Dealing with increasing data volumes and decreasing resources

Depner, Jan; Reed, Barbara; Byrne, Simon; Parker, J.; Paton, Mark; Gee, Lindsay; Mayer, Larry A.; Ware, Colin

doi:10.1109/oceans.2002.1192139

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…This approach has proven useful for evaluating newly installed or upgraded systems prior to starting survey opera tions and for assessing the suitability of all correc tions that have been applied to the data during sur vey operations. [1] The sounding reduction algorithm uses an areabased approach to reduce the full dataset to a sub set that preserves the significant seafloor features using shoal-biased down-sampling. Soundings that have been selected are highlighted in the displays allowing for detailed review and QC against all neighbouring data points.…”

Section: Shipboard Processing Analysis and Quality Controlmentioning

confidence: 99%

“…The 30 years of multibeam data prior to 1997 barely registers on the graph! [1] NAVOCEANO's survey fleet conducts world-wide survey operations. Our approach for most effi ciently generating products and information from this survey data includes; (1) the integration of onboard systems to centralise operation, data management and QC, (2) the integration of pro cessing and Quality Assurance (QA) into the real time data acquisition environment, (3) the automa tion of historically labour intensive processing with integrated quality assurance practices and (4) the smart data-basing of the information produced, while maintaining an effective approach for storing the full-resolution supporting datasets.…”

Section: Introductionmentioning

confidence: 99%

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Space

2013

Encyclopedia of Military Science

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Section: Shipboard Processing Analysis and Quality Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space

2013

Encyclopedia of Military Science

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“…Under current NAVOCEANO procedures, the major blocks of the hydrographic workflow are: (1) defini tion of survey requirements, (2) creation of project plan, (3) definition of survey collectables, (4) col lection of survey data, (5) ingestion of survey data, (6) validation of data, (7) creation of products and (8) delivery of products. The objective of this paper is to provide an overview of the workflow to pro duce hydrographic data products using NAV-OCEANO's currently deployed capabilities.…”

Section: -H -Ph T-h I-h V-h T-h T-hmentioning

confidence: 99%

Russia

2013

Encyclopedia of Military Science

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“…This flagging paradigm may not be the best solution; a tool that interacts with CUBE's multiple hypothesis space and allows the user to remove, merge, or nominate hypotheses may be more efficient. Such tools are currently in development [ Depner et al , 2002].…”

Section: Theorymentioning

confidence: 99%

Automatic processing of high‐rate, high‐density multibeam echosounder data

Calder

Mayer

2003

Geochem Geophys Geosyst

202

173

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[1] Multibeam echosounders (MBES) are currently the best way to determine the bathymetry of large regions of the seabed with high accuracy. They are becoming the standard instrument for hydrographic surveying and are also used in geological studies, mineral exploration and scientific investigation of the earth's crustal deformations and life cycle. The significantly increased data density provided by an MBES has significant advantages in accurately delineating the morphology of the seabed, but comes with the attendant disadvantage of having to handle and process a much greater volume of data. Current data processing approaches typically involve (computer aided) human inspection of all data, with timeconsuming and subjective assessment of all data points. As data rates increase with each new generation of instrument and required turn-around times decrease, manual approaches become unwieldy and automatic methods of processing essential. We propose a new method for automatically processing MBES data that attempts to address concerns of efficiency, objectivity, robustness and accuracy. The method attributes each sounding with an estimate of vertical and horizontal error, and then uses a model of information propagation to transfer information about the depth from each sounding to its local neighborhood. Embedded in the survey area are estimation nodes that aim to determine the true depth at an absolutely defined location, along with its associated uncertainty. As soon as soundings are made available, the nodes independently assimilate propagated information to form depth hypotheses which are then tracked and updated on-line as more data is gathered. Consequently, we can extract at any time a ''current-best'' estimate for all nodes, plus co-located uncertainties and other metrics. The method can assimilate data from multiple surveys, multiple instruments or repeated passes of the same instrument in real-time as data is being gathered. The data assimilation scheme is sufficiently robust to deal with typical survey echosounder errors. Robustness is improved by pre-conditioning the data, and allowing the depth model to be incrementally defined. A model monitoring scheme ensures that inconsistent data are maintained as separate but internally consistent depth hypotheses. A disambiguation of these competing hypotheses is only carried out when required by the user. The algorithm has a low memory footprint, runs faster than data can currently be gathered, and is suitable for real-time use. We call this algorithm CUBE (Combined Uncertainty and Bathymetry Estimator). We illustrate CUBE on two data sets gathered in shallow water with different instruments and for different purposes. We show that the algorithm is robust to even gross failure modes, and reliably processes the vast majority of the data. In both cases, we confirm that the estimates made by CUBE are statistically similar to those generated by hand.

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Dealing with increasing data volumes and decreasing resources

Cited by 5 publications

References 4 publications

Space

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Russia

Automatic processing of high‐rate, high‐density multibeam echosounder data

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