Weak signal enhancement method, device and equipment based on three-dimensional curvelet domain in low signal-to-noise region and medium
By decomposing and processing seismic data using the three-dimensional curve domain method, weak reflection signals are extracted and enhanced, solving the signal identification and enhancement problems in seismic exploration in low signal-to-noise ratio areas, and improving the quality of seismic data and the success rate of exploration.
Patent Information
- Application Number
- CN202410825967.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-25
- Publication Date
- 2025-12-26
Smart Images
Figure CN121208918A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of seismic exploration, specifically relating to a method, device, equipment, and medium for enhancing weak signals in low signal-to-noise regions based on three-dimensional curved wave domain. Background Technology
[0002] As is well known, with my country's oil and gas exploration moving towards complex reservoirs, the importance of seismic exploration is increasing. As oil and gas exploration targets become increasingly complex and sophisticated, seismic exploration must not only ensure the quantity and accuracy of acquired data but also save economic costs; "cost reduction and efficiency improvement" has become a crucial objective. However, seismic data in these areas typically has low signal-to-noise ratios (SNR). Therefore, extracting effective reflection signals from this low SNR seismic data is particularly important and key to improving the success rate of exploration in complex areas. In seismic exploration, increasing the energy of effective reflection signals in low SNR areas can optimize shot density and reduce investment in acquisition equipment, making it an effective method for improving quality and efficiency. To address this, this invention proposes a three-dimensional curve domain weak signal enhancement method for low SNR areas. Summary of the Invention
[0003] This invention aims to solve the problem of how to enhance weak signals in low signal-to-noise ratio areas during seismic exploration. It proposes a method, device, equipment, and medium for weak signal enhancement in low signal-to-noise areas based on three-dimensional curve domain. This solution is applicable to the identification and enhancement of weak reflection signals in seismic acquisition, processing, and interpretation in low signal-to-noise ratio areas.
[0004] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:
[0005] A method for enhancing weak signals in low signal-to-noise regions based on three-dimensional curve domain is characterized by the following steps:
[0006] Step a: Decompose the seismic data into a combination of harmonic signals of different frequencies and amplitudes, and perform three-dimensional curvelet transform;
[0007] Step b: Extract the set of extreme values of the curvelet coefficients of the decomposed signal at various scales and directions in the three-dimensional curvelet transform domain;
[0008] Step c: Establish a time window in the 3D seismic data where weak reflection signals are visible. By establishing a weak signal threshold range, extract the extreme values of the curve wave coefficients of the 3D seismic data within the time window.
[0009] Step d: Use the above steps to filter out weak reflection signals containing effective waves from all seismic data;
[0010] Step e: Perform an inverse transform on the selected curve coefficients to transform them back to the time domain, and obtain the time domain signal with the noise removed.
[0011] Step f: Energy enhancement is performed on the time-domain signal to obtain the final weak reflection signal in the middle and deep layers, thus completing the weak signal identification and noise reduction process.
[0012] Furthermore, in step c, when extracting the extreme values of the curve wave coefficients of the three-dimensional seismic data within the time window, it is necessary to optimize the number of curve wave coefficients participating in each iterative calculation, and ensure convergence accuracy through iterative calculation.
[0013] Furthermore, in step c, the Kaiser window function is used to overlap the curve wave coefficients, so that each iteration selects a portion of the curve wave coefficients from the previous iteration.
[0014] Furthermore, iterative calculations are performed by establishing a novel exponential threshold model, which is as follows:
[0015]
[0016] Where K is the number of iterations, N is the total number of iterations, and λ k C represents the control parameter for the curvelet coefficients used in each iteration of the calculation. mk max and C mk min These represent the maximum and minimum values of the curve coefficients corresponding to weak signals.
[0017] Furthermore, the Kaiser window function is as follows:
[0018]
[0019] Where β is the window size control function, which is determined based on the signal-to-noise ratio of the actual data; N is the window length during calculation, n = 1, 2, 3...N.
[0020] Furthermore, in step d, weak reflection signals containing effective waves are filtered out from all seismic data according to the following formula:
[0021]
[0022] In the formula, C mk (j,l,k) represents the weak reflection signal of the selected effective wave; C D (j,l,k) are the curve wave coefficients obtained after forward transformation of all seismic data.
[0023] This invention also proposes a low signal-to-noise region based three-dimensional curve domain weak signal enhancement device, comprising:
[0024] The seismic signal decomposition module is used to decompose seismic data into a combination of harmonic signals of different frequencies and amplitudes, and to perform three-dimensional curvelet transform.
[0025] The curve wave coefficient extremum extraction module is used to extract the set of curve wave coefficient extrema of the decomposed seismic signal at various scales and directions in the three-dimensional curve wave transform domain.
[0026] The sample extraction module is used to establish time windows in 3D seismic data where weak reflection signals are visible. By establishing a threshold range for weak signals, the extreme values of the curve wave coefficients of 3D seismic data within the time window are extracted.
[0027] The total amount extraction module filters out weak reflection signals containing effective waves from all seismic data;
[0028] The inverse transform module is used to perform an inverse transform on the extracted curve coefficients, transforming them back to the time domain to obtain a time domain signal with noise filtered out.
[0029] The signal enhancement module is used to enhance the energy of the inverse-transformed time-domain signal to obtain the final weak reflection signal in the middle and deep layers.
[0030] Furthermore, it also includes:
[0031] The iterative calculation module is used to optimize the number of curvelet coefficients participating in each iterative calculation when extracting the extreme values of curvelet coefficients in 3D seismic data within a time window, and to ensure convergence accuracy through iterative calculation.
[0032] The iterative optimization module is used to perform overlapping processing on the curve wave coefficients using the Kaiser window function, so that each iteration selects a portion of the curve wave coefficients from the previous iteration.
[0033] The present invention also proposes a low signal-to-noise region based three-dimensional curved wave domain weak signal enhancement device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the low signal-to-noise region based three-dimensional curved wave domain weak signal enhancement method described above.
[0034] The present invention also proposes a computer-readable storage medium storing a computer program, characterized in that running the computer program can execute the steps in the above-described method for enhancing weak signals in a low signal-to-noise region based on a three-dimensional curved wave domain.
[0035] In summary, the present invention has the following advantages:
[0036] 1. This invention starts with signal and noise identification, and utilizes the strong sparse representation capability of three-dimensional curvelet transform to perform sparse transform on seismic data by scale and orientation. This enables the identification of seismic signals of different directions and types, and completes the separation of effective signals and noise in one go, as well as the identification of weak signals in the middle and deep layers. Ultimately, it achieves the purpose of identifying and enhancing weak reflection signals in areas with low signal-to-noise ratio, and improves the quality of seismic data in complex areas.
[0037] 2. This invention utilizes the three-dimensional curvelet transform method to optimize the weak signal threshold range within the sample window and perform enhancement processing, thereby completing the weak signal enhancement processing of the entire data.
[0038] 3. In order to ensure the continuity of data, this invention uses the Kaiser window function to process the overlapping parts of the curve coefficients, avoiding the problems of waveform splicing and large energy variation amplitude, and the processed data has high accuracy. Attached Figure Description
[0039] Figure 1 This is a schematic diagram of continuous three-dimensional curved wave transform segmentation in a specific embodiment of the present invention;
[0040] Figure 2 This is a schematic diagram of discrete three-dimensional curvelet transform segmentation in a specific embodiment of the present invention;
[0041] Figure 3 This is the original seismic profile before processing in a specific embodiment of the present invention;
[0042] Figure 4 These are deep weak signals selected in specific implementations of this invention;
[0043] Figure 5 This is a seismic profile after weak signal enhancement processing in a specific embodiment of the present invention. Detailed Implementation
[0044] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further clarifies the invention. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.
[0045] Example 1
[0046] This invention proposes a three-dimensional curve domain-based weak signal enhancement method for low signal-to-noise ratio regions, which is applicable to the identification and enhancement of weak reflection signals in seismic acquisition, processing and interpretation in low signal-to-noise ratio areas.
[0047] First, the seismic signal is theoretically decomposed.
[0048] According to harmonic theory, any seismic signal (including noise signals) can be decomposed into countless combinations of harmonic signals with different frequencies and amplitudes, and the signal expression is defined as:
[0049] s(t)=a(t)*e 2πimft +n(t) (1)
[0050]
[0051] Where a is the amplitude envelope, T is the scan time, f is the instantaneous frequency, and fL f is the starting frequency. H The termination frequency is given by the above expressions (1) to (3). It can be seen that the reflected signal and noise have the problem of being confused at the same frequency. The reflected signal cannot be separated by frequency alone. The directionality of the seismic reflected signal also needs to be considered.
[0052] The specific implementation steps of this method are as follows:
[0053] Step 1: Perform three-dimensional curvelet transform on the seismic data, which has been decomposed into numerous combinations of harmonic signals with different frequencies and amplitudes. The specific process is as follows:
[0054] A. First, perform a three-dimensional Fourier transform on s(t) to obtain its frequency domain expression. Where -n / 2≤ω1,ω2,ω3≤n / 2;
[0055] B. In In the frequency range, the basis functions of the discrete three-dimensional curvelet transform are gradually covered by a series of wedges. Figure 1 and Figure 2 Select a frequency component of a certain sector angle and scale, and represent it in three-dimensional Cartesian grid form as f(n1,n2,n3), where 0≤n1,n2,n3≤n, and use it as valid input. The corresponding set of curvelet coefficients C D (j,l,k) is:
[0056]
[0057] Where j,l∈Z,k=(k1,k2,k3).
[0058] C. The three-dimensional curvelet transform basis φ in the above equation is composed of a radial window function and a angular window function. First, the radial window function is expressed in the form of a Cartesian radius:
[0059]
[0060] and, in, It is a low-pass smoothing function; φ is the "mother waveform" of the curve transform.
[0061] D. Typically, the basis function volume of a discrete three-dimensional curvelet transform has 6 surfaces. At a fine scale, taking the first part (ω1>0) as an example: assuming (1, α... l ,β l If ) represents the direction of the wedge's centerline, then the angular window function is defined as:
[0062]
[0063] For the other 5 parts, a similar definition is made by swapping ω1 and ω2 or ω1 and ω3.
[0064] frequency domain window for:
[0065]
[0066] E. The basis functions for the transformation of the discrete curvilinear wave with final displacement parameter k at scale j and angle l are:
[0067]
[0068] Where 0≤k1<L 1,j,l ,0≤k2<L 2,j,l ,0≤k3<L 3,j,l .
[0069] Step 2: Extract the set of extreme values (maximums and minimums) of the curvelet coefficients of the decomposed signal at various scales and directions in the three-dimensional curvelet domain. The formula is as follows:
[0070]
[0071] Among them, C max (j,l,k) and C min (j,l,k) represents the maximum and minimum values of the curve wave coefficient in the original seismic data.
[0072] Step 3: Since the seismic data is very large, using the above steps, establish a time window (the time window where weak reflection signals are visible) in the original 3D seismic data. Determine the threshold range by selecting a sample data, and then extract the maximum and minimum curve wave coefficient values of the 3D seismic data within the time window.
[0073] The threshold range for establishing the curve coefficients of weakly reflected signals is as follows:
[0074] (C mk max C mk min )=MK(C s max (j,l,k),C s min (j,l,k)) (11)
[0075] Where MK is the threshold function, and C s max (j,l,k) and C s min (j,l,k) represent the maximum and minimum curvature coefficient values of the reflected signal within the time window; C mk max and Cmk min These represent the maximum and minimum values of the curve coefficients corresponding to weak signals.
[0076] In this step, to ensure computational accuracy, the number of curvelet coefficients participating in each iteration needs to be optimized to guarantee convergence accuracy, while also considering computational efficiency. Therefore, this step uses a novel exponential threshold model for iterative calculation:
[0077]
[0078] Where K is the number of iterations, N is the total number of iterations, and λ k These are the control parameters for the curve coefficients used in each iterative calculation.
[0079] If the curve wave coefficients calculated in each iteration are extracted using the above equation (12), it will result in hard connections between the curve wave coefficients calculated in each iteration, thus causing waveform splicing and large energy variation in the final time-domain seismic profile. Therefore, this step uses the Kaiser window function to overlap the curve wave coefficients, so that a small portion of the curve wave coefficients from the previous iteration will be selected in each iteration.
[0080] The Kaiser window function can adjust the window size based on the data signal-to-noise ratio. Therefore, after processing with the Kaiser window function, the problems of waveform splicing and large energy variation amplitude can be eliminated. The Kaiser window function in this embodiment is as follows:
[0081]
[0082] Where β is the window size control function, which is determined based on the signal-to-noise ratio of the actual data, and is usually chosen as a natural number; N is the window length during calculation, n = 1, 2, 3...N.
[0083] Step 4: Using the above methods, screen the curve wave coefficients of all seismic data within the work area, and select weak reflection signals containing effective waves:
[0084]
[0085] In the formula, C mk (j,l,k) represents the weak reflection signal of the selected effective wave; C D (j,l,k) are the curve wave coefficients obtained after forward transformation of all seismic data.
[0086] Step 5: Perform an inverse transform on the selected curvelet coefficients to return them to the time domain. Since the time-domain signal after the inverse transform consists of curvelet coefficients within a specific range, noise filtering has already been performed. Based on this, energy enhancement is then applied to obtain the final mid-to-deep weak reflection signal, thus completing both weak signal identification and noise reduction. Figure 3 , Figure 4 and Figure 5 ).
[0087] Example 2
[0088] This invention also proposes a low signal-to-noise region based three-dimensional curve domain weak signal enhancement device, comprising:
[0089] The seismic signal decomposition module is used to decompose seismic data into a combination of harmonic signals of different frequencies and amplitudes, and to perform three-dimensional curvelet transform.
[0090] The curve wave coefficient extremum extraction module is used to extract the set of curve wave coefficient extrema of the decomposed seismic signal at various scales and directions in the three-dimensional curve wave transform domain.
[0091] The sample extraction module is used to establish time windows in 3D seismic data where weak reflection signals are visible. By establishing a threshold range for weak signals, the extreme values of the curve wave coefficients of 3D seismic data within the time window are extracted.
[0092] The total amount extraction module filters out weak reflection signals containing effective waves from all seismic data;
[0093] The inverse transform module is used to perform an inverse transform on the extracted curve coefficients, transforming them back to the time domain to obtain a time domain signal with noise filtered out.
[0094] The signal enhancement module is used to enhance the energy of the inverse-transformed time-domain signal to obtain the final weak reflection signal in the middle and deep layers.
[0095] Furthermore, it also includes:
[0096] The iterative calculation module is used to optimize the number of curvelet coefficients participating in each iterative calculation when extracting the extreme values of curvelet coefficients in 3D seismic data within a time window, and to ensure convergence accuracy through iterative calculation.
[0097] The iterative optimization module is used to perform overlapping processing on the curve wave coefficients using the Kaiser window function, so that each iteration selects a portion of the curve wave coefficients from the previous iteration.
[0098] The functions of each of the above-mentioned modules correspond one-to-one with the steps of each method in Example 1, and will not be repeated here.
[0099] Example 3
[0100] This embodiment provides a low signal-to-noise region weak signal enhancement device based on three-dimensional curved wave domain, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the low signal-to-noise region weak signal enhancement method based on three-dimensional curved wave domain described in Embodiment 1 above.
[0101] Preferably, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the device.
[0102] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the device and connects the various parts of the device using various interfaces and lines.
[0103] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card, a secure digital card, and a flash memory card, or the memory can be other volatile solid-state storage devices.
[0104] Example 4
[0105] This invention also proposes a computer-readable storage medium storing a computer program that, when executed, can perform the steps in the low signal-to-noise region weak signal enhancement method based on three-dimensional curved domain as described in Embodiment 1 above.
[0106] Computer storage media can be tangible media that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Any simple modifications or equivalent variations made to the above embodiments based on the technical essence of the present invention fall within the protection scope of the present invention.
Claims
1. A method for enhancing weak signals in the low signal-to-noise region based on three-dimensional curve domain, characterized in that, Includes the following steps: Step a: Decompose the seismic data into a combination of harmonic signals of different frequencies and amplitudes, and perform three-dimensional curvelet transform; Step b: Extract the set of extreme values of the curvelet coefficients of the decomposed signal at various scales and directions in the three-dimensional curvelet transform domain; Step c: Establish a time window in the 3D seismic data where weak reflection signals are visible. By establishing a weak signal threshold range, extract the extreme values of the curve wave coefficients of the 3D seismic data within the time window. Step d: Use the above steps to filter out weak reflection signals containing effective waves from all seismic data; Step e: Perform an inverse transform on the selected curve coefficients to transform them back to the time domain, and obtain the time domain signal with the noise removed. Step f: Energy enhancement is performed on the time-domain signal to obtain the final weak reflection signal in the middle and deep layers, thus completing the weak signal identification and noise reduction process.
2. The method for enhancing weak signals in low signal-to-noise regions based on three-dimensional curve domain as described in claim 1, characterized in that, In step c, when extracting the extreme values of the curve wave coefficients of the 3D seismic data within the time window, it is necessary to optimize the number of curve wave coefficients participating in each iteration calculation, and ensure convergence accuracy through iterative calculation.
3. The method for enhancing weak signals in low signal-to-noise regions based on three-dimensional curve domain as described in claim 2, characterized in that, In step c, the Kaiser window function is used to overlap the curve wave coefficients, so that each iteration selects a portion of the curve wave coefficients from the previous iteration.
4. A method for enhancing weak signals in low signal-to-noise regions based on three-dimensional curve domain as described in claim 2 or 3, characterized in that, A novel exponential threshold model is established for iterative calculation. The novel exponential threshold model is as follows: Where K is the number of iterations, N is the total number of iterations, and λk is the control parameter for the curve coefficients involved in each iteration; C mk max and C mk min These represent the maximum and minimum values of the curve coefficients corresponding to weak signals.
5. The method for enhancing weak signals in low signal-to-noise regions based on three-dimensional curve domain as described in claim 3, characterized in that, The Kaiser window function is as follows: Where β is the window size control function, which is determined based on the signal-to-noise ratio of the actual data; N is the window length during calculation, n = 1, 2, 3...N.
6. The method for enhancing weak signals in low signal-to-noise regions based on three-dimensional curve domain as described in claim 1, characterized in that, In step d, weak reflection signals containing effective waves are filtered out from all seismic data according to the following formula: In the formula, C mk (j,l,k) represents the weak reflection signal of the selected effective wave; C D (j,l,k) are the curve wave coefficients obtained after forward transformation of all seismic data.
7. A low signal-to-noise region weak signal enhancement device based on three-dimensional curve domain, characterized in that, include: The seismic signal decomposition module is used to decompose seismic data into a combination of harmonic signals of different frequencies and amplitudes, and to perform three-dimensional curvelet transform. The curve wave coefficient extremum extraction module is used to extract the set of curve wave coefficient extrema of the decomposed seismic signal at various scales and directions in the three-dimensional curve wave transform domain. The sample extraction module is used to establish time windows in 3D seismic data where weak reflection signals are visible. By establishing a threshold range for weak signals, the extreme values of the curve wave coefficients of 3D seismic data within the time window are extracted. The total amount extraction module filters out weak reflection signals containing effective waves from all seismic data; The inverse transform module is used to perform an inverse transform on the extracted curve coefficients, transforming them back to the time domain to obtain a time domain signal with noise filtered out. The signal enhancement module is used to enhance the energy of the inverse-transformed time-domain signal to obtain the final weak reflection signal in the middle and deep layers.
8. A low signal-to-noise region weak signal enhancement device based on three-dimensional curve domain according to claim 7, characterized in that, Also includes: The iterative calculation module is used to optimize the number of curvelet coefficients participating in each iterative calculation when extracting the extreme values of curvelet coefficients in 3D seismic data within a time window, and to ensure convergence accuracy through iterative calculation. The iterative optimization module is used to perform overlapping processing on the curve wave coefficients using the Kaiser window function, so that each iteration selects a portion of the curve wave coefficients from the previous iteration.
9. A low signal-to-noise region weak signal enhancement device based on three-dimensional curve domain, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the low signal-to-noise region weak signal enhancement method based on three-dimensional curve domain as described in any one of claims 1 to 6.
10. A computer-readable storage medium storing a computer program, characterized in that, Running this computer program can perform the steps in a low signal-to-noise region weak signal enhancement method based on three-dimensional curved domain as described in any one of claims 1 to 6 above.