Building bottom karst detection method based on velocity imaging

Abstract

The invention belongs to the field of geotechnical engineering investigation, and particularly discloses a method for detecting karst at the bottom of a building based on velocity imaging, and the method comprises the following specific steps: S1, equipment arrangement: arranging a seismic wave receiver in a predetermined detection area; s2, data collection: using a seismic source to generate seismic waves, and using a seismic wave receiver to collect seismic wave propagation time data; s3, model construction: constructing a speed model; s4, optimizing the model; optimizing the speed model by using an LSQR algorithm; s5, velocity imaging: using a reverse time migration algorithm to obtain an image of the underground reflector; s6, analysis and verification: determining the position, size and form of the karst according to a velocity imaging graph obtained through analysis, and verifying the accuracy of velocity imaging through drilling; according to the method, the LSQR algorithm serves as an optimization technology, the speed model can be further optimized by minimizing the difference between model prediction and actual observation, a more accurate karst imaging graph is obtained, and the karst risk of the bottom of the building is evaluated more accurately.

Description

Technical Field

[0001] The invention belongs to the field of geotechnical engineering investigation, and in particular relates to a karst detection method at the bottom of a building based on velocity imaging. Background Art

[0002] Karst detection refers to the technical means used to detect the karst erosion morphology, spatial structure and karst materials that may exist in rocks. Karst detection at the bottom of a building is to ensure the safety of the building foundation and prevent the risks that may be caused by underground karst problems. If the building foundation is in a karst development area, karst detection can help avoid potential geological disaster risks and ensure the stability and safety of the building;

[0003] However, the traditional method of core drilling for karst exploration has great limitations, and is prone to human errors and operational difficulties, resulting in unsatisfactory detection results. Summary of the invention

[0004] The purpose of the present invention is to provide a karst detection method at the bottom of a building based on velocity imaging to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The karst detection method at the bottom of a building based on velocity imaging has the following specific steps:

[0007] S1. Equipment layout: Arrange seismic wave receivers in the predetermined detection area;

[0008] S2. Data collection: using a seismic source to generate seismic waves and using seismic wave receivers to collect seismic wave propagation time data;

[0009] S3, model construction: construct velocity model;

[0010] S4, model optimization; use LSQR algorithm to optimize the speed model;

[0011] S5, velocity imaging: using the reverse time migration algorithm to obtain images of underground reflectors;

[0012] S6. Analysis and verification: The velocity imaging map obtained by analysis is used to determine the location, size and shape of the karst, and the accuracy of the velocity imaging is verified by drilling.

[0013] Preferably, the seismic source in step S2 comprises a shock or vibration plate.

[0014] Preferably, the velocity model in step S3 is a preliminary velocity model established using known geological information and simple seismic wave velocity analysis.

[0015] Preferably, the specific calculation formula for model optimization in step S4 is as follows:

[0016]

[0017] Among them, A represents the coefficient matrix, x represents the velocity model to be determined, and b represents the observed seismic wave travel time data.

[0018] Preferably, the specific steps of speed imaging in step S5 are as follows:

[0019] S5.1. Forward modeling: Using the obtained velocity model and earthquake source function, forward model the propagation process of seismic waves in the underground medium to generate synthetic seismic records;

[0020] S5.2, reverse time propagation: using the actual observed earthquake records as boundary conditions, reverse time propagation of the seismic wave field;

[0021] S5.3, stacking and imaging: the reverse propagation wave field is superimposed with the forward simulation wave field, and a high-resolution image of the underground reflection interface is obtained by imaging condition calculation;

[0022] S5.4. Velocity model optimization: Based on the imaging results, identify difficult data where the model predictions differ greatly from the actual data, focus on retraining these data, and adjust the velocity model to better match the actual observation data.

[0023] Preferably, the specific calculation formula for the forward modeling in step S5.1 is as follows:

[0024]

[0025] Among them, u represents the wave field pressure, v(x, z) represents the velocity model, s(x, z, t) represents the source term, represents the time second-order partial derivative, and represents the spatial second-order partial derivative.

[0026] Preferably, in step S6, low-velocity abnormal areas in the imaging image are identified, and these areas may be karst development areas.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) The present invention generates a high-resolution image of the underground reflection interface by combining the reverse time migration algorithm with the seismic wave velocity, providing higher-resolution karst imaging with meter-level accuracy. This can make up for the limitations of traditional drilling methods and effectively reduce detection blind spots, which is crucial for preventing ground collapse and ensuring building safety.

[0029] (2) The present invention uses the LSQR algorithm as an optimization technique to further optimize the velocity model by minimizing the difference between model prediction and actual observation, thereby obtaining a more accurate karst imaging map and more accurately assessing the karst risk at the bottom of the building, thereby avoiding the possibility of errors in the generated image due to the complexity of the geological structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a block diagram of the specific steps of the present invention;

[0031] Figure 2 It is a block diagram of the specific steps of speed imaging of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] As attached Figure 1 To Attachment Figure 2 As shown:

[0034] Embodiment 1: The present invention provides a method for detecting karst at the bottom of a building based on velocity imaging, and the specific steps are as follows:

[0035] S1. Equipment layout: Arrange seismic wave receivers in the predetermined detection area;

[0036] S2. Data collection: Use a seismic source to generate seismic waves, use seismic wave receivers to collect seismic wave propagation time data, and perform necessary filtering and noise suppression on the data;

[0037] S3, model construction: construct velocity model;

[0038] S4, model optimization; use LSQR algorithm to optimize the speed model;

[0039] S5, velocity imaging: using the reverse time migration algorithm to obtain images of underground reflectors;

[0040] S6. Analysis and verification: The velocity imaging map obtained by analysis is used to determine the location, size and shape of the karst, and the accuracy of the velocity imaging is verified by drilling.

[0041] Specifically, the seismic source in step S2 includes shocking or vibrating the plate.

[0042] Specifically, the velocity model in step S3 is a preliminary velocity model established using known geological information and simple seismic wave velocity analysis.

[0043] Specifically, the specific steps of velocity imaging in step S5 are as follows:

[0044] S5.1. Forward modeling: Using the obtained velocity model and earthquake source function, forward model the propagation process of seismic waves in the underground medium to generate synthetic seismic records;

[0045] S5.2, reverse time propagation: using the actual observed earthquake records as boundary conditions, reverse time propagation of seismic wave fields, that is, reversing time and simulating the process of reverse propagation of wave field data over time;

[0046] S5.3, stacking and imaging: the reverse propagation wave field is superimposed with the forward simulation wave field, and a high-resolution image of the underground reflection interface is obtained by imaging condition calculation;

[0047] S5.4. Velocity model optimization: Based on the imaging results, identify difficult data with large differences between model predictions and actual data, focus on retraining these data, adjust the velocity model to better match the actual observation data, improve the robustness of the model, iteratively optimize the velocity model, effectively reduce the error between the velocity model and the actual data, and improve the accuracy of velocity imaging.

[0048] Specifically, the specific calculation formula for the forward modeling in step S5.1 is as follows:

[0049]

[0050] Among them, u represents the wave field pressure, v(x, z) represents the velocity model, which represents the velocity of the underground medium, and s(x, z, t) represents the source term, which represents the location and time of the earthquake source. represents the second-order partial derivative of time, indicating the change of wave field over time, and It represents the second-order partial derivative in space and the rate of change of the wave field with space. It is used to simulate the propagation of seismic waves in underground media. The LSQR algorithm has good convergence and stability and is suitable for processing large sparse matrix problems.

[0051] Specifically, in step S6, low-velocity anomaly areas in the image are identified, and these areas may be karst development areas.

[0052] From the above, we can see that the velocity change information of the underground medium is obtained through the reverse time migration algorithm, and the velocity information of the seismic wave is used to reconstruct the image of the underground structure. These images reflect the details of the underground structure, including the continuity of the rock layer, the location of the fault, and possible karst features. They can make up for the limitations of traditional drilling methods, effectively reduce detection blind spots, and provide more continuous and detailed underground karst distribution images with meter-level accuracy, which is crucial for preventing ground collapse and ensuring the safety of buildings.

[0053] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that the specific calculation formula for model optimization in step S4 is as follows:

[0054]

[0055] Among them, A represents the coefficient matrix, which is composed of seismic wave travel time data, x represents the velocity model to be determined, and b represents the observed seismic wave travel time data. The LSQR algorithm optimizes the velocity model by minimizing the difference between the model prediction and the actual observation, providing high-precision karst imaging.

[0056] From the above, it can be seen that by minimizing the difference between model predictions and actual observations to further optimize the velocity model, a more accurate karst imaging map can be obtained, thereby more accurately assessing the karst risk at the bottom of the building and avoiding the possibility of errors in the generated images due to the complexity of the geological structure.

[0057] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0058] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0061] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without contradiction.

[0062] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0063] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting karst at the bottom of a building based on velocity imaging, characterized in that: The specific steps are as follows: S1. Equipment layout: Arrange seismic wave receivers in the predetermined detection area; S2. Data collection: using a seismic source to generate seismic waves and using seismic wave receivers to collect seismic wave propagation time data; S3, model building: building a velocity model; S4, model optimization; use LSQR algorithm to optimize the speed model; S5, velocity imaging: using the reverse time migration algorithm to obtain images of underground reflectors; S6. Analysis and verification: The velocity imaging map obtained by analysis is used to determine the location, size and shape of the karst, and the accuracy of the velocity imaging is verified by drilling.

2. The method for detecting karst at the bottom of a building based on velocity imaging according to claim 1, characterized in that: The seismic source in step S2 includes a shock or vibration plate.

3. The method for detecting karst at the bottom of a building based on velocity imaging according to claim 2, characterized in that: The velocity model in step S3 is a preliminary velocity model established by using known geological information and simple seismic wave velocity analysis.

4. The method for detecting karst at the bottom of a building based on velocity imaging according to claim 3, characterized in that: The specific calculation formula for model optimization in step S4 is as follows: Among them, A represents the coefficient matrix, x represents the velocity model to be determined, and b represents the observed seismic wave travel time data.

5. The method for detecting karst at the bottom of a building based on velocity imaging according to claim 4, characterized in that: The specific steps of speed imaging in step S5 are as follows: S5.

1. Forward modeling: Using the obtained velocity model and earthquake source function, forward model the propagation process of seismic waves in the underground medium to generate synthetic seismic records; S5.2, reverse time propagation: using the actual observed earthquake records as boundary conditions, reverse time propagation of the seismic wave field; S5.3, stacking and imaging: the reverse propagation wave field is superimposed with the forward simulation wave field, and a high-resolution image of the underground reflection interface is obtained by imaging condition calculation; S5.

4. Velocity model optimization: Based on the imaging results, identify difficult data where the model predictions differ greatly from the actual data, focus on retraining these data, and adjust the velocity model to better match the actual observation data.

6. The method for detecting karst at the bottom of a building based on velocity imaging according to claim 5, characterized in that: The specific calculation formula for the forward modeling in step S5.1 is as follows: Among them, u represents the wave field pressure, v(x, z) represents the velocity model, s(x, z, t) represents the source term, represents the time second-order partial derivative, and represents the spatial second-order partial derivative.

7. The method for detecting karst at the bottom of a building based on velocity imaging according to claim 6, characterized in that: In the step S6, low-velocity abnormal areas in the image are identified, and these areas may be karst development areas.

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