PipeHawk plc Font SizeBigger Reset Smaller You are here: Home e-SafePRO Corporate About PipeHawk plc Technology Division QM Systems Adien Ltd Thomson Engineering Design Ltd Wessex Precision Instruments Ltd Utsi Electronics Ltd Investor Relations Aim Rule 26 Disclosures Corporate Governance Contact us Privacy GPR Products & Services Article Index e-SafePRO depth-estimation All Pages Page 1 of 2 e-SafePRO The All Service Avoidance system for those with a Proactive - Protective - Professional mindset when it comes to avoiding damage to utility services while undertaking streetworks. Designed specifically for those who desire to bring about the eradication of utility service strikes and are therefore looking for the best of the best when it comes to their weapon of choice. The e-SafePRO takes excellence to another level with the introduction of a swathe of new and uprated features. These include 3 different deployment methods “Baseline”, “Syncline” and “Switchback”. The innovative Chronos Masking feature has also received an update and is now enabled with a more intuitive 4 stage, surface down process. To the Depth Estimation feature we have now added something we call “TrackBack”. The biggest change of all however looks you right in the face. The e-SafePRO is equipped with a Progressive 5 lane data View. This means the operator now has the ability to display up to 5 sets of parallel data all at the same time, side by side and in the order of collection. DATA COLLECTION MODES Baseline If you collect data using the Baseline method, then your 5 sets of data will be aligned based on a common starting line such as a straight kerb line at the beginning of your data collection. This is the most common method of data collection and can be conducted in either a forward or reverse direction. Syncline For those occasions when there is no opportunity to start a series of data collections from a topographical straight edge such as a handy kerb line. Syncline enable the operator to start and end each parrallel data collection (transect) at an unrelated or random point from the preceding one. To ensure each set of data collected can be properly aligned, the operator may choose to mark a known point in each set of data collected. This may be something like a visible tar or scar line in the ground surface. A marked centre or junction line in a highway, or an arbitrary line marked by the operator for the purpose. Switchback For those situations where the operator is collecting long swathes of data ie. up to 16 linear metres at a time. Using the Switchback method of data collection enables the operator to alternate their direction of data collection thereby reducing the amount of time the they are simply returning to their common start point rather than collecting data while walking. When you consider some operators may walk anything up to 12km per day, this "return time" can result in a considerable amount of wasted effort. CHRONOS MASKING The Chronos Masking feature provides a means by which data may be manipulated by the operator, in order to better inform the decision making process; with regard to the likely validity of any potential target visible in the data, based on its estimated depth with respect to other items within the same data set. This is achieved by separating all data; prior to display, into distinct groups based on units of time which in GPR terms can be equated to relative depth. These groups of data may then be individually masked (turned on or off) by the operator effectively creating a layering effect within the data. All e-Safe data displayed on screen is the final product of captured signals that have been reflected back; within fixed time intervals, by features within the ground over which the e-Safe has been deployed. Those signals received in the shortest timeframe are reflected back by features nearest to the surface and therefore categorised as being from the layer, or for short. Conversely; those received over the longest timeframe are reflected back by features furthest from the surface and therefore categorised as being from the Deepest layer or for short. All other signals received naturally fall between these two extremes which; for easier reference, are subdivided once more into Shallow (SW) and Deep (DP) respectively. Using these broad but simple categories, an operator may quickly form an opinion as to the depth of a particular target relative to that of another. As the speed of all signals received will not be constant due to changing ground conditions, it is not possible to assign absolute values in respect to the depth to/from, for each of these layers. Each division is therefore calculated based on a percentage of the overall timeframe within each data set. These are preset as follows: - CHRONOS MASKING LEVELS TOTAL TIMEFRAME ACTION ICON ALL CHRONOS MASKING OFF No Chronos Masking is applied and ALL DATA is displayed. 25% LAYER 1 – NEAR SURFACE (NS) Only data formed from signals returning within the shortest timeframe are shown. All other data is hidden. 25% LAYER 2 – SHALLOW (SW) Only data formed from signals returning within a short timeframe (but not the very shortest timeframe) are shown. All other data is hidden 25% LAYER 3 – DEEP (DP) Only data formed from signals returning over a long timeframe (but not the very longest timeframe) are shown. All other data is hidden 25% LAYER 4 – DEEPEST (DT) Only data formed from signals returning over the very longest timeframe are shown. All other data is hidden CHRONOS MASKING - EXAMPLE The Chronos Masking feature; although useful when used with single scan lines, the greatest benefit will most likely be observed when used with a number of Parallel Scan lines. This is because the Chronos Masking will affect the data within each scan line by equal measure. It therefore becomes very easy to visually analyse the data for the likely presence of linear features running through the area of interest. Because no data is ever lost or deleted whilst using the Chronos masking feature, each Level may be turned on and off as many times as may be required. This makes it very simple for even the most novice operator to follow an iterative process and build an informed approximation as to the relative depths of likely services within a common data set. Screen example Next