METHOD AND DEVICE FOR DETECTING ONE OR MORE OBJECTS ON THE SEABED AND STORAGE DEVICE

Abstract

The present invention relates to a method, computer program, and device 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 on one or more objects on the seabed. The receiver signal is generated by a plurality of receivers. The method further comprises grouping parts of the receiver signal at points on a detection grid. The detection grid represents a grid whose points locate one or more objects. The method further comprises performing a time-duration correction of the parts of the receiver signal relative to the points on the detection grid. The method further comprises consolidating the time-duration corrected parts of the receiver signal at the points on the detection grid.The method also involves detecting one or more objects at the detection grid points based on the consolidation of the time-corrected portions of the receiver signal. The detection of one or more objects relies on the dispersion of the various acoustic signals around one or more objects.

Description

"METHOD AND DEVICE FOR DETECTING ONE OR MORE OBJECTS ON THE SEABED AND STORAGE DEVICE" FIELD OF TECHNIQUE

[001] The present invention relates to a method, computer program, and device for detecting one or more objects on the seabed, more precisely, but not exclusively, by detecting objects based on the scattering of an acoustic signal over one or more objects. BACKGROUND

[002] Locating objects of varying sizes in marine sediments is, in many cases, a challenge for the construction of marine infrastructure, as well as for the economic exploitation of the seabed, for example, in wind power installations, platforms, cable routes, and drilling. Such objects can be boulders or other geological inhomogeneities, but also unexploded ordnance (UXO) devices found in the uppermost layers of sediments. Boulders, for example, represent a problem in the Quaternary deposits of the North Sea and the Baltic Sea and generally in many shallow sea areas of moderate and high latitudes for the installation of offshore infrastructure (infrastructure built on the seabed far from the coast), while UXOs are commonly found in the North Sea and the Baltic Sea and may require extensive detection and cleanup before the construction of offshore infrastructure.Conventional methods for exploring the subsurface, such as 2D / 3D reflection seismics, high-resolution acoustics, and magnetism, have different limitations in detecting objects in sediments.

[003] Locating objects in marine sediments is a task performed in the expansion of offshore infrastructure, as well as for Petition 870260057744, dated 06 / 15 / 2026, page 5 / 100 2 / 38 drilling or for the foundations of, for example, platforms or wind turbines, which can often be inadequately addressed with conventional methods. SUMMARY

[004] The present invention relates to the detection of one or more objects on the seabed, which is enabled by data processing of a receiver signal that reproduces a scattering (diffraction) of several acoustic signals on one or more objects.

[005] The invention is based on the fact that the dispersion of various acoustic signals around one or more objects is used to detect one or more objects. In order to evaluate the dispersion, in the present disclosure, parts of the receiver signal originating from different receivers and / or at different times are assigned to points on a detection grid. Since these parts originate from various receivers and / or at various times, a time-duration correction is applied to these parts to compensate for a displacement between the receivers and between the receivers and the source. Then, the time-duration corrected parts are consolidated (“stacked”) into a common signal, based on which it can be determined whether, at a given depth (from a point on the detection grid), there is an object that can be detected by diffraction.

[006] Examples of embodiments develop 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 on one or more objects on the seabed. The receiver signal is generated by a plurality of receivers. The method further comprises grouping parts of the receiver signal at points on a detection grid. The detection grid represents a grid on whose points one or more objects are located. The method further comprises performing time correction for the duration of the parts. Petition 870260057744, dated 06 / 15 / 2026, p. 6 / 100 3 / 38 of the receiver signal relative to the detection grid points. The method further comprises a consolidation of the time-corrected portions of the receiver signal at the detection grid points. The method also comprises detecting one or more objects at the detection grid points based on the consolidation of the time-corrected portions of the receiver signal. The detection of one or more objects is based on the dispersion of the various acoustic signals onto one or more objects.

[007] By grouping parts of the receiver signal into points on a detection grid, performing time-duration correction and subsequent consolidation of the parts, detection of one or more objects based on dispersion of the acoustic signal, which is captured by a large receiver array, is enabled or improved.

[008] For example, the wavelength of the various acoustic signals can be adjusted to an expected dimension of one or more objects. This allows for the perception of acoustic signal dispersion in one or more objects. A distance between adjacent receivers of a plurality of receivers can, for example, be at most half the wavelength of the various acoustic signals. This avoids aliasing (incorrect identification of a signal frequency) in the detection of one or more objects.

[009] For example, one or more objects can be detected based on an amplitude of the consolidation of parts of the receiver signal corrected for duration. The amplitude can represent, for example, an easy-to-evaluate and visualize possibility, in order to recognize the existence of an object at a point on the detection grid.

[010] In many examples of modality, the method still involves calculating an envelope of the amplitude of the consolidation of Petition 870260057744, dated 06 / 15 / 2026, page 7 / 100 4 / 38 parts of the receiver signal are corrected for duration. One or more objects can be detected based on envelopes. This allows for improved representation and interpretation of the detection.

[011] The method can also calculate a coherence function based on the consolidation of the time-corrected parts of the receiver signal. The coherence function can be based on a similarity between consecutive parts of the receiver signal. One or more objects can be detected based on the coherence function. In this case, the coherence function can reproduce, for example, how adjacent values ​​are similar in the consolidation of the time-corrected parts of the receiver signal, for example, in order to evaluate such values ​​with less force in a subsequent step, which represent “leakage”, i.e., leakage in the consolidation of the time-corrected parts of the receiver signal can be suppressed by the coherence function.

[012] In many modality examples, the method comprises a calculation of a weighted envelope based on envelopes and based on a coherence function. The weighted envelope offers a better capacity for interpretation and expressiveness than the consolidation of the parts of the receiver signal corrected in the duration time.

[013] For example, the coherence function can be based on similarity analysis. Similarity analyses allow for an improvement in the resolution of seismic data, although there is background noise in the receiving signal.

[014] The method may also include an adjustment of the consolidation of the time-corrected parts of the receiver signal, to achieve signal reinforcement of signal parts that are based on the dispersion of the various acoustic signals in objects further away from one or more objects. This allows for relative alignment. Petition 870260057744, dated 06 / 15 / 2026, page 8 / 100 5 / 38 of the diffraction amplitude, which are located even further away from the receptors.

[015] In many examples of the modality, the method still involves identifying a reflection of the scattering of the acoustic signal in one or more objects. The reflection of the scattering of the actual acoustic signal in one or more objects can be disregarded in the detection of one or more objects. Thus, echoes of the diffractions in other objects can be filtered out.

[016] In many modality examples, a constant seismic velocity can be supported for duration time correction. This allows duration time correction to be calculated in a simplified manner, with possibly reduced accuracy.

[017] In many modality examples, duration time correction can be performed for a range of possible seismic velocities (referred to as Radon-Transformation). A seismic velocity from the range of possible seismic velocities can be selected based on an extension of a local maximum in the corresponding consolidation of the duration time-corrected parts of the receiver signal for duration time correction. The extension of the local maximum can, in this case, be used as an indicator if the selected average velocity is sufficiently accurate.

[018] Alternatively, for duration time correction at detection grid points, coordinate seismic velocities can be used for different material layers between the plurality of receivers and the detection grid points. This allows for the most precise possible definition of the underlying seismic velocities for duration time correction.

[019] In at least many modality examples, the parts of the receiver signal are grouped based on a distance from the detection grid points to the receivers of the plurality of receivers and the Petition 870260057744, dated 06 / 15 / 2026, p. 9 / 100 6 / 38 at least one signal source from the acoustic signal to the detection grid points. This can be used, for example, as a basis for which components of the receiver signal are considered for which point on the detection grid.

[020] For example, the receiver signal parts for each acoustic signal can be grouped separately at the detection grid points (called Real Aperture Processing).

[021] Alternatively, parts of the receiver signal are grouped into a predefined number of moments in the predefined time sequence consolidated to the detection grid points (called Synthetic Aperture Processing). By means of this, it is possible to increase the resolution in the direction of motion.

[022] In many examples of the modality, the parts of the receiver signal can be grouped for a predefined distance from the detection grid points in relation to the receivers of the plurality of receivers and at least one signal source to the detection grid points. This allows processing of the relevant parts of the receiver signal predicted for a detection grid point.

[023] In at least many examples of the modality, the receiving signal comprises a first part of the signal, which is based on the dispersion of the various acoustic signals in one or more objects. The receiving signal may also comprise a second part of the signal which is based on the reflection of the various acoustic signals. The method may comprise a suppression of the second part of the signal against the first part of the signal. The grouping of the parts of the receiving signal, the realization of the time-duration correction, the consolidation of the time-duration corrected parts and / or the detection of one or more objects may be based (exclusively or predominantly) on large Petition 870260057744, dated 06 / 15 / 2026, page 10 / 100 7 / 38 part) in the first part of the signal. This allows for the suppression or removal of the reflection of acoustic signals from the receiving signal, to facilitate or improve the detection of one or more objects based on diffraction. In other words, the method may involve suppressing, in the receiving signal, a reflection of the various acoustic signals.

[024] In many modality examples, the suppression of the second part of the signal, therefore the reflection of the various acoustic signals, is based on an eigenvalue decomposition of a time-corrected version of the receiver signal. This allows for efficient suppression of the second part of the signal.

[025] In examples of the modality, at least one signal source is provided and the plurality of receivers is intended to be towed by a vessel along a water surface above the seabed. The detection grid may be, for example, a two-dimensional detection grid. The two-dimensional detection grid may be extended along a direction of the vessel's course and along a depth axis between the at least one signal source and / or the plurality of receivers and the seabed. This allows for systematic detection of objects on the seabed.

[026] Examples of the modality also provide a program with a program code for executing the method when the program code is executed on a computer, a processor, a control module, or a programmable hardware component.

[027] Examples of embodiments further provide a device for detecting one or more objects on the seabed. The device comprises an interface for obtaining a receiver signal. The receiver signal is based on the dispersion of several acoustic signals on one or more objects on the seabed. The receiver signal is generated by a plurality of receivers. The device further comprises a processing module, designed for assembling the parts of the signal. Petition 870260057744, dated 06 / 15 / 2026, page 11 / 100 8 / 38 receiver to the points of a detection grid. The detection grid represents a grid on whose points one or more objects are located. The processing module is further formed to perform the time-duration correction of the parts of the receiver signal in relation to the points of the detection grid. The processing module is further formed to consolidate the time-duration corrected parts of the receiver signal at the points of the detection grid. The processing module is further formed to detect one or more objects at the points of the detection grid based on the consolidation of the time-duration corrected parts of the receiver signal. The detection of one or more objects is based on the dispersion of the various acoustic signals in one or more objects. BRIEF DESCRIPTION OF THE FIGURES

[028] Some examples of devices and / or methods are explained in more detail below, with reference to the attached figures, by way of example only. Shown:

[029] Figures 1a and 1b show example flowcharts of a method for detecting one or more objects on the seabed;

[030] Figure 1c shows a block diagram of an example embodiment of a device for detecting one or more objects on the seabed.

[031] Figure 2 shows a schematic diagram of a common midpoint and common fault point classification principle; and

[032] Figures 3a to 3h show a representation of the processing steps based on synthetic data. DESCRIPTION

[033] Different examples are now described in more detail with reference to the attached figures, in which some examples are shown. In the figures, the strengths of the lines, layers and / or Petition 870260057744, dated 06 / 15 / 2026, page 12 / 100 9 / 38 areas may be exaggerated for clarity.

[034] Therefore, although other examples of various modifications and alternative forms are suitable, some specific examples of them are shown in the figures and are described in more detail below. However, this detailed description does not limit other examples to the particular forms described. Other examples may encompass all modifications, equivalents, and alternatives that fall within the scope of disclosure. Throughout the description of the figures, the same or similar reference numbers refer to the same or similar elements which, when compared with each other, can be implemented identically or in a modified form, while providing the same or similar function.

[035] It is understood that when an element is designated as “joined” or “coupled” with another element, the elements may be joined or coupled directly or by one or more intermediate elements. When two elements A and B are combined using an “or”, it should be understood that all possible combinations are disclosed, i.e., only A, only B, as well as A and B, unless explicitly or implicitly defined otherwise. An alternative formulation for equal combinations is “at least one of A and B” or “A and / or B”. The same applies, correspondingly in meaning, to combinations of more than two elements.

[036] The terminology used here to describe certain examples should not be limiting for other examples. When a singular form is used, for example, “a”, “an”, and “, a”, and the use of only a single element is not explicitly or implicitly defined as mandatory, other examples may also use plural elements to implement the same function. If a function is described below as being implemented using multiple Petition 870260057744, dated 06 / 15 / 2026, p. 13 / 100 10 / 38 elements; other examples may implement the same function using a single element or a single processing entity. It is further understood that the terms "comprises," "that comprehends / comprising," "presents," and / or "that presents / presenting," when used, specify the presence of the specified characteristics, integers, steps, operations, processes, elements, components, and / or a group thereof, but do not exclude the presence or addition of one or more other characteristics, integers, steps, operations, processes, elements, components, and / or a group thereof.

[037] Unless defined otherwise, all terms (including technical and scientific terms) are used in this document with their normal meaning in the field to which the examples belong.

[038] At least many embodiments of the present disclosure deal with subsurface characterization of objects with the aid of point diffraction processing. In this case, for example, an arrangement for subsurface characterization in which the acoustic source and receiver are coupled may be used. Embodiment examples include the processing of seismic / acoustic data for the purpose of detecting point diffractors in marine sediments. Data processing is represented, in this case, as a sequence of data processing steps.These steps are optimized for seismic / acoustic data, which require specific recording geometry requirements; that is, data that meets the processing requirements shown here. The data processing described is optimized for use with data obtained from a specialized acquisition unit; however, the processing steps can also be applied to other data types. Petition 870260057744, dated 06 / 15 / 2026, page 14 / 100 11 / 38 Acoustic / Seismic. The terms seismic data and acoustic data should be understood as synonyms for this application, since the frequency content of the data is above the generally assumed boundary range between seismic and acoustic (approximately 100 - 1000 Hz). The described data processing principle, however, can also be performed with seismic / acoustic data that are outside this frequency range.

[039] A prerequisite for at least many examples of the present concept's modality is the recording of a wavefield as a time series (in a receiving signal), where the positions of the recording units and signal sources are known, and sources and recordings are temporally synchronized. The steps described in more detail later lead to a subsurface model resulting from a statistical evaluation of trace groups. The result of data processing is a seismic / acoustic data volume of the study area in which point diffractors can be differentiated in their respective spatial positions of the environment by their amplitude, i.e., signal intensity. This data volume then allows the mapping of point diffractors in the study area.

[040] Although diffracted waves carry important information, there are only a few established techniques in the field of exploration geophysics that make this part of a seismic wave field usable for investigating 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 starting point for such diffractions can be boulders or other geological inhomogeneities, but also unexploded ordnance (UXO) found in the uppermost layers of sediments. These Petition 870260057744, dated 06 / 15 / 2026, p. 15 / 100 12 / 38 Objects or non-homogeneities produce point diffractions, that is, they disperse the wave field spherically as a secondary source when they differ from their surroundings in terms of their physical properties and if they are of an adequate 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 in this case are, most often, the density and propagation speed of seismic waves within the object or in the surrounding subsurface. An adequate object size is when the wavelength of the seismic wave stimulates the object to radial resonance. For this to happen, the radius of the object can be found within a range of approximately 20% to 200% of the wavelength.Based on a wavefield recorded with high precision, with acquisition parameters that are matched to the objects to be visualized, the disclosure described here can reproduce and characterize objects and inhomogeneities of various sizes in marine sediments.

[041] In general, there are several methods for locating objects under the seabed, each with specific advantages and disadvantages. In summary, it can be said that all seismic / acoustic methods established so far either do not show the spatial resolution to reliably depict objects (0.5 - 5 m), or show insufficient signal penetration into the sediments. Magnetic methods for UXO detection exhibit low reliability and high susceptibility to false detections. To avoid data duplication, only alternative possibilities for seismic / acoustic data processing for object detection are shown below. Petition 870260057744, dated 06 / 15 / 2026, p. 16 / 100 13 / 38

[042] In general, two approaches can be distinguished for mapping point diffractions based on 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 diffraction mapping. Migration is the step in seismic data analysis that moves tilted reflectors to their true position underground and projected diffractions back to their origin (Yilmaz, 1991: “Seismic data processing. Society of Exploration Geophysicists”). This step is used to generate a ground image from a wavefield image. To map diffractions with migration filters, the principle can be used that reflections can be approximated locally, as flat surfaces, after a standard migration.This approximation can then be used to determine a filter to suppress reflections, and in a second migration pass, after the filter is applied, only the diffractions at their point of origin are reproduced (Sturzu et al., 2014). In this context, the migration aperture angle and the underlying velocity model are often important factors. Furthermore, real-time processing is generally not possible with migration algorithms, as the complete dataset must always be available to perform a migration. Additionally, migration algorithms require relatively high computational effort, depending on the exact methodology used.

[043] To extract diffractions from the wavefield, at least one sequence of data processing steps is combined. Since diffractions are significantly weaker than reflections, the techniques generally aim to specifically suppress reflections, so that after this suppression, diffractions are left in the field. Petition 870260057744, dated 06 / 15 / 2026, page 17 / 100 14 / 38 datasets only show diffractions and noise. Reflection suppression is generally not perfect, so some attenuated reflections remain in the data.

[044] Another aspect in diffraction reproduction is data classification. The aim of classification is to group all time series containing a certain diffraction and which were recorded at the receivers into a single group. In the case of reflection seismics, traces whose centers between transmitter and receiver are close to each other are grouped. This central point can be assumed to be the reflection point as an approximation. Often this type of sorting is also used for diffraction mapping. However, this approach makes little sense from a statistical point of view. Since a diffraction represents a new secondary source, in at least many modality examples, all time series that are within a certain distance of the diffraction origin can be grouped.This distance is determined, for example, by the fact that if, for instance, the seismic source illuminates the point, the signal-to-noise ratio is large enough, despite the signal attenuation with increasing distance, and the recording time of the receiver is sufficient. In this case, a band may contain several diffractions and thus also be assigned to different groups.

[045] The approach described here is based on the fact that certain subsurface objects retroactively scatter seismic waves, for example, in the form of spherical waves. If seismic waves are generated and recorded in a controlled manner, the locations where these objects can be found can be determined in an exploratory method by duration time correction and statistical evaluations.

[046] Figures 1a and 1b show flowcharts of example modalities of a method for detecting one or more objects on the seabed. The method comprises receiving a receiver signal. The signal Petition 870260057744, dated 06 / 15 / 2026, p. 18 / 100 The method is based on the dispersion of several acoustic signals in one or more objects on the seabed. The receiver signal is generated by a plurality of receivers. The method further comprises a grouping of parts of the receiver signal at points on a detection grid. The detection grid represents a grid on whose points one or more objects are located. The method further comprises performing a time-duration correction of the parts of the receiver signal relative to the points on the detection grid. The method further comprises a consolidation of the time-duration corrected parts of the receiver signal at the points on the detection grid. The method further comprises detecting one or more objects at the points on the detection grid based on the consolidation of the time-duration corrected parts of the receiver signal. The detection of one or more objects is based on the dispersion of the several acoustic signals in one or more objects.

[047] Figure 1c shows a block diagram of an example embodiment of a corresponding device 10 for detecting one or more objects on the seabed. The device 10 comprises an interface 12 for obtaining a receiver signal. The device 10 further comprises a processing module 14 with which the interface 12 is coupled. The processing module is formed for the execution of the method of Figures 1a and / or 1b. For example, the processing module is formed to group the parts of the receiver signal to the points of the detection grid. The processing module is further formed to perform the time-duration correction of the parts of the receiver signal in relation to the points of the detection grid. The processing module is further formed for the consolidation of the time-duration corrected parts of the receiver signal at the points of the detection grid. The processing module is further formed to detect one or more objects at the points of the detection grid based on the Petition 870260057744, dated 06 / 15 / 2026, p. 19 / 100 16 / 38 consolidation of the corrected parts of the receiver signal over time.

[048] The following description deals with both the method of Figures 1a and / or 1b as per the corresponding device 10 in Figure 1c.

[049] Examples of embodiments of the present disclosure relate to a method, a device, as well as a computer program for detecting one or more objects on the seabed. In this case, the term “seabed,” within the meaning of the present disclosure, should not be interpreted as limiting – examples of embodiments are equally applicable to tracking objects on the bed of a river or the bottom of a lake or other watercourse. Correspondingly, the term “seabed,” within the meaning of the present invention, also generally includes the bottom of a watercourse, therefore also the riverbed, lake bottom, or lakebed.

[050] In this case, the system is designed, for example, to detect objects beneath the seabed or riverbed, therefore, for example, objects that are in the seabed sediment. The system can be designed to, for example, detect one or more objects within 10 m (or within 15 m, within 20 m) beneath the seabed. A detection depth of the system can project, for example, at least 10 m (or at least 15 m, at least 20 m) into the seabed. The one or more objects can be rocks (large, isolated), for example, or boulders that are on the seabed. In other embodiments, the one or more objects can be, for example, unexploded ordnance (UXO). These objects can pose a risk if structures, such as wind farms or drilling platforms, are built on seabed foundations that project into the seabed.To detect these objects, the modality examples, unlike other approaches, Petition 870260057744, dated 06 / 15 / 2026, page 20 / 100 17 / 38 do not utilize reflections, which are evoked by a signal source in objects, but rather the dispersion of an acoustic signal within those objects. To utilize this dispersion effect, the wavelength of the acoustic signal used for detection must be compatible with the size of the objects to be detected. To obtain a wide-angle distribution upon which detection can be based, the wavelength of the acoustic signal must be of the same order of magnitude as the dimension of the object itself.

[051] The method involves receiving 110 of the receiver signal. In examples of the modality, the receiver signal is generated by a plurality of receivers. The receivers of the plurality of receivers can be, for example, hydrophones, i.e., microphones that can be used underwater to record or listen to underwater sound. The plurality of receivers can be formed, for example, to capture a wavefront, which results from the dispersion (and optionally also reflection) of the various acoustic signals in one or more objects (and optionally also on the seabed) and, based on the captured wavefront, generate the receiver signal. Thus, the plurality of receivers is formed to generate a receiver signal, at least based on a dispersion of the various acoustic signals in one or more objects.

[052] Frequently, both the scattering and reflection of the acoustic signal are captured by receivers. In these cases, the parts that are based on scattering and the parts that are based on reflection can be separated by the processing module. In other words, the receiving signal may comprise a first part of the signal, which is based on the scattering of various acoustic signals in one or more objects. The receiving signal may comprise a second part of the signal that is based on the reflection of various acoustic signals. The method may comprise a suppression 115 of the second part of the signal against the first part of the signal. The following processing steps, such as, Petition 870260057744, dated 06 / 15 / 2026, page 21 / 100 18 / 38 For example, the grouping of parts of the receiver signal, the implementation of time-duration correction, the consolidation of the time-duration corrected parts, and / or the detection of one or more objects may be based (exclusively or predominantly in large part), for example, on the first part of the signal. In other words, the method may comprise a suppression 115, for example, a weakening of around at least 50% of the reception power in the receiver signal, of the reflection of the various acoustic signals (e.g., on the seabed). Good results are achieved with a methodology that touches on an eigenvalue decomposition (Singular Value Decomposition: SVD; see Bansal and Imhof, 2016: “Diffraction enhancement in prestack seismic data”) after a time-duration correction for reflections. After the eigenvalue decomposition, the time-duration correction is recalculated, such that the original traces are restored without reflections.Other approaches to reflection suppression are conceivable and do not change the subsequent method. In other words, suppression of the second part of the signal, or of reflections, for example, could be based on a high-value decomposition of a time-corrected version of the receiver signal.

[053] It is advantageous for the solution presented here, for example, in some instances, that the inverted wave field is sampled in a sufficiently dense manner. Therefore, it is assumed in the following example that the locations where seismic waves are artificially generated (trigger locations) and recorded (receiver locations) are known with great precision in space. Furthermore, the times at which the waves are generated can be synchronized with the start of the individual recordings. A wave field is sampled, then, in a sufficiently dense manner, when there is no aliasing in space and time, therefore, when Petition 870260057744, dated 06 / 15 / 2026, p. 22 / 100 19 / 38 each wavelength is sampled in space and time at least twice.

[054] The wavelength of the acoustic signal is adjusted, in at least many instances of the modality, to an expected extent of one or more objects. The expected extent of one or more objects may be, for example, a value given by the target of detection. If pebbles are found, then a different wavelength may be used than in a case where ammunition or a shipwreck might be found. Thus, the wavelength of the various acoustic (or seismic) signals may be in the same range as the expected extent of one or more objects themselves. Thus, the wavelength of the various acoustic signals may correspond to at least 10% (or at least 20%, at least 30%, at least 50%) of the expected extent of one or more objects. The wavelength of the acoustic signals may correspond, for example, to at most 1000% (or at most 800%, at most 500%) of the expected extent of one or more objects.The present system and method can be used in many cases to detect significantly larger objects, such as pebbles or unexploded projectiles. Thus, the wavelength of the various acoustic signals can be at least 50 cm (or at least 80 cm, at least 100 cm, at least 150 cm). In this case, the wavelengths of the various acoustic signals, for example, can be substantially equal, that is, they differ from each other by less than 5% of the wavelength. Also, the distance between adjacent receivers of a plurality of receivers can be made wavelength-dependent to avoid, for example, aliasing. Thus, a distance between adjacent receivers of a plurality of receivers can be at most half the wavelength of the various acoustic signals.

[055] In an exploratory method, it can then be tested in Petition 870260057744, dated 06 / 15 / 2026, p. 23 / 100 20 / 38 all positions that the artificially created seismic wave reaches, if, at those positions, there is an object with the properties described above. The limiting factors are the attenuation of the seismic signal with increasing duration time and the radiation characteristics of the objects. In order to test all possible locations of point diffractors in the study area, a grid network (e.g., the detection grid) can be created on which calculations are performed for the acoustic data. To follow common conventions, the individual grid points (the detection grid points) are called CFPs.

[056] Figure 2 shows a schematic diagram of a Common-Mid-Point (CMP) and Common-Fault-Point (CFP) classification principle.While in CMP 210 time series of receivers 212 are consolidated, whose midpoint 216 coincides between the source 214 and the receiver 212, in contrast, in CFP 220, all time series containing a secondary spherical wave originating from a point diffraction source 222 are grouped together (reference number 224 shows the acoustic sources, reference number 226 shows the receiver).

[057] In the next stage, the CFPs are systematically tested. In this case, the CFPs correspond to the detection grid points. The following steps are performed for each point on the detection grid. For example, at least one part of the same step sequence is repeated at each CFP, as shown in Figures 3a-3h and as described in detail. In other words, steps 120-190 can be performed for each point on the detection grid. Thus, the 120 grouping of the receiver signal parts, the execution of the duration time correction, the consolidation of the duration-corrected parts, and the detection of one or more objects (as well as the steps described as optional below) can be performed. Petition 870260057744, dated 06 / 15 / 2026, p. 24 / 100 21 / 38 (separate) for each point on the detection grid. This procedure makes the exploratory method the solution. If there is an object at the tested location, a coherent signal is generated through a time correction of the acoustic recordings. One or more objects are detected with the aid of this corrected and thus coherent signal. If there is no object at the location, then no coherent signal is created through the time correction and therefore there is no detection.

[058] The individual processing steps are explained below based on schematic representations. Figures 3a3h show a representation of the processing steps based on synthetic data. The Source-Receiver Offset describes the distance between the source and receiver, while the Distance to CFP specifies the distance from a receiver to a Common Fault Point; Common Fault Points (i.e., the points on the detection grid) are potential positions of a scattering source.

[059] In the first representation of the sequence, Figure 3a, the receiver signal is shown, which includes, in addition to diffractions, reflections. Figure 3a shows a synthetic example for raw shot data, in which reflections can be recognized as hyperbolas. In Figure 3a, artifact patterns 302, reflections caused by the seabed 304, and another group of reflections and diffractions 306 can be recognized. In normal cases, reflections are more visible, such as the seabed reflection marked as "Seafloor" in marine seismic datasets. In the display of raw data from all signals recorded for a shot, reflections appear as hyperbolas. In fact, diffractions are contained in the data; however, due to their weak amplitude and the different path of the reflections, Petition 870260057744, dated 06 / 15 / 2026, page 25 / 100 22 / 38 can only be seen very faintly in this representation, or not at all. On the x-axis of Figure 3a and Figure 3b, the Source-Receiver-Offset (in m) can be seen, while on the y-axis of Figures 3a to 3h, the TWT (Two-Way-Travel Time) is specified in ms.

[060] Figure 3b illustrates a relative signal reinforcement of diffractions in the capture of the acoustic signal (“shot gather”). This signal reinforcement can be achieved, for example, by suppressing reflections in the receiver signal. The objective of this step is to strengthen weak diffractions compared to reflections. For this, reflections are, for example, weakened pointwise. This is possible because the time duration curves of diffractions and reflections are distinct. In Figure 3b, noise and, especially, diffractions, which cause a chaotic impression in the representation in relation to the displacement of the original receiver, can be better differentiated by a weakening of reflections. As represented, diffractions cannot yet be clearly recognized even after suppressing reflections in the representation of all records for a shot. In the case of strong diffractions, this step can also be skipped.Again, models of artifacts 314 can be recognized in Figure 3b.

[061] Next, the parts of the receiver signal are assigned to the detection grid points. The detection grid points are also called CFPs (Common-Fault-Points). Figure 3c illustrates a classification of existing seismic data (the parts of the receiver signal) into Common Fault Points (the grouping of the parts of the receiver signal to the detection grid points), as performed, for example, by Kanasewich and Phadke, 1988 in “Imaging discontinuities on seismic sections”. In the representation of the signals in relation to the distance from the receiver to a CFP (x-axis, also in Figure 3d) in Figure 3c, it is now possible to recognize the diffractions 322 as Petition 870260057744, dated 06 / 15 / 2026, p. 26 / 100 23 / 38 hyperbolas. It is also possible to see in Figure 3c the artifact models 324. Figure 3c shows, in this case, the diffractions in the CFP capture (“CFP gather”).

[062] To allow statistical evaluation and improved representation, all firings and receivers are consolidated into a CFP, for example, which would contain the diffraction of an object at those positions. In other words, the receiver signal may comprise parts of several receivers and parts of several firings (acoustic signals). These parts are now assigned to the grid points.

[063] In this case, there are at least three different possibilities for assigning acoustic recordings to CFPs. The classification or representation of the recording in the CFP consolidation occurs according to the distance of the CFP in relation to the reference recording receiver. In other words, parts of the receiver signal can be grouped based on a distance from the detection grid points to the receivers of the plurality of receivers and at least one signal source of the various acoustic signals to the detection grid points.

[064] A first possibility represents the so-called Real Aperture Processing (processing based on actual aperture). In this approach, each shot is processed individually; that is, the parts of the receiver signal are grouped separately to the detection grid points for each acoustic signal. To do this, based on the positions of the receiver and the source, the detection range (which can be formed as a grid) of the CFPs is extended to a maximum distance from the source, which depends on the properties of the source used (e.g., aperture angle). The traces, i.e., the record of 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.

[065] Alternatively, one can use, for example, the Synthetic Aperture Processing, therefore, processing with the aid of synthetic aperture processing. Petition 870260057744, dated 06 / 15 / 2026, p. 27 / 100 24 / 38 of a synthetic aperture. By using a synthetic aperture, multiple receivers in the direction of movement can be synthetically amplified, sending multiple acoustic signals. Thus, in this approach, a fixed number of consecutive triggers are consolidated. In other words, parts of the receiver signal are grouped for a predefined number of moments in the predefined time sequence consolidated to the detection grid points. Starting from the positioning of the receivers and trigger points, a CFP-Grid (detection grid) is extended. The traces of the consolidated triggers are assigned to each CFP.

[066] Alternatively, the parts of the receiver signal can be assigned purely based on their distance to the detection grid points, i.e., all traces within a given distance to a CFP are assigned to that CFP. In other words, the parts of the receiver signal can be grouped for a predefined distance from the detection grid points relative to the receivers of the plurality of receivers and at least one signal source to the detection grid points. The distance must be large enough to include a large part of the expected diffraction hyperbola. If the chosen distance is too large, the signal quality will decrease. A usable distance can be estimated based on the raw data or according to the signal enhancement of the diffractions by measuring the size of the contained diffraction hyperbolas.

[067] Subsequently, for the assigned receiver signal parts, the duration time correction is performed in relation to the detection grid points, i.e., a positioning of the receiver and the signal source to the detection grid point is included, to achieve that different distances from the receiver and the receiver to the respective point in the combination of receiver signal parts are compensated. Figure 3d illustrates the duration time correction of Petition 870260057744, dated 06 / 15 / 2026, p. 28 / 100 25 / 38 receiver signal. Through an appropriate duration time correction, the diffractions become a coherent and seismic event, as can be seen in Figure 3d. The objective of the duration time correction is to correct the diffraction signal of an object at the CFP location so that it occurs in all traces at the same time.

[068] In this case, reference number 332 shows the time-corrected diffraction duration, reference number 334 shows a multiple of the diffraction, and reference number 336 shows the artifact models. Figure 3d shows, in this case, the time-corrected diffractions in the CFP capture (“CFP gather, Diffraction Move Out (DiffMO)”).

[069] In this case, different duration time correction approaches can be pursued. For example, a constant velocity duration time correction can be used, i.e., for the duration time correction, a constant seismic velocity can be assumed. This allows for an improved second-order duration time correction equation when a constant velocity can be assumed:

[070] The duration time correction described here at constant speeds is based on a second-order duration time calculation for a point diffraction (e.g., Yilmaz 1991: “Seismic data processing”; Sheriff and Geldart 1995; Clearbout 2010). The correction depends on the spatial situation of the diffraction source = (, ,), the receiver = (,,), the source = (,,) and the speed of the Root-Mean-Square (mean-square value): equation 5: WTTA-

[071] Equation 5 can be simplified, based on the record of Petition 870260057744, dated 06 / 15 / 2026, page 29 / 100 26 / 38 so that the source and receiver are located on the sea surface. Thus, both are defined as equal to zero. Furthermore, the positions of the source and receiver are reduced to a distance of one CFP (equation 6):

[072] This results in the following simplified duration time equation: equation 7: dfZddrZd TWTa= ^ + ^-+ + ?'?hj 'rins

[073] In equation 7 the vertical duration time is included in the following format: equation 8: 2zd= —

[074] This vertical duration time is constant for a point diffractor in a CFP and thus a DiffMO correction can be derived from equation 7: equation 9: l(tf+W22TWT^dj + d^ J V4V2 d ---—--

[075] As can be seen in Figure 3d, this equation corrects diffraction in a CFP for a horizontal and therefore coherent event.

[076] Alternatively, a Radon-Transformation at unknown velocities can be used, for example. One of the most important variables for the time-duration correction described above is the assumed average seismic velocity. When this is not known, the time-duration correction can be Petition 870260057744, dated 06 / 15 / 2026, page 30 / 100 27 / 38 performed, alternatively, for a range of possible velocities. In other words, the duration time correction can be performed for a range of possible seismic velocities.

[077] When diffraction is contained in the processed CFP data, the stacked signal is displayed locally and a maximum is displayed with the appropriate average velocity. Thus, for example, a seismic velocity from the range of possible seismic velocities can be selected based on an extension of a local maximum in the corresponding consolidation of the time-corrected parts of the receiver signal for the time-duration correction. Through this procedure, positions of scattered bodies and average velocities can be determined. This methodology represents a Radon Transformation, since it is transformed from a signal data space into a velocity data space.

[078] Alternatively, a duration-time correction can be used at known velocities. In most cases, it is not possible to assume that the seismic velocity remains constant with increasing duration time, for example, due to compacted sediments, which exhibit high seismic velocity. On the contrary, it is possible to assume that this velocity is a function of the duration time: (). Following the nomenclature described above, according to Guigné et al. (2014), a duration-time curve for diffractions can also be described using the following equation: equation 10: TWTA=

[079] The terms or describe, in this case, separately, the duration times of the source and the receptors to the respective CFP to be treated. These duration time equations Petition 870260057744, dated 06 / 15 / 2026, p. 31 / 100 28 / 38 can be formulated as one that depends on vlw(7WT) (see, for example, Yilmaz, 1991). Consequently, for duration time correction at the detection grid points, coordinate seismic velocities can be used for different material layers between the receiver plurality and the detection grid points.

[080] After time-duration correction, the traces are summed (consolidated) within the CFPs (i.e., at each point on the detection grid, separately) and the number of traces, for example, is distributed. This results, for example, in a single total trace, which may correspond to the consolidation of parts of the receiver signal, in which the amplitude is applied against the time-duration. If the time-duration corrected traces, as shown in Figure 3e, are now stacked, a clearly recognizable signal boost results for the time of the coherent events (as stacked diffraction, reference number 342). Consequently, the detection of one or more objects can now be performed on the consolidation of parts of the receiver signal (therefore, without the inclusion of other method steps), i.e., one or more objects can be detected based on an amplitude of the consolidation of parts of the receiver signal corrected for time-duration.The time duration (TWT) shown in Figure 3eh corresponds to the vertical duration time, i.e., if the velocity model is available, the depth can be converted. This step represents a first statistical evaluation and aims, for example, to suppress noise and strengthen the signal. Here, all signals that are not diffractions are designated as noise. Individual traces can also be scaled to compensate for energy loss due to spatial propagation of the signal (Spherical Divergence Correction). In other words, the method, as shown in Figure 1b, may involve a 145 adjustment of the consolidation of the corrected receiver signal parts. Petition 870260057744, dated 06 / 15 / 2026, page 32 / 100 29 / 38 in duration time, to achieve signal reinforcement of signal parts that are based on the dispersion of various acoustic signals in objects further away from one or more objects (e.g., by using spherical divergence correction). The reference number 344 shows a diffraction multiple, which can be caused, for example, by diffraction reflection. In Figure 3e, the amplitude of the stacked signal is applied on the x-axis, from -4 to +4 x 10-9.

[081] To improve the ability to consolidate the time-corrected portions of the receiver signal, a so-called consolidation envelope of the time-corrected portions of the receiver signal can be calculated. In other words, the method can optionally calculate one of the amplitudes of the consolidation of the time-corrected portions of the receiver signal. One or more objects can be detected based on envelopes. Figure 3f illustrates the optional calculation of the stacked signal envelopes. Through the optional calculation of the envelopes (in English “stack envelope”), as shown in Figure 3f, the representation of the stack amplitude in a single maximum 352 is simplified. It is possible to evaluate the signal with phase information and it can also contain other information about the point diffractor formed. It is also possible to see in Figure 3f the diffraction multiple 354. In Figure 3f, on the x-axis, the envelope is applied, on a scale of 0 to approximately 4.5 x 10-9.

[082] Additionally, optionally, a coherence function can be calculated by consolidating the parts of the receiver signal corrected over a duration of 160, for example, one called Semblance. In Figure 3g, Semblance / coherence (a measure of similarity or coherence) is optionally calculated as a measure of coherence for Figure 3d (the signals corrected over duration). Semblance, i.e., the similarity of features within a CFP over a defined time window, is a measure of coherence. Petition 870260057744, dated 06 / 15 / 2026, page 33 / 100 30 / 38 of the time-corrected signals. In other words, the coherence function can be based on a similarity between consecutive parts of the receiving signal. The coherence function can be based, for example, on a similarity analysis. The use of other coefficients, for example, correlation coefficients, is conceivable and does not change anything in the overall solution. One or more objects can be detected based on the coherence function. The reference number 362 indicates the acoustic signal in diffraction, and the reference number 364 indicates the diffraction multiple. In Figure 3g, on the x-axis, Semblance (from 0 to 0.4) is applied.

[083] The chosen coherence measure (e.g., Semblance) is (optionally) multiplied by the envelopes of the stacks (called signal weighting), as shown in Figure 3h. Artifacts are attenuated and resolution is increased, diffractions (reference number 372) can now be easily identified, and many of the diffraction multiples 374 are also easily differentiated. In other words, the method can calculate a weighted envelope based on envelopes and on the coherence function, for example, by multiplying the envelopes and the coherence function. Weighting results in improved resolution and expressiveness. The detection of one or more objects can be based, for example, on the weighted envelope. This step, however, is not strictly necessary for the solution.

[084] In many examples of the modality, the method may further comprise identifying a reflection of the acoustic signal dispersion in one or more objects, for example, by removing all other maxima close to a principal maximum. The reflection of the current acoustic signal dispersion in one or more objects may be disregarded in the detection of one or more objects, or in the detection or by adjusting the consolidation of the parts of the receiver signal corrected in Petition 870260057744, dated 06 / 15 / 2026, page 34 / 100 31 / 38 duration time.

[085] The method further comprises detecting one or more objects at the detection grid points based on the consolidation of the time-corrected portions of the receiver signal. For example, an object may be detected from one or more objects when the amplitude of the time-corrected consolidation portions of the receiver signal, the envelope of the amplitude of the time-corrected consolidation portions of the receiver signal, or the weighted envelope exceed a threshold value, for example, if the corresponding maximum of the amplitude of the time-corrected consolidation portions of the receiver signal, the envelope of the amplitude of the time-corrected consolidation portions of the receiver signal, or the weighted envelope coincide with a position of the detection grid point and / or indicate a coherent signal. For example, the method may comprise a determination of an underground model based on one or more detected objects.

[086] The detection of one or more objects is based (exclusively) on the dispersion of the various acoustic signals in one or more objects, i.e., the reflection of the acoustic signal, for example, can be ignored or disregarded in the detection of one or more objects.

[087] The data processing process described has the great advantage that, through this method, only point diffractions with very good resolution can be partially mapped and real-time data processing is possible.

[088] All parts of the wavefield, except point diffractions, are in some modality examples considered as noise and suppressed as much as possible. The relative signal enhancement described for diffractions initially causes a strong suppression of reflections, which constitute the dominant part of the wavefield. The subsequent duration time correction corrects, in many examples Petition 870260057744, dated 06 / 15 / 2026, p. 35 / 100 32 / 38 modality, (only) point diffractions for coherent events. As a result, (only) point diffractions are enhanced by subsequent stacking. Optional weighting of stacked data with Semblance provides additional noise suppression and allows for improved interpretation of results. In a grid of CFPs above the study area, relative maxima are shown (only) at the points and depths where diffractions can be found. This allows the spatial location of a large number of existing point diffractors to be determined. Because a large number of data traces in each CFP are used for the evaluation, it is possible to arrive at statistically significant conclusions.

[089] In at least many modality examples, very good resolution is achieved, since, in classification, traces of a wide area around the CFPs are used. For example, the best possible coverage of the study area can be guaranteed in the prior registration of the data and aliasing of the data in space can be avoided. The spatial extent of the positions of the source and receiver during data collection can define how good the spatial resolution achievable will be. The greater the expansion, the smaller the distance from which two close point diffractions can be distinguished.

[090] Real-time data processing is possible in at least some modality examples, since reflection suppression is applied to individual shots and Synthetic Aperture Processing is applied to shots registered one after the other. Thus, this method, in these cases, can provide results that can be used for evaluation even during data collection.

[091] In contrast to techniques that are based on migration, the disclosure described here is not very susceptible to errors in the field of assumed speed. In suppressing reflections, the correction Petition 870260057744, dated 06 / 15 / 2026, page 36 / 100 33 / 38 of the duration time for reflections is applied forward and in reverse, such that the assumed velocity field does not distort the traces. Tests have shown that the duration time correction for diffractions is less prone to errors in the velocity domain. Errors resulting from an incorrect velocity field appear in the accuracy of object localization, but the method's ability to detect point diffractors is not significantly affected. Furthermore, the migration algorithms are comparatively significantly more computationally complex.

[092] The modality examples create a combination of CFP classification and Synthetic Aperture Processing to locate point diffractors, which is not yet known.

[093] Point diffractors in marine sediments can be different types of objects, for example, glacial pebbles, other geological inhomogeneities, or UXOs.

[094] Technology is also of interest for geoscientific studies. With the method described here, for example, fluid discharge points, subsurface fault areas and concretions can be mapped and analyzed.

[095] Examples of modality can be used, for example, with a specific signal source system and a plurality of receivers. The plurality of receivers can be distributed, for example, over a surface. This surface forms the opening of the plurality of receivers, i.e., the larger the surface, the larger the opening of the plurality of receivers. Thus, the receivers of the plurality of receivers can be arranged, in a regular or irregular way, in a grid, which forms the opening of the plurality of receivers. The method (or processing module) can detect, based on the dispersion of the acoustic signal in one or more objects, one or more objects both under the grid (of the surface, of the Petition 870260057744, dated 06 / 15 / 2026, p. 37 / 100 34 / 38 opening) when offset from the grid, for example, offset by at least 10° (or offset by at least 20°, offset by at least 30°, offset by at least 45°). With a large opening, the angle may be greater than 45°.

[096] The acoustic signal can be generated, for example, from at least one signal source. The at least one signal source can be arranged in different positions in this case, for example, inside or outside the surface, in a plurality of receivers. The at least one signal source can be, in this case, an acoustic and / or seismic signal source, for example, a GI-Gun (Generator-Injector-Gun), a Sparker (sound source with electrical discharge), or a Boomer (sound source that stores energy in capacitors and emits it through a flat coil in the shape of a spiral, so that water is displaced through an adjacent copper plate). The terms acoustic and seismic can be used interchangeably here, since in the present approach wavelengths are used, which can be assigned to acoustic and seismic signals.

[097] For this purpose, at least one signal source and a plurality of receivers may be provided to be towed by a ship along a water surface above the seabed. The detection grid may be, for example, a two-dimensional detection grid. The two-dimensional detection grid may be extended along the direction of the ship's course and along the depth axis between at least one signal source and / or the plurality of receivers and the extended seabed. Alternatively, the detection grid may be a three-dimensional detection grid, which is extended along the direction of the ship's course, orthogonal to the direction of the ship's course and along the depth axis between at least one signal source and / or the plurality of receivers and the seabed.

[098] Interface 12 may correspond, for example, to a or Petition 870260057744, dated 06 / 15 / 2026, p. 38 / 100 35 / 38 more inputs and / or one or more outputs to receive and / or transmit information, for example, in digital bit values, based on a code, within a module, between modules, or between modules of different entities.

[099] In the embodiment examples, the processing module can correspond to any controller or processor or to a programmable hardware component. For example, processing module 14 can also be implemented as software, which is programmed for a corresponding hardware component. In this sense, processing module 14 can be implemented as programmable hardware with correspondingly tuned software. In this case, any processors can be used, such as digital signal processors (DSPs). The embodiment examples are not restricted, in this case, to a specific type of processor. Any number of processors or multiple processors are conceivable to implement processing module 14.

[100] The aspects and characteristics that are described together with one or more of the examples and figures detailed above may also be combined with one or more of the other examples, in order to replace an identical characteristic of the other example or to additionally present the characteristic in the other example.

[101] In addition, examples may be a computer program with program code to execute one or more of the above methods or refer to when the computer program is executed on a computer or processor. Steps, operations, or processes of the various methods described above may be performed by programmed computers or processors. Examples may also include program storage devices, for example, digital data storage media that are readable. Petition 870260057744, dated 06 / 15 / 2026, page 39 / 100 36 / 38 per machine, processor, or computer and which encode machine-executable, processor-executable, or computer-executable instruction programs. The instructions execute some or all of the steps of the method described above or cause them to be executed. Program storage devices may comprise or be, for example, digital storage, magnetic storage media such as magnetic disks and magnetic tapes, hard disk drives, or optically readable digital data storage media. Other examples may also include computers, processors, or control units that are programmed to execute the steps of the method described above, or field-programmable logic arrays ((F)PLAs) or field-programmable gate arrays ((F)PGAs) that are programmed to execute the steps of the method described above.

[102] The description and drawings only illustrate the principles of disclosure. Furthermore, all examples listed here are expressly intended to serve only illustrative purposes, in order to assist the reader in understanding the principles of disclosure and the concepts for the further development of the technology contributed by the inventor(s). All statements herein concerning principles, aspects and examples of disclosure, as well as specific examples thereof, include their equivalents.

[103] A block of functions referred to as “means to…” perform a specific function may refer to a circuit that is designed to perform a specific function. Thus, a means to something may be implemented as a means designed for or suitable for something, for example, a component or a circuit designed for or suitable for the respective task.

[104] The functions of various elements shown in the figures, Petition 870260057744, dated 06 / 15 / 2026, p. 40 / 100 37 / 38 including each of the function blocks referred to as means, means to provide a signal, means to generate a signal, etc. can be implemented in the form of dedicated hardware, for example, a signal provider, a signal processing unit, a processor, a controller, etc., as well as hardware capable of executing the software in conjunction with the associated software. When provided by a processor, the functions can be provided by a single dedicated processor, a single shared processor, or a plurality of individual processors, some or all of which may be shared.However, the term processor or controller is by no means limited to hardware that is exclusively capable of executing software, but may include digital signal processor (DSP) hardware, network processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), read-only memory (ROM) for software storage, random access memory (RAM), and non-volatile storage. Other hardware, conventional and / or customized, may also be included.

[105] For example, a block diagram can represent a high-level circuit diagram that implements the principles of disclosure. Similarly, a flowchart, a sequence diagram, a state transition diagram, pseudocode, and the like can represent various processes, operations, or steps that are essentially represented, for example, in a computer-readable medium and thus are executed by a computer or processor, regardless of whether such a computer or processor is explicitly shown. The method disclosed in the description or in Petition 870260057744, dated 06 / 15 / 2026, page 41 / 100 38 / 38 claims can be implemented by a device with the means to execute each of the respective steps of that method.

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

[107] In addition, the following claims are incorporated into the detailed description, where each claim may constitute a separate example. Although each claim may stand in itself as a separate example, it should be noted that while a dependent claim in the claims may refer to a particular combination with one or more other claims, other examples also include a combination of the dependent claim with the object of each of the dependent or independent claims. Such combinations are explicitly suggested here unless it is indicated that a particular combination is not desired. Furthermore, the features of a claim are also intended to be included in any other independent claim, even if that claim is not made directly dependent on the independent claim.

Claims

CLAIMS 1. Method for detecting one or more objects on the seabed, characterized in that it comprises: receiving (110) a receiver signal, wherein the receiver signal is based on a dispersion of several acoustic signals on one or more objects on the seabed, wherein the receiver signal is generated by a plurality of receivers; generating a detection grid, wherein the detection grids represent a grid in whose points one or more objects are located, wherein the detection grid is distributed both below a surface covered by the plurality of receivers, and also displaced from the surface; grouping (120) the parts of the receiver signal to the points of the detection grid, performing (130) the time correction of the duration of the parts of the receiver signal in relation to the points of the detection grid.consolidate (140) the time-corrected portions of the receiver signal at the detection grid points; detect (190) one or more objects at the detection grid points both below the surface covered by the plurality of receivers and displaced from the surface based on the consolidation of the time-corrected portions of the receiver signal, wherein the detection of one or more objects is enhanced by the dispersion of the various acoustic signals on one or more objects; and calculate (150) an amplitude envelope of the consolidation of the time-corrected portions of the receiver signal, wherein one or more objects can be detected based on envelopes.

2. Method, according to claim 1, characterized in that one or more objects can be detected based on an amplitude of the consolidation of the parts of the receiver signal corrected in the time duration.

3. Method, according to any one of claims 1 or 2, characterized in that it further comprises calculating (160) a coherence function based on consolidation of parts of the receiver signal corrected in the duration time, wherein the coherence function is based on a similarity between consecutive parts of the receiver signal, wherein, one or more objects are detected based on the coherence function.

4. Method according to claim 3, characterized in that it further comprises calculating (170) a weighted envelope based on envelopes and based on a coherence function.

5. A method, according to either of claims 3 or 4, characterized in that the coherence function is based on a similarity analysis.

6. A method, according to any one of claims 1 to 5, characterized in that it further comprises scaling the individual parts of the receiver signal corrected in duration, to achieve a signal reinforcement of signal parts that is based on the dispersion of the various acoustic signals in objects further away from one or more objects.

7. Method, according to any one of claims 1 to 6, characterized in that it further comprises identifying (180) a reflection of the acoustic signal dispersion in one or more objects, wherein the reflection of the acoustic signal dispersion in one or more objects is disregarded in the detection of one or more objects.

8. A method, according to any one of claims 1 to 7, characterized in that a constant seismic velocity is assumed for the duration time correction.

9. Method, according to any of the claims Petition 870260057744, dated 06 / 15 / 2026, p. 44 / 100 3 / 5 1 to 7, characterized in that the duration time correction is performed for a range of possible seismic velocities, wherein a seismic velocity is selected from the range of possible seismic velocities based on an extension of a local maximum in the corresponding consolidation of the duration time-corrected parts of the receiver signal for duration time correction.

10. A method, according to any one of claims 1 to 7, characterized in that, for the time duration correction at the detection grid points, coordinated seismic velocities for different material layers between the plurality of receivers and the detection grid points can be used.

11. A method, according to any one of claims 1 to 10, characterized in that the parts of the receiver signal can be grouped based on a distance from the detection grid points to the receivers of the plurality of receivers and to at least one signal source of the various acoustic signals to the detection grid points.

12. Method, according to claim 11, characterized in that the parts of the receiver signal are grouped for each acoustic signal to the detection grid points, separately.

13. Method, according to claim 11, characterized in that the parts of the receiver signal are grouped for a predefined number of moments in the predefined time sequence consolidated to the detection grid points.

14. Method, according to claim 11, characterized in that the parts of the receiver signal can be grouped to a predefined distance from the detection grid points in relation to the receivers of the plurality of receivers and at least one signal source to the detection grid points.

15. Method, according to any of the claims in Petition 870260057744, dated 06 / 15 / 2026, page 45 / 100 4 / 5 1 to 14, characterized in that the receiving signal comprises a first part of the signal, which is based on the dispersion of the various acoustic signals in one or more objects, and in that the receiving signal comprises a second part of the signal, which is based on a reflection of the various acoustic signals, in which the method comprises a suppression of the second part of the signal relative to the first part of the signal, in which the grouping of the parts of the receiving signal, the realization of the time-duration correction, the consolidation of the time-duration corrected parts and / or the detection of one or more objects are based on the first part of the signal.

16. Method, according to claim 15, characterized in that the suppression of the second part of the signal is based on a high-value decomposition of a time-corrected version of the receiver signal.

17. Method, according to any one of claims 1 to 16, characterized in that it comprises suppression (115), in the receiver signal, of the reflection of the various acoustic signals.

18. Storage devices characterized by containing instructions for executing the method, as defined in any of the preceding claims, when the instructions are executed in a computer, a processor, a control module, or a programmable hardware component.

19. Device (10) for detecting one or more objects on the seabed, characterized in that it comprises: an interface (12) for receiving a receiver signal, wherein the receiver signal is based on a dispersion of several acoustic signals on one or more objects on the seabed, wherein the receiver signal is generated by a plurality of receivers; and a processing module (14), configured to: generate a detection grid, in which the detection grids represent a grid in which the points of the one or more objects are located, wherein the detection grid is distributed both below a surface covered by the plurality of receivers, and also displaced from the surface; group the parts of the receiver signal to the points of the detection grid, perform the time correction of the duration of the parts of the receiver signal in relation to the points of the detection grid.consolidate the time-corrected portions of the receiver signal at the detection grid points; and detect one or more objects at the detection grid points both below the surface covered by the plurality of receivers and displaced from the surface based on the consolidation of the time-corrected portions of the receiver signal, where the detection of one or more objects is enhanced by the dispersion of the various acoustic signals in one or more objects.

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