METHOD, DEVICE AND COMPUTER PROGRAM FOR DETECTING ONE OR MORE OBJECTS IN THE SEA SUBSTANCE

Description

Technisches Gebiet

[0001] Exemplary embodiments deal with a method, a computer program and a device for detecting one or more objects in the seabed, more precisely, but not exclusively, with the detection of objects based on the scattering of an acoustic signal by the one or more objects. Hintergrund

[0002] The localization of objects of various sizes in marine sediments is often a prerequisite for the construction of marine infrastructure and for the economic use of the seabed, for example, for wind turbines, pipelines, platforms, cable routes, and drilling operations. Such objects can be boulders or other geological inhomogeneities, but also unexploded ordnance (UXO) located in the uppermost sediment layers. Boulders, for example, pose a problem in the Quaternary deposits of the North and Baltic Seas and generally in many shallow sea areas of the temperate and higher latitudes for the installation of offshore infrastructure (infrastructure built on the seabed away from the coast), while UXO, for example, are found in the North and Baltic Seas and may require complex detection and removal before offshore infrastructure construction can begin.Conventional methods for exploring the subsurface, such as 2D / 3D reflection seismics, high-resolution acoustics, and magnetics, show different limitations in object detection in sediments.

[0003] Several publications address seismic methods based on wave diffraction at diffraction sensors. For example, Chinese patent disclosure CN 107 817523 A establishes a modeling method for seismic surveys, and in particular a method for analyzing the migration velocities of diffraction waves. Berkovitch et al., "Diffraction imaging by multifocusing," GEOPHYSICS, Vol. 74, November 2009; Kanasewich et al., "Imaging discontinuities on seismic sections," GEOPHYSICS, Vol. 53, March 1988; and Bansal and Imhof, "Diffraction enhancement in prestack seismic data," GEOPHYSICS, Vol. 70, May 2005, also deal with diffraction-based seismic analyses.

[0004] Locating objects in marine sediments is a task that is often only inadequately solved using conventional methods, as well as in the development of offshore infrastructure, drilling, or foundations for platforms or wind turbines. Zusammenfassung

[0005] The present invention relates to the detection of one or more objects in the seabed, which is made possible by processing data from a receiver signal that represents the diffraction of several acoustic signals at the one or more objects. The invention is defined by the attached claims.

[0006] The invention is based on the use of diffraction of multiple acoustic signals at one or more objects for their detection. To evaluate this diffraction, components of the receiver signal originating from different receivers and / or different times are assigned to points on a detection grid. Since these components originate from multiple receivers and / or multiple times, a time-of-flight correction is applied to these components, for example, to compensate for an offset between the receivers and between the receivers and the source. The time-of-flight-corrected components are then combined ("stacked") into a single signal, which can be used to determine whether an object is located at a specific depth (a point on the detection grid) that can be detected by diffraction.

[0007] Exemplary embodiments provide a method for detecting one or more objects on the seabed. The method comprises receiving a receiver signal. The receiver signal is based on the scattering of multiple acoustic signals at the one or more objects on the seabed. The receiver signal is generated by a plurality of receivers. The method further comprises grouping portions of the receiver signal into points of a detection grid. The detection grid is a grid at whose points the one or more objects are located. The method further comprises performing a time-of-flight correction of the portions of the receiver signal with respect to the points of the detection grid. The method further comprises combining the time-of-flight-corrected portions of the receiver signal at the points of the detection grid.The method further includes detecting one or more objects at the points of the detection grid based on the summation of the time-corrected components of the receiver signal. The detection of the one or more objects is based on the dispersion of the multiple acoustic signals at the one or more objects.

[0008] By grouping the components of the receiver signal into points of a detection grid, performing a time-of-flight correction, and subsequently summing the components, detection of one or more objects based on the dispersion of the acoustic signal picked up by a large receiver array is enabled or improved.

[0009] For example, the wavelength of one or more acoustic signals can be matched to the expected size of the one or more objects. This makes it possible to detect the dispersion of the acoustic signal among the one or more objects. For instance, the distance between adjacent receivers of the majority of receivers can be no more than half the wavelength of the multiple acoustic signals. This avoids aliasing in the detection of the one or more objects.

[0010] The one or more objects are detected based on the amplitude of the summed time-corrected components of the receiver signal. The amplitude can, for example, provide an easy-to-evaluate and visualize way to determine the presence of an object at a point on the detection grid.

[0011] According to the invention, the method further comprises calculating an envelope of the amplitude of the summed time-corrected components of the receiver signal. The one or more objects are detected based on this envelope. This enables improved visualization and interpretation of the detection.

[0012] The method can further calculate a coherence function based on the summation of the time-corrected components of the receiver signal. The coherence function can be based on the similarity between temporally successive components of the receiver signal. One or more objects can then be detected based on this coherence function. For example, the coherence function can represent how similar adjacent values ​​are in the summation of the time-corrected components of the receiver signal, perhaps to give less weight in a subsequent step to values ​​that represent "outliers"—that is, outliers in the summation of the time-corrected components of the receiver signal can be suppressed by the coherence function.

[0013] In some embodiments, the method includes calculating a weighted envelope based on the envelope and the coherence function. The weighted envelope offers improved interpretability and informative value compared to summing the time-corrected components of the receiver signal.

[0014] For example, the coherence function can be based on a semblance analysis. Semblance analyses enable an improvement in the resolution of seismic data, even when background noise is present in the receiver signal.

[0015] The method can further include adjusting the summation of the time-corrected components of the receiver signal to achieve signal amplification of signal components based on the scattering of multiple acoustic signals at more distant objects of the one or more objects. This allows for a relative equalization of the amplitude of diffractions occurring further away from the receivers.

[0016] In some embodiments, the method further includes identifying a reflection of the scattering of the acoustic signal at the one or more objects. The reflection of the scattering of the acoustic signal at the one or more objects can be disregarded in the detection of the one or more objects. For example, echoes of diffractions at other objects can be filtered out.

[0017] In some implementations, a constant seismic velocity can be assumed for the time-of-flight correction. This allows for a simplified calculation of the time-of-flight correction, albeit with potentially reduced accuracy.

[0018] In some implementations, the time-of-flight correction can be performed for a range of possible seismic velocities (so-called Radon transformation). A seismic velocity from this range can be selected based on the magnitude of a local maximum in the corresponding summary of the time-of-flight-corrected components of the receiver signal. The magnitude of this local maximum can then be used as an indicator that the chosen average velocity is sufficiently accurate.

[0019] Alternatively, seismic velocities for different material layers between the majority of receivers and the detection grid points, tailored to the points of the detection grid, can be used for time-of-flight correction. This allows for the most precise possible definition of the seismic velocities used for the time-of-flight correction.

[0020] In at least some embodiments, the components of the receiver signal are grouped based on the distance of the points of the detection grid to the receivers of the majority of receivers and to at least one signal source of the acoustic signal relative to the points of the detection grid. This can be used, for example, as a basis for determining which components of the receiver signal are considered for which point on the detection grid.

[0021] For example, the components of the receiver signal for each acoustic signal can be grouped separately to the points of the detection grid (so-called Real Aperture Processing).

[0022] Alternatively, the components of the receiver signal for a predefined number of time points in the predefined temporal sequence can be summarized and grouped into the points of the detection grid (so-called Synthetic Aperture Processing). This allows the resolution in the direction of motion to be increased.

[0023] In some embodiments, the components of the receiver signal for a predefined distance between the points of the detection grid and the receivers of the majority of receivers, as well as between the receivers and the at least one signal source, can be grouped. This allows for the processing of the components of the receiver signal that are likely to be relevant for a given point of the detection grid.

[0024] In at least some embodiments, the receiver signal comprises a first signal component based on the scattering of multiple acoustic signals by the one or more objects. The receiver signal may further comprise a second signal component based on the reflection of the multiple acoustic signals. The method may include suppressing the second signal component relative to the first. Grouping the components of the receiver signal, performing time-of-flight correction, combining the time-of-flight-corrected components, and / or detecting the one or more objects may be based (exclusively or predominantly) on the first signal component. This allows for the suppression or removal of the reflection of the acoustic signals from the receiver signal to facilitate or improve the detection of the one or more objects by diffraction.In other words, the method can involve suppression of the receiver signal, a reflection of multiple acoustic signals.

[0025] In some embodiments, the suppression of the second signal component, i.e., the reflection of the multiple acoustic signals, is based on an eigenvalue decomposition of a time-corrected version of the receiver signal. This enables efficient suppression of the second signal component.

[0026] In exemplary embodiments, the at least one signal source and the plurality of receivers are designed to be towed by a ship along a water surface above the seabed. The detection grid can, for example, be a two-dimensional detection grid. This two-dimensional detection grid can be extended along a direction of travel of the ship and along a depth axis between the at least one signal source and / or the plurality of receivers and the seabed. This enables the systematic detection of objects on the seabed.

[0027] Further embodiments provide a program with program code for carrying out the method if the program code is executed on a computer, a processor, a control module or a programmable hardware component.

[0028] Exemplary embodiments further provide a device for detecting one or more objects in the seabed. The device includes an interface for receiving a receiver signal. The receiver signal is based on the scattering of several acoustic signals at the one or more objects in the seabed. The receiver signal is generated by a plurality of receivers. The device further includes a processing module configured to group components of the receiver signal into points of a detection grid. The detection grid is a grid at whose points the one or more objects are located. The processing module is further configured to perform a time-of-flight correction of the components of the receiver signal with respect to the points of the detection grid. The processing module is further configured to combine the time-of-flight-corrected components of the receiver signal at the points of the detection grid.The processing module is further designed to detect one or more objects at the points of the detection grid based on the summation of the time-corrected components of the receiver signal. The detection of the one or more objects is based on the dispersion of the multiple acoustic signals at the one or more objects. Figurenkurzbeschreibung

[0029] Some examples of devices and / or methods are explained in more detail below with reference to the accompanying figures. These show: Fign. 1a and 1b show flowcharts of exemplary implementations of a method for detecting one or more objects in the seabed; Fig. 1c Figure 1 shows a block diagram of an embodiment of a device for detecting one or more objects in the seabed. Fig. 2 shows a schematic diagram of a common-mid-point and common-fault-point sorting principle; and Fign. 3a bis 3h show a representation of processing steps based on synthetic data. Beschreibung

[0030] Several examples will now be described in more detail with reference to the accompanying figures, which illustrate some of these examples. The thickness of lines, layers, and / or areas may be exaggerated in the figures for clarity.

[0031] While further examples of various modifications and alternative forms are suitable, some specific examples are accordingly shown in the figures and are described in detail below. However, this detailed description does not limit further examples to the specific forms described. Further examples may encompass all modifications, correspondences, and alternatives that fall within the scope of revelation. Equal or similar reference signs throughout the description of the figures refer to identical or similar elements that, upon comparison, may be implemented identically or in a modified form while providing the same or a similar function.

[0032] It is understood that when an element is described as "connected" or "coupled" to another element, the elements may be connected or coupled directly or via one or more intermediate elements. When two elements A and B are combined using "or," this is to be understood as revealing all possible combinations, i.e., only A, only B, and A and B, unless explicitly or implicitly defined otherwise. An alternative formulation for the same combinations is "at least one of A and B" or "A and / or B." The same applies, mutatis mutandis, to combinations of more than two elements.

[0033] The terminology used here to describe certain examples is not intended to be limiting for other examples. Where a singular form, e.g., "a" and "the," "a," "a," is used, and the use of only a single element is neither explicitly nor implicitly defined as mandatory, further examples may also use plural elements to implement the same function. Similarly, where a function is subsequently described as being implemented using multiple elements, further examples may implement the same function using a single element or a single processing entity.It is further understood that the terms "include", "comprehensive", "exhibit" and / or "exhibit" when used specify the presence of the indicated features, integers, steps, operations, processes, elements, components and / or a group thereof, but do not exclude the presence or addition of one or more other features, integers, steps, operations, processes, elements, components and / or a group thereof.

[0034] Unless otherwise defined, all terms (including technical and scientific terms) are used herein in their usual sense within the field to which examples belong.

[0035] At least some embodiments of the present disclosure deal with the subsurface characterization of objects using point diffractor processing. For subsurface characterization, for example, an arrangement can be used in which the acoustic source and the receivers are decoupled. Embodiments include the processing of seismic / acoustic data for the purpose of detecting point diffractors in marine sediments. The data processing is represented as a sequence of data processing steps. These steps are optimized for seismic / acoustic data that have specific requirements regarding the acquisition geometry, i.e., data that meet the prerequisites for processing by the steps shown here.The described data processing is optimized for use with data acquired using a specialized acquisition unit; however, the processing steps can also be applied to other acoustic / seismic data. For this application, the terms seismic data and acoustic data are to be understood as synonymous, since the frequency content of the data lies above the generally accepted boundary between seismics and acoustics (approximately 100–1000 Hz). The described data processing principle can also be applied to seismic / acoustic data that lies outside this frequency range.

[0036] A prerequisite for at least some embodiments of the present concept is the recording of a wave field as a time series (in a receiver signal), where the positions of the recording units and the signal sources are known and the source and recording are time-synchronized. The steps described in more detail later lead to a subsurface model resulting from a statistical analysis of trace groups. The result of the data processing is a seismic / acoustic data volume of the study area in which point diffractions at their respective spatial positions can be distinguished from the surroundings by their amplitude, i.e., signal strength. This data volume subsequently enables the mapping of point diffractions within the study area.

[0037] Although diffracted waves carry important information, there are few established techniques in exploration geophysics that utilize this component of a seismic wave field to investigate the subsurface (Landa and Keydar, 1998: "Seismic monitoring of diffraction images for detection of local heterogeneities."; Moser and Howard, 2008: "Diffraction imaging in depth"). The source of such diffractions can be erratics or other geological inhomogeneities, but also unexploded ordnance (UXO) located in the uppermost sedimentary layers. These objects, or rather,Inhomogeneities generate point diffractions, meaning they scatter the wave field spherically as a secondary source, if they differ from their surroundings in their physical properties and are of a suitable size (Wu and Aki, 1985: "Elastic wave scattering by a random medium and the small-scale inhomogeneities in the lithosphere", 1988: "Introduction: Seismic Wave Scattering in Three-dimensionally Heterogeneous Earth, in: Scattering and Attenuations of Seismic Waves, Part I"). The most important physical properties are usually the density and the propagation speed of seismic waves within the object or in the surrounding subsurface. A suitable object size exists when the wavelength of the seismic wave excites the object to radial resonance. For this to occur, the object's radius can range from approximately 20% to approximately 200% of the wavelength.Based on a highly accurate recorded wave field with acquisition parameters tailored to the objects to be imaged, the revelation described here can image and characterize objects and inhomogeneities of various sizes in marine sediments.

[0038] In general, there are several methods for locating objects beneath the seabed, each with its own specific advantages and disadvantages. In summary, all currently established seismic / acoustic methods either lack the spatial resolution to reliably image objects (0.5–5 m) or exhibit insufficient signal penetration into the sediments. Magnetic methods for UXO detection show low reliability and a high susceptibility to false detections. To avoid duplication, only alternative data processing options for seismic / acoustic data used for object detection are presented below.

[0039] In general, two approaches can be distinguished for imaging point diffractions from seismic / acoustic data: either filters are applied during data migration, or a portion of the wavefield is extracted from the raw dataset (Moser and Howard, 2008; Sturzu et al. 2014: "Diffraction imaging using specularity gathers"). Migration algorithms are one possible method for imaging diffractions. Migration refers to the step in seismic data analysis that shifts tilted reflectors to their true position in the subsurface and projects diffractions back onto their origin (Yilmaz, 1991: "Seismic data processing. Society of Exploration Geophysicists"). This step is used to generate an image of the subsurface from an image of the wavefield.To model diffractions using migration filters, the principle can be applied that reflections can be locally approximated as planar surfaces after a standard migration. This approximation allows a filter to be determined for suppressing reflections, and in a second migration pass, after applying the filter, only diffractions at their origin are modeled (Sturzu et al., 2014). In this context, the opening angle of the migration and the underlying velocity model are often important factors. Furthermore, real-time processing is usually not possible with migration algorithms, as the complete dataset is always required to perform a migration. Additionally, depending on the specific methodology used, migration algorithms require a relatively high computational effort.

[0040] Extracting diffractions from the wave field typically involves combining several data processing steps. Since diffractions are significantly weaker than reflections, these techniques usually aim to selectively suppress reflections, so that after this suppression, the data sets contain only diffractions and noise. Reflection suppression is usually not perfect, meaning that some attenuated reflections may still remain in the data.

[0041] Another aspect of diffraction mapping is data sorting. The goal of sorting is to group all time series containing a specific diffraction pattern and recorded at the receivers. In reflection seismics, the traces whose midpoints lie close together between the transmitter and receiver are grouped. This midpoint can be approximated as the reflection point. This type of sorting is often used for mapping diffraction patterns. However, from a statistical perspective, this approach is not very useful. Since a diffraction represents a new, secondary source, in at least some implementations, all time series within a certain distance of the diffraction origin can be grouped together.This distance is determined, for example, by whether the seismic source illuminates the point, whether the signal-to-noise ratio is sufficiently high despite signal attenuation with increasing distance, and whether the recording time of the receivers is adequate. A single trace can contain multiple diffractions and thus be assigned to different groups.

[0042] The approach described here is based on the fact that certain objects in the subsurface scatter seismic waves, for example in the form of spherical waves. If the seismic waves are generated and recorded in a controlled manner, the locations where these objects can be found can be determined in an exploratory process using time-of-flight corrections and statistical analyses.

[0043] Fign. 1a and 1bFigure 1 shows flowcharts of exemplary embodiments of a method for detecting one or more objects on the seabed. The method comprises receiving a receiver signal. The receiver signal is based on the dispersion of several acoustic signals at the one or more objects on the seabed. The receiver signal is generated by a plurality of receivers. The method further comprises grouping portions of the receiver signal into points of a detection grid. The detection grid is a grid at whose points the one or more objects are located. The method further comprises performing a time-of-flight correction of the portions of the receiver signal with respect to the points of the detection grid. The method further comprises summarizing the time-of-flight-corrected portions of the receiver signal at the points of the detection grid.The method further comprises detecting 190 of the one or more objects at the points of the detection grid based on the summation of the time-corrected components of the receiver signal. The detection of the one or more objects is based on the dispersion of the multiple acoustic signals at the one or more objects.

[0044] Fig. 1c Figure 1 shows a block diagram of an exemplary embodiment of a corresponding device 10 for detecting one or more objects in the seabed. The device 10 includes an interface 12 for receiving a receiver signal. The device 10 further includes a processing module 14, which is coupled to the interface 12. The processing module is configured to execute the method of Fign. 1a and / or 1b. For example, the processing module is configured to group the components of the receiver signal into points of the detection grid. The processing module is further configured to perform time-of-flight correction of the components of the receiver signal with respect to the points of the detection grid. The processing module is further configured to combine the time-of-flight-corrected components of the receiver signal at the points of the detection grid. The processing module is further configured to detect the one or more objects at the points of the detection grid based on the combination of the time-of-flight-corrected components of the receiver signal.

[0045] The following description refers to both the procedure of Fign. 1a and / or 1b as well as the corresponding device 10 of Fig. 1c .

[0046] Embodiments of the present disclosure relate to a method, a device, and a computer program for detecting one or more objects in the seabed. The term "seabed" within the context of this disclosure is not to be interpreted restrictively—embodiments are equally applicable to detecting objects in a riverbed or in the bottom of a lake or other body of water. Accordingly, in light of this application, the term "seabed" also generally encompasses the bottom of bodies of water, including "riverbed," "bottom of a lake," or "lakebed."

[0047] The system is designed, for example, to detect objects below the seabed or riverbed, such as objects located in the seabed sediment. The system can be configured, for instance, to detect one or more objects within 10 m (or within 15 m, or within 20 m) below the seabed. The system's detection depth can extend at least 10 m (or at least 15 m, or at least 20 m) into the seabed. The one or more objects could be, for example, (large, isolated) boulders, such as erratics, located in the seabed. In other embodiments, the one or more objects could be unexploded ordnance (UXO).These objects can pose a hazard, for example, when structures such as wind turbines or drilling platforms are to be built on foundations that extend into the seabed. Unlike other approaches, some implementations of this technology do not utilize the reflections caused by a signal source at the objects, but rather the scattering of an acoustic signal by the objects themselves. To take advantage of these scattering effects, the wavelength of the acoustic signal used for detection is matched to the size of the objects to be detected. To achieve wide-angle scattering on which the detection can be based, the wavelength of the acoustic signal can be on the same order of magnitude as the size of the objects themselves.

[0048] The method comprises receiving the receiver signal. In exemplary embodiments, the receiver signal is generated by a plurality of receivers. The receivers of the plurality of receivers can, for example, be hydrophones, i.e., microphones that can be used underwater to record or listen to underwater sound. The plurality of receivers can, for example, be configured to detect a wavefront that has arisen from the scattering (and optionally also reflection) of the multiple acoustic signals at the one or more objects (and optionally also at the seabed) and to generate the receiver signal based on the detected wavefront. Thus, the plurality of receivers is configured to generate the receiver signal at least based on the scattering of the multiple acoustic signals at the one or more objects.

[0049] Receivers often detect both the scattering and reflection of the acoustic signal. In these cases, the components based on scattering and those based on reflection can be separated by the processing module. In other words, the receiver signal may include a first signal component based on the scattering of the multiple acoustic signals from the one or more objects. The receiver signal may also include a second signal component based on the reflection of the multiple acoustic signals. The process may involve suppressing the second signal component relative to the first.Subsequent processing steps, such as grouping the components of the received signal, performing time-of-flight correction, combining the time-of-flight-corrected components, and / or detecting the one or more objects, can be based (exclusively or predominantly) on the first signal component. In other words, the method can involve suppressing, for example, attenuating by at least 50% of the received signal power, the reflection of the multiple acoustic signals (e.g., from the seabed). Good results are achieved with a methodology based on singular value decomposition (SVD; see Bansal and Imhof, 2016: "Diffraction enhancement in prestack seismic data") after time-of-flight correction for reflections. After the SVD, the time-of-flight correction is reverse-engineered so that the original traces without reflections are restored.Other approaches to reflection suppression are conceivable and do not change the overall process. In other words, suppressing the second signal component, or the reflections, could, for example, be based on an eigenvalue decomposition of a time-corrected version of the receiver signal.

[0050] In some embodiments, the solution approach presented here is advantageous because the backscattered wave field is sampled with sufficient density. Therefore, it is assumed for the purposes of this example that the locations where the seismic waves are artificially generated (shooting locations) and recorded (receiving locations) are known very precisely in space. Furthermore, the times of wave generation can be synchronized with the start of each recording. A wave field is sampled with sufficient density, for example, if no aliasing occurs in space and time, i.e., if each wavelength is sampled at least twice in space and time.

[0051] In at least some embodiments, the wavelength of the acoustic signal is adapted to the expected size of the one or more objects. The expected size of the one or more objects can, for example, be a value determined by the detection objective. For instance, a different wavelength can be used to locate boulders than in a case where the aim is to find munitions or a sunken ship. Thus, the wavelength of the multiple acoustic (or seismic) signals can be in the same range as the expected size of the one or more objects themselves. For example, the wavelength of the multiple acoustic signals can correspond to at least 10% (or at least 20%, at least 30%, at least 50%) of the expected size of the one or more objects. The wavelength of the acoustic signals can, for example, correspond to at most 1000% (or at most 800%, at most 500%) of the expected size of the one or more objects.The present system and method can be used in many cases to detect larger objects, such as boulders or unexploded ordnance. The wavelength of the multiple acoustic signals can be at least 50 cm (or at least 80 cm, at least 100 cm, at least 150 cm). The wavelengths of the multiple acoustic signals can be substantially similar, meaning they differ by less than 5% of their wavelength. The distance between adjacent receivers can also be made dependent on the wavelength, for example, to avoid aliasing. Thus, the distance between adjacent receivers can be at most half the wavelength of the multiple acoustic signals.

[0052] In an exploratory procedure, it can then be checked at all positions reached by the artificially generated seismic wave whether an object with the properties described above is located at that point. Limiting factors are the attenuation of the seismic signal with increasing travel time and the radiation characteristics of the objects. To test all possible locations of point diffraction in the study area, a grid (such as the detection grid) can be created in which the calculations are performed on the acoustic data. To follow common conventions, the individual grid points (the points of the detection grid) are referred to as CFPs.

[0053] Fig. 2 Figure 1 shows a schematic diagram of a Common-Mid-Point (CMP) and Common-Fault-Point (CFP) sorting principle. While in CMPs 210 time series from receivers 212 are grouped whose midpoint 216 coincides between source 214 and receiver 212, in contrast, in CFPs 220 all time series containing a secondary spherical wave emanating from a point diffraction source 222 are grouped (reference 224 indicates the acoustic source, reference 226 indicates the receivers).

[0054] In the next step, the CFPs are systematically checked. The CFPs correspond to the points of the detection grid. The following steps are performed for each point of the detection grid. For example, at least part of the same sequence of steps is repeated for each of the CFPs as described in Fig. 3a-3h These steps are shown and described in detail below. In other words, steps 120-190 can be performed for each point of the detection grid. For example, grouping the components of the receiver signal (120), performing the time-of-flight correction, combining the time-of-flight-corrected components, and detecting the one or more objects (as well as the steps described below as optional) can be performed separately for each point of the detection grid. This approach makes the solution method exploratory. If an object is present at the tested location, a coherent signal is generated by time-of-flight correction of the acoustic recordings. The one or more objects are then detected using this corrected and therefore coherent signal. If no object is present at the location, no coherent signal is generated by the time-of-flight correction, and therefore no detection takes place.

[0055] The individual processing steps are explained below using schematic diagrams. Fign. 3a-3h The figures illustrate the processing steps using synthetic data. The source-receiver offset describes the distance between the source and receiver, while the distance to CFP indicates the distance of a receiver to a common fault point; common fault points (i.e., the points of the detection grid) are potential locations of a source of variation.

[0056] In the first depiction of the series, Fig. 3a , the receiver signal is represented, which includes reflections in addition to diffractions. Fig. 3a shows a synthetic example of raw data (raw shot gather) in which reflections can be identified as hyperbleeds. Fig. 3a Model artifacts 302, reflections caused by the seafloor 304, and another group of reflections and diffractions 306 can be identified. Normally, reflections, such as the seafloor reflection labeled "Seafloor," are most clearly visible in marine seismic datasets. In the raw data representation of all recorded signals for a shot, reflections appear as hyperstatic. Diffractions are present in the data, but due to their weak amplitude and the different propagation paths of reflections, they are difficult or impossible to see in this representation. On the x-axis of Fig. 3a und Fig. 3b The source-receiver offset (in m) is shown, while on the y-axis the Fign. 3a bis 3h The TWT (Two-Way-Traveltime) is plotted in ms.

[0057] Fig. 3b Figure 1 illustrates a relative signal amplification of diffractions in the acquisition of the acoustic signal ("shot gather"). This signal amplification can be achieved, for example, by suppressing reflections in the receiver signal. The aim of this step is to amplify the diffractions, which are weak compared to the reflections. For this purpose, the reflections are selectively attenuated. This is possible because the time-of-flight curves of diffractions and reflections are different. Fig. 3b Due to the attenuation of reflections, the noise and especially the diffractions 312, which appear chaotic in the representation against the source-receiver offset, are more easily distinguishable. As shown, even after suppressing the reflections, diffractions are still not clearly recognizable in the representation of all recordings for a single shot. This step can also be skipped in the case of strong diffractions. Again, in Fig. 3b Model artifacts 314 can be identified.

[0058] The components of the received signal are then assigned to the points of the detection grid. These points of the detection grid are subsequently referred to as CFPs (Common Fault Points). Fig. 3c Figure 1 illustrates a sorting of the available seismic data (the components of the receiver signal) into Common Fault Points (the grouping of the receiver signal components to the points of the detection grid), as performed, for example, by Kanasewich and Phadke in 1988 in "Imaging discontinuities on seismic sections". The signals are plotted against the distance of the receivers to a CFP (x-axis, also in Fig. 3d ) in Fig. 3c Diffractions 322 are now recognizable as hypernuisances. Also in Fig. 3c Model artifacts 324 can be seen. Fig. 3c This shows the diffractions in CFP acquisition ("CFP gather").

[0059] To enable statistical analysis and improve visualization, all shots and receivers in a CFP (Central Field Profile) that would contain the diffraction of an object at that location are grouped together. In other words, the receiver signal can include components from multiple receivers and components from multiple shots (acoustic signals). These components are then assigned to the points of the grid.

[0060] There are at least three different ways to assign the acoustic recordings to the CFPs. The sorting or display of the recordings in the CFP summary is based on the distance of the CFP to the respective receiver of the recording. In other words, the components of the receiver signal can be grouped based on the distance of the points of the detection grid to the receivers of the majority of receivers and to at least one signal source of the multiple acoustic signals from the points of the detection grid.

[0061] One possibility is so-called Real Aperture Processing. In this approach, each shot is processed individually; that is, the components of the receiver signal are grouped separately for each acoustic signal into the points of the detection grid. For this purpose, starting from the positions of the receivers and the source, the detection grid (which can be a grid) is spanned by CFPs up to a maximum distance from the source, which depends on the characteristics of the source used (e.g., aperture angle). The tracks, i.e., the recordings from all receivers for a shot from the source, of the shot to be processed are assigned to each individual CFP / point of the detection grid.

[0062] Alternatively, Synthetic Aperture Processing (SAP) can be used. With an AP, the number of receivers in the direction of movement can be synthetically increased by emitting multiple acoustic signals. Consequently, this approach groups a defined number of consecutive shots. In other words, the components of the receiver signal for a predefined number of time points in a predefined temporal sequence are grouped into the points of the detection grid. Based on the positions of receivers and firing points, a CFP grid (detection grid) is established. The traces of the grouped shots are then assigned to each individual CFP.

[0063] Alternatively, the components of the receiver signal can be assigned solely based on their distance to the points of the detection grid; that is, all traces within a certain distance of a CFP are assigned to that CFP. In other words, the components of the receiver signal for a predefined distance of the points of the detection grid to the receivers of the majority of receivers and to the at least one signal source can be grouped to the points of the detection grid. The distance should be large enough to encompass a large portion of the expected diffraction hyperboloid. If this distance is chosen too large, the signal quality decreases. A usable distance can be estimated from the raw data or after signal amplification of the diffractions by measuring the size of the diffraction hyperboloids contained within.

[0064] Subsequently, for the assigned components of the receiver signal, the time-of-flight correction is performed with respect to the points of the detection grid, i.e., a positioning of the receivers and the signal source to the point of the detection grid is included in order to compensate for different distances of the receivers and the receiver to the respective point in the combination of the components of the receiver signal. Fig. 3d This illustrates the time-of-flight correction of the receiver signal. Through appropriate time-of-flight correction, the diffractions are reduced, as shown in... Fig. 3d This can be seen as a coherent seismic event. The goal of time-of-flight correction is to correct the diffraction signal of an object at the location of the CFP so that it appears in all tracks at the same time.

[0065] Reference numeral 332 shows the time-of-flight corrected diffraction, reference numeral 334 shows a multiple of the diffraction, and reference numeral 336 shows model artifacts. Fig. 3d This shows the time-corrected diffractions in the CFP acquisition ("CFP gather, Diffraction Move Out (DiffMO)").

[0066] Various time-of-flight correction approaches can be pursued. For example, a time-of-flight correction at constant velocities can be used, i.e., a constant seismic velocity can be assumed for the time-of-flight correction. This allows for an improved second-order time-of-flight correction equation when a constant velocity can be assumed: The time-of-flight correction at constant velocities described here is based on a second-order time-of-flight calculation for point diffraction (e.g., Yilmaz 1991: "Seismic data processing"; Sheriff and Geldart 1995; Clearbout 2010). The correction depends on the spatial location of the diffraction source. D = ( x d , y d , z d ) , of the recipient R = ( x r , y r , z r ) , the source S = ( x s , y s , z s ) and the root mean square (square mean) velocity v rms away: TWT d = x d − x s 2 + y d − y s 2 + z d − z s 2 v rms + x d − x r 2 + y d − y r 2 + z d − z r 2 v rms

[0067] Equation 5 can be simplified based on the assumption that the source and receiver are located at the sea surface. This allows both z r as well as z s are set to zero. Furthermore, the positions of the source and receiver are reduced to their distance from a CFP. d s , r = x d − x s , r 2 + y d − y s , r 2

[0068] This results in the following simplified runtime equation: TWT d = d s 2 v rms 2 + z d 2 v rms 2 + d r 2 v rms 2 + z d 2 v rms 2

[0069] Equation 7 contains the vertical runtime in the following form: TWT d , 0 = 2 z d v rms

[0070] This vertical runtime is constant for a point diffraction in a CFP, and therefore a DiffMO correction can be derived from equation 7: TWT d , 0 TWT d d s d r = d s 2 + d r 2 2 v rms 4 − 2 TWT d 2 d s 2 + d r 2 v rms 2 + TWT d 4 TWT d

[0071] As in Fig. 3d As can be seen, this equation corrects a diffraction in a CFP to a horizontal and therefore coherent event.

[0072] Alternatively, a Radon transformation can be used, for example, when velocities are unknown. One of the most important variables for the previously described time-of-flight correction is the assumed average seismic velocity. If this is unknown, the time-of-flight correction can alternatively be performed for a range of possible velocities. In other words, the time-of-flight correction can be performed for a range of possible seismic velocities.

[0073] If diffraction is present in the processed CFP data, the stacked signal will exhibit a maximum at the location and with the most suitable average velocity. For example, a seismic velocity can be selected from the range of possible seismic velocities based on the magnitude of a local maximum in the corresponding summary of the time-corrected components of the receiver signal for time-of-flight correction. This approach allows for the determination of scattering body positions and average velocities. This methodology represents a Radon transformation, as it transforms from a signal data space to a velocity data space.

[0074] Alternatively, a runtime correction can be used for known speeds. In most cases, the seismic speed cannot be assumed to be known. v rms It remains constant with increasing travel time, e.g., due to compacted sediments that exhibit an increased seismic velocity. Rather, it can be assumed that this velocity is a function of the travel time: v rms ( TWT ) . Following the nomenclature described above, according to Guigné et al. (2014), a transit time curve for diffractions can also be described using the following equation: TWT d = 0.5 √ TWT 2 d s 2 + 0.5 √ TWT 2 d r 2

[0075] The terms TWT 2 d s 2 or TWT 2 d r 2 These equations describe separately the transit times from the source and from the recipients to the respective CFP being processed. These transit time equations can be expressed as equations depending on... v rms ( TWT ) can be formulated (see, for example, Yilmaz, 1991). Consequently, seismic velocities for different material layers between the majority of receivers and the points of the detection grid, tuned to the points of the detection grid, can be used for the time-of-flight correction.

[0076] After time-of-flight correction, the tracks within the CFPs (i.e., at each point of the detection grid separately) are summed (summarized), and the number of tracks is divided, for example. This results in a single summed track, which can correspond to the summation of the components of the receiver signal, plotted against the amplitude and time of flight. If the time-of-flight-corrected tracks are, as in Fig. 3e As shown, now stacked, a clearly recognizable signal amplification at the time of the coherent event results (as stacked diffraction, reference numeral 342). Consequently, the detection of one or more objects can be performed directly on the summation of the components of the receiver signal (i.e., without including further processing steps); that is, the one or more objects can be detected based on the amplitude of the summation of the time-corrected components of the receiver signal. The in Fig. 3e-h The displayed transit time (TWT) corresponds to the vertical transit time; therefore, if a velocity model is available, it can be converted to depth. This step represents an initial statistical evaluation and serves, for example, to suppress noise and amplify the signal. Here, all signals that are not diffractions are referred to as noise. The individual tracks can also be scaled to compensate for energy loss due to the spatial propagation of the signal (Spherical Divergence Correction). In other words, the procedure, as described in Fig. 1b Figure 145 shows an adjustment of the summation of the time-corrected components of the receiver signal to achieve signal amplification of signal components based on the scattering of the multiple acoustic signals at more distant objects of one or more objects (for example, by using spherical divergence correction). Reference numeral 344 shows a multiple of the diffraction, which may be caused, for example, by reflection of the diffraction. In Fig. 3e The amplitude of the stacked signal is plotted on the x-axis, from -4 to +4 x 10 -9< .

[0077] To improve the interpretability of the summary of the time-corrected components of the receiver signal, a so-called envelope of the summary of the time-corrected components of the receiver signal can be calculated. In other words, the method can optionally calculate one of the amplitudes of the summary of the time-corrected components of the receiver signal. One or more objects can be detected based on the envelope. Fig. 3f This illustrates the optional calculation of the stacked signal's envelope. The optional calculation of the stack envelope, as shown in Fig. 3f As shown, the representation of the stack's amplitude is simplified to a single maximum (352). An evaluation of the signal using the phase information is possible and can also contain further information about the depicted point diffraction. Also in Fig. 3f The diffraction is visible at a multiple of 354. Fig. 3f The envelope is plotted on the x-axis, on a scale from 0 to approximately 4.5 x 10 -9< .

[0078] Optionally, a coherence function can be calculated by summing the time-corrected components of the receiver signal, such as a so-called semblance. Fig. 3g Optionally, semblance / coherence (a measure of similarity or coherence) is used as a measure of coherence. Fig. 3d (the time-corrected signals). The semblance, i.e., the similarity of the traces within a CFP over a defined time window, is a measure of the coherence of the time-corrected signals. In other words, the coherence function can be based on a similarity between temporally successive components of the receiver signal. The coherence function can, for example, be based on a semblance analysis. The use of other coefficients, e.g., correlation coefficients, is conceivable and does not change the general solution method. The one or more objects can be detected based on the coherence function. Reference 362 refers to the diffraction of the acoustic signal, and reference 364 to the multiple of the diffraction. In Fig. 3g The semblance is plotted on the x-axis (from 0 to 0.4).

[0079] If one (optionally) multiplies the chosen measure of coherence (such as semblance) by the envelope of the stack (so-called signal weighting), as in Fig. 3h As shown, artifacts are attenuated and the resolution is increased; diffractions (reference symbol 372) can now be easily identified and also easily distinguished from the multiples 374 of the diffraction. In other words, the method can calculate a weighted envelope based on the envelope and the coherence function, for example by multiplying the envelope and the coherence function. The weighting results in an improvement in resolution and informative value. The detection of one or more objects can, for example, be based on the weighted envelope. However, this step is not strictly necessary for the solution.

[0080] In some embodiments, the method may further include identifying a reflection of the scattering of the acoustic signal at the one or more objects, for example by removing all other maxima besides a main maximum. The reflection of the scattering of the acoustic signal at the one or more objects may be disregarded in the detection of the one or more objects, either in the detection itself or by adjusting the summation of the time-corrected components of the receiver signal.

[0081] The method further comprises detecting 190 of the one or more objects at the points of the detection grid based on the summation of the time-corrected components of the receiver signal. For example, an object of the one or more objects can be detected if the amplitude of the summation of the time-corrected components of the receiver signal, the envelope of the amplitude of the summation of the time-corrected components of the receiver signal, or the weighted envelope exceeds a threshold value, such as if the corresponding maximum of the amplitude of the summation of the time-corrected components of the receiver signal, the envelope of the amplitude of the summation of the time-corrected components of the receiver signal, or the weighted envelope coincides with a position of the point of the detection grid and / or indicates a coherent signal.For example, the procedure may include determining a subsurface model based on the one or more detected objects.

[0082] The detection of one or more objects is based (exclusively) on the scattering of the multiple acoustic signals at the one or more objects, i.e., the reflection of the acoustic signal may, for example, be disregarded or ignored in the detection of one or more objects.

[0083] The described data processing method has the great advantage that it can exclusively depict point diffractions with sometimes very good resolution and enables real-time data processing.

[0084] In some implementations, all parts of the wavefield except point diffractions are considered noise and suppressed as much as possible. The described relative signal amplification of the diffractions initially results in strong suppression of reflections, which constitute a dominant part of the wavefield. In some implementations, the subsequent time-of-flight correction corrects (only) point diffractions to coherent events. This means that (only) point diffractions are amplified by the subsequent stacking. The optional weighting of the stacked data with the semblance provides additional noise suppression and allows for improved interpretation of the results. In a grid of CFPs over the study area, relative maxima appear (only) at the locations and depths where point diffractions are found. This allows the spatial location of a large number of existing point diffractions to be determined.Since a large number of data traces in each CFP are used for evaluation, statistically significant statements can be made.

[0085] In at least some implementations, a very fine resolution is achieved because traces from a large area around the CFPs are used during sorting. For example, the preceding data acquisition ensures the best possible coverage of the study area and avoids spatial aliasing. The spatial extent of the source and receiver positions during data acquisition can define the achievable spatial resolution. The larger the extent, the smaller the distance at which two closely spaced point diffractions can be distinguished.

[0086] Real-time data processing is possible in at least some implementations, since reflection suppression is applied to individual shots and synthetic aperture processing is applied to successively recorded shots. Therefore, in these cases, this method can provide results during data recording that can be used for analysis.

[0087] Unlike migration-based techniques, the revelation described here is less susceptible to errors in the assumed velocity field. When suppressing reflections, the time-of-flight correction for reflections is applied forward and inversely, so the assumed velocity field does not distort the traces. Tests have shown that the time-of-flight correction for diffractions is also less susceptible to errors in the velocity field. Errors resulting from a faulty velocity field are noticeable in the accuracy of object localization; however, the method's ability to detect point diffractions is not significantly affected. Additionally, migration algorithms are considerably more computationally intensive in comparison.

[0088] Examples of implementations create a combination of CFP sorting and synthetic aperture processing for the localization of point diffraction patterns in a way that is not yet known. Point diffraction patterns in marine sediments can be various types of objects, e.g., glacial erratics, other geological inhomogeneities, or UXOs.

[0089] This technique is also of interest for geoscientific studies. The methodology described here can be used, for example, to map and analyze fluid emergence points, fault surfaces in the subsurface, and concretions.

[0090] Exemplary implementations can be used, for example, with a specific system consisting of a signal source and a plurality of receivers. The plurality of receivers can be distributed over an area. This area forms the aperture of the plurality of receivers; that is, the larger the area, the larger the aperture of the plurality of receivers. Thus, the receivers of the plurality of receivers can be arranged in a regular or irregular grid, which forms the aperture of the plurality of receivers. Based on the dispersion of the acoustic signal at the one or more objects, the method (or the processing module) can detect the one or more objects both below the grid (the area, the aperture) and offset from the grid, for example, at least 10° (or at least 20°, at least 30°, at least 45°). With a large aperture, the angle can be greater than 45°.

[0091] The acoustic signal can be generated by at least one signal source. This at least one signal source can be located in different positions, such as inside or outside the area where the majority of receivers are located. The at least one signal source can be an acoustic and / or seismic signal source, such as a GI gun (generator-injector gun), a sparker (a sound source with an electrical discharge), or a boomer (a sound source that stores energy in capacitors and releases it via a flat spiral coil, displacing water through an adjacent copper plate). The terms acoustic and seismic can be used interchangeably here, since the present approach uses wavelengths that can be associated with both acoustic and seismic signals.

[0092] The at least one signal source and the plurality of receivers can, for example, be designed to be towed by a ship along a water surface above the seabed. The detection grid can be, for example, a two-dimensional detection grid. This two-dimensional detection grid can be extended along the ship's direction of travel and along a depth axis between the at least one signal source and / or the plurality of receivers and the seabed. Alternatively, the detection grid can be a three-dimensional detection grid extended along the ship's direction of travel, orthogonal to the ship's direction of travel, and along the depth axis between the at least one signal source and / or the plurality of receivers and the seabed.

[0093] Interface 12 can, for example, correspond to one or more inputs and / or one or more outputs for receiving and / or transmitting information, such as digital bit values, based on a code, within a module, between modules, or between modules of different entities.

[0094] In exemplary embodiments, the processing module 14 can correspond to any controller, processor, or programmable hardware component. For example, the processing module 14 can also be implemented as software programmed for a corresponding hardware component. In this respect, the processing module 14 can be implemented as programmable hardware with appropriately adapted software. Any processor, such as digital signal processors (DSPs), can be used. These exemplary embodiments are not limited to a specific type of processor. Any processor, or even multiple processors, are conceivable for implementing the processing module 14.

[0095] The aspects and features described together with one or more of the previously detailed examples and figures can also be combined with one or more of the other examples to replace an identical feature of the other example or to additionally introduce the feature into the other example.

[0096] Examples may also include a computer program with program code for performing one or more of the above procedures, or refer to the execution of the computer program on a computer or processor. Steps, operations, or processes of various procedures described above may be performed by programmed computers or processors. Examples may also include program storage devices, such as digital data storage media, that are machine-, processor-, or computer-readable and encode machine-executable, processor-executable, or computer-executable programs of instructions. The instructions perform or cause some or all of the steps of the procedures described above. The program storage devices may, for example,Digital storage media, magnetic storage media such as magnetic disks and magnetic tapes, hard disk drives, or optically readable digital data storage media. Further examples may also include computers, processors, or control units programmed to perform the steps of the procedures described above, or (field) programmable logic arrays (PLAs) or (field) programmable gate arrays (PGAs) programmed to perform the steps of the procedures described above.

[0097] The descriptions and drawings only illustrate the principles of revelation. Furthermore, all examples presented here are expressly intended for illustrative purposes only, to assist the reader in understanding the principles of revelation and the concepts contributed by the inventor(s) to the advancement of technology. All statements made here regarding principles, aspects, and examples of revelation, as well as specific examples thereof, include their corresponding references.

[0098] A functional block designated as a "means for..." performing a specific function can refer to a circuit configured to perform that function. Thus, a "means for something" can be implemented as a "means configured for or suitable for something," e.g., a component or circuit configured for or suitable for the specific task.

[0099] The functions of various elements shown in the figures, including each functional block designated as "means," "means of providing a signal," "means of generating a signal," etc., can be implemented in the form of dedicated hardware, e.g., "a signal provider," "a signal processing unit," "a processor," "a controller," etc., as well as in hardware capable of executing software in conjunction with associated software. When provided by a processor, the functions can be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some or all of which can be shared.However, the term "processor" or "controller" is by no means limited to hardware capable solely of executing software, but can include digital signal processor hardware (DSP hardware; DSP = Digital Signal Processor), network processors, application-specific integrated circuits (ASICs = Application Specific Integrated Circuits), field-programmable gate arrays (FPGAs = Field Programmable Gate Arrays), read-only memory (ROMs = Read Only Memory) for storing software, random-access memory (RAMs = Random Access Memory), and non-volatile storage devices. Other hardware, both conventional and / or custom-designed, may also be included.

[0100] A block diagram, for example, can represent a rough circuit diagram that implements the principles of the disclosure. Similarly, a flowchart, a process flowchart, a state transition diagram, pseudocode, and the like can represent various processes, operations, or steps that are, for example, substantially depicted in a computer-readable medium and thus executed by a computer or processor, regardless of whether such a computer or processor is explicitly shown. Methods disclosed in the description or in the claims can be implemented by a component that includes means for performing each of the respective steps of these methods.

[0101] It is understood that the disclosure of multiple steps, processes, operations, or functions in the description or claims should not be interpreted as being in a specific order unless explicitly or implicitly stated otherwise, for example, for technical reasons. Therefore, the disclosure of multiple steps or functions does not restrict them to a specific order unless these steps or functions are not interchangeable for technical reasons. Furthermore, in some examples, a single step, function, process, or operation may include and / or be broken down into multiple sub-steps, sub-functions, sub-processes, or sub-operations. Such sub-steps may be included and form part of the disclosure of that single step unless explicitly excluded.

Claims

1. A method for detecting one or more objects in the seabed, the method comprising: obtaining (110) a receiver signal, the receiver signal being based on a scattering of a plurality of acoustic signals at the one or more objects in the seabed, the receiver signal being generated by a plurality of receivers; grouping (120) portions of the receiver signal into points of a detection grid, the detection grid representing a grid at which points the one or more objects are located; performing (130) a propagation time correction of the portions of the receiver signal with respect to the points of the detection grid; aggregating (140) the propagation time-corrected portions of the receiver signal at the points of the detection grid; calculating (150) an envelope of an amplitude of the aggregation of the propagation time-corrected portions of the receiver signal; and detecting (190) the one or more objects at the points of the detection grid based on the envelope of the amplitude of the aggregation of the propagation time-corrected portions of the receiver signal, wherein the detection of the one or more objects is based on the scattering of the plurality of acoustic signals at the one or more objects.

2. The method of claim 1, further comprising calculating (160) a coherence function based on the aggregation of the propagation time-corrected portions of the receiver signal, wherein the coherence function is based on a similarity between temporally consecutive portions of the receiver signal, wherein the one or more objects are detected based on the coherence function.

3. The method of claim 2, further comprising calculating (170) a weighted envelope based on the envelope and based on the coherence function.

4. The method of any of claims 2 or 3, wherein the coherence function is based on a semblance analysis.

5. The method of any of claims 1 to 4, further comprising adjusting (145) the aggregation of the propagation time-corrected portions of the receiver signal to achieve a signal gain of signal portions based on the scattering of the plurality of acoustic signals at more distant objects of the one or more objects.

6. The method of any of claims 1 to 5, further comprising identifying (180) a reflection of the scattering of the acoustic signal at the one or more objects, wherein the reflection of the scattering of the acoustic signal at the one or more objects is disregarded in the detection of the one or more objects.

7. The method of any of claims 1 to 6, wherein the propagation time correction is performed for a range of possible seismic velocities, wherein a seismic velocity from the range of possible seismic velocities is selected for the propagation time correction based on a magnitude of a local maximum in the corresponding aggregation of the propagation time-corrected portions of the receiver signal, or wherein seismic velocities tuned to the points of the detection grid are used for the propagation time correction for different material layers between the plurality of receivers and the points of the detection grid, or wherein a constant seismic velocity is assumed for the propagation time correction.

8. The method of any of claims 1 to 7, wherein the portions of the receiver signal are grouped to the points of the detection grid based on a distance of the points of the detection grid to the receivers of the plurality of receivers and to at least one signal source of the plurality of acoustic signals.

9. The method of claim 8, wherein the portions of the receiver signal are separately grouped to the points of the detection grid for each acoustic signal.

10. The method of claim 8, wherein the portions of the receiver signal are grouped together to the points of the detection grid for a predefined number of points in time in the predefined temporal sequence.

11. The method of claim 8, wherein the portions of the receiver signal are grouped to the points of the detection grid for a predefined distance of the points of the detection grid to the receivers of the plurality of receivers and to the at least one signal source.

12. A program having a program code for performing the method of any of the preceding claims when the program code is executed on a computer, a processor, a control module or a programmable hardware component.

13. An apparatus (10) for detecting one or more objects in the seabed, the apparatus comprising: an interface (12) for obtaining a receiver signal, the receiver signal being based on a scattering of a plurality of acoustic signals at the one or more objects in the seabed, the receiver signal being generated by a plurality of receivers; and a processing module (14) configured to: group portions of the receiver signal into points of a detection grid, the detection grid representing a grid at which points the one or more objects are located, perform a propagation time correction of the portions of the receiver signal with respect to the points of the detection grid, aggregate the propagation time-corrected portions of the receiver signal at the points of the detection grid, calculate an envelope of an amplitude of the aggregation of the propagation time-corrected portions of the receiver signal, and detect the one or more objects at the points of the detection grid based on the envelope of the amplitude of the aggregation of the propagation time-corrected portions of the receiver signal, wherein the detection of the one or more objects is based on the scattering of the plurality of acoustic signals at the one or more objects.