Detection and characterization of CWA dumpsites
Transkrypt
Detection and characterization of CWA dumpsites
WP. 4 Detection and characterization of CWA dumpsites Zygmunt Klusek Ulf Olsson Stockholm 02.03.2013 Zygmunt Klusek & Ulf Olsson WP. 4 Detection and characterization of CWA dumpsites 0.2.03.2013 This page is intentionally left blank. 2 Contents: 1. Goals .................................................................................................................................................... 4 2. Area and survey .................................................................................................................................... 5 3. Results .................................................................................................................................................. 7 4. Expectations ....................................................................................................................................... 11 5. Summary ............................................................................................................................................. 12 3 Zygmunt Klusek & Ulf Olsson WP. 4 Detection and characterization of CWA dumpsites 0.2.03.2013 4 1. Goals The strategy of the hydroacoustic surveillance and assessment defined in the WP4 goals are – to find, detect and categorize targets on the sea bottom in the entire area under examination. The main goal of the bottom scanning effort, performed with the acoustic techniques, was focused on the chemical weapon munition detection with afterward mine-like targets categorization and cataloging. For the validation of the categorization performed on the basis of acoustic images by trained operators and assessment of technical condition of examined objects ROV missions were carried out. 5 Zygmunt Klusek & Ulf Olsson WP. 4 Detection and characterization of CWA dumpsites 0.2.03.2013 2. Area and Survey The presented results of surveillance and assessment are intended to provide preliminary information about the site, targets number, target detection and categorization with trained operators. The material should be analyzed at the next phase of the postprocessing, with computer aided methods to diminish the uncertainity in the targets classification. The C-Area with the total surface of 1760 km2 was divided into 40 sub-areas each with the size approximately 13.000 x 3-5.000 meter. The Multibeam (MBS) and Side Scan Sonar (SSS) activities performed in the frame of CHEMSEA WP 4 task covered the full area marked on maps as the explosives dump site. During the acoustic surveys data were collected using towing side-scan sonar from the bow and multibeam sonar. Survey tracks were chosen parallel west-east (east-west) direction in the southern part of the area, and north-south (south-north) in the central region as more practicable concerning bottom. The side-scan sonar system was towed at the height such that it was capable of detect an object on the sea bed with size about 1m at the outer range limits. Data were typically acquired up to the 140-meter range, or a little less, on either side of tracklines providing a swath of approximately 280 meters for each pass. Assuming that it is impracticable to register every feature on the sea bottom we determined the minimum size of feature which should be detected, catalogued and possibly searched for in particular area. So, the acoustic system was towed at a speed 6-7 knots and at pinging rate that allows a minimum of 3 to 4 pings per 1 meter of track distance. At some sites magnetic coverage was performed by the SMA (C-Area) and PNA (Gdansk Deep Area). The actions were carried out at the selected the most probably chemical weapon sites with the spacing 10 m which enabled full magnetic coverage. Despite the fact that the sonar scan range along each trackline did not provide usual full overlap of the survey area, it was decided at the start to neglect it in favor of keeping search costs down and to the look for targets distribution in the full area inside the mapped boundaries. In 8 southern sectors of the C-area (A,B,D,E,G,H,L,K) the side scan sonar operations were performed also by “Oceania” to provide full coverage for further statistical prognosis. The entire survey operation performed by the SMA (“Baltica”) lasted 130 days, consisting of surveys of 19, 61 and 50days long. The productivity of whole operations in the C-Area encompasses following: 35% of the ship time included the MBS, SSS and ROV activities, 36% of the ship time weather related interruptions, and 29% additional ship time (preparations and arrangements). 6 Fig. 1. The map of the C-Area division into sectors. 7 Zygmunt Klusek & Ulf Olsson WP. 4 Detection and characterization of CWA dumpsites 0.2.03.2013 3. Results The primary targets categorization into five classes has been based on the size of highlights of detected target and its acoustic shadow. Typical acceptable size and shape for the first class, the most probably munition pieces, are with characteristic dimensions not more than 1.8 x 0.5 m. The second class contains targets with strong echo with clearly visible pit/shadow, but wrong size. The most likely sunken in the mud or covered with fine sands; hardly recognized targets characterized by strong reflection but without the pit/shadow encompass the third class. Wrecks were classified as the fourth and other unrecognized targets as the fifth classes. Table 1. The number of target cataloged into the five classes. Class Number Munition 17 267 Other strong echoes 6 476 Unrecognizable, in sediments or flat objects 12 476 Wrecks 33 Other echoes 3 008 Together 39.260 Maps based on this classification based on the human analysis of images are completed to identify the location and depth for each category of targets. Space distribution of detected objects in the C-area categorized as the first (red dots) or second class (black dots) is shoved in Fig. 2. 8 Fig. 2. Space distribution of all detected objects in the entire C-area. Red dots represents classified as the first class targets. (SMA data collection) Example of distribution of different classes targets in one of the sectors (E sector) is given in Figs. 3. Red dots represent first class targets, other targets depicted as blue dots. Some uncertainity in the target identification process is observed in the left panel of Fig. 3. In rather homogenous concerning the sediments type area, the density of detected and classified as first class objects predicted by two operators (presented between and outside of the two green lines in the panel) is contrastingly different. From acoustic observations, supported by the ROV sediment samples we could state that in the area prevailed hardy sediments. As a result, a number of the targets are clearly visible in side scan sonar images. Having regard to the above, very little support in the munition identification from the gradiometer (magnetic) monitoring was expected. To distinguish chemical munitions from ordinary items, identification by numerous ROV investigations is required to get statistical support and to accomplish statistically significant extrapolation of the number for the whole area. 9 Zygmunt Klusek & Ulf Olsson WP. 4 Detection and characterization of CWA dumpsites 0.2.03.2013 Targets with features characteristic for mine-like objects which have been classified on the SSS image as the first class required visual checking. About 120+ objects were investigated by the ROV operations (the total ROV diving by SMA and PNA went beyond 250, not each of with success). Fig. 3. Distribution of targets in two sub-areas. Red dots are for first class targets. According to the SMA data in 23 ROV trials, the targets detected by means of hydroacoustics system were not found. It turned out also that in the 21 cases, instead of the chemical munition anchored mines, and 51 bombs were found. Among them 17 is believed to be gas bombs. In the area 18 wrecks were imaged and positioned. Efficiency in objects categorization using side scan images, eliminating not founded with the ROV operation objects, could be summarized as follows – the correct class - 65%, against NOT correct class 35%. The preliminary location of targets at the sea bottom with the SSS was based on positioning the sonar in relation to the ship position by the USBL or by calculation the towing cable layback. It was expected from the earlier SMA practice that positioning precision should be about 15 meters (95%). Comparing the target positions obtained by the SSS with following the ROV findings, it was obtained that for 82 targets – the mean distance from predicted locations is 14.4 meter, with standard deviation equal 13.9m. The uppermost value of the disparity was 51.0 meter and minimum 0.0 meter. Number of object within 15 meter radius spot - 52 (65%) and 95% of the objects were at the distance less than 44.85 meter from predicted position. Side scan sonar images representative for different small size class 1 objects are given in Fig. 4. Fig. 4. Representative examples of the side scan sonar images of small targets (images delivered by SMA). 10 4. Expectations To diminish/improve the probability of false/true detection postprocessing of the sidescan sonar images has been started. The images had been segmented and after of 2D filtering and/or improving image contrasts, different texture characteristics of the bottom and morphological characteristics of targets were extracted for further decision making procedures. Classification of the seafloor and mines on the basis of the acoustic imaging has been employed for mine detection operations for many years but the automatic classification software allowed more confident usage, has been implemented relatively recently and are still at in statu nascendi level. These techniques provide limited area coverage – working for single targets (mines). However, their introduction, at least in limited scale is highly required Further processing and analysis of side scan sonar images and multibeam backscattering will allow to determine extent of different type of sediments. Consequently, the sidescan sonar images analysis could help in the future to predict possibility of contamination spreading. As for example, in the area with fine-grained sediment (muddy bottom) released contaminants are very likely to be accumulated in the sediments. Whereas in case of hardy clay or coarse-grained sediments or exposed bedrock due to bottom currents the contaminants are very probable to be more easily dispersed. Also, targets identification could be more efficiently performed when there is a priori information about the geology and the processes that influence the existing sediments character. Some part of the material should be reprocessed with computer aided methods to get better consistency between target classes and diminish the uncertainity in of target classification . For the further refining the ability of target classification, a planned postprocessing of the raw side scan data i.e. includes: • • • • “slant range correction”; beam directionality correction ( beam pattern correction); TVG corection (if needed) – which form at source used; spikes /diminishing of a single ping echo energy normalisation. We expect that as the results of the postprocessing we could reduce the required number of the ROV launches, number of sediments sampling as well as diminish the ship time, number of analyses, and in the general project costs. 11 Zygmunt Klusek & Ulf Olsson WP. 4 Detection and characterization of CWA dumpsites 0.2.03.2013 5. Summary The study of the official chemical ammunition dumping area within the Gotland Deep, revealed that this region is characterized by the presence of large quantity of items of discarded waste material. In some area the tens of nautical miles long naval mines chains were recognized and position of placement catalogued. On the basis of the collected side scan sonar images the preliminary classification and estimation of local densities and clusters of small bottom targets and their mapping has been performed. The bottom coverage area for the minewarfare objects was performed at unprecedented not only on the Baltic Sea but European scale. The ROV launches - filming the objects, their visual recognition, water and sediment samples from/around the munition items authenticated the classification with the rate exceeding 50%. Two another important findings originate from this study. Firstly, the SSS checking of the sea bottom at relatively high ship speed was found to be sufficiently detailed and to be more time and cost efficient than magnetometer. Even if, magnetometer data could be collected at higher spatial resolutions and allow to distinguish the ferromaterial targets. Secondly, the side scan sonar images have the ability to identify and categorize similar seafloor features, although additional improvements of the categorization is needed . The type of sediments in the area needs to be considered when determining finally efficiency of targets detection both with the acoustic techniques or ROV operations. Side scan sonar and swath bathymetry techniques are a significant aid in the investigation of bottom sediment character distribution providing almost continuous coverage of the seafloor. Final maps based on these techniques could be completed to identify the location of category of targets on the basis of texture analysis of images. 12