A method and device for correcting depth data measured using a sonar system

Abstract

A method correcting depth measurement data obtained using a sonar system is disclosed. The data is collected along a plurality of track lines and two or more adjacent track lines partly overlap with each other. The method is performed by a processor and comprises the steps of: creating a topology network comprising a plurality of nodes each having links connected thereto, each node representing a sectional correction value to be applied to a section of depth measurement data in an overlapping part of a track line, a link connecting to a node representing a sectional depth mismatch between a section of an overlapping part between the track line and an adjacent track line and uncertainty of the sectional depth mismatch, a sectional depth mismatch being associated with a section of difference grid database related to depth difference between depth measurement data at overlapping grid points of adjacent track lines; obtaining a solved sectional correction value for each of the plurality of nodes, by collectively minimizing imbalances of all nodes of the topology network; and correcting the depth measurement data by applying sectional correction values to respective sections of depth measurement data in the overlapping part of the track line.

Description

A METHOD AND DEVICE FOR CORRECTING DEPTH DATA MEASURED USING A SONAR SYSTEMFIELD OF THE INVENTION

[0001] The present disclosure generally relates to the field of geospatial surveying, and more specifically to a method and device for correcting depth measurement data obtained using a sonar system. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.BACKGROUND OF THE INVENTION

[0002] Different surveys might collect data at various times, locations, using diverse or same measurement methods or instruments. In order to create a more comprehensive and complete dataset, it is often necessary that a plurality of measurements taken at different locations and time are combined. By merging data from multiple measurements, it is possible to cover a wider area, longer time frame, or capture various aspects of the phenomenon under study.

[0003] Mismatches between adjacent measurements, often referred to as misalignments or data discrepancies, can occur in various types of surveys and measurements, including for example marine survey, land surveying, seismic surveys, Lidar surveys and so on.

[0004] When combining measurements made at different time and locations, mismatches present between adjacent measurement covering an overlapping area can lead to inconsistencies in the data, making it difficult to compare, analyse, or visualize. Also, inconsistent data can hinder meaningful interpretation. Moreover, when combining data, the errors can accumulate, amplifying the overall uncertainty.

[0005] One specific example of mismatches is vertical mismatches in overlapping multibeam echo sounder, MBES, data collected during a marine survey project. When creating a Digital Terrain Model, DTM, from multibeam data, vertical differences of multibeam data in adjacent survey lines is visible in a DTM. Such vertical differences of multibeam data have to be corrected so that more accurate DTM can be created.

[0006] One known method of correcting the mismatches between the overlapping part of adjacent multibeam measurements involves simultaneously estimating sound speed correctionsfor a set of chosen pings and their neighbours by computing a best-fit solution that minimizes the mismatch in the areas of overlap between lines. The algorithm is a physics-based approach, which can be highly analytical and not easily comprehensible or implementable for some users.

[0007] In consideration of the above it is desirable there is an improved method for correcting depth data measured using a sonar system.BRIEF SUMMARY OF THE INVENTION

[0008] According to one aspect of the present disclosure, there is presented a method for correcting depth measurement data obtained using a sonar system, the data being collected along a plurality of track lines and two or more adjacent track lines partly overlapping with each other, the method is performed by a processor and comprising the steps of:

[0009] - creating a topology network comprising a plurality of nodes each having links connected thereto, each node representing a sectional correction value to be applied to a section of depth measurement data in an overlapping part of a track line, a link connecting to a node representing a sectional depth mismatch between a section of an overlapping part between the track line and an adjacent track line and uncertainty of the sectional depth mismatch, a sectional depth mismatch being associated with a section of difference grid database related to depth difference between depth measurement data at overlapping grid points of adjacent track lines;

[0010] - obtaining a solved sectional correction value for each of the plurality of nodes, by collectively minimizing imbalances of all nodes of the topology network; and

[0011] - correcting the depth measurement data by applying sectional correction values to respective sections of depth measurement data in the overlapping part of the track line.

[0012] The present disclosure is based on the insight that depth correction values for correcting mismatches between depth measurement values at overlapping grid points of adjacent track lines of a survey can be obtained based on the original depth measurement data and the mismatch observations.

[0013] Based on the idea of the present disclosure, a system or a topology network comprising nodes and links is built. Each node of the topology network represents a sectional correction value to be applied to a section of depth measurement data in an overlapping part of a track line. Links connected to the nodes represent mismatch observations between the node and other nodes and uncertainty of the mismatch observations. A mismatch observation, that is, a depth difference between grid points of two track lines having same navigationcoordinates, at any instance in time will affect an estimated position error at nearby times with a slow decay over time.

[0014] The method of the present disclosure solves for the sectional correction values by way of a minimization procedure that minimizes the depth mismatches between overlapping grid points of adjacent track line by considering the decay and propagation of errors over time. This process allows optimal sectional correction values to be obtained, providing a coherent and accurate representation of the seabed depths in overlapping regions.

[0015] In other words, the disclosed method acknowledges the complex interplay of depth errors between overlapping part of the survey lines, depth measurements, mismatch observations and associated uncertainty, and their temporal evolution. By treating the correction values collectively and iteratively, the method of the present disclosure endeavors to harmonize the corrections, addressing not only the immediate mismatches but also the lingering effects of errors over time. This holistic approach contributes to the effectiveness of the correction process and ensures the resulting depth data aligns with the intended survey objectives.

[0016] The method of the present disclosure is numerical and its implementation is more straightforward comparing to analytical methods.

[0017] Following the correction of the depth measurement data at overlapping parts of adjacent survey lines, a digital Terrain Model accurately representing the aera under survey can be built.

[0018] In an example of the present disclosure, the sectional depth mismatches are calculated by the steps of:

[0019] - obtaining, for each pair of adjacent track lines, a depth difference grid database by calculating depth difference between the depth measurement data of overlapping grid points of the pair of adjacent track lines, based on grid database created for each of the pair adjacent track lines from data points collected by the sonar system along the pair of track lines;

[0020] - dividing depth difference grid databases for all track lines along a reference survey direction, into multiple sections based on a defined interval;

[0021] for each section of a depth difference grid database, deriving a sectional depth mismatch by calculating an average value of depth difference values of all grid points in the section.

[0022] As can be contemplated by those skilled in the art, a model of an object under survey, such as a region of the seabed, built based on measurement data obtained during a survey starts with building a grid based on the measured data including navigations, depthmeasurement and timestamps for the measurement. The method of the present disclosure is built upon the grid of the model to be created from the measurement or survey data.

[0023] Instead of finding a correction value for each grid point of the digital model to be derived from the measurement data, the method of the present disclosure aims at computing correction values for sections of the overlapping part of the track lines. This allows requirements on computational resources to be reduced. It also allows the accuracy of the model to be adapted based on the available computational resources. As can be understood by those skilled in the art, finer grid can be used to obtain more accurate correction while coarser gird can be used to reduce the computational load.

[0024] It will be understood by those skilled in the art that the defined interval for dividing the difference grid databases into sections can be defined by way of receiving a user input, or based on a default setting in a software implementation the method of the present disclosure.

[0025] In an example of the present disclosure, the method further comprises the following step before the obtaining step:

[0026] - creating a grid database for each track line from data points collected by the sonar system along track lines.

[0027] As can be contemplated by those skilled in the art, this step creates a grid of data points where depth values are assigned to specific coordinates in a two-dimensional space (latitude and longitude, for example). This grid dataset is essentially a digital representation of the region under survey, such as an underwater terrain.

[0028] In an example of the present disclosure, the plurality of nodes of the topology network are sorted according to time stamps of the nodes.

[0029] This is performed for chronological alignment. Sorting the nodes in the topology network create a temporal sequence representing different points in time, corresponding to the order of obtaining the measured depth data. Sorting nodes by time helps in understanding the temporal evolution of the surveyed area and in applying corrections or adjustments in a logically sequenced manner. It ensures that adjustments are made in a meaningful order based on the progression of time during the survey.

[0030] In an example of the present disclosure, the method further comprises the step of filling in a gap between two consecutive nodes if a time difference between the two consecutive nodes is larger than a temporal threshold.

[0031] Filling in the gap is implemented by inserting one or more nodes between the two nodes having too large time difference. The insertion of nodes allows better control over howerrors propagate over time, which helps to maintain accuracy in the topology network as large time gaps may allow errors to accumulate without correction.

[0032] Moreover, the inserted nodes help in interpolating errors between the observed nodes. This is particularly useful when dealing with continuous processes, ensuring that error adjustments are distributed smoothly over time rather than in large steps.

[0033] In an example of the present disclosure, the obtaining step comprises the steps of:

[0034] for each node of the model, computing an imbalance of the node by considering current sectional correction value of the node and sectional depth mismatch and uncertainty of links connected to the node;

[0035] comparing imbalances of all nodes with a defined threshold value;

[0036] moving the nodes based on a step size if the imbalances of all nodes are not below the defined threshold value;

[0037] - repeating an iteration comprising the computing, comparing and moving steps until the imbalances of all nodes are below the defined threshold value.

[0038] The iteration procedure is performed to allow the system of the topology network of nodes to enter a state of equilibrium, under which the overall imbalance of the system is minimized. The values of the nodes obtained when the iteration ends are the sectional correction values to be applied for each section of the overlapping part of the track lines.

[0039] The procedure calculates the imbalance of each node by taking into account the depth mismatch of the node, the depth measurements and their temporal evolution and the uncertainty of the links connecting these nodes. Each of the sectional correction value is adjusted with the iteration, until the system of the topology network reaches a balanced state with minimal imbalance.

[0040] The imbalance of a node is computed by considering uncertainty from all links connected to the nodes. By computing the imbalance of a node considering all links connected to the node, the influence of adjacent nodes on the current is taken into consideration. This allows the network of nodes to be collectively optimized, which will give the optimal correction value to each section of the depth measurement data.

[0041] In an example of the present disclosure, the imbalance of a node is related to a sum of scaled uncertainty associated with all links connected to the nodes.

[0042] The uncertainty of adjacent nodes is therefor also taken into account, which allows more accurate correction values to be derived.

[0043] In an example of the present disclosure, the imbalance of a node is a multiplication of the sum by a reactance representing net uncertainty of all links connected to the node.

[0044] The imbalance is in essence the deviation or mismatch associated with a particular node in the topology network. In this context, it's related to the difference between the observed depth values and the modelled or expected depth values. The reactance represents a combined or overall measure of uncertainty associated with the vertical position or depth of the node. As the uncertainty associated with the vertical difference plays a role in determining the final imbalance, it is taken into account when calculating the imbalance.

[0045] The net uncertainty associated with this difference is an important factor, and the reactance encapsulates this uncertainty.

[0046] Overall, the imbalance of a node is not only about the raw differences but also considers the net uncertainty associated with the vertical differences. The reactance term, being a multiplicative factor, indicates the influence of uncertainty in determining the overall imbalance. This aligns with the broader context of optimizing the system to reach a state of equilibrium where depth mismatches are minimized, and corrections are applied with consideration of uncertainties.

[0047] In an example of the present disclosure, the moving step comprises moving the node by increasing its current sectional correction value by a distance scaled by the step size.

[0048] The sectional correction value is calculated based on a varied step size, this allows the system of the topology network to converge in a shorter period of time, which helps to improve the efficiency of the method of the present disclosure.

[0049] In an example of the present disclosure, the step size is decreased when a largest imbalance of all node of a current iteration increases comparing to a largest imbalance of all node of a previous iteration.

[0050] This shows that that the system is not converging and therefore a smaller step size is used.

[0051] In another example of the present disclosure, the step size is increased when a largest imbalance of all node of a current iteration decreases comparing to a largest imbalance of all node of a previous iteration.

[0052] When the system converges, a larger step size can be used, which will allow the system to converge more rapidly.

[0053] In an example of the present disclosure, each sectional correction value comprises a time stamp and a delta value to be applied to a measured depth value.

[0054] The time stamp allows the correction value to be applied the right measurement data.

[0055] In an example of the present disclosure, depth data is collected using a multibeam echo sounder, MBES.

[0056] The method can be advantageously used to correct depth measurement data obtained using MBES.

[0057] In a second aspect of the present disclosure, there is presented a device correcting depth data measured using a sonar system, the data is collected along a plurality of track lines and two adjacent track lines partly overlap with each other, the device comprises a processor configured to perform the method according to the first aspect of the disclosure.

[0058] In a third aspect of the present disclosure, there is presented a computer program product comprising a computer readable storage medium storing instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to the first aspect of the present disclosure or the fourth aspect of the present disclosure.

[0059] The above mentioned and other features and advantages of the disclosure will be best understood from the following description referring to the attached drawings. In the drawings, like reference numerals donate identical parts or parts performing an identical or comparable function or operation.BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are therefore not to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[0061] FIG. 1 schematically illustrates, in a flow chart type diagram, an embodiment of a method for correcting depth data measured using a sonar system, in accordance with the present disclosure.

[0062] FIG. 2 schematically illustrates a diagram comprising segmented depth difference grid database.

[0063] FIG. 3 illustrates vertical mismatches present in the overlapping areas between the survey lines shown in Figure 2.

[0064] FIG. 4 illustrates, in a flow chart type diagram, an embodiment of a method for minimizing imbalances of nodes in the topology network.DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0065] Embodiments contemplated by the present disclosure will now be described in more detail with reference to the accompanying drawings. The disclosed subject matter should not be construed as limited to only the embodiments set forth herein. Rather, the illustrated embodiments are provided by way of example to covey the scope of the subject matter to those skilled in the art.

[0066] In this disclosure, the wordings “mismatch observation”, “observed mismatch”, “depth difference”, “depth mismatch”, and “vertical difference” are used interchangeably and refer to the difference between two or more depth measurements at a grid point with the same latitude and longitude coordinates.

[0067] In this disclosure, the terms “survey lines” and “track lines” are used interchangeably.

[0068] In the following description, the method of present disclosure will be described with reference to correcting vertical differences or mismatches present in overlapping multibeam echo sounder, MBES, data. The method of the present disclosure is, however, not limited to the correction of MBES data only. Instead, it can be used to correct mismatches in data collected for various survey scenario, including for example correcting depth data collected by other sonar systems, where mismatches are present in overlapping part of adjacent survey or measurement data.

[0069] MBES data is obtained by collecting a series of individual echo soundings at distinct time points. When a sound wave is transmitted through the water and returns to a sensor of the MBES, it captures data points that are distributed discretely in space and time. These data points are recorded with specific location coordinates, such as longitude, latitude, and depth, as well as a time stamp indicating when the measurement was taken. These data points are crucial for generating bathymetric models or topographic maps, providing valuable insights into underwater or surface topography.

[0070] When conducting a survey of an area using multiple survey lines, it is common for these lines to overlap at certain points. This overlapping is essential to ensure comprehensive coverage of the surveyed area. However, due to various factors such as inaccuracies innavigation or equipment and varying ocean conditions, depth measurements collected at the overlapping sections of these survey lines may not perfectly align.

[0071] The present disclosure provides a method that minimizes vertical or depth differences or mismatches between overlapping parts of adjacent survey lines while maintaining data integrity.

[0072] More specifically, when constructing a digital model for a survey area, each of grid points with the same latitudes and longitudes for a part of the surveyed area scanned twice (or more times) by two track lines would have two depth values dl and d2. In practice, the true depth value is unknown, and dl and d2 may have a mismatch dl-d2. The purpose of the present disclosure is to find a first correction value Adi for correcting the first depth value dl and a second correction value Ad2 for correcting the second depth d2 which allow the mismatch dl- d2 between the two depth measurements to be minimized.

[0073] The present disclosure is based on the idea that mismatches between measured depth data can be used, in addition to the depth measurement data, as part of inputs to solve for corrections to the measured depth data. Based on this idea of the inventors, two or more survey lines which have measurement overlap are examined to find or obtain mismatch observations between the survey lines.

[0074] A complete list of all mismatch observations found for a survey is compiled. The list of mismatch observations and a list of original measured data for the survey is then run through a network minimizer to find corrections for the measured data. The corrections comprise a time series of adjustments, in this case, adjustment to depth measurement, that minimizes the vertical mismatches between the overlapping survey lines while also best adheres to the original measured data based on the provided uncertainties.

[0075] Figure 1 schematically illustrates, in a flow chart type diagram, an embodiment of a method for correcting depth data measured using a sonar system, in accordance with the present disclosure.

[0076] Before running the method of the present disclosure, preparation steps and / or preprocessing of MBES data are performed. The pre-processing may comprise validating that the measurement data to be processed is in a desired data format. An example of the desired data format can be Hydrographic Data Acquisition and Processing, HDCS, format. The preprocessing may also comprise ensuring that the measurement data is transformed or converted, from a generic geodesy such as WGS84, into a local geodetic reference system more relevant to the specific region or area where the data was collected.

[0077] Total vertical uncertainty, TVU, of all depth measurement data is calculated and used for the uncertainty of the depth observations in the minimizing process. The standard deviations of the depth differences within each depth section are used for uncertainty of the depth mismatch observation for that section.

[0078] When the preparatory steps are finished, an evaluation area covering the survey region is constructed. The evaluation area may be defined by a given width, such as for example 50,000 meters. A reference line indicating the direction of survey is also defined, which will be used as a reference line for dividing the overlapping area between adjacent survey lines into a plurality of sections at a subsequent processing step.

[0079] The ultimate purpose of correcting the vertical differences in the overlapping area between survey lines is to create a more accurate bathymetric model, which is a digital representation of a topography bathymetry of the sea floor by coordinates and depths. An exemplary bathymetric model used in the present disclosure is the DTM, and the method of the present disclosure is applied or performed on the same gridding as used in the DTM.

[0080] At step 11, a grid database for each track line is created from data collected by the sonar system along track lines.

[0081] To create a DTM, point cloud data is transformed into a regular grid. This involves dividing the survey area into a grid of cells and assigning each cell a value based on the MBES data points within it. This process is known as gridding or interpolation. Interpolation methods that can be used include for example Inverse Distance Weighting, IDW, Kriging, or TIN-based interpolation. This is beyond the scope of the present disclosure and will not be elaborated herein.

[0082] The gridding procedure results in a structured grid where data points are organized in a regular pattern both spatially and temporally. This regularity facilitates analysis, interpretation, and comparison since it provides a systematic structure for the data.

[0083] As can be contemplated by those skilled in the art, the gridding procedure creates a grid database for each survey line based on the original MBSE data. Cell size for the grid can be defined by a user for example.

[0084] Following that, at step 12, for each pair of adjacent survey or track lines, a depth difference grid database is created or obtained. This is realised by calculating depth difference between the measured depth data of overlapping grid points of for example a pair of adjacent survey lines.

[0085] For each overlapping area between two adjacent survey lines, the depth difference grid database comprises a list of data items each associated with two time instants where adepth measurement is respectively taken, for a same grid point, by the MBES and a depth difference between the two depth measurements. The data items are arranged sequentially following a time order, in other words, the data items in the depth difference grid database are sorted in time or based on their timestamps.

[0086] If an area is covered by three survey lines, then a depth difference grid database is created for each pair of lines, in this case, three depth difference grid database will be obtained.

[0087] In other words, the original measurements are depths and time of measurement. Mismatch observations are pairs of measurements of the same location but difference times. There may be multiple measurements of the same location at different times producing multitudes of mismatch observation pairs.

[0088] An exemplary data item in the depth difference grid database is:

[0089] (tl, t2, LineNamel, LineName2, depth difference, uncertainty of the depth difference)

[0090] Here, tl represents a first timestamp when a grid point is measured when surveying is performed along LineNamel, t2 represents a second timestamp when the grid point is measured when surveying is performed along LineName2, depth difference represents a difference between depths respectively obtained for the grid point at tl and t2, uncertainty represents an uncertainty of the depth difference, which is also referred to as a mismatch observation.

[0091] At step 13, each depth difference grid database is then segmented into different sections along the direction of the survey line, based on a defined distance interval. The distance interval can be defined based on a user input.

[0092] Figure 2 schematically illustrates a diagram comprising segmented depth difference grid database. Figure 2 illustrates four survey lines respectively marked by numerals 21 to 24. Survey lines 21 and 22 have an overlapping area 210, survey lines 22 and 23 overlaps at area 211 and survey lines 23 and 24 overlaps at area 212. Surveys are conducted along a reference line 220. As an example, survey line 21 may be obtained while a vessel goes upwards along line 220, while survey line 22 is obtained when the vessel turns around and goes downwards along line 220, and so on.

[0093] Each overlapping area between adjacent lines are divided into five sections along the reference line 220. As an example, the overlapping area between the survey lines 21 and 22 are divided into sections 201.1-210.5.

[0094] Due to various factors influencing the accuracy of measurement by the MBES, the depth results in the overlapping area between two adjacent lines may not precisely align witheach other. Figure 3 illustrates vertical mismatches present in the overlapping areas between the survey lines shown in Figure 2. This is a cross section taken a long a line vertical to the reference line 220, for example within the section 210.2, across all the survey lines.

[0095] It is seen from Figure 3 that the depths, indicated in the dashed boxes, respectively measured along two adjacent survey lines differ from each other in the overlapping area between the two adjacent survey lines. Those skilled in the art will understand that the mismatches or depth differences shown in Figure 3 are simplified and for illustrative purpose only.

[0096] In Figures 2 and 3 it is shown that two adjacent lines overlap with each other. It will be understood by those skilled in the art that in practice more lines, such as three lines next to each other, can overlap. In this case, vertical difference is calculated per pair of overlapping survey lines.

[0097] Referring back to Figure 1, at step 14, for each section of a depth difference grid database, a sectional depth difference value is derived by calculating an average value of depth difference values of all grid points in the section.

[0098] For the example as illustrated in Figure 2, 15 sectional difference values are obtained for all overlapping part between adjacent survey lines.

[0099] For the derived sectional depth difference, an average of latitudes and longitudes of all grid points in the section is taken as its latitude and longitude coordinates (x, y). Based on this derived latitude and longitude coordinates of the sectional depth difference, line navigation (position of the vessel) comprising time, X,Y is read, and interpolation is then used to find the nearest x,y to this average depth difference and the interpolated time stamp is used as the timestamp of this average sectional depth difference.

[0100] In other words, the original navigation for the track lines is used to get the nearest navigation point in position to the difference point and then by using interpolation the difference point is given its the time stamp.

[0101] The data item further comprises uncertainty of the depth difference. In the present disclosure, the uncertainty of each section of the depth difference grid database is derived by calculating a standard deviation of the depth differences in that section.

[0102] The sectional difference values will be input to a minimizer and uncertainties thereof, to find a series of depth corrections values that will be used to correct the original depth data. The original measured depth data are respectively obtained from the survey lines at coordinates corresponding to that of the sectional depth difference. The minimization procedure also makes reference to the original measured depth data.

[0103] Steps 15 to 17 briefly summarize the minimization procedure, which will be described in more detail in the following.

[0104] At step 15 of Figure 1, a topology network comprising a plurality of nodes and a plurality of links is created. Each node represents a correction value to be applied to a section of the depth difference grid. Each node is connected to a corresponding node by links representing the sectional depth difference between the pair of nodes and an uncertainty of the sectional depth difference. The pair of nodes are with the save navigation but different depth measurement.

[0105] As an example, a sectional depth difference represents a mismatch between two observations or depth measurements performed at two time instants tl and t2. Two correction values are respectively needed for the depth measurements obtained at tl and t2. Therefore, a first node N1 associated with time tl (during scanning along survey line 1 for example) and a second node N2 associated time t2 (during scanning along survey line 2 for example) will be created in the topology network.

[0106] Herein, the node N1 represents a correction value to be applied to a corresponding section of the grid database of survey line 1, which will be obtained through the minimization procedure to be described below, and N2 represents a correction value to be applied to a corresponding section of the grid database of survey line 2. After having the depth data in the sections of both survey lines 1 and 2 corrected using the derived correction values, the vertical difference in the overlapping part between the survey lines will be eliminated or at least significantly reduced, allowing improved terrain model of the survey area to be created.

[0107] As mentioned, the minimizer, which is based on the topology network created at step 15, will then generate a correction value, for each section of the overlapping part of a grid database, by collectively minimizing imbalances of all nodes of the topology network. This is done at step 16. The minimization procedure will be detailed in the following with reference to Figure 4.

[0108] At step 17, the correction values obtained at step 16 will be applied to the original depth data to correct the depth mismatches between adjacent survey lines.

[0109] After the depth measurement data at overlapping parts of adjacent survey lines are corrected, a digital Terrain Model accurately representing the aera under survey can be built.

[0110] In the following, the minimizer or minimization procedure of the present disclosed will be describe. The minimization is an iterative procedure that brings the topology network into a steady state with minimum imbalance of mismatches. During the minimization procedure, the topology network is considered as a system comprising nodes and links where a positionerror represented by each node exhibits a certain imbalance. Adjusting the position error of one node will influence the imbalances exhibited by other nearby nodes.

[0111] During each iteration, the minimizer of the present disclosure (re)calculates the total imbalance of each node, based on an adjusted correction value applied to each node in the network. It will be understood by those skilled in the art that for the first iteration the imbalances are calculated based on the initial values of the nodes and no adjustment is made to the correction values or position errors of the nodes.

[0112] When the iteration procedure leads to a converging system, that is, the imbalances of the nodes converge over the iteration procedure, it shows that the system of the network is getting stable. The iteration can stop when a defined threshold value is reached. The position error or correction value of each node at this moment is the correction value to be applied to the grid points.

[0113] The topology network of the present disclosure comprises nodes interconnected by links. Most nodes represent depth error at a point in time. As an example, a node is identified by a time when a depth is taken, that is, a time of validity such as StarFix seconds in floating point. The best estimate of position error at that point in time is represented by deltaZ, which is initialized to zero, and to be calculated as the major outcome of the minimizer. That is, an output value of a node by the minimizer represents a correction value to be applied to a measure depth value at that point.

[0114] Most nodes also contain a list of links to other nodes. Nodes which contain no forward link to other nodes are those that do not move.

[0115] A node is created for each sectional vertical difference value in one survey line. For the example as illustrated in Figure 2, a topology network comprising 30 nodes will be created, which will allow 30 correction values to be applied to corresponding part of each grid database of the survey lines.

[0116] Links will be added to connect different nodes. A link starts from one node and points to other nodes and contains extra information such as the observed mismatch (dz and the uncertainty of the mismatch (uncertainty ). Theoretically, a node can have any number of links. Each represents a mismatch observation. For each measurement, a mismatch observation (link) is generated to every other measurement for that section. In most cases, a node has two links representing the vertical difference between the node and a corresponding node with the same navigation.

[0117] The topology network is initialized by logging the sectional depth difference to respective nodes. Nodes are sorted in time and consecutive nodes are linked together with bi-directional links (if they do not exceed a time gap limit). The links are to model the Kalman filter of the inertial navigation system and to limit the drift rate of the position errors. That is the predicted rate of drift of depth errors. The depth errors (and therefore the corrections) should be a slowly changing (drifting) value over time.

[0118] In other words, it is assumed that position error is a slow drift or a random walk. A mismatch observation or vertical difference at any instance in time will affect the estimated position error at nearby times with a slow decay over time. The drift links connecting consecutive time nodes have position error (dz) set to zero and the uncertainty computed as the product of the delta time between the nodes and an error drift rate (a constant to model the error drift of the inertial navigation system).

[0119] Any nodes that are spaced farther apart in time than a defined interval, of for example a few seconds, will have one or more nodes inserted between them at the desired interval. If the gap in time between a pair of nodes is too large (thousands of seconds) then the nodes are left disconnected as error adjustments on one should not affect the position of the other at that separation.

[0120] For the purpose of obtaining a correction value that helps to reduce the vertical mismatch between measurements obtained by two adjacent survey lines, the nodes (representing the vertical difference) will be moved based on the mismatch observation distances and the uncertainties.

[0121] The topology network formed by nodes and links may be considered as a system, an imbalance of the system will change when the position errors represented by the nodes change. In a sense, the minimization involves a procedure of studying how the imbalance will change with a given change in the position errors represented by the nodes of the topology network.

[0122] Figure 4 illustrates, in a flow chart type diagram 400, an embodiment of a method for minimizing imbalances of nodes in the topology network.

[0123] After starting the procedure at step 401, the minimizer first calculates imbalances exhibited by all nodes of the network. This involves the procedure as described below.

[0124] At step 402, a scaled uncertainty associated a link connected to a node is calculated. The node and the link connected to the node when being processed is referred to as the current node and the current link specifically in the present disclosure for clarity purposes.

[0125] The scaled uncertainty, which may also be referred to sigma of uncertainty, represents how the position error of the current node is influenced by uncertainty of the current link.

[0126] An exemplary method of calculating the scaled uncertainty is by subtracting the node’s mismatch observation dZ in a link from the position error deltaZ of the node for this iteration, which is then divided by the uncertainty of the link. The system works by trying to balance the network tension by equalizing the number of sigmas that each node is moved to reach equilibrium (convergence).

[0127] At step 403, it is checked whether all links connected to the nodes are considered. If there are links connected to the current node is not processed, the procedure moves to step 404, to consider the next link. The scaled uncertainty related to the next link, which is now the current link, is calculated following step 402, in the same way as described above.

[0128] When step 403 has a positive result, that is, all links connected to the current nodes are considered, the procedure moves to step 405, where the imbalance of the current node is derived from the scaled uncertainty related to all links.

[0129] The imbalance of the node is calculated by multiplying a sum of all the scaled uncertainty obtained from all links connected to the node by a reactance of the node. The reactance is calculated as a collective uncertainty from all links connected to a node. An example of calculating the reactance can be based on the parallel resistor equation: reactance = 1 / sum (1 / uncertainty(n)), n=l . . .N, N being the number of links connected to the node.

[0130] The result of multiplying the sum of all scaled uncertainty from all links connected to the node and reactance of the node is an estimate of the position error represented by the node in the current iteration.

[0131] Following that, the procedure proceeds to step 406, where it checks if all nodes in the network are processed. When there is unprocessed node, the procedure moves to the unprocessed node at step 407, followed by repeat steps 402 to 406 until all nodes are processed, that is, when the imbalances of all nodes are derived.

[0132] The above steps are repeated until imbalances exhibited by all nodes of the network are calculated, that is when step 406 has a positive result. Those skilled in the art that the above procedure is describe for facilitating understanding of the minimization procedure. In practice, the imbalances of all nodes can be computed in parallel when the computational resources of a device implementing the method of the present disclosure allows.

[0133] Following that, the procedure checks whether a condition for terminating the iteration procedure is met, which comprises steps 408 and 409. It will be understood by those skilled in the art this illustrates the procedure from a perspective of a skilled person implementing the method in software.

[0134] At step 408, the flow is shown to check if imbalances of all nodes are smaller than a defined threshold value. At step 409 the flow checks whether a defined number of maximum iterations has been reached while the system does not converge. When either of the conditions is met, the minimization procedure stops.

[0135] If it is determined that further minimization is needed following steps 408 and 409, the procedure determines a step size to be used to move the nodes of the network. Moving a node representing adjusting the depth error or correction value represented by the node. The purpose is to find optimal correction values for the depth measurement data which allows the system to reach a balanced state.

[0136] This is done at step 410 by checking if a largest one of imbalances of all nodes is smaller than a largest imbalance of the previous iteration. Based on the decision of step 410 a step size used for moving the nodes is made smaller (step 411) or larger (step 412). The goal is to find the largest step size (to reach equilibrium as fast as possible) but to avoid overshooting and leading to instability and oscillations.

[0137] If it is shown that the largest imbalance decreases than the last iteration, then the step size used for moving the nodes can be made larger at step 412. This can be done by multiplying the current step size by a factor which is larger than 1 (one). An exemplary factor for increasing the step size is chosen as 1.2.

[0138] On the contrary, if it is shown that the largest imbalance increases than the last iteration, then the step size used for moving the nodes can be made smaller at step 411. This can be done by multiplying the current step size by a factor which is smaller than 1 (one), or dividing the current step size by a factor larger than 1 (one). In an example, the current step size is divided by a factor of 2.

[0139] When the step size used for moving the nodes is determined, at step 413, each node is moved by a distance which is equal to the current imbalance times the step size. In other words, the correction value Az represented by the node is updated to Az_next= Az+Az*step size.

[0140] The flow then goes back to step 402 to calculate the imbalances of all nodes once again and check if the movement of the nodes allows the system to converge.

[0141] The iteration procedure stops when the network reaches equilibrium, which in the present disclosure is considered as meeting the condition when the imbalances of all nodes is below a defined threshold value. The threshold value may be set to for example 0.001 meter, of the uncertainty. It will be understood by those skilled in the art that the threshold value maybe defined based on a required precision for correction. A smaller threshold allows more accurate correction values to be obtained.

[0142] The values of all nodes when the iteration finishes are the solved correction values to be applied to the measured depth data in the grid depth database. It will be understood by those skilled in the art that each correction value comprises a time step and a delta value for correcting the depth data.

[0143] As a correction value is derived for each section of overlapping part of the grid database of a survey line, the correction value is applied to all grid points in the section of the overlapping part of the grid database. This is advantageous especially for large volume of data, which allows the correction to be performed in a more time efficient way.

[0144] The invention has been described by reference to certain embodiments discussed above. It will be recognized that these embodiments are susceptible to various modifications and alternative forms well known to those of skill in the art.

[0145] Further modifications in addition to those described above may be made to the structures and techniques described herein without departing from the spirit and scope of the invention. Accordingly, although specific embodiments have been described, these are examples only and are not limiting upon the scope of the invention.

Claims

CLAIMS1. A method for correcting depth measurement data obtained using a sonar system, the data being collected along a plurality of track lines and two or more adjacent track lines partly overlapping with each other, the method being performed by a processor and comprising the steps of: creating a topology network comprising a plurality of nodes each having links connected thereto, each node representing a sectional correction value to be applied to a section of depth measurement data in an overlapping part of a track line, a link connecting to a node representing a sectional depth mismatch between a section of an overlapping part between the track line and an adjacent track line and uncertainty of the sectional depth mismatch, a sectional depth mismatch being associated with a section of difference grid database related to depth difference between depth measurement data at overlapping grid points of adjacent track lines; obtaining a solved sectional correction value for each of the plurality of nodes, by collectively minimizing imbalances of all nodes of the topology network; and correcting the depth measurement data by applying sectional correction values to respective sections of depth measurement data in the overlapping part of the track line.

2. The method according to claim 1, wherein the sectional depth mismatches are calculated by the steps of: obtaining, for each pair of adjacent track lines, a depth difference grid database by calculating depth difference between the depth measurement data of overlapping grid points of the pair of adjacent track lines, based on grid database created for each of the pair adjacent track lines from data points collected by the sonar system along the pair of track lines; dividing depth difference grid databases for all track lines along a reference survey direction, into multiple sections based on a defined interval; for each section of a depth difference grid database, deriving a sectional depth mismatch by calculating an average value of depth difference values of all grid points in the section.

3. The method according to claim 2, further comprising the following step before the obtaining step: creating a grid database for each track line from data points collected by the sonar system along track lines.

4. The method according to any of the previous claims, wherein the plurality of nodes of the topology network are sorted according to time stamps of the nodes.

5. The method according to claim 4, further comprising the step of filling in a gap between two consecutive nodes if a time difference between the two consecutive nodes is larger than a temporal threshold.

6. The method according to any of the previous claims, wherein the obtaining step comprises the steps of: for each node of the model, computing an imbalance of the node by considering current sectional correction value of the node and sectional depth mismatch and uncertainty of links connected to the node; comparing imbalances of all nodes with a defined threshold value; moving the nodes based on a step size if the imbalances of all nodes are not below the defined threshold value; repeating an iteration comprising the computing, comparing and moving steps until the imbalances of all nodes are below the defined threshold value.

7. The method according to claim 6, wherein the imbalance of a node is related to a sum of scaled uncertainty associated with all links connected to the nodes.

8. The method according to claim 7, wherein the imbalance of a node is a multiplication of the sum by a reactance representing a net uncertainty of all links connected to the node9. The method according to claim 6, wherein the moving step comprises moving the node by increasing its current sectional correction value by a distance scaled by the step size.

10. The method according to claim 9, wherein the step size is decreased when the largest imbalance of all nodes of a current iteration increases comparing to the largest imbalance of all nodes of a previous iteration.

11. The method according to claim 9, wherein the step size is increased when the largest imbalance of all node of a current iteration decreases comparing to the largest imbalance of all nodes of a previous iteration.

12. The method according to any of the previous claims, wherein each sectional correction value comprises a time stamp and a delta value to be applied to a measured depth value.

13. The method according to any of the previous claims, wherein the depth data is collected using a multibeam echo sounder, MBES.

14. A device for correcting depth data measured using a sonar system, the data being collected along a plurality of track lines and two adjacent track lines partly overlapping with each other the device comprising a processor configured to perform the method according to any of the previous claims 1 to 13.

15. A computer program product, comprising a computer readable storage medium storing instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any of the claims 1 to 13.

Images