonomy","category-applications-to-ocean-dynamics","category-uncertainty-quantification-and-predictions","category-data-assimilation","category-optimal-path-planning","category-acoustical-physical-interactions","category-publications","category-adaptive-sampling","category-computational-acoustics","category-papers-in-refereed-journals-data-assimilation","category-papers-in-refereed-journals-adaptive-sampling","category-papers-in-refereed-journals-optimal-path-planning","category-papers-in-refereed-journals-uncertainty-quantification-and-predictions","category-papers-in-refereed-journals-acoustical-physical-interactions","category-papers-in-refereed-journals-computational-acoustics"],"_links":{"self":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1038"}],"version-history":[{"count":4,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":5722,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=\/wp\/v2\/posts\/1038\/revisions\/5722"}],"wp:attachment":[{"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mseas.mit.edu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}