Automated Seabed Mapping and Data Delivery in the Cloud

Transcription

1 Automated Seabed Mapping and Data Delivery in the Cloud London, 2nd November /20/2017 1

2 2 WE CALL IT SEABED INTELLIGENCE Ocean Infinity are explorers. We go to unmapped locations to survey the seabed using the most advanced fleet of autonomous vehicles in the world.

3 MAPPING THE DEEP Black areas represent areas of seabed below 6000m, the remaining seabed region is within the operating range of Ocean Infinity 3

4 4 MONTHS AGO 4

5 SEABED CONSTRUCTOR LAYOUT 5

6 6

7 SURVEY EQUIPMENT 7

8 AUV SHOALING 8

9 Autonomous Underwater Vehicle The main survey system for seabed mapping HUGIN

10 SENSORS Multi Beam Echosounder (Kongsberg Maritime EM 2040) Side Scan Sonar (EdgeTech 75, 225 and 410 khz Sub-bottom Profiler (EdgeTech DW- 216) Camera (CathX Ocean Still Color) Conductivity/Temperature/Depth (SAIV) Self Compensating Magnetometer (Ocean Floor Geophysics SCM) Optional: Turbidity Sensor Methane Sensor Cathx Laser 10

11 AUV DATA TRANSFER 11

12 APPLICATIONS Geophysical Seabed Mapping Cable Route Surveys Decommisioning Surveys Benthic Habitat Mapping Archeological Survey Marine Debris Search Pipeline Inspection Survey / Asset Integrity Surveys Environmental mapping 12

13 OPERATIONAL STATUS 13

14 OPERATIONAL STATUS Performed 4 projects so far with the AUV solution: 1st project was the size of the Faroe Islands or Singapore, this was surveyed in 9 days, 1700km 2. 2nd project, area surveyed was 1440km 2,18x AUV dives 6 AUV s surveying simultaneously Area surveyed last month 5944 km 2 Planned installation of hull mounted MBES December

15 UN-AIDED OPERATIONS 15

16 MAGNETOMETER DATA 16

17 CathX Rectified Subsea Images 17

18 CathX Photo Mosaic 18

19 STANDARDISATION & AUTOMATION 19

20 NOT 20

21 BIG DATA Being big is not necessary! Can involve a high Variety of data from complex sources Ability to analyse unstructured and structured data together to generate incite High Velocity streaming data Velocity, Variety and Volume 21

22 DATA PROCESSING Each AUV per 24 hours: 1 TB digital data 430,000 still images (max.) Processing packages: Delph ixblue EIVA NaviSuite ESRI ArcGIS CathX Deliverables: SSDM format GIS data.bag compatibility Mosaic raster catalogs Field Reports Turnaround production: Preliminary results within 24 hours after data on disk. 22

23 AUTOMATIC DATA PROCESSING Virtual machines set up on 7 workplaces 3-5 monitors depending on use One virtual macine workplace is for the Eiva WFM user interface This in turn remote control one or more additional virtual machines. One data processor on shift dedicated to navigation and MBE basic processing only. Record so far is processing of 3 auv dives in less than one 12h shift. Total of 125h of data. 23

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25 GIS DELIVERABLES Data Transferred from the Vessel Data is prepared on the vessel then transferred onshore for upload to the Cloud. This data comprises of 4 core components: 1. Mosaic geodatabase Side Scan Sonar TIFFs, Back scatter TIFFs, Navimodel depth TIFFs 2. Sub bottom profile geodatabase SEG-Y files (generated by Geocap extension) 3. SSDM geodatabase AUV tracks, run lines, target listings, survey key sheets 4. CathX georeferenced still images TIFFs 25

26 OFFSHORE GIS WORKFLOWS Scripts developed by Geodata to populate Geodatabases offshore Data Reviewer used for QC 26

27 WORKING WITH MOSAICS A source mosaic is created for each AUV dive Dive mosaics are combined into a single derived mosaic dataset, per data type (the project mosaic) 27

28 MOSAICS AND COVERAGES TOOL 28

29 AUTOMATION IN VECTOR DATA CREATION Dive footprint polygons created 29

30 EXAMPLES OF PROBLEMS Errors with footprints and boundaries for Back Scatter data: 30

31 OFFSETS BETWEEN TIFF AND TRACKS 31

32 MISSING TIFFS 32

33 COVERAGE AND INFILL AREAS GENERATED AUTOMATICALLY FROM THE DATA 33

34 ONSHORE DATA MANAGEMENT AND DELIVERY 34

35 ArcGIS Server 35

36 WHERE TO STORE OUR BIG DATA?

37 WHY AUTOMATE OUR ONSHORE WORKFLOW? Manual Necessary to establish a clear process manual Prone to human error Tacit knowledge held by employee (single point failure) Difficult to scale environment to ensure performance Costly to run Automated Reduce Risk Ensures consistent data Error can be detected Enables scaling to handle workload Reduces costs o Use AWS spot resources o Reduce manpower 37

38 DATA SHARING Allow the data owner to retain the responsibility for their data and its maintenance, as well as the technological mechanisms to share it efficiently with others. 38

39 Thank you for your attention Any Questions Ocean Infinity Inc 1400 Broadfield Blvd, Suite 200, Houston, Texas, Ocean Infinity Limited First Floor, 6 Grosvenor Street, London W1K 4PZ +44 (0)