Geological unit attribute characterization method and system based on multi-scale cascade fusion

Abstract

The invention provides a geological unit attribute characterization method and system based on multi-scale cascade fusion, and belongs to the technical field of geotechnical engineering. The method comprises the steps that seismic wave data and sound wave data of a target area are acquired and preprocessed; performing parameter consistency conversion on the seismic wave data and the sound wave data by using the scale conversion coefficient; dividing the target area into grids of different scales based on the geological attribute change rate; on the basis of the divided grids, the converted seismic wave data and the converted sound wave data are input into a cascade fusion model, the converted seismic wave data serve as the basis of a macroscopic model, the sound wave data serve as local fine constraints, and attribute fusion of the seismic wave data and the sound wave data is achieved through a multi-scale coupling algorithm; and carrying out attribute prediction by using an attribute prediction algorithm to obtain a geological unit attribute representation graph. The synergistic effect of macroscopic and microscopic scales is effectively realized, and the problems of insufficient seismic wave and drilling sound wave data fusion and insufficient precision in the prior art are solved.

Description

Technical Field

[0001] This invention belongs to the field of geotechnical engineering technology, and in particular relates to a method and system for characterizing the properties of geological units based on multi-scale cascade fusion. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In geological exploration, accurate characterization of geological unit properties is of great significance for underground engineering construction and geological disaster prevention. Traditional methods of geological property characterization usually rely on a single detection method, among which the most commonly used techniques include seismic wave technology and borehole acoustic logging technology.

[0004] Seismic wave technology is a method for obtaining geological information by measuring the propagation characteristics of seismic waves in subsurface media. Its advantages include wide coverage, providing extensive geological profile data, and suitability for large-scale regional exploration. In particular, seismic wave inversion techniques can yield macroscopic properties of the subsurface medium, such as elastic parameters and velocity fields. However, when dealing with complex geological structures, seismic wave technology suffers from low spatial resolution, making it difficult to accurately describe local geological changes, especially in areas with complex geological layers or significant differences in physical properties, where data inaccuracies or deficiencies are likely to occur.

[0005] In contrast, borehole acoustic logging technology, by directly measuring the propagation velocity of sound waves within the borehole, can obtain high-resolution local geological property information, such as sound velocity and acoustic impedance. Due to its high spatial resolution, acoustic logging technology can accurately reflect the physical characteristics of local areas, making it valuable for refined exploration. However, acoustic logging data is limited to the local area around the borehole and cannot provide large-scale geological information, resulting in insufficient coverage and an inability to comprehensively reflect the geological characteristics of the entire study area.

[0006] Existing methods for characterizing geological unit properties cannot effectively integrate the advantages of seismic wave and borehole acoustic technologies. They cannot fully utilize the macroscopic coverage of seismic wave technology, nor can they leverage the high-resolution characteristics of acoustic logging technology for geological exploration. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a method and system for characterizing the properties of geological units based on multi-scale cascade fusion. By cascading and fusing seismic waves and borehole acoustic waves, the synergistic effect of macroscopic and microscopic scales is achieved, solving the problems of insufficient fusion and inadequate accuracy of seismic wave and borehole acoustic wave data in the prior art.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for characterizing the properties of geological units based on multi-scale cascade fusion; Geological unit body attribute characterization methods based on multi-scale cascade fusion include: Acquire seismic and acoustic wave data for the target area and perform preprocessing; Scale transformation coefficients are used to perform parameter consistency transformation on seismic wave data and acoustic wave data; Based on the rate of change of geological attributes, the target area is divided into grids of different scales. Based on the grids, the converted seismic wave data and acoustic wave data are input into the cascaded fusion model to achieve attribute fusion of the seismic wave data and acoustic wave data. The cascaded fusion model uses the converted seismic wave data as the basis of the macroscopic model and the converted acoustic wave data as the local fine constraint. The attribute fusion of the seismic wave data and acoustic wave data is achieved through a multi-scale coupling algorithm. The fused data is input into the attribute prediction algorithm to predict the attributes of the un-drilled area, resulting in an attribute characterization map of the geological unit.

[0009] As a further technical solution, during the acquisition of seismic wave data of the target area, the underground structure of the target area is also obtained through inversion using the acquired seismic wave data; the inversion formula is as follows:

[0010] In the formula, It is the travel time of wave propagation. For the longitudinal wave velocity, For each location along the propagation path of the seismic wave.

[0011] As a further technical solution, in the process of acquiring acoustic data of the target area, acoustic logging is used to transmit acoustic waves to the borehole in the target area and receive the reflected waves; the sound velocity of each layer is calculated based on the propagation time of the acoustic waves and the borehole depth. Acoustic impedance is obtained by measuring sound velocity and formation density, and the acoustic impedance reflects the elastic properties of the formation.

[0012] As a further technical solution, the process of performing parameter consistency transformation on seismic wave data and acoustic wave data using scaling transformation coefficients is as follows: Numerical simulations of the propagation processes of seismic waves and acoustic waves were performed based on the wave equation, yielding the propagation characteristics of the two types of waves in different geological strata. Establish the proportional relationship between seismic waves and sound waves, and obtain the scale conversion coefficient; Scale conversion factors are used to convert seismic and acoustic data, adjusting their values ​​to the same parameter range.

[0013] As a further technical solution, the process of dividing the target area into grids of different scales based on the rate of change of geological attributes is as follows: For regions with drastic property changes, high-density meshing is used to improve computational accuracy; For regions with gradual changes in properties, low-density meshing is used to improve computational efficiency.

[0014] As a further technical solution, the multi-scale coupling algorithm is as follows:

[0015] in, These are the attribute values ​​after fusion; These are macroscopic properties provided by seismic waves; It is a local property provided by sound waves; and It's a weight, set based on the reliability of the data, to satisfy... + =1; Three-dimensional interpolation is performed on the acoustic wave data, and the interpolation results are superimposed onto the seismic wave model.

[0016] As a further technical solution, the attribute prediction algorithm adopts the Kriging interpolation method, as shown in the following equation:

[0017] in, Unknown point The predicted value; For known points Observed values; The weighting coefficients represent the contribution of each known point to the predicted value. The number of known data points.

[0018] The second aspect of the present invention provides a geological unit body attribute characterization system based on multi-scale cascade fusion.

[0019] A geological unit attribute characterization system based on multi-scale cascade fusion includes: The data acquisition module is configured to acquire seismic wave data and acoustic wave data of the target area and perform preprocessing. The consistency conversion module is configured to perform parameter consistency conversion on seismic wave data and acoustic wave data using scale conversion coefficients. The data fusion module is configured to: divide the target area into grids of different scales based on the rate of change of geological attributes; and input the converted seismic wave data and acoustic wave data into the cascaded fusion model based on the divided grids to achieve attribute fusion of the seismic wave data and acoustic wave data. The cascaded fusion model uses the converted seismic wave data as the basis of the macroscopic model and the converted acoustic wave data as the local fine constraint, and achieves attribute fusion of the seismic wave data and acoustic wave data through a multi-scale coupling algorithm. The geological unit attribute characterization module is configured to input the fused data into the attribute prediction algorithm, perform attribute prediction on the un-drilled area, and obtain the geological unit attribute characterization map.

[0020] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the method for characterizing geological unit body attributes based on multi-scale cascade fusion as described in the first aspect of the present invention.

[0021] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the geological unit body attribute characterization method based on multi-scale cascade fusion as described in the first aspect of the present invention.

[0022] The above one or more technical solutions have the following beneficial effects: (1) This invention utilizes the spatial breadth of seismic wave data and the high-resolution characteristics of sonic logging by cascading and fusing seismic wave and borehole acoustic wave data, thereby achieving a comprehensive characterization of geological units from macroscopic to microscopic levels. Seismic wave data provides a large-scale macroscopic model to ensure overall coverage; sonic logging data is used to supplement local attributes and improve local accuracy; multi-scale fusion of data avoids the problems of insufficient accuracy or missing local information caused by a single method.

[0023] (2) Multi-scale collaboration was achieved using cascade fusion technology. A multi-scale coupling algorithm was proposed, which solved the mismatch between seismic wave and acoustic wave data in terms of resolution and spatial coverage through scale conversion, parameter consistency and weight allocation. By introducing spatial scale and time scale conversion coefficients, the seismic wave data was adjusted to the same scale as the acoustic wave logging, realizing seamless connection between the two types of data during fusion, and improving the consistency and reliability of geological attribute characterization.

[0024] (3) Through attribute fusion and optimization, the consistency and complementarity of seismic wave and acoustic wave data in the fused model are ensured. Weight control technology is used to balance the influence of seismic waves and acoustic waves on the final model, and the fusion result takes into account both macroscopic constraints and microscopic details.

[0025] (4) This invention can adapt to a variety of complex geological environments. By adjusting and optimizing parameters to suit different underground media characteristics, it can improve the ability to identify underground structural features in geological disaster monitoring and provide high-precision reference data for the design of underground engineering such as tunnels in the field of engineering geology.

[0026] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0027] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0028] Figure 1 This is a flowchart of the method in the first embodiment.

[0029] Figure 2 This is a system structure diagram of the second embodiment. Detailed Implementation

[0030] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0032] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0033] This invention cascades and fuses seismic waves and borehole acoustic waves to obtain a geological attribute characterization method that possesses both spatial breadth and local precision, achieving a more comprehensive and accurate characterization of geological unit attributes. By analyzing the wave equations of seismic and acoustic waves, correlation expressions at different scales are derived, and scale and consistency parameters are transformed through correlation analysis. By dividing the target area into attribute grids and adaptively assigning dynamic values ​​to grid attributes based on geological characteristics, the rationality and consistency of data in each grid are ensured.

[0034] Example 1 This embodiment discloses a method for characterizing the properties of geological units based on multi-scale cascade fusion; like Figure 1 As shown, the geological unit body attribute characterization method based on multi-scale cascade fusion includes: Step S1: Acquire seismic wave data and acoustic wave data of the target area and perform preprocessing; First, during the acquisition of seismic wave data for the target area, artificial sources (such as hammers, ground vibrators, or blast sources) are used to generate seismic waves. These waves include P-waves (longitudinal waves) and S-waves (transverse waves), whose propagation velocities and waveforms reflect the different physical properties of the underground geological layers. Next, seismic detectors are deployed on the ground in a grid pattern. These detectors receive seismic waves (reflected waves, refracted waves, etc.) propagating from underground and record their arrival times (or travel times). By analyzing the arrival times recorded by different detectors, the medium properties of different underground strata (such as wave velocity and elastic modulus) can be deduced. Based on this, inversion techniques are used to reconstruct the underground structure from the acquired seismic wave data. The inversion process is typically based on wave propagation time (T), and the travel time method can be used to calculate the wave velocity of the underground medium. The specific inversion formula is as follows:

[0035] in, It is the travel time of wave propagation. For the longitudinal wave velocity, For each location along the seismic wave propagation path, the velocity field model is optimized using the least squares inversion method to obtain a seismic wave velocity profile. This not only optimizes the velocity field model but also provides a reliable basis for subsequent geological property inferences. If the simulation results are consistent with the measured data, it ensures that the predicted geological properties (such as density and porosity) are also reliable. Through multiple iterations, the velocity field model is continuously adjusted until the simulation results are consistent with the measured data. This ensures that the simulated subsurface velocity field (especially the P-wave velocity) matches the actual measured seismic wave travel time data as closely as possible, guaranteeing the accuracy of the velocity field model. Iteratively aligning the simulation results with the measured data effectively improves the model's accuracy. Through inversion optimization, the final velocity field model more accurately represents the true state of the subsurface medium, thereby improving the accuracy of geological analysis.

[0036] Secondly, in the process of acquiring acoustic data, acoustic logging is used for data acquisition. The acoustic logging instrument emits acoustic waves through the borehole and receives the reflected waves. Based on the propagation time of the acoustic waves and the borehole depth, different acoustic velocity values ​​are calculated for each depth segment or layer in the borehole. , ,in, v Let L be the velocity of sound, L be the measured borehole length, and T be the sound wave propagation time. The acoustic impedance is obtained by measuring the sound velocity and formation density, as shown in the equation:

[0037] in, Acoustic impedance, The density of the formation; obtaining the acoustic impedance helps to reflect the elastic properties of the formation.

[0038] The acquired seismic and acoustic data undergo preprocessing, including denoising and interpolation. High-pass or low-pass filters are used to remove excessively high or low frequency noise, eliminating random noise interference and retaining effective signals. Based on velocity data obtained from seismic wave inversion and borehole acoustic logging data (where the velocity data obtained from seismic wave inversion is P-wave velocity and S-wave velocity obtained from reflected wave travel time data acquired during seismic exploration), a complete velocity field map is generated using interpolation methods to ensure spatial continuity of the data. The velocity field map is a visual image of underground velocity distribution, reflecting P-wave and S-wave velocities at different depths or locations within the target area. The velocity field map primarily provides a macroscopic description of underground geological structures and serves as the foundational data for subsequent attribute prediction and geological analysis. In the data fusion process, the velocity field map acts as a fundamental constraint for the macroscopic model and provides the necessary framework and accuracy guarantee for subsequent prediction and optimization. This not only improves the accuracy of geological unit attribute characterization but also ensures consistency and coherence in subsequent attribute prediction and visualization processes.

[0039] Step S2: Perform parameter consistency transformation on seismic wave data and acoustic wave data using scaling transformation coefficients; In step S2, the propagation processes of seismic waves and sound waves are numerically simulated based on the wave equation to obtain the propagation characteristics of the two types of waves in different geological strata; whereby the wave equation describes the propagation behavior of seismic waves in the subsurface medium. The propagation processes of seismic waves and sound waves are numerically simulated to obtain the propagation characteristics of the two types of waves in different geological strata:

[0040] in, For displacement, For wave speed, For the Laplace operator.

[0041] Based on the acquired propagation characteristics, the proportional relationship between seismic waves and sound waves is established, and the scale conversion coefficient is obtained; the scale conversion coefficient includes the spatial scale conversion coefficient. and time scale conversion factor ,

[0042]

[0043] in, For the longitudinal wave velocity, Let T be the transverse wave velocity and T be the propagation time.

[0044] Scaling transformation factors are used to transform seismic and acoustic data, adjusting their values ​​to the same parameter range. The propagation characteristics of the two waves are compared to ensure parameter consistency during fusion. Weighting coefficients are then set (…). and This is used to control the contributions of seismic wave and acoustic wave data in the final model, ensuring a reasonable fusion of the two data sets. Furthermore, the cascaded fusion model can be calculated using the following weighted average method:

[0045] in, This refers to the velocity of seismic waves; The speed of sound waves; and These are weighting coefficients. Corresponding to the macroscopic attribute weights provided by seismic waves, The weights of the corresponding local attributes provided by the sound waves are set according to the data reliability and satisfy the following conditions: This ensures that the fusion results take into account both the macroscopic coverage of seismic waves and the local high resolution of acoustic waves, thereby improving the accuracy of geological unit attribute characterization. In the above weighting process, the seismic wave data (corresponding to...) Scoring is based on coverage integrity, signal-to-noise ratio, and inversion error; for acoustic data (corresponding to...) The average score was obtained by scoring the logging depth coverage, data stability, and formation matching degree. , According to the formula , Calculate the weighting coefficients mentioned above.

[0046] Step S3: Based on the rate of change of geological attributes, the target area is divided into grids of different scales; the rate of change of geological attributes includes porosity, density, etc. During the gridding process, in areas with drastic attribute changes, the grid is finer to improve computational accuracy; while in areas with relatively gradual attribute changes, the grid is coarser to improve computational efficiency. The formula is:

[0047] In the formula, The rate of change of attributes is represented. Then, a weighted average method is used to fuse seismic wave and acoustic wave data into the grid. The weighted average method is used to initially fuse seismic wave and acoustic wave data into the grid so as to provide basic data for subsequent attribute prediction and refined fusion within each grid cell, help to form a rough overall model, lay the foundation for the next step of refined model optimization, and ensure that the final geological attribute characterization map can accurately reflect the geological characteristics of the entire region.

[0048] The specific assignment can be done using the following weighted formula:

[0049] Here, "Attribute" represents different geological properties (such as density, porosity, etc.). and These are weighting coefficients, set based on data reliability and satisfying the following conditions: .

[0050] Furthermore, based on the divided grid, the transformed seismic wave data and acoustic wave data are input into a cascaded fusion model to achieve attribute fusion of the seismic wave data and acoustic wave data. The cascaded fusion model uses the transformed seismic wave data as the basis for a macroscopic model, which refers to a large-scale model describing the geological characteristics of the target area constructed using the seismic wave data. The transformed acoustic wave data is used as a local fine-grained constraint, and attribute fusion of the seismic wave data and acoustic wave data is achieved through a multi-scale coupling algorithm. The specific details of the multi-scale coupling algorithm are as follows:

[0051] in, These are the attribute values ​​after fusion; These are macroscopic properties provided by seismic waves; It is a local property provided by sound waves; and These are weighting coefficients, set based on data reliability and satisfying certain conditions. .

[0052] Three-dimensional interpolation is performed on the acoustic data, and the interpolated results are superimposed onto the seismic wave model. Three-dimensional interpolation is used to extend the acoustic data distributed at the borehole location to the entire target area. Since acoustic logging is typically performed only at the borehole location, three-dimensional interpolation expands local data (borehole acoustic data) to the entire area, compensating for the insufficient resolution of seismic wave data and achieving global attribute estimation. Subsequently, the interpolated acoustic data is combined with the seismic wave model through a superposition operation to obtain a more comprehensive and accurate geological attribute characterization model.

[0053] Step S4: Input the fused data into the attribute prediction algorithm to predict the attributes of the un-drilled area and obtain the attribute characterization map of the geological unit.

[0054] In step S4, the attribute prediction algorithm employs Kriging interpolation, a statistically based optimal linear unbiased estimation method. It predicts the value of unknown points based on the spatial relationships of known points, while considering the variability and correlation of spatial data. Its core formula is as follows: Predicted value formula:

[0055] In the formula, Unknown point The predicted value; For known points Observed values; is the weighting coefficient, representing the contribution of each known point to the predicted value; n is the number of known data points.

[0056] Weight coefficient calculation: weight The determination of is based on the semi-variogram function, with the aim of ensuring that the estimated value has optimal linear unbiasedness. The specific calculation is as follows: Semivariogram formula: The semivariogram describes the relationship between geological attribute values ​​as a function of spatial distance.

[0057] In the formula, It is the semivariogram value when the spatial interval is h; It is the number of point pairs with a spatial interval of h; It is the attribute value at the i-th position.

[0058] Kriging system equations: Constructing the Kriging system equations using semi-variograms and solving for the weight coefficients. :

[0059] In the formula, It is the semivariogram between known points; The semivariogram between the known point and the predicted point; It is a Lagrange multiplier used to constrain the weights to a sum of 1.

[0060] By solving the above system of linear equations, the weight corresponding to each known point can be obtained. By substituting the weights into the prediction formula, the interpolation result of the target point can be obtained.

[0061] Ultimately, the system enables attribute prediction for un-drilled areas, generating attribute characterization maps of geological units that display key physical parameters such as porosity and density. GIS software (such as ArcGIS or QGIS) is then used for 3D visualization of the geological attributes, generating isosurfaces for specific physical properties (such as porosity) to demonstrate their distribution in 3D space.

[0062] Example 2 This embodiment discloses a geological unit body attribute characterization system based on multi-scale cascade fusion; like Figure 2 As shown, the geological unit body attribute characterization system based on multi-scale cascade fusion includes: The data acquisition module is configured to acquire seismic wave data and acoustic wave data of the target area and perform preprocessing. The consistency conversion module is configured to perform parameter consistency conversion on seismic wave data and acoustic wave data using scale conversion coefficients. The data fusion module is configured to: divide the target area into grids of different scales based on the rate of change of geological attributes; and input the converted seismic wave data and acoustic wave data into the cascaded fusion model based on the divided grids to achieve attribute fusion of the seismic wave data and acoustic wave data. The cascaded fusion model uses the converted seismic wave data as the basis of the macroscopic model and the converted acoustic wave data as the local fine constraint, and achieves attribute fusion of the seismic wave data and acoustic wave data through a multi-scale coupling algorithm. The geological unit attribute characterization module is configured to input the fused data into the attribute prediction algorithm, perform attribute prediction on the un-drilled area, and obtain the geological unit attribute characterization map.

[0063] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0064] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the geological unit body attribute characterization method based on multi-scale cascade fusion as described in Example 1.

[0065] Example 4 The purpose of this embodiment is to provide an electronic device.

[0066] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the geological unit body attribute characterization method based on multi-scale cascade fusion as described in Example 1.

[0067] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0068] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0069] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for characterizing the attributes of geological units based on multi-scale cascade fusion, characterized in that, include: Acquire seismic and acoustic wave data for the target area and perform preprocessing; Scale transformation coefficients are used to perform parameter consistency transformation on seismic wave data and acoustic wave data; Based on the rate of change of geological attributes, the target area is divided into grids of different scales. Based on the grids, the converted seismic wave data and acoustic wave data are input into the cascaded fusion model to achieve attribute fusion of the seismic wave data and acoustic wave data. The cascaded fusion model uses the converted seismic wave data as the basis of the macroscopic model and the converted acoustic wave data as the local fine constraint. The attribute fusion of the seismic wave data and acoustic wave data is achieved through a multi-scale coupling algorithm. The fused data is input into the attribute prediction algorithm to predict the attributes of the un-drilled area, resulting in an attribute characterization map of the geological unit.

2. The geological unit body attribute characterization method based on multi-scale cascade fusion as described in claim 1, characterized in that, In the process of acquiring seismic wave data of the target area, the underground structure of the target area is also obtained through inversion using the acquired seismic wave data; the inversion formula is as follows: In the formula, It is the travel time of wave propagation. For the longitudinal wave velocity, For each location along the propagation path of the seismic wave.

3. The geological unit body attribute characterization method based on multi-scale cascade fusion as described in claim 1, characterized in that, In the process of acquiring acoustic data of the target area, acoustic logging is used to transmit acoustic waves to the borehole in the target area and receive the reflected waves; the sound velocity of each layer is calculated based on the propagation time of the acoustic waves and the borehole depth. Acoustic impedance is obtained by measuring sound velocity and formation density, and the acoustic impedance reflects the elastic properties of the formation.

4. The geological unit body attribute characterization method based on multi-scale cascade fusion as described in claim 1, characterized in that, The process of performing parameter consistency transformation on seismic wave data and acoustic wave data using scaling transformation coefficients is as follows: Numerical simulations of the propagation processes of seismic waves and acoustic waves were performed based on the wave equation, yielding the propagation characteristics of the two types of waves in different geological strata. Establish the proportional relationship between seismic waves and sound waves, and obtain the scale conversion coefficient; Scale conversion factors are used to convert seismic and acoustic data, adjusting their values ​​to the same parameter range.

5. The geological unit body attribute characterization method based on multi-scale cascade fusion as described in claim 1, characterized in that, The process of dividing the target area into grids of different scales based on the rate of change of geological properties is as follows: For regions with drastic property changes, high-density meshing is used to improve computational accuracy; For regions with gradual attribute changes, low-density meshing is used to improve computational efficiency.

6. The geological unit body attribute characterization method based on multi-scale cascade fusion as described in claim 1, characterized in that, The multi-scale coupling algorithm is as follows: in, These are the attribute values ​​after fusion; These are macroscopic properties provided by seismic waves; It is a local property provided by sound waves; and It's a weight, set based on the reliability of the data, to satisfy... + =1; Three-dimensional interpolation is performed on the acoustic wave data, and the interpolation results are superimposed onto the seismic wave model.

7. The geological unit body attribute characterization method based on multi-scale cascade fusion as described in claim 1, characterized in that, The attribute prediction algorithm uses Kriging interpolation, and the formula is: in, Unknown point The predicted value; For known points Observed values; The weighting coefficients represent the contribution of each known point to the predicted value. This represents the number of known data points.

8. A geological unit body attribute characterization system based on multi-scale cascade fusion, characterized in that, include: The data acquisition module is configured to acquire seismic wave data and acoustic wave data of the target area and perform preprocessing. The consistency conversion module is configured to perform parameter consistency conversion on seismic wave data and acoustic wave data using scale conversion coefficients. The data fusion module is configured to: divide the target area into grids of different scales based on the rate of change of geological attributes; and input the converted seismic wave data and acoustic wave data into the cascaded fusion model based on the divided grids to achieve attribute fusion of the seismic wave data and acoustic wave data. The cascaded fusion model uses the converted seismic wave data as the basis of the macroscopic model and the converted acoustic wave data as the local fine constraint, and achieves attribute fusion of the seismic wave data and acoustic wave data through a multi-scale coupling algorithm. The geological unit attribute characterization module is configured to input the fused data into the attribute prediction algorithm, perform attribute prediction on the un-drilled area, and obtain the geological unit attribute characterization map.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the geological unit body attribute characterization method based on multi-scale cascade fusion as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the geological unit body attribute characterization method based on multi-scale cascade fusion as described in any one of claims 1-7.

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