Optimization methods, apparatus, equipment, and storage media for speed models

Abstract

This disclosure provides a velocity model optimization method, apparatus, device, and storage medium, belonging to the field of seismic exploration technology. The method includes: updating a first velocity model to obtain a second velocity model; identifying a first disadvantageous region in the second velocity model; and backfilling the portion of the first velocity model corresponding to the first disadvantageous region into the first disadvantageous region to obtain an optimized second velocity model. Since the first disadvantageous region represents the area where the accuracy of the second velocity model is lower than that of the first velocity model, backfilling the portion of the first velocity model corresponding to the disadvantageous region into the first disadvantageous region of the second velocity model removes the area of ​​lower accuracy compared to the first velocity model, thus improving the optimization efficiency of the velocity model.

Description

Technical Field

[0001] This disclosure relates to the field of seismic exploration technology, and in particular to a method, apparatus, device, and storage medium for optimizing a velocity model. Background Technology

[0002] In the field of seismic exploration technology, a velocity model refers to a model of how seismic wave propagation velocity varies with subsurface depth. The accuracy of the velocity model affects the imaging precision of migration imaging; an inaccurate velocity model will lead to inaccurate migration imaging. To obtain high-quality migration imaging profiles, the velocity model needs to be optimized to provide the most accurate velocity model possible.

[0003] In related technologies, velocity model optimization is achieved by continuously updating the velocity model multiple times.

[0004] Each updated velocity model has regions where accuracy is higher and regions where accuracy is lower compared to the original velocity model. The velocity update requires multiple iterations to achieve the optimization goal, resulting in low optimization efficiency. Summary of the Invention

[0005] This disclosure provides a speed model optimization method, apparatus, device, and storage medium, which can improve the efficiency of speed model optimization. The technical solution is as follows:

[0006] In a first aspect, a method for optimizing a speed model is provided, the method comprising: updating the speed of a first speed model to obtain a second speed model; determining a first disadvantage region in the second speed model, the first disadvantage region being a region where the accuracy of the second speed model is lower than that of the first speed model; and backfilling the portion of the first speed model corresponding to the first disadvantage region into the first disadvantage region to obtain an optimized second speed model.

[0007] Optionally, determining the first disadvantage region in the second velocity model includes: determining first energy distribution data of a first time-domain superimposed profile based on the first velocity model; determining second energy distribution data of a second time-domain superimposed profile based on the second velocity model; determining a second disadvantage region in the second time-domain superimposed profile based on the first energy distribution data and the second energy distribution data, wherein the second disadvantage region is a region where the second energy distribution data is less than the first energy distribution data; and determining the first disadvantage region based on the second disadvantage region.

[0008] Optionally, determining the second disadvantage region in the second time-domain overlay profile based on the first energy distribution data and the second energy distribution data includes: subtracting the first energy distribution data from the second energy distribution data to obtain a time-domain energy difference profile; and determining the region in the time-domain energy difference profile where the energy data difference is negative as the second disadvantage region.

[0009] Optionally, before determining the region with a negative energy data difference in the time-domain energy difference profile as the second disadvantage region, the method further includes at least one of the following: removing energy data differences in the time-domain energy difference profile whose absolute value is less than a difference threshold; removing regions in the time-domain energy difference profile where the energy data difference is negative and the region area is less than an area threshold; and removing regions in the time-domain energy difference profile where the energy data difference is positive and the region area is less than the area threshold.

[0010] Optionally, the first energy distribution data and the second energy distribution data are calculated using the following formula: A(t)=||sqrt(x) 2 (t)+H 2 (x(t)))||2; where A(t) represents the first energy distribution data or the second energy distribution data, x(t) represents the discrete signal sequence, H(x(t)) represents the Hilbert transform of x(t), t represents time, and ||sqrt(x)||2 represents the time interval. 2 (t)+H 2 (x(t)))||2 represents the expression for sqrt(x) 2 (t)+H 2 (x(t))) takes the L2 norm; or, the first energy distribution data and the second energy distribution data are calculated using the following formula: A(t)=|sqrt(x) 2 (t)+H 2 (x(t)))|;where, A(t) represents the first energy distribution data or the second energy distribution data, x(t) represents the discrete signal sequence, H(x(t)) represents the Hilbert transform of x(t), t represents time, and A(t) = |sqrt(x(t))|; 2 (t)+H 2 (x(t)))| represents the expression for sqrt(x) 2 (t)+H 2 (x(t)) takes the absolute value.

[0011] Secondly, a speed model optimization device is provided, the device comprising: a speed update module for updating a first speed model to obtain a second speed model; a determination module for determining a first disadvantage region in the second speed model, the first disadvantage region being a region where the accuracy of the second speed model is lower than that of the first speed model; and a speed model optimization module for backfilling the portion of the first speed model corresponding to the first disadvantage region into the first disadvantage region to obtain an optimized second speed model.

[0012] Optionally, the determining module is used to determine first energy distribution data of a first time-domain superimposed profile based on the first velocity model; determine second energy distribution data of a second time-domain superimposed profile based on the second velocity model; determine a second disadvantage region in the second time-domain superimposed profile according to the first energy distribution data and the second energy distribution data, wherein the second disadvantage region is a region where the second energy distribution data is less than the first energy distribution data; and determine the first disadvantage region according to the second disadvantage region.

[0013] Optionally, the determining module is used to subtract the first energy distribution data from the second energy distribution data to obtain a time-domain energy difference profile; and to determine the region in the time-domain energy difference profile where the energy data difference is negative as the second disadvantage region.

[0014] Optionally, the determining module is further configured to optimize the time-domain energy difference profile in any of the following ways: removing energy data differences in the time-domain energy difference profile whose absolute value is less than a difference threshold; removing regions in the time-domain energy difference profile where the energy data difference is negative and the region area is less than an area threshold; and removing regions in the time-domain energy difference profile where the energy data difference is positive and the region area is less than the area threshold.

[0015] Optionally, the determining module is used to calculate the first energy distribution data and the second energy distribution data using the following formula: A(t)=||sqrt(x 2 (t)+H 2 (x(t)))||2;where, A(t) represents the first energy distribution data or the second energy distribution data, x(t) represents the discrete signal sequence, H(x(t)) represents the Hilbert transform of x(t), t represents time, ||sqrt(x)||2; 2 (t)+H 2 (x(t)))||2 represents the expression for sqrt(x) 2 (t)+H 2(x(t))) takes the L2 norm; or, the determining module is used to calculate the first energy distribution data and the second energy distribution data using the following formula: A(t)=|sqrt(x) 2 (t)+H 2 (x(t)))|;

[0016] Where A(t) represents the first energy distribution data or the second energy distribution data, x(t) represents the discrete signal sequence, H(x(t)) represents the Hilbert transform of x(t), t represents time, and A(t) = |sqrt(x(t)|t ... 2 (t)+H 2 (x(t)))| represents the expression for sqrt(x) 2 (t)+H 2 (x(t)) takes the absolute value.

[0017] Thirdly, a computer device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to perform the method described in the first aspect.

[0018] Fourthly, a computer-readable storage medium is provided, wherein when instructions in the computer-readable storage medium are executed by a processor of a computer device, the computer device is enabled to perform the method described in the first aspect.

[0019] Fifthly, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the method described in the first aspect above.

[0020] The beneficial effects of the technical solutions provided in this disclosure are:

[0021] In this embodiment, a second speed model is obtained by updating the speed of a first speed model; a first disadvantageous region in the second speed model is determined; and the portion of the first speed model corresponding to the first disadvantageous region is backfilled into the first disadvantageous region to obtain an optimized second speed model. Since the first disadvantageous region is the area where the accuracy of the second speed model is lower than that of the first speed model, backfilling the portion of the first speed model corresponding to the disadvantageous region into the first disadvantageous region of the second speed model removes the area where the accuracy is lower than that of the first speed model, thus improving the optimization efficiency of the speed model. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the offset imaging result of a first velocity model provided in an embodiment of this disclosure;

[0024] Figure 2 This is a schematic diagram of the offset imaging result of a second velocity model provided in an embodiment of this disclosure;

[0025] Figure 3 This is a simplified schematic diagram of the offset imaging result of a first velocity model provided in an embodiment of this disclosure;

[0026] Figure 4 This is a simplified schematic diagram of the offset imaging result of a second velocity model provided in an embodiment of this disclosure;

[0027] Figure 5 This is a flowchart of a speed model optimization method provided in an embodiment of this disclosure;

[0028] Figure 6 This is a flowchart of another speed model optimization method provided in this embodiment of the disclosure;

[0029] Figure 7 This is a schematic diagram of a first depth domain overlay profile provided in an embodiment of this disclosure;

[0030] Figure 8 This is a schematic diagram of a first time-domain superimposed profile provided in an embodiment of this disclosure;

[0031] Figure 9 This is a schematic diagram illustrating the principle of extracting the signal envelope according to an embodiment of the present disclosure;

[0032] Figure 10 This is a schematic diagram of a second depth domain overlay profile provided in an embodiment of this disclosure;

[0033] Figure 11 This is a schematic diagram of a second time-domain overlay profile provided in an embodiment of this disclosure;

[0034] Figure 12 This is a simplified distribution diagram of first energy distribution data provided in an embodiment of this disclosure;

[0035] Figure 13 This is a simplified distribution diagram of a second energy distribution data provided in an embodiment of this disclosure;

[0036] Figure 14 This is a simplified schematic diagram of a time-domain energy difference profile provided in an embodiment of this disclosure;

[0037] Figure 15 This is a simplified schematic diagram of a depth-domain energy difference profile provided in an embodiment of this disclosure;

[0038] Figure 16 This is a schematic diagram of a first velocity model provided in an embodiment of this disclosure;

[0039] Figure 17 This is a schematic diagram of a second velocity model provided in an embodiment of this disclosure;

[0040] Figure 18 This is a schematic diagram of another second velocity model provided in an embodiment of this disclosure;

[0041] Figure 19 This is a schematic diagram of an optimized second velocity model provided in an embodiment of this disclosure;

[0042] Figure 20 This is a schematic diagram of a depth domain overlay profile corresponding to an optimized second velocity model provided in an embodiment of this disclosure;

[0043] Figure 21 This is a schematic diagram of the structure of a speed model optimization device provided in an embodiment of this disclosure;

[0044] Figure 22 This is a structural block diagram of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0046] The imaging quality of subsurface medium models (depth domain images) obtained through pre-stack depth migration is mainly affected by factors such as seismic wave propagation velocity, anisotropy parameters, absorption attenuation parameters, and subsurface medium density. Among these, seismic wave propagation velocity has the greatest impact on the imaging quality of the subsurface medium model. Therefore, it is necessary to optimize the velocity model to establish an accurate velocity model.

[0047] The general process of optimizing a velocity model includes: updating the velocity model; determining whether there are regions with poor imaging quality in the migration imaging results corresponding to the updated velocity model; if there are regions with poor imaging quality in the migration imaging results, then continuing to update the velocity model until the imaging quality of the migration imaging results after multiple updates meets the requirements.

[0048] If the accuracy of the velocity model after each velocity update is relatively high, the number of velocity updates can be reduced, thereby improving the optimization efficiency of the velocity model. Velocity update methods mainly include layer-by-layer velocity analysis, vertical velocity analysis, mesh tomography, and full waveform inversion. These methods all have certain limitations; the velocity model after each update contains regions with higher accuracy and regions with lower accuracy compared to the original model. Therefore, multiple iterations are required to achieve the optimization goal, resulting in low optimization efficiency.

[0049] Figure 1 This is a schematic diagram of the migration imaging result of a first velocity model provided in an embodiment of this disclosure. The schematic diagram is obtained by performing migration imaging on the first velocity model. Figure 1 As shown, Figure 1 The mid-horizontal axis comprises 10 stripes, each representing a different common imaging point gather. Each stripe has several fringes from top to bottom, such as... Figure 1 The stripes marked with number 1 are called phase axes. The flatness of the phase axes reflects the accuracy of the velocity model. The flatter the phase axes, the higher the accuracy of the velocity model; the more tilted the phase axes, the lower the accuracy of the velocity model.

[0050] Figure 2 This is a schematic diagram of the offset imaging result of a second velocity model provided in an embodiment of this disclosure. The schematic diagram is obtained by offset imaging of the second velocity model. The second velocity model is a velocity model updated from the first velocity model.

[0051] Figure 3 This is a simplified schematic diagram of the offset imaging result of a first velocity model provided in an embodiment of this disclosure. Figure 4 This is a simplified schematic diagram of the migration imaging result of a second velocity model provided in an embodiment of this disclosure. The migration imaging result of the second velocity model exhibits a region of flattened phase axis compared to the migration imaging result of the first velocity model, for example... Figure 3 The area circled by the thin black dashed circle. Meanwhile, the migration imaging results of the second velocity model show areas of in-phase axis tilt relative to the migration imaging results of the first velocity model, for example... Figure 4 The area circled by the thick black dashed circle. Since the flattening of the phase axis reflects the accuracy of the velocity model, the second velocity model will have some areas with higher accuracy and some areas with lower accuracy compared to the first velocity model.

[0052] Therefore, this disclosure provides a speed model optimization method that can optimize the speed model after each speed update to improve the optimization efficiency of the speed model.

[0053] Figure 5This is a flowchart illustrating a speed model optimization method provided in an embodiment of this disclosure. This method can be executed by a computer device, such as a computer. See also... Figure 5 The method includes:

[0054] In step 501, the first velocity model is updated to obtain the second velocity model.

[0055] In some implementations, the first velocity model may be an initial velocity model obtained from the original seismic data. In other implementations, the first velocity model may be a velocity model resulting from multiple velocity updates to the initial velocity model.

[0056] For example, the velocity update method can be any of the following: layer-by-layer velocity analysis, vertical velocity analysis, mesh tomography, and full waveform inversion.

[0057] In step 502, a first disadvantage region in the second velocity model is determined. The first disadvantage region is the region where the accuracy of the second velocity model is lower than that of the first velocity model.

[0058] For information on the accuracy of velocity models, please refer to [link / reference]. Figure 1 The embodiments shown are omitted in detail here.

[0059] In step 503, the portion of the first velocity model corresponding to the first disadvantage region is backfilled into the first disadvantage region of the second velocity model to obtain the optimized second velocity model.

[0060] In this embodiment, a second speed model is obtained by updating the speed of a first speed model; a first disadvantageous region in the second speed model is determined; and the portion of the first speed model corresponding to the first disadvantageous region is backfilled into the first disadvantageous region of the second speed model to obtain an optimized second speed model. Since the first disadvantageous region is the area where the accuracy of the second speed model is lower than that of the first speed model, backfilling the portion of the first speed model corresponding to the disadvantageous region into the first disadvantageous region of the second speed model removes the area where the accuracy is lower than that of the first speed model, thus improving the optimization efficiency of the speed model.

[0061] Figure 6 This is a flowchart of another speed model optimization method provided in this disclosure embodiment. This method can be executed by a computer device, such as a computer. See also... Figure 6 The method includes:

[0062] In step 601, the first velocity model is updated to obtain the second velocity model.

[0063] For details regarding the first velocity model and velocity update, please refer to step 501 above; detailed descriptions are omitted here.

[0064] In step 602, the first energy distribution data of the first time-domain superimposed profile is determined based on the first velocity model.

[0065] In this embodiment of the disclosure, step 602 includes: determining a first time-domain superimposed profile based on a first velocity model; and determining first energy distribution data of the first time-domain superimposed profile.

[0066] In some implementations, a migration imaging process is performed on the first velocity model to obtain a migration imaging result of the first velocity model. This migration imaging result is then stacked to obtain a first depth-domain stacked profile. Finally, a depth-time conversion process is performed on the first velocity model and the first depth-domain stacked profile to obtain a first time-domain stacked profile. The depth-time conversion refers to converting the depth-domain stacked profile from the depth domain to the time domain. The depth domain refers to the profile's vertical coordinate axes being represented by depth, showing the depth position of seismic waves excited from the surface reaching the underground reflecting layer. The time domain refers to the profile's vertical coordinates being represented by time, showing the time it takes for seismic waves excited from the surface to reach the surface from the underground reflecting layer.

[0067] Figure 7 This is a schematic diagram of a first depth domain overlay profile provided in an embodiment of this disclosure. Figure 7 Curve a in the middle is equivalent to Figure 1 The result of stacking a single strip (common imaging point gather). Figure 8 This is a schematic diagram of a first time-domain superimposed profile provided in an embodiment of this disclosure. Figure 8 Curve b in the middle is obtained by... Figure 7 The curve 'a' is obtained by performing a deep time-mapping transformation. It should be noted that... Figure 7 Including multiple curves a, Figure 8 This includes multiple curves b, but only one curve is used for illustration in this embodiment.

[0068] In some examples, the first energy distribution data of the first time-domain overlay profile is calculated using formula (1):

[0069] A(t) = ||sqrt(x) 2 (t)+H 2 (x(t)))||2 (1)

[0070] In formula (1), A(t) represents the first energy distribution data; x(t) represents... Figure 8 Discrete points on curve b; H(x(t)) represents the Hilbert transform of x(t); t represents time; sqrt(x 2 (t)+H 2(x(t))) represents the envelope of curve b; ||sqrt(x 2 (t)+H 2 (x(t)))||2 represents the expression for sqrt(x) 2 (t)+H 2 Taking the L2 norm of (x(t)) for sqrt(x) 2 (t)+H 2 Taking the L2 norm of (x(t)) means taking the L2 norm of multiple sqrt(x) 2 (t)+H 2 The purpose of summing (x(t)) and then taking the square root is to ensure that the calculated first energy distribution data is positive, which facilitates subsequent data processing.

[0071] In other examples, the first energy distribution data of the first time-domain overlay profile is calculated using formula (2):

[0072] A(t) = |sqrt(x) 2 (t)+H 2 (x(t)))| (2)

[0073] In formula (2), A(t) represents the first energy distribution data; x(t) represents... Figure 8 Discrete points on curve b; H(x(t)) represents the Hilbert transform of x(t); t represents time; sqrt(x 2 (t)+H 2 (x(t))) represents the envelope of curve b; |sqrt(x 2 (t)+H 2 (x(t)))| represents the expression for sqrt(x) 2 (t)+H 2 Take the absolute value of (x(t)) to make the calculated first energy distribution data positive.

[0074] Figure 9 This is a schematic diagram of the envelope of curve b provided in an embodiment of this disclosure, as shown below. Figure 9 As shown in the figure, the curve labeled 1 represents Figure 8 Curve b in the diagram, with the curve labeled 2, represents the envelope of curve b. The magnitude of the amplitude corresponding to the envelope reflects the magnitude of the first energy distribution data. Furthermore, from... Figure 9It can be seen that in the first time-domain superimposed profile corresponding to the first velocity model, curve 1 changes drastically, while curve b changes relatively smoothly. Generally, the frequency band of the first velocity model is between 1 and 5 Hz, while the frequency band of the first time-domain superimposed profile is between 8 and 100 Hz. The significant difference in frequency band resolution between these two is detrimental to the subsequent determination of the dominant and disadvantageous regions. By performing a Hilbert transform on curve 1 to extract the envelope of the first time-domain superimposed profile, the frequency band of the transformed profile can be made similar to that of the first velocity model.

[0075] In step 603, the second energy distribution data of the second time-domain superimposed profile is determined based on the second velocity model.

[0076] In this embodiment of the disclosure, step 603 includes: determining a second time-domain superimposed profile based on a second velocity model; and determining second energy distribution data of the second time-domain superimposed profile.

[0077] In some implementations, the second velocity model is subjected to offset imaging processing to obtain the offset imaging result of the second velocity model, the offset imaging result of the second velocity model is superimposed to obtain the second depth domain superimposed profile, and then the second velocity model and the second depth domain superimposed profile are subjected to depth-time conversion processing to obtain the second time domain superimposed profile.

[0078] The second energy distribution data is calculated using formula (1) or formula (2) in step 602.

[0079] Figure 10 This is a schematic diagram of a second depth domain overlay profile provided in an embodiment of this disclosure. Figure 7 Compared to the first depth domain overlay profile shown, Figure 10 In the second depth domain overlay profile shown, the area circled by the black solid circle is relatively blurry, indicating that the energy in the area circled by the black solid circle in the second depth domain overlay profile is weakened and the imaging quality is poor.

[0080] Figure 11 This is a schematic diagram of a second time-domain overlay profile provided in an embodiment of this disclosure. Figure 8 Compared with the first time-domain superimposed profile, Figure 11 In the second time-domain overlay profile shown, the area circled by the black solid circle is relatively blurry, indicating that the energy in the area circled by the black solid circle in the second time-domain overlay profile is weakened and the imaging quality is poor.

[0081] In step 604, a second disadvantage region is determined based on the first energy distribution data and the second energy distribution data.

[0082] The second disadvantageous region is the region where the second energy distribution data is less than the first energy distribution data.

[0083] In this embodiment of the disclosure, step 604 includes: subtracting the first energy distribution data from the second energy distribution data to obtain a time-domain energy difference profile; and determining the region in the time-domain energy difference profile where the energy data difference is negative as the second disadvantage region.

[0084] In some implementations, the second energy distribution data in the second time-domain overlay profile can be subtracted from the first energy distribution data at the same location in the first time-domain overlay profile to obtain a time-domain energy difference profile. Then, the regions in the time-domain energy difference profile where the energy data difference is negative are delineated as the second disadvantage region.

[0085] Figure 12 This is a simplified distribution diagram of first energy distribution data provided in an embodiment of this disclosure, such as... Figure 12 As shown in the figure, area 1 corresponds to the region with a higher value of the first energy distribution data, and area 2 corresponds to the region with a lower value of the first energy distribution data. In practical applications, areas 1 and 2 are filled with different gradient colors. For example, area 1 is filled with a red gradient, with the red gradually increasing to represent a gradual increase in the value of the first energy distribution data; area 2 is filled with a blue gradient, with the blue gradually decreasing to represent a gradual increase in the value of the first energy distribution data.

[0086] Figure 13 This is a simplified distribution diagram of second energy distribution data provided in an embodiment of this disclosure, such as... Figure 13 As shown in the figure, the area marked with the dashed circle is the same as... Figure 12 Compared to the reduction in area, it indicates Figure 13 The value of the second energy distribution data in the region circled by the middle dashed circle has decreased.

[0087] Figure 14 This is a schematic diagram of a time-domain energy difference profile provided in an embodiment of this disclosure, as shown below. Figure 14 As shown, region 3 represents a region with a negative energy difference, that is, a region where the energy of the second time-domain superimposed profile corresponding to the second velocity model is weaker than that of the first time-domain superimposed profile corresponding to the first velocity model. Region 4 represents a region with a positive energy difference, that is, a region where the energy of the second time-domain superimposed profile corresponding to the second velocity model is stronger than that of the first time-domain superimposed profile corresponding to the first velocity model. In practical applications, regions 3 and 4 can be filled with different gradient colors. For example, region 3 is filled with a blue gradient, where the blue changes from dark to light to represent the absolute value of the energy data difference changing from large to small; region 4 is filled with a red gradient, where the red changes from dark to light to represent the energy data difference changing from large to small.

[0088] Optionally, in this embodiment of the disclosure, before determining the region with a negative energy data difference in the time-domain energy difference profile as the second disadvantage region, the velocity model optimization method further includes at least one of the following:

[0089] Method 1: Remove energy data differences in the time-domain energy difference profile whose absolute value is less than the difference threshold.

[0090] For example, the method for processing the time-domain energy difference profile can be any of the following: filtering, regularization, and thresholding. The difference threshold can be set by relevant technical personnel according to actual needs. By removing regions where the energy data difference changes little before and after the velocity update, and retaining regions where the energy data difference changes significantly, it is easier for the computer equipment to determine the second disadvantage region, thereby improving the efficiency of the computer equipment in determining the second disadvantage region.

[0091] Method 2: Remove regions in the time-domain energy difference profile where the energy data difference is negative and the region area is less than the area threshold, and remove regions in the time-domain energy difference profile where the energy data difference is positive and the region area is less than the area threshold. The area threshold can be set by relevant technical personnel according to actual needs.

[0092] By removing regions in the time-domain energy difference profile where the energy difference is negative and the area is smaller than the area of ​​the region, and regions in the regional time-domain energy difference profile where the energy difference is positive and the area is smaller than the area threshold, the time-domain energy difference profile can retain regions with negative energy difference and relatively concentrated distribution, as well as regions with positive energy difference and relatively concentrated distribution, making it easier for the computer to quickly determine the second disadvantage region.

[0093] In step 605, the first disadvantage region in the second velocity model is determined based on the second disadvantage region.

[0094] In this embodiment of the disclosure, step 605 includes:

[0095] The first step is to perform time-depth conversion on the time-domain energy difference profile to obtain the depth-domain energy difference profile, and the third disadvantage region in the depth-domain energy difference profile corresponding to the second disadvantage region.

[0096] Since the velocity model is in the depth domain, the time domain energy difference profile needs to be converted into the depth domain energy difference profile in order to determine the first disadvantage region in the second velocity model.

[0097] Figure 15 This is a schematic diagram of a depth-domain energy difference profile provided in an embodiment of this disclosure, as shown below. Figure 15 As shown, with Figure 14 Similarly, region numbered 3 represents a region with a negative energy difference, and region numbered 4 represents a region with a positive energy difference. Figure 14 Compared to the time-domain energy difference profile shown, Figure 15 The third disadvantageous area ( Figure 15 The shape of the area marked 3 has changed slightly.

[0098] The second step is to determine the fourth disadvantage region in the second velocity model that corresponds to the third disadvantage region.

[0099] In this embodiment of the disclosure, the portion of the second velocity model corresponding to the third disadvantage region in the depth domain energy difference profile is determined as the fourth disadvantage region.

[0100] The third step is to determine the first disadvantage region in the second velocity model based on the fourth disadvantage region.

[0101] In some implementations, the fourth disadvantage region in the second velocity model can be directly used as the first disadvantage region.

[0102] In other implementations, ray analysis theory is used to analyze the velocity field range that affects the fourth disadvantage region in the second velocity model. If the velocity field range is larger than the fourth disadvantage region, this velocity field range is taken as the first disadvantage region.

[0103] Since the formation of the fourth disadvantage region in the second velocity model is related not only to the velocity field of the fourth disadvantage region but also to the velocity field changes of the strata above the fourth disadvantage region, it is more accurate to regard the range of the velocity field affecting the fourth disadvantage region as the first disadvantage region.

[0104] In this embodiment, regions with high velocity model accuracy have high energy and high imaging quality in the corresponding regions of the time-domain overlay profile, while regions with low velocity model accuracy have low energy and low imaging quality. Furthermore, it is difficult to delineate regions in the second velocity model where accuracy decreases. Therefore, in this embodiment, the region with decreased accuracy in the second velocity model is determined by the sign of the difference between the second energy distribution data corresponding to the second velocity model and the first energy distribution data corresponding to the first velocity model.

[0105] In the time-domain energy difference profile, regions with negative energy data differences indicate areas where energy decreases after velocity updates, meaning the accuracy of the second velocity model decreases, and these regions need to be removed. Conversely, regions with positive energy differences in the time-domain energy difference profile indicate areas where energy increases after velocity updates, meaning the accuracy of the second velocity model increases, and these regions need to be retained.

[0106] The first disadvantage region in the second velocity model can be determined through steps 602 to 605.

[0107] In step 606, the portion of the first velocity model corresponding to the first disadvantage region is backfilled into the first disadvantage region of the second velocity model to obtain the optimized second velocity model.

[0108] Backfilling the portion of the first velocity model corresponding to the disadvantaged region into the region of the second velocity model corresponding to the first disadvantaged region means filling the portion of the first velocity model corresponding to the disadvantaged region into the region of the second velocity model corresponding to the first disadvantaged region. In this way, the second velocity model retains the regions where the accuracy has increased after the velocity update, while eliminating the regions where the accuracy has decreased after the velocity update.

[0109] The following example illustrates the effect of optimizing the velocity model using the optimization method provided in the embodiments of this disclosure. Figure 16 This is a schematic diagram of a first velocity model provided in an embodiment of this disclosure. For example... Figure 16 As shown in the figure, different shades of gray represent geological layers with different seismic wave propagation velocities. Figure 17 This is a schematic diagram of a second velocity model provided in an embodiment of this disclosure. The second velocity model is based on... Figure 16 The velocity is obtained by updating the velocity using the first velocity model. Figure 18 This is a schematic diagram of another second velocity model provided in an embodiment of this disclosure. For example... Figure 18 As shown, Figure 18 The shaded area labeled I is the first disadvantage region identified in step 605 above. Figure 19 This is a schematic diagram of an optimized second velocity model provided in an embodiment of this disclosure. Figure 16 The first velocity model and Figure 18 The portion corresponding to the first disadvantage region of the second velocity model is backfilled to... Figure 18 The first disadvantage region of the second velocity model is obtained.

[0110] Figure 20 This is a schematic diagram of a depth domain overlay profile corresponding to an optimized second velocity model provided in an embodiment of this disclosure. Figure 20 As shown, this image was obtained by offset imaging and stacking processing from the optimized second velocity model. Figure 7 Compared to the first depth domain overlay profile shown, Figure 20 The image of the region circled by the black solid circle becomes clearer, indicating that the velocity model optimization method provided in this embodiment can improve the accuracy of the second velocity model.

[0111] In this embodiment, a second speed model is obtained by updating the speed of a first speed model; a first disadvantageous region in the second speed model is determined; and the portion of the first speed model corresponding to the first disadvantageous region is backfilled into the first disadvantageous region to obtain an optimized second speed model. Since the first disadvantageous region is the area where the accuracy of the second speed model is lower than that of the first speed model, backfilling the portion of the first speed model corresponding to the disadvantageous region into the first disadvantageous region of the second speed model removes the area where the accuracy is lower than that of the first speed model, thus improving the optimization efficiency of the speed model.

[0112] Figure 21 This is a structural block diagram of a speed model optimization device 2100 provided in an embodiment of this disclosure. For example... Figure 21 As shown, the device includes a speed update module 2101, a determination module 2102, and a speed model optimization module 2103.

[0113] The speed update module 2101 is used to update the speed of the first speed model to obtain a second speed model. The determination module 2102 is used to determine a first disadvantage region in the second speed model, which is the region where the accuracy of the second speed model is lower than that of the first speed model. The speed model optimization module 2103 is used to backfill the portion of the first speed model corresponding to the first disadvantage region into the first disadvantage region to obtain an optimized second speed model.

[0114] Optionally, the determining module 2102 is used to determine first energy distribution data of a first time-domain superimposed profile based on the first velocity model; determine second energy distribution data of a second time-domain superimposed profile based on the second velocity model; determine a second disadvantage region in the second time-domain superimposed profile according to the first energy distribution data and the second energy distribution data, wherein the second disadvantage region is a region where the second energy distribution data is less than the first energy distribution data; and determine the first disadvantage region according to the second disadvantage region.

[0115] Optionally, the determining module 2102 is used to subtract the first energy distribution data from the second energy distribution data to obtain a time-domain energy difference profile; and to determine the region in the time-domain energy difference profile where the energy data difference is negative as the second disadvantage region.

[0116] Optionally, the determining module 2102 is further configured to optimize the time-domain energy difference profile in any of the following ways: removing energy data differences in the time-domain energy difference profile whose absolute value is less than a difference threshold; removing regions in the time-domain energy difference profile where the energy data difference is negative and the region area is less than an area threshold; and removing regions in the time-domain energy difference profile where the energy data difference is positive and the region area is less than the area threshold.

[0117] Optionally, the determining module 2102 is used to calculate the first energy distribution data and the second energy distribution data using formula (1) or formula (2).

[0118] It should be noted that the speed model optimization device 2100 provided in the above embodiments is only illustrated by the division of the above functional modules when optimizing the speed model. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the speed model optimization device and the speed model optimization method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0119] Figure 22 This is a structural block diagram of a computer device provided in an embodiment of this disclosure. Figure 22 As shown, the computer device can be a computer or the like. The computer device includes a processor 2201 and a memory 2202.

[0120] Processor 2201 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 2201 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 2201 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 2201 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen.

[0121] The memory 2202 may include one or more computer-readable storage media, which may be non-transitory. The memory 2202 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 2202 is used to store at least one instruction, which is executed by the processor 2201 to implement the speed model optimization method provided in the embodiments of this application.

[0122] Those skilled in the art will understand that Figure 22 The structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0123] This invention also provides a non-transitory computer-readable storage medium, wherein when the instructions in the storage medium are executed by the processor of a computer device, the computer device is able to execute the speed model optimization method provided in this disclosure.

[0124] A computer program product includes a computer program / instructions that, when executed by a processor, implement the speed model optimization method provided in embodiments of this disclosure.

[0125] The above description is merely an optional embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A method of optimization of a velocity model, characterized in that, The method comprises: performing velocity updating on a first velocity model to obtain a second velocity model; determining a first disadvantaged area in the second velocity model, the first disadvantaged area being an area in which the second velocity model has lower accuracy relative to the first velocity model; backfilling a part of the first velocity model corresponding to the first disadvantaged area to the first disadvantaged area to obtain an optimized second velocity model; The determination of the first disadvantaged area in the second velocity model comprises: determining first energy distribution data of a first time-domain stack section based on the first velocity model; determining second energy distribution data of a second time-domain stack section based on the second velocity model; determining a second disadvantaged area in the second time-domain stack section according to the first energy distribution data and the second energy distribution data, the second disadvantaged area being an area in which the second energy distribution data is smaller than the first energy distribution data; determining the first disadvantaged area according to the second disadvantaged area.

2. The method of claim 1, wherein, The determination of the second disadvantaged area in the second time-domain stack section according to the first energy distribution data and the second energy distribution data comprises: subtracting the first energy distribution data from the second energy distribution data to obtain a time-domain energy difference section; determining an area in which energy data difference is negative in the time-domain energy difference section as the second disadvantaged area.

3. The method of claim 2, wherein, Before the area in which energy data difference is negative in the time-domain energy difference section is determined as the second disadvantaged area, the method further comprises at least one of the following: removing energy data difference with an absolute value smaller than a difference threshold value in the time-domain energy difference section; removing an area in which energy data difference is negative and area size is smaller than an area threshold value in the time-domain energy difference section, and removing an area in which energy data difference is positive and area size is smaller than the area threshold value in the time-domain energy difference section.

4. The method according to any one of claims 1 to 3, characterized in that, The first energy distribution data and the second energy distribution data are calculated by using the following formula: ； Wherein, A(t) represents the first energy distribution data or the second energy distribution data, x(t) represents a discrete signal sequence, H(x(t)) represents a Hilbert transform on x(t), t represents time, represents a Hilbert transform on taking L2 norm; or, The first energy distribution data and the second energy distribution data are calculated by using the following formula: ； wherein A(t) represents the first energy distribution data or the second energy distribution data, x(t) represents a discrete signal sequence, H(x(t)) represents a Hilbert transform of x(t), and t represents time, represents a Hilbert transform of taking an absolute value.

5. An apparatus for optimizing a velocity model, characterized by, The device comprises: a velocity updating module configured to perform velocity updating on a first velocity model to obtain a second velocity model; a determination module configured to determine a first disadvantaged area in the second velocity model, the first disadvantaged area being an area in which the second velocity model has lower accuracy relative to the first velocity model; a velocity model optimization module configured to backfill a part of the first velocity model corresponding to the first disadvantaged area to the first disadvantaged area to obtain an optimized second velocity model; The determining module is configured to determine first energy distribution data of a first time-domain stack section based on the first velocity model, determine second energy distribution data of a second time-domain stack section based on the second velocity model, and determine a second disadvantageous area in the second time-domain stack section according to the first energy distribution data and the second energy distribution data, the second disadvantageous area being an area where the second energy distribution data is less than the first energy distribution data, and determine the first disadvantageous area according to the second disadvantageous area.

6. A computer device, comprising: Comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method of any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the computer device, the computer device is enabled to perform the method of any one of claims 1 to 4.

8. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the method of any one of claims 1 to 4.

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