Sea Technology

SEP 2017

The industry's recognized authority for design, engineering and application of equipment and services in the global ocean community

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Page 23 of 68 September 2017 / st 23 H ydrographic survey operations are evolving to allow multiple users to exist across a broader range of plat- forms within a single project. Large vessels are concurrently mapping alongside autonomous vessels collecting data sets that must be processed, merged and sent to chart. Issues arise due to the incorporation of a variety of software solu- tions within a single nonseamless (acquisition through de- liverable) workflow. This results in an accumulation of hu- man error, which leads to inaccurate final products and/or poor decisions with undesirable consequences. To improve accuracy, Quality Positioning Services (QPS) has automated the mundane and reduced human error by guiding users through a seamless hydrographic workflow. In addition to reducing processing error, the QPS workflow maximizes ef- ficiency by providing options for automated live processing, the distribution of large workloads and the merger of multi- user/multiplatform survey efforts. The production of high- quality products from high-resolution hydrographic data is a difficult task even for the experienced hydrographer. Despite field procedure improvements, the hydrographic workflow is complex since it requires a human to connect all the pieces to produce a final product. A paradigm shift is currently underway to isolate the human error in the mod- ern-day hydrographic workflow while maximizing advance- ments in computing technology to automate the mundane, error-prone tasks. Hydrographers across the globe, such as the ones at eTrac in San Rafael, California, have embraced this paradigm shift to improve efficiencies at every stage of the hydrographic workflow. This article outlines the key workflow improvements and the details driving them. Creating a Seamless Hydrographic Workflow Reducing Human Error by Increasing Automated Data-Processing Power By Chris Malzone Data flow diagram for vertical uncertainty component of the Hare-Godin-Mayer MBES uncertainty model (Calder, 2007).

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