Seabed node image processing method and device

Abstract

The invention provides a seabed node image processing method, system and device, and a storage medium. The method comprises the following steps: respectively carrying out one-dimensional dual-tree complex wavelet domain band-pass filtering processing on uplink and downlink wave imaging gathers in an original seabed node image one by one; performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink wave imaging gathers subjected to the one-dimensional dual-tree complex wavelet domain band-pass filtering processing; performing matched filtering on the uplink and downlink wave imaging gathers subjected to the two-dimensional dual-tree complex wavelet transform; and performing two-dimensional dual-tree complex wavelet inverse transformation on the complex wavelet coefficients of the uplink and downlink wave imaging gathers after matched filtering processing to obtain a final seabed node image. According to the method, the quality of uplink and downlink wave imaging gathers is remarkably improved, the consistency of the uplink and downlink wave imaging gathers is improved, and the imaging definition is improved.

Description

Technical Field

[0001] The present invention relates to the field of detection data processing, and more specifically, to a method, system, device and storage medium for seabed node image processing. Background Art

[0002] In the seismic exploration of ocean bottom nodes (OBN), the sea surface is a strong reflection interface, which will generate ghost waves in the seabed received data. The ghost wave interference will form false phase axes in the seismic records and cause notch effects in the spectrum, thereby reducing the resolution of the underground effective reflection seismic signals and affecting the accuracy of seismic data interpretation. In the pre-stack common imaging point gathers of up and down waves, due to the existence of random noise, long-offset gather stretching, and high-steep offset noise, the quality of subsequent joint imaging of up and down waves will be seriously affected. Summary of the invention

[0003] In view of this, the purpose of the embodiments of the present invention is to propose a method, system, electronic device and computer-readable storage medium for seabed node image processing. The present invention improves the quality of uplink and downlink imaging gathers and improves the consistency of uplink and downlink imaging gathers by first performing one-dimensional complex wavelet domain bandpass filtering on the uplink and downlink wave gathers respectively, and then performing matched filtering on the results of the above bandpass filtering in the two-dimensional complex wavelet domain.

[0004] Based on the above purpose, one aspect of an embodiment of the present invention provides a method for processing an ocean bottom node image, comprising the following steps: performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original ocean bottom node image one by one; performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering; performing matched filtering on the uplink and downlink imaging gathers that have been subjected to two-dimensional dual-tree complex wavelet transform; and performing two-dimensional dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the uplink and downlink imaging gathers that have been subjected to matched filtering to obtain a final ocean bottom node image.

[0005] In some embodiments, the step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seabed node image, respectively, includes: setting the low-level complex wavelet coefficients dominated by high-frequency random noise in the one-dimensional complex wavelet domain to zero, and setting the high-level complex wavelet coefficients dominated by stretching noise of the one-dimensional complex wavelet domain low-frequency far-offset gathers to zero.

[0006] In some embodiments, the step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers of the data in the original seabed node image respectively includes: performing a nine-level one-dimensional dual-tree complex wavelet transform decomposition on the uplink and downlink imaging gathers, wherein levels one to two are high-frequency random noise, and levels seven to nine are low-frequency far-offset gather stretching noise.

[0007] In some embodiments, the step of performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering includes: performing two-dimensional dual-tree complex wavelet transform according to the number of rows and columns of each pulse in the uplink and downlink imaging gathers to form a three-level decomposition complex wavelet coefficient amplitude spectrum.

[0008] In some embodiments, the step of matching filtering the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform includes matching filtering the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers.

[0009] In some embodiments, the step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers includes: determining the phase angle of the complex wavelet coefficient according to the size of the real part and the imaginary part of the complex wavelet coefficient and the angle determined by the quadrant position of the Cartesian coordinate system in which they are located.

[0010] In some embodiments, the step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers includes: in response to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers being greater than a threshold, setting the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers to zero.

[0011] In another aspect of an embodiment of the present invention, a system for processing seabed node images is provided, including: a bandpass filtering module, used to perform one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seabed node image one by one; a transformation module, used to perform two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering; a matched filtering module, used to perform matched filtering on the uplink and downlink imaging gathers that have been subjected to two-dimensional dual-tree complex wavelet transform; and an inverse transformation module, used to perform two-dimensional dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the uplink and downlink imaging gathers that have been subjected to matched filtering to obtain a final seabed node image.

[0012] According to another aspect of an embodiment of the present invention, there is provided an electronic device, comprising: at least one processor; and a memory, wherein the memory stores computer instructions executable on the processor, and the instructions implement the steps of the above method when executed by the processor.

[0013] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program that implements the above method steps when executed by a processor.

[0014] The present invention has the following beneficial technical effects: by first performing one-dimensional complex wavelet domain bandpass filtering on the uplink and downlink wave gathers respectively, and then performing matched filtering on the results after the above bandpass filtering in the two-dimensional complex wavelet domain, the quality of the uplink and downlink wave imaging gathers is improved and the consistency of the uplink and downlink wave imaging gathers is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic diagram of an embodiment of a method for processing seabed node images provided by the present invention;

[0017] Figure 2 (a) and Figure 2 (b) is a schematic diagram of the one-dimensional dual-tree complex wavelet forward transform and inverse transform provided by the present invention;

[0018] 3(a) to 3(j) are schematic diagrams of 9-level decomposition of one-dimensional dual-tree complex wavelet transform of downlink imaging gathers provided by the present invention;

[0019] Figure 4 A schematic diagram of a pulse provided by the present invention and its two-dimensional dual-tree complex wavelet transform three-level decomposition amplitude spectrum;

[0020] Figure 5 A schematic diagram of the waveform response of the three-level decomposition of the point pulse two-dimensional dual-tree complex wavelet transform provided by the present invention;

[0021] 6(a) to 6(f) are comparison diagrams of the joint denoising effect of uplink and downlink in the dual-tree complex wavelet domain provided by the present invention;

[0022] Figure 7 A schematic diagram of an embodiment of a system for seabed node image processing provided by the present invention;

[0023] Figure 8 A schematic diagram of the hardware structure of an embodiment of an electronic device for seabed node image processing provided by the present invention;

[0024] Fig. 9 A schematic diagram of an embodiment of a computer storage medium for seafloor node image processing provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0026] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0027] A first aspect of the embodiments of the present invention provides an embodiment of a method for processing an ocean bottom node image. Figure 1 FIG. 2 is a schematic diagram of an embodiment of a method for processing an image of a seafloor node provided by the present invention. Figure 1 As shown, the embodiment of the present invention includes the following steps:

[0028] S1, performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seafloor node image one by one;

[0029] S2, performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers processed by one-dimensional dual-tree complex wavelet domain bandpass filtering respectively;

[0030] S3, performing matched filtering on the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform; and

[0031] S4. Perform two-dimensional dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the uplink and downlink imaging gathers after matched filtering to obtain the final seafloor node image.

[0032] The embodiment of the present invention utilizes the dual-tree complex wavelet transform to perform frequency division and multi-directional decomposition characteristics of signals. To address the problems of high-frequency random noise, low-frequency far-offset gather stretching noise and high-steep offset noise in the pre-stack common imaging point gathers of the uplink and downlink wave fields, a joint denoising technical solution is adopted in which one-dimensional complex wavelet domain bandpass filtering is first performed on the uplink and downlink wave gathers respectively, and then matched filtering is performed on the results after the bandpass filtering in the two-dimensional complex wavelet domain, so as to achieve the purpose of improving the quality of the uplink and downlink wave imaging gathers and improving the consistency of the uplink and downlink wave imaging gathers.

[0033] The up and down wave imaging gathers in the original seafloor node image are bandpass filtered in the one-dimensional dual-tree complex wavelet domain. The dual-tree complex wavelet transform (DTCWT) is realized by two sets of parallel real discrete wavelet transforms, one of which is completed in tree 1 and the other in tree 2. The subband coefficients generated in tree 1 and tree 2 are the real and imaginary parts of the complex wavelet coefficients, respectively. Compared with the real discrete wavelet transform, the dual-tree complex wavelet transform has better translation invariance and more directional sorting in addition to the same multi-scale frequency division characteristics.

[0034] In some embodiments, the step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the upstream and downstream wave imaging gathers in the original seafloor node image respectively includes: setting the low-level complex wavelet coefficients dominated by high-frequency random noise in the one-dimensional complex wavelet domain to zero, and setting the high-level complex wavelet coefficients dominated by the stretched noise of the low-frequency far-offset gather in the one-dimensional complex wavelet domain to zero. One-dimensional dual-tree complex wavelet domain bandpass filtering refers to setting the low-level complex wavelet coefficients dominated by high-frequency random noise in the one-dimensional complex wavelet domain to zero to suppress the high-frequency random noise; setting the high-level complex wavelet coefficients dominated by the stretched noise of the low-frequency far-offset gather in the one-dimensional complex wavelet domain to zero to suppress the stretched noise of the low-frequency far-offset gather.

[0035] Figure 2 A schematic diagram of the one-dimensional dual-tree complex wavelet forward transform and inverse transform provided by the present invention, Figure 2 (a) is a positive transformation, Figure 2 (b) is the inverse transformation. Figure 2 (a) and Figure 2 In (b), the subband coefficients generated in tree 1 are the real parts of the complex wavelet coefficients, and the subband coefficients generated in tree 2 are the imaginary parts of the complex wavelet coefficients.

[0036] In some embodiments, the step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers of the data in the original seabed node image respectively includes: performing a nine-level one-dimensional dual-tree complex wavelet transform decomposition on the uplink and downlink imaging gathers, wherein levels one to two are high-frequency random noise, and levels seven to nine are low-frequency far-offset gather stretching noise.

[0037] Fig. 3 is a schematic diagram of the nine-level decomposition of the one-dimensional dual-tree complex wavelet transform of the downlink wave imaging gather provided by the present invention, Fig. 3(a) is the original downlink wave imaging gather, Fig. 3(b) to Fig. 3(j) are the bandpass filtering results of levels 1 to 9 obtained by performing the nine-level decomposition of the one-dimensional dual-tree complex wavelet transform on Fig. 3(a), respectively. It can be seen that levels 1 to 2 are the dominant components of high-frequency random noise; levels 7 to 9 are the dominant components of the stretching noise of the low-frequency far-offset gather.

[0038] The uplink and downlink imaging gathers processed by one-dimensional dual-tree complex wavelet domain bandpass filtering are respectively subjected to two-dimensional dual-tree complex wavelet transform.

[0039] In some embodiments, the step of performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering includes: performing two-dimensional dual-tree complex wavelet transform according to the number of rows and columns of each pulse in the uplink and downlink imaging gathers to form a three-level decomposition complex wavelet coefficient amplitude spectrum.

[0040] Figure 4 A schematic diagram of the pulse and its two-dimensional dual-tree complex wavelet transform three-level decomposition amplitude spectrum provided by the present invention, Figure 4(a) The number of pulse data rows nr0 = 128, the number of columns nc0 = 128; Figure 4 (b) is the amplitude spectrum after the third-level decomposition of the second-level dual-tree complex wavelet transform, with the number of rows nr=2*nr0=258 and the number of columns nc0=2*nc0=258; among them, the three quadrants q11, q12 and q13 are the first-level decomposition complex wavelet coefficient amplitude spectrum; the three quadrants q21, q22 and q23 are the second-level decomposition complex wavelet coefficient amplitude spectrum; the four quadrants q31, q32, q33 and q34 are the last level, i.e., the third-level decomposition complex wavelet coefficient amplitude spectrum.

[0041] Figure 5 This is a schematic diagram of the waveform response of the three-level decomposition of the point pulse two-dimensional dual-tree complex wavelet transform provided by the present invention. Figure 5 The top row is the waveform response corresponding to the real and imaginary parts of the first-order complex wavelet coefficients q11, q12, and q13 in the three quadrants; Figure 5 The middle row is the waveform response corresponding to the real and imaginary parts of the second-level complex wavelet coefficients q21, q22, and q23 in the three quadrants; Figure 5 The bottom line is the waveform response corresponding to the real and imaginary parts of the three quadrants of the third-level complex wavelet coefficients q31, q32, and q33. It can be seen that the dual-tree complex wavelet transform has the ability to decompose data in frequency and direction. This feature is conducive to fine matching filtering of uplink and downlink waves in the two-dimensional dual-tree complex wavelet domain, thereby effectively suppressing random noise, long-offset gather stretching noise, offset high steepness noise and other non-consistent noise.

[0042] Matched filtering is performed on the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform.

[0043] In some embodiments, the step of matching filtering the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform includes matching filtering the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers.

[0044] The phase angle C of the complex wavelet coefficient refers to the angle determined by the size of the real part Pr and the imaginary part Pi of the complex wavelet coefficient and the quadrant position of the Cartesian coordinate system. Its radian value range is greater than or equal to 0 and less than 2Π:

[0045] When Pr>0, Pi>0, C=ATAN(|Pi| / |Pr|);

[0046] When Pr<0, Pi>0, C=0.5Π+ATAN(|Pi| / |Pr|);

[0047] When Pr<0, Pi<0, C=1.0Π+ATAN(|Pi| / |Pr|);

[0048] When Pr>0, Pi<0, C=1.5Π+ATAN(|Pi| / |Pr|);

[0049] When Pr=0, Pi=0, C=0;

[0050] When Pr=0, Pi>0, C=0.5Π;

[0051] When Pr=0, Pi<0, C=1.5Π;

[0052] When Pi=0, Pr<0, C=1.0Π;

[0053] When Pi=0, Pr>0, C=0.

[0054] In some embodiments, the step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers includes: determining the phase angle of the complex wavelet coefficient according to the size of the real part and the imaginary part of the complex wavelet coefficient and the angle determined by the quadrant position of the Cartesian coordinate system in which they are located.

[0055] In some embodiments, the step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers comprises: in response to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers being greater than a threshold, the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers are set to zero. Matched filtering means that when the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers is greater than a threshold, the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers are set to zero.

[0056] The complex wavelet coefficients of the uplink and downlink imaging gathers after matched filtering are subjected to two-dimensional double-tree complex wavelet inverse transform to obtain the final seafloor node image.

[0057] Figure 6 is a comparison diagram of the joint denoising effect of uplink and downlink waves in the dual-tree complex wavelet domain provided by the present invention, and Figures 6(a)-6(b) are respectively the uplink and downlink wave gathers before the dual-tree complex wavelet domain joint denoising; Figures 6(c)-6(d) are respectively the uplink and downlink wave gathers after the dual-tree complex wavelet domain joint denoising; Figures 6(e)-6(f) are respectively the uplink and downlink wave gathers after the dual-tree complex wavelet domain joint denoising process to remove noise. It can be seen from the above figures that the dual-tree complex wavelet domain joint denoising method can effectively suppress the high-frequency random noise, low-frequency far-offset gather stretching noise and high-steep offset noise in the pre-stack common imaging point gathers of the uplink and downlink wave fields, significantly improve the quality of the uplink and downlink wave imaging gathers, and improve the consistency of the uplink and downlink wave imaging gathers.

[0058] It should be pointed out in particular that the various steps in the various embodiments of the above-mentioned method for processing seabed node images can be cross-linked, replaced, added, and deleted. Therefore, these reasonable permutations, combinations and transformations of the method for processing seabed node images should also fall within the scope of protection of the present invention, and the scope of protection of the present invention should not be limited to the embodiments.

[0059] Based on the above purpose, the second aspect of the embodiment of the present invention provides a system for processing seabed node images. Figure 7 As shown, the system 200 includes the following modules: a bandpass filtering module, used to perform one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seabed node image one by one; a transformation module, used to perform two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering; a matched filtering module, used to perform matched filtering on the uplink and downlink imaging gathers that have been subjected to two-dimensional dual-tree complex wavelet transform; and an inverse transformation module, used to perform two-dimensional dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the uplink and downlink imaging gathers that have been subjected to matched filtering to obtain the final seabed node image.

[0060] In some embodiments, the bandpass filtering module is used to set low-level complex wavelet coefficients dominated by high-frequency random noise in the one-dimensional complex wavelet domain to zero, and set high-level complex wavelet coefficients dominated by low-frequency far-offset gather stretching noise in the one-dimensional complex wavelet domain to zero.

[0061] In some embodiments, the bandpass filtering module is used to perform a one-dimensional dual-tree complex wavelet transform nine-level decomposition on the uplink and downlink imaging gathers, wherein levels one to two are high-frequency random noise, and levels seven to nine are low-frequency far-offset gather stretching noise.

[0062] In some embodiments, the transformation module is used to perform a two-dimensional dual-tree complex wavelet transform according to the number of rows and columns of each pulse in the uplink and downlink imaging gathers to form a three-level decomposition complex wavelet coefficient amplitude spectrum.

[0063] In some implementations, the matched filtering module is used to perform matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers.

[0064] In some implementations, the matched filter module is used to determine the phase angle of the complex wavelet coefficient according to the magnitude of the real part and the imaginary part of the complex wavelet coefficient and the angle determined by the quadrant position of the Cartesian coordinate system where the complex wavelet coefficient is located.

[0065] In some embodiments, the matched filtering module is used to set the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers to zero in response to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers being greater than a threshold.

[0066] Based on the above purpose, the third aspect of the embodiment of the present invention proposes an electronic device, including: at least one processor; and a memory, the memory storing computer instructions that can be run on the processor, the instructions being executed by the processor to implement the following steps: S1, performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seabed node image one by one; S2, performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering; S3, performing matched filtering on the uplink and downlink imaging gathers that have been subjected to two-dimensional dual-tree complex wavelet transform; and S4, performing two-dimensional dual-tree complex wavelet inverse transform on the complex wavelet coefficients of the uplink and downlink imaging gathers that have been subjected to matched filtering to obtain the final seabed node image.

[0067] In some embodiments, the step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seabed node image, respectively, includes: setting the low-level complex wavelet coefficients dominated by high-frequency random noise in the one-dimensional complex wavelet domain to zero, and setting the high-level complex wavelet coefficients dominated by stretching noise of the one-dimensional complex wavelet domain low-frequency far-offset gathers to zero.

[0068] In some embodiments, the step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers of the data in the original seabed node image respectively includes: performing a nine-level one-dimensional dual-tree complex wavelet transform decomposition on the uplink and downlink imaging gathers, wherein levels one to two are high-frequency random noise, and levels seven to nine are low-frequency far-offset gather stretching noise.

[0069] In some embodiments, the step of performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers that have been subjected to one-dimensional dual-tree complex wavelet domain bandpass filtering includes: performing two-dimensional dual-tree complex wavelet transform according to the number of rows and columns of each pulse in the uplink and downlink imaging gathers to form a three-level decomposition complex wavelet coefficient amplitude spectrum.

[0070] In some embodiments, the step of matching filtering the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform includes matching filtering the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers.

[0071] In some embodiments, the step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers includes: determining the phase angle of the complex wavelet coefficient according to the size of the real part and the imaginary part of the complex wavelet coefficient and the angle determined by the quadrant position of the Cartesian coordinate system in which they are located.

[0072] In some embodiments, the step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers includes: in response to the difference in phase angles of the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers being greater than a threshold, setting the complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers to zero.

[0073] like Figure 8 FIG. 1 is a schematic diagram of the hardware structure of an embodiment of the electronic device for seabed node image processing provided by the present invention.

[0074] As Figure 8 Taking the device shown as an example, the device includes a processor 301 and a memory 302.

[0075] The processor 301 and the memory 302 may be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0076] The memory 302 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the method for processing the image of the seabed node in the embodiment of the present application. The processor 301 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 302, that is, implementing the method for processing the image of the seabed node.

[0077] The memory 302 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the method for processing the seabed node image, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 302 may optionally include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] Computer instructions 303 corresponding to one or more methods of processing seafloor node images are stored in the memory 302. When executed by the processor 301, the method of processing seafloor node images in any of the above method embodiments is executed.

[0079] Any embodiment of an electronic device that executes the above-mentioned method for processing seabed node images can achieve the same or similar effect as any corresponding embodiment of the above-mentioned method.

[0080] The present invention also provides a computer-readable storage medium, which stores a computer program for executing the method for processing seabed node images when the computer-readable storage medium is executed by a processor.

[0081] like Fig. 9 FIG. 1 is a schematic diagram of an embodiment of a computer storage medium for the above-mentioned seafloor node image processing provided by the present invention. Fig. 9 Taking the computer storage medium shown as an example, the computer readable storage medium 401 stores a computer program 402 that performs the above method when executed by a processor.

[0082] Finally, it should be noted that a person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a computer program, and the program of the method for processing the seabed node image can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program can be a disk, an optical disk, a read-only storage memory (ROM) or a random access memory (RAM), etc. The above-mentioned computer program embodiment can achieve the same or similar effect as any of the corresponding aforementioned method embodiments.

[0083] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0084] It should be understood that, as used herein, the singular forms "a", "an" are intended to include the plural forms as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations including one or more of the associated listed items.

[0085] The serial numbers of the embodiments disclosed in the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0086] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0087] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for processing seabed node images, characterized in that: The steps include: The up and down wave imaging gathers in the original seafloor node image are processed by one-dimensional double-tree complex wavelet domain bandpass filtering. The uplink and downlink imaging gathers processed by one-dimensional dual-tree complex wavelet domain bandpass filtering are respectively transformed into two-dimensional dual-tree complex wavelet transform; Perform matched filtering on the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform; as well as The complex wavelet coefficients of the uplink and downlink imaging gathers after matched filtering are subjected to two-dimensional double-tree complex wavelet inverse transform to obtain the final seafloor node image.

2. The method for processing seabed node images according to claim 1, characterized in that: The step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seafloor node image respectively and one by one comprises: The low-level complex wavelet coefficients dominated by high-frequency random noise in the one-dimensional complex wavelet domain are set to zero, and the high-level complex wavelet coefficients dominated by low-frequency far-offset gather stretching noise in the one-dimensional complex wavelet domain are set to zero.

3. The method for processing seafloor node images according to claim 1, characterized in that: The step of performing one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seafloor node image respectively and one by one comprises: The uplink and downlink imaging gathers are decomposed into nine levels by one-dimensional double-tree complex wavelet transform, where levels one to two are high-frequency random noises, and levels seven to nine are low-frequency far-offset gather stretching noises.

4. The method for processing seafloor node images according to claim 1, characterized in that: The step of performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink imaging gathers respectively after the one-dimensional dual-tree complex wavelet domain bandpass filtering process comprises: A two-dimensional dual-tree complex wavelet transform is performed according to the number of rows and columns of each pulse in the uplink and downlink imaging gathers to form a three-level decomposition complex wavelet coefficient amplitude spectrum.

5. The method for processing seafloor node images according to claim 1, characterized in that: The step of performing matched filtering on the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform comprises: Matched filtering is performed on the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers.

6. The method for seafloor node image processing according to claim 5, characterized in that: The step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers comprises: The phase angle of the complex wavelet coefficient is determined according to the size of the real part and the imaginary part of the complex wavelet coefficient and the angle determined by the quadrant position of the Cartesian coordinate system.

7. The method for processing seafloor node images according to claim 5, characterized in that: The step of performing matched filtering on the uplink and downlink imaging gathers according to the difference in phase angles of complex wavelet coefficients at corresponding positions of the uplink and downlink imaging gathers comprises: In response to the difference in phase angles of complex wavelet coefficients at corresponding positions of uplink and downlink imaging gathers being greater than a threshold, the complex wavelet coefficients at the corresponding positions of the uplink and downlink imaging gathers are set to zero.

8. A system for seabed node image processing, characterized in that: include: The bandpass filtering module is used to perform one-dimensional dual-tree complex wavelet domain bandpass filtering on the uplink and downlink imaging gathers in the original seabed node image one by one; A transformation module, used for performing two-dimensional dual-tree complex wavelet transformation on the uplink and downlink imaging gathers processed by one-dimensional dual-tree complex wavelet domain bandpass filtering; A matched filter module is used to perform matched filtering on the uplink and downlink imaging gathers after two-dimensional dual-tree complex wavelet transform; as well as The inverse transformation module is used to perform two-dimensional dual-tree complex wavelet inverse transformation on the complex wavelet coefficients of the uplink and downlink imaging gathers after matched filtering to obtain the final seabed node image.

9. An electronic device, characterized in that: include: at least one processor; as well as A memory storing computer instructions executable on the processor, wherein the instructions, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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