Seabed waterway thick-layer sandstone earthquake prediction method
The seismic prediction method for thick sandstones of subsea waterways combined with well recording, logging and sparse pulse methods has been solved in the prior art. The problem of poor prediction effect of thick sandstones of subsea waterways of 'V' or 'U' type undercut morphology in the existing technology is solved. , and the accurate depiction and feature prediction of thick sandstone in the subsea waterway are achieved.
Patent Information
- Application Number
- CN202411151799.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-21
- Publication Date
- 2024-12-10
AI Technical Summary
The prior art is difficult to accurately predict thick layer of subsea waterways with 'V' or 'U' undercut morphology, especially sandstone with a thickness greater than 100m. The inversion result shows an illusion inside the thick layer of sandstone, which cannot effectively characterize its development characteristics. .
A comprehensive histogram of well recording and logging curves was used to establish a longitudinal wave impedance curve interpretation diagram, and the configuration characteristics of the subsea waterway were explained through instantaneous phase attributes, a formation grid model was established, and the longitudinal wave impedance data was inverted by sparse pulse method, combining thick sandstone factors. The attributes are low-frequency correction, overcoming the shortcomings of the existing layered lattice model.
The ‘V’ or ‘U’-shaped undercut morphology of the subsea waterway is accurately portrayed, eliminating the mudstone artifact inside the thick sandstone, and significantly improving the prediction effect of the characteristics of thick sandstone.
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Figure CN119105108A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of oil and gas exploration geophysics, and more specifically, relates to a method for predicting thick-layer sandstone earthquakes in submarine waterways. Background Art
[0002] Thick sandstone in submarine channels often has large reservoir thickness and good physical properties, and is easy to form large-scale oil and gas reservoirs. It is one of the important goals of oil and gas exploration and development research. Using seismic prediction methods to accurately estimate sandstone thickness is one of the most important research tasks.
[0003] There are several main problems in the seismic prediction of thick sandstone in submarine waterways: First, submarine waterways usually present a "V" or "U" shaped downcut. The stratigraphic framework established using the existing top and bottom layers is a layered framework model, which cannot reflect the structural morphology of submarine waterways; Second, the sand bodies of submarine waterways are usually thick sandstones hundreds of meters deep. The existing seismic inversion technology based on the convolution model has a good prediction effect for medium-thickness (λ / 4~3λ / 4, taking the velocity of 4000m / s, the main frequency of 40Hz as an example, the thickness is 25m~75m) sandstone. For thick sandstones with a thickness greater than 100m, the inversion results will appear in the thick sandstone. Illusions cannot be well predicted for the development characteristics of thick sandstones. It can be seen that the existing technology is not ideal for predicting thick sandstones in submarine waterways that present a "V" or "U" shaped downcut. Summary of the invention
[0004] In order to overcome the problem in the prior art that the prediction effect of thick sandstone in submarine waterways presenting a "V"-shaped or "U"-shaped undercut morphology is not ideal, the present invention provides a method for seismic prediction of thick sandstone in submarine waterways.
[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:
[0006] A method for predicting thick sandstone earthquakes in a submarine waterway comprises the following steps:
[0007] S1: Use the comprehensive histogram of logging and well logging curves to establish the interpretation chart of the longitudinal wave impedance curve of thick sandstone in the submarine channel;
[0008] S2: Obtain instantaneous phase attributes from the seismic data volume and use them to interpret the structural characteristics of thick sandstone layers in submarine channels;
[0009] S3: using the thick sandstone configuration characteristics of the submarine channel obtained in step S2, establishing a stratigraphic framework model capable of characterizing the morphology of the submarine channel;
[0010] S4: Under the constraints of the stratigraphic framework model established in step S3, the P-wave impedance data volume is inverted using the sparse pulse method:
[0011] S5: Construct thick-layer sandstone factor attributes that can reflect thick-layer sandstone;
[0012] S6: Using the thick-layer sandstone factor attribute, the P-wave impedance data volume inverted by the sparse pulse method is subjected to low-frequency correction to obtain a low-frequency corrected P-wave impedance data volume;
[0013] S7: interpreting the longitudinal wave impedance data volume after the low-frequency correction in step S6 according to the longitudinal wave impedance curve interpretation chart of the thick sandstone in the submarine waterway established in step S1.
[0014] Preferably, in step S2, the complex seismic trace attribute is the attribute obtained by performing Hilbert transform on the original seismic trace, the seismic signal of the seismic data volume is f(t), the Hilbert transform of the seismic signal is g(t), the complex seismic trace is expressed as F(t)=f(t)+ig(t), and the expression of the instantaneous phase attribute is
[0015] Ph(t)=arctan[f(t) / g(t)].
[0016] Preferably, in step S3, during the establishment of the stratigraphic framework model, information fusion technology is used to unify geological, logging and seismic multi-geological information into the same model, thereby achieving organic fusion of various types of information in the model space, and improving the information usage of inversion, information matching accuracy and confidence of inversion results.
[0017] Preferably, in step S3, the constraints of the thick sandstone configuration characteristics of the submarine channel are taken into consideration when establishing the stratigraphic framework model, so as to be able to characterize the stratigraphic framework model of the submarine channel morphology.
[0018] Preferably, in step S4, based on norm deconvolution and maximum likelihood deconvolution, it is assumed that the reflection coefficient sequence of the formation is composed of a series of sparse and discontinuous large reflection coefficients and Gaussian distributed small reflection coefficients; from a geological perspective, the large reflection coefficient represents the underground discontinuous interface and lithological interface, and the objective function is obtained based on the basic assumptions of the constrained sparse pulse method:
[0019]
[0020] In the formula, R 2 is the mean square value of the reflection coefficient, N 2 is the mean square value of noise, the reflection coefficient of the Kth sampling point is r(K), n(K) is the noise, M is the number of reflection layers, L is the total number of samples, and λ is the likelihood value of the given reflection coefficient;
[0021] Then, the reflection coefficient is calculated iteratively, and then the maximum likelihood deconvolution is used to obtain the reflection coefficient. The recursive algorithm is combined with the initial longitudinal wave impedance trend to obtain the longitudinal wave impedance model;
[0022] The sparse pulse method inversion objective function adjusts the initial longitudinal wave impedance calculated from the seismic traces one by one, and adjusts the reflection coefficient to obtain the final objective function:
[0023] F=L p (r)+λL q (sd)+a -1 L1ΔZ
[0024] Where r is the reflection coefficient, ΔZ is the impedance trend difference, d is the seismic trace, s is the synthetic seismic trace, λ is the residual weight factor, a is the trend weight factor, L is the modulus function, L1 is the first-order modulus function, and p,q are L modulus factors. Sparse pulse inversion is based on seismic data and is constrained by wave impedance trends in order to optimally match synthetic seismic records with seismic traces using the least reflection coefficient pulses.
[0025] Preferably, in step S5, the seismic signal is subjected to Hilbert transform to establish thick-layer sandstone factor attributes, that is, to calculate the change of amplitude with frequency. The thick-layer sandstone factor attribute calculation formula is as follows:
[0026]
[0027] Preferably, in step S6, the frequency spectrum characteristics of the thick sandstone factor attributes and the sparse pulse inversion P-wave impedance data volume are extracted respectively, and the P-wave impedance data volume of the sparse pulse inversion in step S5 is subjected to low-frequency correction using the frequency components of the thick sandstone factor attributes to obtain the corrected P-wave impedance data volume.
[0028] Preferably, the frequency spectrum envelopes of the thick sandstone factor attributes and the sparse pulse inversion longitudinal wave impedance data body intersect at point A, that is, the dominant frequency band of the thick sandstone factor is the frequency corresponding to 0 to the intersection point A, and the dominant frequency band of the sparse pulse inversion longitudinal wave impedance is greater than the frequency corresponding to the intersection point A. The frequency components corresponding to the thick sandstone factor attributes 0 to the intersection point A are used to perform low-frequency correction on the longitudinal wave impedance data body of the sparse pulse inversion in step S5.
[0029] Preferably, in step S7, the thick sandstone wave impedance curve interpretation chart established in step S1 is used as the standard, and the longitudinal wave impedance is less than B as the boundary to clearly depict the thick sandstone. By comparing with the sandstone and mudstone interpreted by the longitudinal wave impedance curve measured in the well, it can be seen that the inversion result has a good correspondence with the well. Then, the frequency spectrum of the corrected longitudinal wave impedance data body is extracted to see the range of its dominant frequency band, which is compensated for the range of the low-frequency component after the sparse pulse inversion of the longitudinal wave impedance.
[0030] Preferably, the B is 8200 g / cm3*m / s. In the step S7, the range of the dominant frequency band is 0-80 Hz, and the range of the low-frequency component is 0-10 Hz.
[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains instantaneous phase attributes for seismic attributes, explains the top and bottom morphology and internal structure of thick sandstone in submarine waterways, accurately describes the configuration characteristics of the waterway, and uses the configuration characteristics to establish a stratigraphic framework model, thereby overcoming the defects of the existing layered framework model and accurately describing the "V"-shaped or "U"-shaped undercut morphology of submarine waterways; then, the seismic data body is subjected to Hilbert transformation, and the thick sandstone factor attributes are established. The longitudinal wave impedance data body is inverted using the sparse pulse method, and the longitudinal wave impedance data body is subjected to low-frequency correction using the thick sandstone factor attributes to obtain a low-frequency corrected longitudinal wave impedance data body. By comparing it with the wells that have been drilled, the mudstone illusion appearing inside the thick sandstone is overcome, and the characteristics of the thick sandstone are well described. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 It is a comprehensive histogram of logging and well logging curves.
[0033] Figure 2 This is the interpretation chart of the longitudinal wave impedance curve of thick sandstone.
[0034] Figure 3 The seismic data volume.
[0035] Figure 4 for Figure 3 Calculated instantaneous phase properties.
[0036] Figure 5 for Figure 3 Stratigraphic configuration features derived from the interpretation of seismic data volumes.
[0037] Figure 6 for Figure 4 Architecture characteristics of thick sandstones in submarine channels interpreted from instantaneous phase attributes.
[0038] Figure 7 The stratigraphic framework model is established without considering the structural characteristics constraints of thick sandstone in the submarine channel.
[0039] Figure 8 A stratigraphic framework model was established considering the structural characteristics constraints of thick sandstone in submarine channels.
[0040] Fig. 9 Thick sandstone factor properties.
[0041] Fig.10 P-wave impedance data obtained by sparse pulse inversion.
[0042] Fig.11 Schematic diagram of the method for determining the low-frequency correction frequency.
[0043] Fig.12 It is the low frequency corrected longitudinal wave impedance data volume.
[0044] Fig.13 Spectrum of low-frequency corrected longitudinal wave impedance data volume.
[0045] Fig.14 The present invention is a flow chart of the method for predicting thick-layer sandstone earthquakes in submarine waterways. DETAILED DESCRIPTION
[0046] The drawings are only for illustrative purposes and cannot be construed as limiting the present invention. To better illustrate the present embodiment, some parts of the drawings may be omitted, enlarged, or reduced, and do not represent the size of the actual product. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the drawings. The positional relationships described in the drawings are only for illustrative purposes and cannot be construed as limiting the present invention.
[0047] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "long", "short" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.
[0048] The technical solution of the present invention is further described in detail below through specific embodiments and in conjunction with the accompanying drawings:
[0049] Example 1
[0050] like Fig.14 As shown, a method for predicting thick sandstone earthquakes in submarine waterways comprises the following steps:
[0051] S1: Use the comprehensive histogram of logging and well logging curves to establish the interpretation chart of the longitudinal wave impedance curve of thick sandstone in the submarine channel;
[0052] S2: Obtain instantaneous phase attributes from the seismic data volume and use them to interpret the structural characteristics of thick sandstone layers in submarine channels;
[0053] S3: using the thick sandstone configuration characteristics of the submarine channel obtained in step S2, establishing a stratigraphic framework model capable of characterizing the morphology of the submarine channel;
[0054] S4: Under the constraints of the stratigraphic framework model established in step S3, the P-wave impedance data volume is inverted using the sparse pulse method:
[0055] S5: Construct thick-layer sandstone factor attributes that can reflect thick-layer sandstone;
[0056] S6: Using the thick-layer sandstone factor attribute, the P-wave impedance data volume inverted by the sparse pulse method is subjected to low-frequency correction to obtain a low-frequency corrected P-wave impedance data volume;
[0057] S7: interpreting the longitudinal wave impedance data volume after the low-frequency correction in step S6 according to the longitudinal wave impedance curve interpretation chart of the thick sandstone in the submarine waterway established in step S1.
[0058] Example 2
[0059] The difference from implementation 1 is that in step S6, Fig.11 As shown in FIG. 1 , the spectrum envelope of the thick-layer sandstone factor attribute and the sparse pulse inversion P-wave impedance data body intersects at point A. The frequency corresponding to point A is 10 Hz, that is, the dominant frequency band of the thick-layer sandstone factor is the frequency corresponding to 0 to 10 Hz, and the dominant frequency band of the sparse pulse inversion P-wave impedance is 10 Hz to 80 Hz. The frequency components corresponding to 0 to 10 Hz of the thick-layer sandstone factor attribute are used to perform low-frequency correction on the P-wave impedance data body of the sparse pulse inversion in step S5, and the corrected P-wave impedance data body (as shown in FIG. 1 ) is obtained. Fig.12 shown).
[0060] In addition, in step S7, the thick sandstone wave impedance curve interpretation chart established in step S1 is used as the standard, and the longitudinal wave impedance is less than B as the limit, and B is 8200 g / cm 3 *m / s, which can clearly depict thick sandstone. By comparing the sandstone and mudstone interpreted by the measured P-wave impedance curve of the well, it can be seen that the inversion results have a good correspondence with the well. Further, the spectrum of the corrected P-wave impedance data volume (such as Fig.13 As shown), it can be seen that the range of its dominant frequency band is 0-80Hz, which is compensated for the low-frequency component (0-10Hz) after sparse pulse inversion of longitudinal wave impedance.
[0061] Example 3
[0062] A method for predicting thick sandstone earthquakes in a submarine waterway comprises the following steps:
[0063] S1: Use the comprehensive histogram of logging and well logging curves to establish the interpretation chart of the longitudinal wave impedance curve of thick sandstone in the submarine channel. Figure 1 This is a comprehensive histogram of logging and well logging curves for drilling in thick sandstone layers of submarine waterways. The first track is the depth measurement, the second track is the natural gamma curve (GR), the third track is the longitudinal wave impedance curve, the fourth track is the cuttings logging, and the fifth track is the sandstone and mudstone interpretation conclusion obtained based on the fourth track. Figure 1 The interpretation conclusions of sandstone and mudstone in the paper and the GR and Pimp curves are used to establish the interpretation chart of the longitudinal wave impedance curve of thick sandstone, such as Figure 2 As shown, the sandstone interpretation standard is that Pimp is less than 8200g / cm3*m / s.
[0064] S2: Obtain the instantaneous phase attributes of the seismic data body, and use the instantaneous phase attributes to explain the thick sandstone configuration characteristics of the submarine channel. The physical meaning of the instantaneous phase attribute is the angle between the phase vector and the real axis as a function of time. The instantaneous phase attribute is independent of the seismic energy, can enhance weak reflection signals, highlight the lateral variation characteristics of the event axis, and has a good effect on the lateral boundary characteristics of special geological bodies. The complex seismic trace attributes are the attributes obtained by Hilbert transforming the original seismic trace. The seismic signal and its Hilbert transform are expressed as f(t) and g(t), respectively, then the complex seismic trace is expressed as:
[0065] F(t)=f(t)+ig(t)
[0066] The expression of instantaneous phase is:
[0067] Ph(t)=arctan[f(t) / g(t)]
[0068] Figure 3 is the seismic data volume, Figure 4 for Figure 3 The instantaneous phase attributes are calculated. The instantaneous phase attributes are used to explain the architectural characteristics of thick sandstones in submarine channels, that is, the top and bottom morphology and internal structure of thick sandstones in submarine channels. Figure 5 for Figure 3 The stratigraphic configuration characteristics obtained by interpreting the seismic data volume, Figure 6 for Figure 4 Architecture characteristics of thick sandstones in submarine channels interpreted from instantaneous phase attributes.
[0069] S3: Using the thick sandstone configuration characteristics of the submarine channel obtained in step S2, a stratigraphic framework model capable of characterizing the submarine channel morphology is established. In the process of establishing the stratigraphic framework model, information fusion technology is used to unify multiple geological information such as geology, logging, and seismic information into the same model, so as to achieve the organic fusion of various types of information in the model space, improve the information usage of inversion, information matching accuracy, and confidence in the inversion results. In addition, the constraints of the thick sandstone configuration characteristics of the submarine channel are taken into account during modeling, so as to characterize the stratigraphic framework model of the submarine channel morphology. Figure 7 The stratigraphic framework model was established without considering the structural characteristics of thick sandstone in submarine channels. Figure 8 The stratigraphic framework model was established to consider the structural characteristics of thick sandstone in submarine channels. Figure 8 It can be seen that the stratigraphic framework model is established by utilizing the configuration characteristics, which accurately depicts the channel configuration characteristics, overcomes the defects of the existing layered framework model, and can accurately depict the "V"-shaped or "U"-shaped incision morphology of the submarine channel.
[0070] S4: Under the constraints of the stratigraphic framework model established in step S3, the sparse pulse method is used to invert the P-wave impedance data volume. Sparse pulse inversion is a recursive seismic impedance inversion method based on norm deconvolution and maximum likelihood deconvolution. It assumes that the reflection coefficient sequence of the stratum is composed of a series of sparse and discontinuous large reflection coefficients and Gaussian distributed small reflection coefficients. From a geological perspective, the large reflection coefficient represents the underground discontinuous interface and lithological interface. Based on the basic assumption of constrained sparse pulses, the objective function is obtained:
[0071]
[0072] In the formula, R 2 is the mean square value of the reflection coefficient, N 2 is the mean square value of noise, the reflection coefficient of the Kth sampling point is r(K), n(K) is the noise, M is the number of reflection layers, L is the total number of samples, and λ is the likelihood value of the given reflection coefficient. The reflection coefficient can be calculated iteratively, and then the maximum likelihood deconvolution is used to obtain the reflection coefficient. The recursive algorithm is combined with the initial longitudinal wave impedance trend to obtain the longitudinal wave impedance model.
[0073] Sparse pulse inversion adjusts the initial P-wave impedance calculated from the seismic traces one by one according to the objective function, and adjusts the reflection coefficient to obtain the final objective function:
[0074] F=L p (r)+λL q (sd)+a -1 L1ΔZ
[0075] Where r is the reflection coefficient, ΔZ is the impedance trend difference, d is the seismic trace, s is the synthetic seismic trace, λ is the residual weight factor, a is the trend weight factor, L is the modulus function, L1 is the first-order modulus function, and p,q are L modulus factors. Sparse pulse inversion is based on seismic data and is constrained by wave impedance trends in order to use the least reflection coefficient pulses to best match the synthetic seismic record with the seismic trace. Fig.10 As shown in the figure, the P-wave impedance data volume is obtained by sparse pulse inversion. Since the sparse pulse inversion is equivalent to the seismic resolution, it cannot fully characterize the characteristics of thick sandstone, and mudstone artifacts appear inside the thick sandstone.
[0076] S5: Construct thick sandstone factor attributes that can reflect thick sandstone. Perform Hilbert transform on seismic signals to establish thick sandstone factor attributes, that is, calculate the change of amplitude with frequency, highlight the reflection characteristics of abnormal geological bodies, and better characterize the boundaries of thick sandstone.
[0077]
[0078] like Fig. 9As shown, the thick sandstone factor attribute is established using the above formula. It can be seen that the thick sandstone characteristics can be clearly characterized, but its value is a relative seismic attribute and cannot quantitatively characterize the thick sandstone.
[0079] S6: Use the thick-layer sandstone factor attribute to perform low-frequency correction on the P-wave impedance data volume inverted by the sparse pulse method to obtain a low-frequency corrected P-wave impedance data volume. The frequency spectrum characteristics of the thick-layer sandstone factor attribute and the sparse pulse inversion P-wave impedance data volume are extracted respectively, such as Fig.11 As shown in Figure 1, the intersection point of the two spectrum envelopes is 10 Hz, that is, the dominant frequency band of the thick sandstone factor is 0-10 Hz, and the dominant frequency band of the sparse pulse inversion P-wave impedance is 10-80 Hz. Therefore, the 0-10 Hz frequency component of the thick sandstone factor attribute is used to perform low-frequency correction on the sparse pulse inversion P-wave impedance data volume in step 5, and the corrected P-wave impedance data volume is obtained (as shown in Figure 1). Fig.12 shown).
[0080] S7: Interpret the P-wave impedance data volume after low-frequency correction in step S6 according to the P-wave impedance curve interpretation chart of thick sandstone in submarine waterway established in step S1. The thick sandstone wave impedance curve interpretation chart established in step 1 is used as the standard, and the P-wave impedance is less than 8200g / cm3*m / s as the limit, which can clearly depict the thick sandstone. By comparing with the sandstone and mudstone interpreted by the P-wave impedance curve measured in the well, it can be seen that the inversion result has a good correspondence with the well. Further extract the spectrum of the P-wave impedance data volume after correction (such as Fig.13 As shown in the figure, it can be seen that its dominant frequency band is 0-80Hz. Compared with the sparse pulse inversion longitudinal wave impedance, the low-frequency component (0-10Hz) is compensated, overcoming the mudstone illusion inside the thick sandstone, and can better characterize the thick sandstone in the submarine waterway.
[0081] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.
Claims
1. A method for predicting thick sandstone earthquakes in submarine waterways, characterized in that: The following steps are involved: S1: Use the comprehensive histogram of logging and well logging curves to establish the interpretation chart of the longitudinal wave impedance curve of thick sandstone in the submarine channel; S2: Obtain instantaneous phase attributes from the seismic data volume and use them to interpret the structural characteristics of thick sandstone layers in submarine channels; S3: using the thick sandstone configuration characteristics of the submarine channel obtained in step S2, establishing a stratigraphic framework model capable of characterizing the morphology of the submarine channel; S4: Under the constraints of the stratigraphic framework model established in step S3, the P-wave impedance data volume is inverted using the sparse pulse method: S5: Construct thick-layer sandstone factor attributes that can reflect thick-layer sandstone; S6: Using the thick-layer sandstone factor attribute, the P-wave impedance data volume inverted by the sparse pulse method is subjected to low-frequency correction to obtain a low-frequency corrected P-wave impedance data volume; S7: interpreting the longitudinal wave impedance data volume after the low-frequency correction in step S6 according to the longitudinal wave impedance curve interpretation chart of the thick sandstone in the submarine waterway established in step S1.
2. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 1, characterized in that: In step S2, the complex seismic trace attribute is the attribute obtained by performing Hilbert transform on the original seismic trace. The seismic signal of the seismic data volume is f(t), the Hilbert transform of the seismic signal is g(t), the complex seismic trace is expressed as F(t)=f(t)+ig(t), and the expression of the instantaneous phase attribute is Ph(t)=arctan[f(t) / g(t)].
3. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 1, characterized in that: In step S3, during the establishment of the stratigraphic framework model, information fusion technology is used to unify geological, logging, and seismic multi-geological information into the same model, thereby achieving organic fusion of various types of information in the model space, and improving the information usage of inversion, information matching accuracy, and confidence in the inversion results.
4. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 1, characterized in that: In step S3, the constraints of the thick sandstone configuration characteristics of the submarine channel are taken into consideration when establishing the stratigraphic framework model, so as to be able to characterize the stratigraphic framework model of the submarine channel morphology.
5. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 1, characterized in that: In step S4, based on norm deconvolution and maximum likelihood deconvolution, it is assumed that the reflection coefficient sequence of the formation is composed of a series of sparse and discontinuous large reflection coefficients and Gaussian distributed small reflection coefficients. From a geological perspective, the large reflection coefficient represents the underground discontinuous interface and lithological interface. Based on the basic assumptions of the constrained sparse pulse method, the objective function is obtained: In the formula, R 2 is the mean square value of the reflection coefficient, N 2 is the mean square value of noise, the reflection coefficient of the Kth sampling point is r(K), n(K) is the noise, M is the number of reflection layers, L is the total number of samples, and λ is the likelihood value of the given reflection coefficient; Then, the reflection coefficient is calculated iteratively, and then the maximum likelihood deconvolution is used to obtain the reflection coefficient. The recursive algorithm is combined with the initial longitudinal wave impedance trend to obtain the longitudinal wave impedance model; The sparse pulse method inversion objective function adjusts the initial longitudinal wave impedance calculated from the seismic traces one by one, and adjusts the reflection coefficient to obtain the final objective function: F=L p (r)+λL q (s-d)+a -1 L1ΔZ Where r is the reflection coefficient, ΔZ is the impedance trend difference, d is the seismic trace, s is the synthetic seismic trace, λ is the residual weight factor, a is the trend weight factor, L is the modulus function, L1 is the first-order modulus function, and p,q are L modulus factors; Sparse pulse inversion is based on seismic data and is constrained by wave impedance trends in order to optimally match synthetic seismic records with seismic traces using the least reflection coefficient pulses.
6. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 1, characterized in that: In step S5, the seismic signal is subjected to Hilbert transformation to establish thick-layer sandstone factor attributes, that is, to calculate the change of amplitude with frequency. The thick-layer sandstone factor attribute calculation formula is as follows:
7. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 1, characterized in that: In step S6, the frequency spectrum characteristics of the thick sandstone factor attributes and the sparse pulse inversion P-wave impedance data volume are extracted respectively, and the P-wave impedance data volume of the sparse pulse inversion in step S5 is low-frequency corrected using the frequency components of the thick sandstone factor attributes to obtain the corrected P-wave impedance data volume.
8. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 7, characterized in that: The frequency spectrum envelopes of the thick sandstone factor attributes and the sparse pulse inversion longitudinal wave impedance data body intersect at point A, that is, the dominant frequency band of the thick sandstone factor is the frequency corresponding to 0 to the intersection point A, and the dominant frequency band of the sparse pulse inversion longitudinal wave impedance is greater than the frequency corresponding to the intersection point A. The frequency components corresponding to the thick sandstone factor attributes 0 to the intersection point A are used to perform low-frequency correction on the longitudinal wave impedance data body of the sparse pulse inversion in step S5.
9. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 8, characterized in that: In step S7, the thick sandstone wave impedance curve interpretation chart established in step S1 is used as a standard, and the longitudinal wave impedance is less than B as a boundary to clearly depict the thick sandstone. By comparing with the sandstone and mudstone interpreted by the longitudinal wave impedance curve measured in the well, it can be seen that the inversion result has a good correspondence with the well. Then, the frequency spectrum of the corrected longitudinal wave impedance data body is extracted to see the range of its dominant frequency band, which is compensated for the range of the low-frequency component after the sparse pulse inversion of the longitudinal wave impedance.
10. The method for predicting thick sandstone earthquakes in submarine waterways according to claim 9, characterized in that: The B is 8200 g / cm3*m / s. In the step S7, the range of the dominant frequency band is 0-80 Hz, and the range of the low-frequency component is 0-10 Hz.