CN111025399B - Depth control method and system for effective model for first-arrival chromatography near-surface modeling - Google Patents
Depth control method and system for effective model for first-arrival chromatography near-surface modeling Download PDFInfo
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Abstract
A depth control method and system for the first-arrival chromatography near-surface modeling effective model are disclosed. The method comprises the following steps: obtaining a ray maximum depth and optimized speed model of the 1 st iteration and the 2 nd iteration; setting a threshold value, and performing Nth iteration: step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration; step 2: calculating a depth difference; and step 3: calculating the effective model depth of the Nth iteration; and 4, step 4: obtaining an optimized speed model; and 5: whether N is equal to the threshold value, if yes, obtaining a near-surface speed model, and if not, step 6; step 6: and (4) whether the optimized speed model reaches a preset target or not, if so, obtaining a near-surface speed model, and if not, performing the (N + 1) th iteration. According to the method, the effective model depth of the iteration is determined through the maximum ray depth of the last iteration in the first-arrival chromatography iteration process, and the speed units outside the effective model depth are excluded so as to improve the forward efficiency.
Description
Technical Field
The invention relates to the field of seismic exploration data processing, in particular to a depth control method and system for an effective model of first-arrival chromatography near-surface modeling.
Background
The near-surface modeling technology of the first-arrival chromatography by utilizing the first-arrival time inversion near-surface velocity model is widely applied to the seismic data processing process. The first-arrival chromatography near-surface modeling technology is an iterative process, and each iteration comprises 2 basic steps of forward modeling and inversion. The forward process is that ray tracing technology is utilized to calculate ray paths and time of seismic waves which are excited at each shot point and reach each receiving point after being propagated by a given initial velocity model; the inversion is to calculate the correction amount of the initial velocity model according to the difference between the forward time and the first arrival time of the actual data pickup and by combining the ray path data obtained by the forward. And applying the initial velocity model correction quantity calculated in the inversion process to the initial velocity model to obtain an optimized velocity model, taking the optimized velocity model as the initial velocity model of the next iteration, and entering the next iteration.
The forward modeling process is a step that the first-arrival tomography near-surface modeling technology consumes the most CPU computing time, although different ray tracing technologies are available, the CPU computing time consumed by the ray tracing is related to the range of the velocity model used for computing the ray path in the ray tracing process, the CPU computing time consumed by the ray tracing process is more when the range of the velocity model used is larger, and wrong ray tracing results can be caused if the range of the velocity model used is too small. The prior art fixes a large velocity model range, which does not result in erroneous ray tracing results for each iteration, but also results in more CPU computation time since the fixed large velocity model range is not used for each iteration. Therefore, there is a need to develop an effective model depth control method and system for first-arrival tomography near-surface modeling.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a depth control method and a system for an effective model for first-arrival tomography near-surface modeling, which can determine the effective model depth of a speed model required by the current iteration through the maximum ray depth related to the latest secondary iteration in the first-arrival tomography iteration process, and exclude speed units outside the effective model depth from the ray path tracking range so as to improve the forward efficiency.
According to one aspect of the invention, a depth control method for an effective model for first-arrival tomography near-surface modeling is provided. The method may include: according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained; taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining an optimized velocity model; setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps: step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration; step 2: calculating a depth difference according to the maximum depth of the ray of the preprocessed N-2 th iteration and the maximum depth of the ray of the preprocessed N-1 th iteration; and step 3: calculating the effective model depth of the Nth iteration according to the maximum depth of the preprocessed ray of the (N-1) th iteration and the depth difference; and 4, step 4: taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of first-arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model; and 5: judging whether N is equal to the iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6; step 6: and judging whether the optimized speed model reaches a preset target, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
Preferably, N is equal to or greater than 3.
Preferably, the step 2 includes: subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference; calculating an amplified depth difference according to the initial depth difference and a proportional coefficient; setting a depth difference threshold, if the amplified depth difference is smaller than the depth difference threshold, using the depth difference threshold as the depth difference, and if the amplified depth difference is larger than or equal to the depth difference threshold, using the amplified depth difference as the depth difference.
Preferably, the step 3 comprises: adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth; and carrying out smoothing treatment on the initial effective model depth to obtain the effective model depth of the Nth iteration.
Preferably, the smoothing process is performed on the initial effective model depth, and obtaining the effective model depth of the nth iteration includes: setting a cycle number threshold, and performing the following steps: step a: calculating a moving average value of the initial effective model depth according to a smooth window; step b: judging whether the cycle number is equal to the cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c; step c: and (c) aiming at the point of the initial effective model depth, the depth value of which is smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c.
According to another aspect of the present invention, there is provided a depth control system for an efficient model for modeling near-surface of first-arrival tomography, having a computer program stored thereon, the system comprising: a memory storing computer-executable instructions; a processor executing computer executable instructions in the memory to perform the steps of: according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained; taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining an optimized velocity model; setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps: step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration; step 2: calculating a depth difference according to the maximum depth of the ray of the preprocessed N-2 th iteration and the maximum depth of the ray of the preprocessed N-1 th iteration; and step 3: calculating the effective model depth of the Nth iteration according to the maximum depth of the preprocessed ray of the (N-1) th iteration and the depth difference; and 4, step 4: taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of first-arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model; and 5: judging whether N is equal to the iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6; step 6: and judging whether the optimized speed model reaches a preset target, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
Preferably, N is equal to or greater than 3.
Preferably, the step 2 includes: subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference; calculating an amplified depth difference according to the initial depth difference and a proportional coefficient; setting a depth difference threshold, if the amplified depth difference is smaller than the depth difference threshold, using the depth difference threshold as the depth difference, and if the amplified depth difference is larger than or equal to the depth difference threshold, using the amplified depth difference as the depth difference.
Preferably, the step 3 comprises: adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth; and carrying out smoothing treatment on the initial effective model depth to obtain the effective model depth of the Nth iteration.
Preferably, the smoothing process is performed on the initial effective model depth, and obtaining the effective model depth of the nth iteration includes: setting a cycle number threshold, and performing the following steps: step a: calculating a moving average value of the initial effective model depth according to a smooth window; step b: judging whether the cycle number is equal to the cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c; step c: and (c) aiming at the point of the initial effective model depth, the depth value of which is smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c.
The present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
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The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
FIG. 1 shows a flow chart of the steps of a method for efficient model depth control for first arrival tomographic near-surface modeling in accordance with the present invention.
FIG. 2 shows a schematic of a near-surface velocity model obtained from 13 iterations of first-arrival tomography without a depth control method.
Fig. 3 shows a schematic diagram of the ray maximum depth of the 1 st and 2 nd iterations of the first-arrival tomography and the initial effective model depth of the 3 rd iteration and the effective model depth of the 3 rd iteration, which are calculated by the depth control method.
FIG. 4 shows a comparison of the ray maximum depth of the 4 th iteration when the depth control method is not used for the first-arrival tomography and the effective model depth of the 4 th iteration calculated by the depth control method.
FIG. 5 shows a comparison of the ray maximum depth for the 7 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 7 th iteration calculated by the depth control method.
FIG. 6 shows a comparison of the ray maximum depth of the 10 th iteration when the depth control method is not used for the first-arrival tomography and the effective model depth of the 10 th iteration calculated by the depth control method.
FIG. 7 shows a comparison of the ray maximum depth of the 13 th iteration when the depth control method is not used for the first-arrival tomography and the effective model depth of the 13 th iteration calculated by the depth control method.
FIG. 8 shows a comparison of CPU time for 1-13 iterations of first arrival tomography with or without the depth control method.
FIG. 9 shows a comparison of the root mean square error of the first arrival time for 1-13 iterations of the first arrival tomography, whether the depth control method is used or not.
FIG. 10 shows a schematic of a near-surface velocity model obtained using 13 iterations of the first-arrival tomography of the depth control method.
Fig. 11 shows a schematic diagram of the ray maximum depth of the 1 st and 2 nd iterations of the first-arrival tomography and the initial effective model depth of the 3 rd iteration and the effective model depth of the 3 rd iteration, which are calculated by the depth control method.
FIG. 12 shows a comparison of the ray maximum depth for the 4 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 4 th iteration calculated by the depth control method.
FIG. 13 shows a comparison of the ray maximum depth for the 7 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 7 th iteration calculated by the depth control method.
FIG. 14 shows a comparison of the ray maximum depth for the 10 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 10 th iteration calculated by the depth control method.
FIG. 15 shows a comparison of the ray maximum depth for the 13 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 13 th iteration calculated by the depth control method.
FIG. 16 shows a comparison of CPU time for 1-13 iterations of first arrival tomography with or without depth control.
FIG. 17 shows a comparison of the root mean square error of the first arrival time for 1-13 iterations of the first arrival tomography, whether the depth control method is used or not.
FIG. 18 shows a schematic of a near-surface velocity model obtained using 13 iterations of the first-arrival tomography of the depth control method.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 shows a flow chart of the steps of a method for efficient model depth control for first arrival tomographic near-surface modeling in accordance with the present invention.
In this embodiment, the method for controlling the depth of the effective model for the first-arrival tomography near-surface modeling according to the present invention may include: according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained; taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining the optimized velocity model; setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps: step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration; step 2: calculating a depth difference according to the maximum depth of the ray of the preprocessed N-2 th iteration and the maximum depth of the ray of the preprocessed N-1 th iteration; and step 3: calculating the effective model depth of the Nth iteration according to the maximum depth and the depth difference of the preprocessed ray of the (N-1) th iteration; and 4, step 4: taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of first arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model; and 5: judging whether N is equal to an iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6; step 6: and judging whether the optimized speed model reaches a preset target, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
In one example, N is greater than or equal to 3.
In one example, step 2 comprises: subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference; calculating an amplified depth difference according to the initial depth difference and the proportional coefficient; and setting a depth difference threshold, wherein if the amplified depth difference is smaller than the depth difference threshold, the depth difference threshold is used as the depth difference, and if the amplified depth difference is larger than or equal to the depth difference threshold, the amplified depth difference is used as the depth difference.
In one example, step 3 comprises: adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth; and smoothing the initial effective model depth to obtain the effective model depth of the Nth iteration.
In one example, smoothing the initial effective model depth, and obtaining the effective model depth for the nth iteration comprises: setting a cycle number threshold, and performing the following steps: step a: calculating a moving average value of the initial effective model depth according to a smooth window; step b: judging whether the cycle number is equal to a cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c; step c: and (c) aiming at the point of the initial effective model depth with the depth value smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c.
Specifically, the process of establishing a near-surface velocity model of a work area by using a first-arrival chromatography technology may include one or more first-arrival chromatography processes, each first-arrival chromatography process includes an iteration process, one iteration process includes a plurality of iterations, each iteration can obtain a velocity model correction amount for correcting the velocity model at the beginning of the iteration, and each iteration includes 2 basic steps of forward modeling and inversion. The forward process is to calculate the ray path and time of the seismic wave excited at each shot point and arriving at each receiving point after propagating through a given initial velocity model by using a ray path tracking technology. The inversion is to calculate the correction amount of the initial velocity model according to the difference between the forward time and the first arrival time of the actual data pickup and by combining the ray path data obtained by the forward. And applying the initial velocity model correction quantity calculated in the inversion process to the initial velocity model to obtain an optimized velocity model, taking the optimized velocity model as the initial velocity model of the next iteration, and entering the next iteration. The first-arrival chromatography iteration process is finished when the iteration number reaches a given iteration number threshold or the optimization speed model reaches a preset target, and the optimization speed model reaches the preset target, the first-arrival time root mean square error is always the preset target, namely the first-arrival time root mean square error is smaller than or equal to a given value, and the judgment index is used.
The forward process is a step that the first-arrival tomography consumes the most CPU computing time, although different ray path tracking technologies are available, the CPU computing time consumed by the ray path tracking is related to the range of the velocity model used for computing the ray path in the ray path tracking process, the CPU computing time consumed by the ray path tracking process is more when the used range of the velocity model is larger, and an error ray path tracking result can be caused if the used range of the velocity model is too small. The prior art fixes a large velocity model range, which does not result in an erroneous ray path tracing result for each iteration, but also results in a large CPU computation time, because such a large velocity model range is not required for each iteration.
The maximum depth reached by the ray path in the first-arrival tomography forward process, namely the maximum ray depth, is related to the initial velocity model. With each iteration modifying the initial velocity model, the maximum depth reached by the ray path for the next iteration varies. In general, the maximum depth reached by the ray path is gradually increased, or may start to be gradually decreased after reaching a certain depth, and finally stabilize within a certain range. The maximum depth reached by the ray path of each adjacent iteration has a certain rule, and the change trend of the maximum depth reached by the ray path of the previous 2 iterations can be used for predicting the maximum depth reached by the ray path of the current iteration. If the maximum depth reached by the ray path of the previous 2 iterations is increased, the maximum depth reached by the ray path of the current iteration is more than half larger, and the maximum depth possibly reached by the current iteration can be predicted and properly widened according to the maximum depth reached by the ray path of the previous 2 iterations and the increase amount thereof. If the maximum depth reached by the ray path of the previous 2 iterations is reduced, the maximum depth reached by the ray path of the current iteration is mostly smaller, and the predicted maximum depth possibly reached by the ray path of the current iteration can be kept at the maximum depth reached by the ray path of the last iteration and can be properly relaxed.
The effective model depth control method for the first-arrival tomography near-surface modeling is characterized in that the effective depth range of a speed model related to the current iteration, namely the effective model depth, is determined according to the maximum ray depth related to the latest second iteration in the first-arrival tomography iteration process, only speed units with the depth smaller than the effective model depth in the speed model are used for ray path tracking to improve forward efficiency, and the speed units with the depth smaller than the effective model depth in the speed model are a subset of the speed model. Only the first 2 iterations in the first-arrival tomography iteration process, the entire velocity model range is used for ray path tracing since the maximum depth and change trend that the ray can reach are not known yet.
The core of the depth control method of the first-arrival chromatography near-surface modeling effective model is to calculate the effective model depth of the iteration according to the ray maximum depth data of the last 2 iterations, and the method also comprises the matching technologies of allocating and storing the memory space of the ray maximum depth data, excluding the speed model units except the effective model depth from the forward process (namely only taking the speed units with the depth smaller than the effective model depth in the speed model for ray path tracking), extracting and storing the ray maximum depth data and the like.
And allocating memory space for storing ray maximum depth data. Before the first-arrival tomography iteration process is started, a memory space capable of storing the maximum ray depth of 2 iterations is allocated and used for storing the maximum ray depth data of the last 2 iterations, and the size of the maximum ray depth data is equal to 2 times of the number of units on the speed model plane. Assuming that the number of cells of the velocity model in the x, y, z directions is Nx, Ny, Nz, respectively, the number of cells on the plane of the velocity model is Nx × Ny, and the size of the memory space for storing the maximum depth data of the ray is equal to 2 × Nx × Ny. The ray maximum depth is a function of the velocity model plane element, and a plane element corresponds to a value representing the maximum depth value in all ray paths traversed in all Nz vertical elements belonging to the plane element.
The depth control method for the first-arrival chromatography near-surface modeling effective model can comprise the following steps:
according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained; taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining the optimized velocity model;
setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps, wherein N is more than or equal to 3:
step 1: and preprocessing the maximum ray depth of the (N-2) th iteration and the maximum ray depth of the (N-1) th iteration, setting that rays pass through the plane unit twice and the depth is the same for the case that rays do not pass through the plane unit twice in two iterations, namely setting the maximum ray depth value of the iteration of the two iterations, in which rays do not pass through the plane unit twice, to be the same as the maximum ray depth value of the iteration of which rays pass through the plane unit twice.
Step 2: subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference; multiplying the initial depth difference by a proportional coefficient Fc to obtain an amplified depth difference; setting a depth difference threshold Dd, setting the depth difference threshold as a depth difference if the amplified depth difference is smaller than the depth difference threshold, and setting the amplified depth difference as a depth difference if the amplified depth difference is equal to or greater than the depth difference threshold, which indicates that the depth difference is set to the set value even if the depth difference is smaller than 0, resulting in no negative depth difference; both a larger Fc and a larger Dd can lead to a larger calculation result of the effective model depth; planar elements that do not have rays passing through both iterations do not have depth differences, and are marked with a negative value (e.g., -999).
And step 3: adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth; carrying out smoothing treatment on the initial effective model depth, setting a cycle threshold, and carrying out the following steps: step a: calculating a moving average value of the initial effective model depth according to a smooth window, namely calculating the average value of the initial effective model depths of all points falling into a given window taking the calculated point as the window center point by point, wherein the initial effective model depths and the effective model depths are three-dimensional curved surfaces; step b: judging whether the cycle number is equal to a cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c; step c: and (c) aiming at the point of the initial effective model depth with the depth value smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c. The smooth window for calculating the moving average value is a closed area, which is usually rectangular or circular, the larger the window range is, the larger the cycle number Nr is, and the larger the final effective model depth value is overall and the more stable the transverse direction is. The length of the rectangle side or the diameter of the circle is more suitable about 3000 meters, and the technical personnel in the field can set the length according to the specific situation, a smaller window needs to be matched with a larger cycle number, and a larger window is not favorable for realizing the purpose of improving the calculation efficiency of the invention. Regarding the difference between the rectangle and the circle as the smooth window, when the window dimensions (the diameter of the circle and the side length of the rectangle) are the same, the smoothing effect of the rectangular window is relatively large, and there is no essential difference, and the influence on the result is slight.
And 4, step 4: and taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of the first-arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model.
And 5: and judging whether the N is equal to the iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6.
Step 6: and judging whether the optimized speed model reaches a preset target or not, judging whether the root mean square error of the first arrival time is smaller than a preset error threshold or not, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
Ray maximum depth data is extracted and saved. After the forward step of each tomography iteration, i.e. the ray path tracking step, is finished, the ray maximum depth of each plane unit is extracted and stored in the memory space capable of storing ray maximum depth data of two iterations, and the ray maximum depth values of the plane units without rays passing through are given a negative value, such as-999, to mark.
The method determines the effective model depth of the speed model required by the current iteration through the maximum ray depth of the last secondary iteration in the first-arrival chromatography iteration process, and excludes the speed units outside the effective model depth from the ray path tracking range so as to improve the forward efficiency.
Application example
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Take southern mountain seismic exploration work area as an example. The same first-arrival chromatography method was applied, comparing the accuracy and efficiency of the experiment with and without the depth control method.
FIG. 2 shows a near-surface velocity model obtained from 13 iterations of first-arrival tomography without the use of a depth control method.
And setting the proportionality coefficient Fc to be 1.2 and the depth difference threshold Dd to be 200m, and when the effective model depth is calculated according to the initial effective model depth, wherein the cycle number Nr to be 3, the smoothing window is a rectangle with the length Lx to be 3000m and the width Ly to be 3000 m.
Fig. 3 shows a schematic diagram of the ray maximum depth of the 1 st and 2 nd iterations of the first-arrival tomography and the initial effective model depth of the 3 rd iteration and the effective model depth of the 3 rd iteration, which are calculated by the depth control method. And (4) visually representing the relationship among the maximum ray depth, the initial effective model depth and the effective model depth of the last 2 iterations.
FIG. 4 shows a comparison of the ray maximum depth of the 4 th iteration when the depth control method is not used for the first-arrival tomography and the effective model depth of the 4 th iteration calculated by the depth control method. The effective model depth of the 4 th iteration calculated by the control method is larger than the maximum ray depth of the 4 th iteration when the depth control method is not used, which shows that the effective model depth calculated by the depth control method adopting the parameters does not influence the ray tracing precision.
FIG. 5 shows a comparison of the ray maximum depth for the 7 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 7 th iteration calculated by the depth control method.
FIG. 6 shows a comparison of the ray maximum depth of the 10 th iteration when the depth control method is not used for the first-arrival tomography and the effective model depth of the 10 th iteration calculated by the depth control method.
FIG. 7 shows a comparison of the ray maximum depth of the 13 th iteration when the depth control method is not used for the first-arrival tomography and the effective model depth of the 13 th iteration calculated by the depth control method.
And sampling for quality monitoring purposes to show results of 7 th iteration, 10 th iteration and 13 th iteration, wherein effective model depths of the 7 th iteration, 10 th iteration and 13 th iteration calculated by the control method are all larger than the maximum ray depths of the 7 th iteration, 10 th iteration and 13 th iteration when the depth control method is not used, and the ray tracing precision is not influenced by the depth control method adopting the parameters.
FIG. 8 shows a comparison of CPU time for 1-13 iterations of first-arrival tomography using the depth control method, with a significant reduction in CPU time for first-arrival tomography using the depth control method from iteration 3.
FIG. 9 is a graph showing the comparison of the RMS error of the first-arrival time of 1-13 iterations of the first-arrival tomography using the depth control method, where the RMS error is the RMS error between the first-arrival time calculated based on the model ray tracing and the first-arrival time of the actual seismic data acquisition, the RMS variation curve represents the variation of the coincidence between the first-arrival time calculated by the ray tracing and the first-arrival time of the actual seismic data acquisition as the number of iterations increases, and the RMS error decreases continuously, which represents that the coincidence is increasing. The complete coincidence of the 2 rms error curves in fig. 9 with or without the depth control method also represents the complete coincidence of the first arrival times calculated with or without the ray tracing method.
FIG. 10 shows a schematic of a near-surface velocity model obtained using 13 iterations of the first-arrival tomography of the depth control method. The comparison with fig. 2 is completely consistent, which shows that the depth control method using the above parameters obviously improves the efficiency without changing the precision.
When the scaling coefficient Fc is set to 1.2 and the depth difference threshold Dd is set to 100m, and the effective model depth is calculated from the initial effective model depth, where the number of cycles Nr is 3, the smoothing window is a rectangle with a length Lx of 3000m and a width Ly of 3000 m. In contrast to "implementation parameter 1", only the depth difference threshold value is changed from "implementation parameter 1" with Dd 200m to here with Dd 100 m.
Fig. 11 shows a schematic diagram of the ray maximum depth of the 1 st and 2 nd iterations of the first-arrival tomography and the initial effective model depth of the 3 rd iteration and the effective model depth of the 3 rd iteration, which are calculated by the depth control method. The initial effective model depth and the effective model depth for the 3 rd iteration are relatively shallow compared to fig. 3.
FIG. 12 shows a comparison of the ray maximum depth for the 4 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 4 th iteration calculated by the depth control method. FIG. 13 shows a comparison of the ray maximum depth for the 7 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 7 th iteration calculated by the depth control method. FIG. 14 shows a comparison of the ray maximum depth for the 10 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 10 th iteration calculated by the depth control method. FIG. 15 shows a comparison of the ray maximum depth for the 13 th iteration when the depth control method is not used for first-arrival tomography and the effective model depth for the 13 th iteration calculated by the depth control method.
The results of the 7 th iteration, 10 th iteration and 13 th iteration are shown for sampling for quality monitoring, wherein the effective model depth of the 7 th iteration, 10 th iteration and 13 th iteration calculated by the control method is mostly larger than the maximum ray depth of the 7 th iteration, 10 th iteration and 13 th iteration when the depth control method is not used, the former is slightly smaller than the latter when the local positions are partially overlapped, and the depth control method adopting the parameters can influence the ray tracing accuracy of some ray paths.
FIG. 16 shows a comparison of CPU time for 1-13 iterations of first-arrival tomography using the depth control method, starting from iteration 3, with significantly less CPU time for first-arrival tomography using the depth control method, and with more reduction than shown in FIG. 8.
Fig. 17 shows a comparison graph of the first-arrival time root mean square error of 1-13 iterations of the first-arrival tomography using the depth control method, 2 root mean square error curves are visually completely overlapped, the maximum numerical comparison is only 0.03 difference, and most of the numerical comparison is within 0.01, which indicates that the ray tracing accuracy is affected but slightly affected by using the depth control method.
FIG. 18 shows a schematic of a near-surface velocity model obtained using 13 iterations of the first-arrival tomography of the depth control method. In contrast to fig. 2, the other parts are substantially identical except for a slight difference visible at the deeper part. Therefore, by adopting the depth control method of the parameters, the efficiency is improved more obviously under the condition that the first-arrival chromatography precision is slightly influenced in a deeper layer.
In summary, the present invention determines the effective model depth of the velocity model required by the current iteration through the maximum ray depth of the last iteration of the first-arrival tomography iteration process, and excludes the velocity units outside the effective model depth from the ray path tracking range, so as to improve the forward performance efficiency.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
The system for controlling the depth of an effective model for modeling a near-surface of a first arrival tomography according to the invention, having a computer program stored thereon, is characterized in that the program, when executed by a processor, implements the steps of: according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained; taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining the optimized velocity model; setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps: step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration; step 2: calculating a depth difference according to the maximum depth of the ray of the preprocessed N-2 th iteration and the maximum depth of the ray of the preprocessed N-1 th iteration; and step 3: calculating the effective model depth of the Nth iteration according to the maximum depth and the depth difference of the preprocessed ray of the (N-1) th iteration; and 4, step 4: taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of first arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model; and 5: judging whether N is equal to an iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6; step 6: and judging whether the optimized speed model reaches a preset target, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
In one example, N is greater than or equal to 3.
In one example, step 2 comprises: subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference; calculating an amplified depth difference according to the initial depth difference and the proportional coefficient; and setting a depth difference threshold, wherein if the amplified depth difference is smaller than the depth difference threshold, the depth difference threshold is used as the depth difference, and if the amplified depth difference is larger than or equal to the depth difference threshold, the amplified depth difference is used as the depth difference.
In one example, step 3 comprises: adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth; and smoothing the initial effective model depth to obtain the effective model depth of the Nth iteration.
In one example, smoothing the initial effective model depth, and obtaining the effective model depth for the nth iteration comprises: setting a cycle number threshold, and performing the following steps: step a: calculating a moving average value of the initial effective model depth according to a smooth window; step b: judging whether the cycle number is equal to a cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c; step c: and (c) aiming at the point of the initial effective model depth with the depth value smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c.
The system determines the effective model depth of the speed model required by the iteration through the maximum ray depth of the last iteration of the first-arrival chromatography iteration process, and excludes the speed units outside the effective model depth from the ray path tracking range so as to improve the forward efficiency.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. A depth control method for an effective model of first-arrival chromatography near-surface modeling is characterized by comprising the following steps:
according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained;
taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining an optimized velocity model;
setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps:
step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration;
step 2: calculating a depth difference according to the maximum depth of the ray of the preprocessed N-2 th iteration and the maximum depth of the ray of the preprocessed N-1 th iteration;
and step 3: calculating the effective model depth of the Nth iteration according to the maximum depth of the preprocessed ray of the (N-1) th iteration and the depth difference;
and 4, step 4: taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of first-arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model;
and 5: judging whether N is equal to the iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6;
step 6: and judging whether the optimized speed model reaches a preset target, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
2. The method for controlling the depth of an effective model for first arrival tomography near-surface modeling according to claim 1, wherein N is greater than or equal to 3.
3. The first arrival tomography near-surface modeling effective model depth control method of claim 1, wherein the step 2 comprises:
subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference;
calculating an amplified depth difference according to the initial depth difference and a proportional coefficient;
setting a depth difference threshold, if the amplified depth difference is smaller than the depth difference threshold, using the depth difference threshold as the depth difference, and if the amplified depth difference is larger than or equal to the depth difference threshold, using the amplified depth difference as the depth difference.
4. The first arrival tomography near-surface modeling effective model depth control method of claim 1, wherein the step 3 comprises:
adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth;
and carrying out smoothing treatment on the initial effective model depth to obtain the effective model depth of the Nth iteration.
5. The method of claim 4, wherein smoothing the initial effective model depth to obtain the effective model depth for the Nth iteration comprises:
setting a cycle number threshold, and performing the following steps:
step a: calculating a moving average value of the initial effective model depth according to a smooth window;
step b: judging whether the cycle number is equal to the cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c;
step c: and (c) aiming at the point of the initial effective model depth, the depth value of which is smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c.
6. A system for efficient model depth control for first-arrival tomographic near-surface modeling, having a computer program stored thereon, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
according to the initial velocity model, forward modeling and inversion of first arrival chromatography are carried out to obtain the maximum ray depth of the 1 st iteration and the velocity model correction of the 1 st iteration, and then an optimized velocity model is obtained;
taking the optimized velocity model as an initial velocity model, performing forward modeling and inversion of first arrival chromatography to obtain the maximum ray depth of the 2 nd iteration and the velocity model correction of the 2 nd iteration, and further obtaining an optimized velocity model;
setting an iteration time threshold, taking an optimized speed model obtained by the (N-1) th iteration as an initial speed model, carrying out the Nth iteration, and carrying out the following steps:
step 1: preprocessing the maximum ray depth of the N-2 iteration and the maximum ray depth of the N-1 iteration;
step 2: calculating a depth difference according to the maximum depth of the ray of the preprocessed N-2 th iteration and the maximum depth of the ray of the preprocessed N-1 th iteration;
and step 3: calculating the effective model depth of the Nth iteration according to the maximum depth of the preprocessed ray of the (N-1) th iteration and the depth difference;
and 4, step 4: taking a speed unit with the depth smaller than the effective model depth of the Nth iteration in the initial speed model to perform forward modeling and inversion of first-arrival chromatography, obtaining the maximum ray depth of the Nth iteration and the speed model correction of the Nth iteration, and further obtaining an optimized speed model;
and 5: judging whether N is equal to the iteration time threshold, if so, taking the optimized speed model as a near-surface speed model, and if not, performing the step 6;
step 6: and judging whether the optimized speed model reaches a preset target, if so, taking the optimized speed model as a near-surface speed model, if not, taking the optimized speed model as an initial speed model, setting N to be N +1, and repeating the steps 1-6 to carry out iteration for the (N + 1) th time.
7. The first-arrival tomography near-surface modeling efficient model depth control system of claim 6, wherein N is greater than or equal to 3.
8. The first-arrival tomographic near-surface modeling effective model depth control system of claim 6, wherein the step 2 comprises:
subtracting the maximum depth of the ray of the preprocessed N-2 iterations from the maximum depth of the ray of the preprocessed N-1 iteration to obtain an initial depth difference;
calculating an amplified depth difference according to the initial depth difference and a proportional coefficient;
setting a depth difference threshold, if the amplified depth difference is smaller than the depth difference threshold, using the depth difference threshold as the depth difference, and if the amplified depth difference is larger than or equal to the depth difference threshold, using the amplified depth difference as the depth difference.
9. The first-arrival tomographic near-surface modeling effective model depth control system of claim 6, wherein the step 3 comprises:
adding the depth difference to the maximum depth of the preprocessed ray of the (N-1) th iteration to obtain the initial effective model depth;
and carrying out smoothing treatment on the initial effective model depth to obtain the effective model depth of the Nth iteration.
10. The first-arrival tomographic near-surface modeling effective model depth control system of claim 9, wherein smoothing the initial effective model depth to obtain the effective model depth for the nth iteration comprises:
setting a cycle number threshold, and performing the following steps:
step a: calculating a moving average value of the initial effective model depth according to a smooth window;
step b: judging whether the cycle number is equal to the cycle number threshold value, if so, taking the moving average value as the effective model depth of the Nth iteration, and if not, executing the step c;
step c: and (c) aiming at the point of the initial effective model depth, the depth value of which is smaller than the moving average value, setting the depth value as the moving average value to obtain the optimized initial effective model depth, taking the optimized initial effective model depth as the initial effective model depth, and repeating the steps a-c.
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