survey methods that only image the seafl oor itself, as a multibeam. This inherently decreases the resolution of detection. Using multiple transducers in an array increases the signal/noise ratio in the overlapping ellipse. Nevertheless, the bigger beam width can be used as an advantage for the detection of cables and pipelines. These features are visible in sub bottom data as hyperbolas. As can be seen in Figure 1, part of the signal refl ects on top of an object before the vessel sails right above it, due to this larger beam width. It is, however, plotted as if it is detected right below the vessel, but at greater depth. The closer the vessel sails to the pipeline or cable, the shallower it shows up in the sub-bottom data. The shallowest point is reached right on top of the cable. A reverse trend occurs moving away from the linear object, resulting in a hyperbola. Acquisition and processing The acquisition of sub bottom data for the detection of cables and pipelines involves the sailing of crosslines on top of the linear object. The more pings refl ect on top of the linear object, the more clear the hyperbola can occur in the data, so a slow survey speed and a high pingrate are recommended. As described in the fi rst section, the recorded data is a sub-bottom acoustic profi le. As always the case with sub-bottom data, the raw signal is processed, but interpretation to identify the seafl oor, geological layers and objects has to be made. Results are less straight forward hydrographical methods like multibeam surveys, that give direct results of the depth of the seafl oor. Other than multichannel data, single channel sub-bottom profi les can be viewed directly as recorded without any processing needed. However, no fi lters or heave reduction are (fully) applied to the online data. This makes it less clear if objects are detected directly. For data quality purposes data should be processed quickly and verifi ed. An easy option to apply a quick review of the data is to use an automated batch processing as is available in the Silas Processing software suite. Figure 1: Object detection in acoustic data. Above the different path lengths to the object are displayed depending on the location. Below the corresponding acoustic traces, resulting in a hyperbole, highlighted in red. Another advantage that sub-bottom profi ling (and seismic data in general) have above other detection methods is that, besides the detection of the objects themselves, the complete subsurface is imaged. This allows also to detect bottom features related to the constructions of pipes and cables as trenches, initially dredged to lay them in. The extra information helps interpretation and could give an indication, even if the pipelines and cables are not detected themselves. Interpretation of the seafl oor is relatively easy and is mostly done by auto-tracing algorithms. For layers in the subsurface, interpretation need to be a bit more manual, especially if layers are not as distinct. The interpretation of objects can even be more complicated. Several diffi culties that can occur during this interpretation, as is also stated by (Wunderlich et al., 2005) , are: • Objects can be masked by refl ections of nearby layers (as the seafl oor or other layer boundaries) and other structures. • Weak echo strength due to acoustic attenuation in the sediment. • Small refl ection coeffi cients due to small acoustic difference (density, sound velocity) to surrounding material. • Small dimensions of objects and unknown or imprecise know positions. The picking of cables, pipelines and objects in sub-bottom data is mainly interpretation work that requires experience of the personnel and these man made interpretations change between different individuals. 25
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