Geological unit attribute representation method and system based on multi-scale cascaded fusion

Abstract

The application 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 following steps: acquiring seismic wave data and acoustic wave data of a target region and performing pretreatment; performing parameter consistency conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient; dividing the target region into grids of different scales based on a geological attribute change rate; inputting the converted seismic wave data and acoustic wave data into a cascade fusion model based on the divided grids; taking the converted seismic wave data as the basis of a macro model and taking the acoustic wave data as local fine constraints; and realizing attribute fusion of the seismic wave data and the acoustic wave data by using a multi-scale coupling algorithm; and performing attribute prediction by using an attribute prediction algorithm to obtain a geological unit attribute characterization map. The method effectively realizes the synergistic effect of macro and micro scales and solves the problems of insufficient fusion and insufficient precision of seismic wave and borehole acoustic wave data in the prior art.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of geotechnical engineering, and particularly relates to a geological unit property characterization method and system based on multi-scale cascade fusion. BACKGROUND

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

[0003] In geological exploration, accurate geological unit property characterization is of great significance for underground engineering construction and geological disaster prevention. Traditional geological property characterization methods 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 of obtaining geological information by measuring the propagation characteristics of seismic waves in underground media. The advantage of this technology is wide coverage, which can provide large-scale geological profile data and is suitable for large-scale regional exploration. In particular, through seismic wave inversion technology, macroscopic properties such as elastic parameters and velocity field of underground media can be obtained. However, seismic wave technology has low spatial resolution when dealing with complex geological structures, making it difficult to accurately describe local geological changes, especially in areas with complex geological layers or large physical property differences, where data may be inaccurate or lacking.

[0005] In contrast, borehole acoustic logging technology can obtain high-resolution local geological property information such as sound velocity and acoustic impedance by directly measuring the sound propagation velocity in the borehole. Due to its high spatial resolution, acoustic logging technology can accurately reflect the physical characteristics of local areas, making it valuable in fine 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 inability to fully reflect the geological characteristics of the entire study area.

[0006] In existing geological unit property characterization methods, the advantages of seismic wave and borehole acoustic logging technology cannot be effectively combined, and the macroscopic coverage of seismic wave technology cannot be fully utilized, nor can the high-resolution characteristics of acoustic logging technology be used for geological exploration. SUMMARY

[0007] To overcome the shortcomings of the above-mentioned prior art, the present application provides a geological unit property characterization method and system based on multi-scale cascade fusion, which realizes the synergistic effect of macroscopic and microscopic scales by cascading fusion of seismic waves and borehole acoustic waves, solving the problem of insufficient fusion and insufficient precision of seismic wave and borehole acoustic wave data in the prior art.

[0008] To achieve the above object, one or more embodiments of the present application provide the following technical solutions:

[0009] The first aspect of the present application provides a geological unit attribute characterization method based on multi-scale cascade fusion.

[0010] The geological unit attribute characterization method based on multi-scale cascade fusion comprises:

[0011] Seismic wave data and acoustic wave data of a target area are acquired and preprocessed.

[0012] The seismic wave data and the acoustic wave data are converted for parameter consistency by using a scale conversion coefficient.

[0013] Based on a geological attribute change rate, the target area is divided into grids of different scales; based on the divided grids, the converted seismic wave data and acoustic wave data are input into a cascade fusion model to realize attribute fusion of the seismic wave data and the acoustic wave data; wherein the cascade fusion model takes the converted seismic wave data as a basis of a macro model and takes the converted acoustic wave data as a local fine constraint, and realizes attribute fusion of the seismic wave data and the acoustic wave data by a multi-scale coupling algorithm.

[0014] The fused data are input into an attribute prediction algorithm to predict attributes of an un-drilled area, and a geological unit attribute characterization map is obtained.

[0015] As a further technical solution, in the process of acquiring seismic wave data of a target area, the underground structure of the target area is acquired by inversion based on the acquired seismic wave data; wherein the inversion formula is:

[0016]

[0017] In the formula, t is a wave propagation travel time, is a P-wave velocity, is a P-wave velocity, is a P-wave velocity,

[0018] As a further technical solution, in the process of acquiring acoustic wave data of a target area, acoustic logging is used to emit acoustic waves from a drilled hole of the target area and receive reflected waves; based on the time of acoustic wave propagation and the depth of the drilled hole, the acoustic velocity of each layer is calculated.

[0019] By measuring the acoustic velocity and the formation density, the acoustic impedance is acquired, and the acoustic impedance reflects the elastic properties of the formation.

[0020] As a further technical solution, the process of converting the seismic wave data and the acoustic wave data for parameter consistency by using a scale conversion coefficient is:

[0021] Based on wave equation, the propagation process of seismic wave and acoustic wave is simulated, and the propagation characteristics of the two waves in different strata are obtained.

[0022] The proportional relationship between the seismic wave and the acoustic wave is established, and the scale conversion coefficient is obtained.

[0023] The scale conversion coefficient is used to convert the seismic wave and acoustic wave data, and the numerical values of the seismic wave and acoustic wave data are adjusted in the same parameter range.

[0024] As a further technical solution, the process of dividing the target area into different scale grids based on the change rate of geological properties is:

[0025] For the area with rapid property change, high-density grid division is carried out to improve the calculation accuracy;

[0026] For the area with gentle property change, low-density grid division is carried out to improve the calculation efficiency.

[0027] As a further technical solution, the multi-scale coupling algorithm is:

[0028]

[0029] Wherein, is the fused attribute value; is the macro attribute provided by the seismic wave; is the local attribute provided by the acoustic wave; and is the weight, which is set according to the reliability of the data, and satisfies + =1;

[0030] The acoustic wave data is three-dimensionally interpolated, and the interpolation result is superimposed into the seismic wave model.

[0031] As a further technical solution, the attribute prediction algorithm adopts the Kriging interpolation method, and the formula is:

[0032]

[0033] Wherein, is the predicted value of the unknown point is the observation value of the known point is the weight coefficient, which represents the contribution of each known point to the predicted value; is the number of known data points. The second aspect of the present application provides a geological unit attribute characterization system based on multi-scale cascade fusion.

[0034] The second aspect of the present application provides a geological unit attribute characterization system based on multi-scale cascade fusion.

[0035] ​The geological unit attribute characterization system based on multi-scale cascade fusion comprises:

[0036] The data acquisition module is configured to acquire seismic wave data and acoustic wave data of a target area and perform preprocessing.

[0037] The consistency conversion module is configured to perform parameter consistency conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient.

[0038] The data fusion module is configured to divide the target area into grids of different scales based on a geological attribute change rate; and input the converted seismic wave data and acoustic wave data into a cascade fusion model based on the divided grids to realize attribute fusion of the seismic wave data and the acoustic wave data; wherein the cascade fusion model takes the converted seismic wave data as a basis of a macro model, takes the converted acoustic wave data as a local fine constraint, and realizes attribute fusion of the seismic wave data and the acoustic wave data by a multi-scale coupling algorithm.

[0039] The geological unit attribute characterization module is configured to input the fused data into an attribute prediction algorithm to perform attribute prediction on an undrilled area and obtain a geological unit attribute characterization map.

[0040] The third aspect of the present application provides a computer readable storage medium having a program stored thereon, the program being executed by a processor to implement the steps of the geological unit attribute characterization method based on multi-scale cascade fusion according to the first aspect of the present application.

[0041] The fourth aspect of the present application provides an electronic device comprising 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 of the geological unit attribute characterization method based on multi-scale cascade fusion according to the first aspect of the present application.

[0042] The above one or more technical solutions have the following beneficial effects:

[0043] (1) The present application realizes comprehensive characterization of geological units from macro to micro by cascade fusion of seismic wave data and acoustic logging data, which not only utilizes the spatial breadth of seismic wave data but also combines the high-resolution characteristics of acoustic logging, thereby ensuring overall coverage by providing a macro model of seismic wave data and improving local accuracy by supplementing local attributes with acoustic logging data.

[0044] (2) Utilizing cascading fusion technology to achieve multi-scale collaboration, a multi-scale coupling algorithm is proposed, which solves the mismatch between seismic wave and acoustic wave data in resolution and spatial coverage through scale conversion, parameter consistency and weight distribution. By introducing spatial scale and time scale conversion coefficients, the seismic wave data is adjusted to the same scale as the acoustic logging, realizing seamless docking of the two data in fusion, and improving the consistency and reliability of geological attribute characterization.

[0045] (3) Through attribute fusion and optimization, the consistency and complementarity of seismic wave and acoustic wave data in the fused model are ensured. Using weight control technology to balance the influence of seismic wave and acoustic wave on the final model, the fusion result takes into account macroscopic constraints and microscopic details.

[0046] (4) The present application can adapt to various complex geological environments, and through parameter adjustment and optimization, it can adapt to different underground medium characteristics, and can improve the recognition ability of underground structure characteristics in geological disaster monitoring, and provide high-precision reference data for underground engineering design such as tunnels in engineering geology field.

[0047] The advantages of the additional aspects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be known by the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings, which form a part of this description, are included to provide further understanding of the application, and are incorporated in and constitute a part of this description. The illustrative embodiments of the application and their description serve to explain the application. They do not, however, limit the present application, nor do they express the only operating combinations of the application.

[0049] Figure 1 The method flowchart of the first embodiment.

[0050] Figure 2 The system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0051] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs.

[0052] It should be noted that the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application.

[0053] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0054] 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.

[0055] Example 1

[0056] This embodiment discloses a method for characterizing the properties of geological units based on multi-scale cascade fusion;

[0057] like Figure 1 As shown, the geological unit body attribute characterization method based on multi-scale cascade fusion includes:

[0058] Step S1: Acquire seismic wave data and acoustic wave data of the target area and perform preprocessing;

[0059] 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:

[0060]

[0061] in, It is the travel time of wave propagation. For the longitudinal wave velocity, For each location of the seismic wave propagation path. By least squares inversion method to optimize the velocity field model, to obtain the seismic wave velocity profile, not only can optimize the velocity field model, but also for subsequent geological attribute speculation to provide a reliable basis. If the simulation results are consistent with the measured data, it can ensure that the predicted geological properties (such as density, porosity, etc.) are also reliable. Through multiple iterations, constantly adjust the velocity field model, until the simulation results and measured data consistent, so that the simulation of the underground velocity field (especially P-wave velocity) can be as much as possible with the actual measured seismic wave propagation travel time data match, ensure the accuracy of the velocity field model. Through iteration to make the simulation results consistent with the measured data can effectively improve the accuracy of the model, through inversion optimization, the final velocity field model can more accurately represent the real situation of the underground medium, and further improve the accuracy of geological analysis.

[0062] Secondly, in the process of obtaining acoustic data, acoustic logging is used for data acquisition, and the acoustic logging instrument transmits acoustic waves through the borehole and receives reflected waves. According to the time of acoustic wave propagation and the depth of the borehole, the different sound velocity values corresponding to each depth section or layer in the borehole are calculated , , wherein, v is the sound velocity, L is the measured borehole length, T is the time of acoustic wave transmission. By measuring the sound velocity and the density of the stratum, the acoustic impedance is obtained, and the formula is:

[0063]

[0064] , wherein, is the acoustic impedance, is the density of the stratum; by obtaining the acoustic impedance, the elastic properties of the stratum can be reflected.

[0065] The obtained seismic wave and acoustic wave data are also preprocessed, and the preprocessing process includes denoising and interpolation processing of the obtained seismic wave data and acoustic wave data. High-pass filters or low-pass filters are used to remove noise with excessively high or low frequencies, eliminate the interference of random noise, and retain effective signals. Based on the velocity data obtained by seismic wave inversion and the acoustic logging data of the borehole, the complete velocity field map is generated by an interpolation method, ensuring the spatial continuity of the data. The velocity field map is a visual image of the underground velocity distribution, reflecting the P-wave velocity and S-wave velocity at different depths or positions in the target area. The velocity field map is mainly used to provide a macro description of the underground geological structure and provide basic data for subsequent attribute prediction and geological analysis. The velocity field map serves as the basis for macro model constraints in the data fusion process and provides the necessary framework and precision guarantee for subsequent prediction and optimization. Not only does it improve the accuracy of attribute characterization of geological units, but it also ensures consistency and coherence in subsequent attribute prediction and visualization processes.

[0066] In step S2, the seismic wave data and acoustic wave data are converted for parameter consistency using scale conversion coefficients.

[0067] In step S2, the propagation process of seismic waves and acoustic waves is numerically simulated based on the wave equation to obtain the propagation characteristics of the two waves in different strata; wherein the wave equation describes the propagation behavior of seismic waves in underground media. Numerical simulation of the propagation process of seismic waves and acoustic waves obtains the propagation characteristics of the two waves in different strata:

[0068]

[0069] wherein, is the displacement, is the wave velocity, is the Laplace operator.

[0070] Based on the obtained propagation characteristics, a proportional relationship between the seismic wave and the acoustic wave is established, and a scale conversion coefficient is obtained; wherein the scale conversion coefficient includes a spatial scale conversion coefficient and a time scale conversion coefficient ,

[0071]

[0072]

[0073] wherein, is the P-wave velocity, is the S-wave velocity, and T is the propagation time.

[0074] The seismic wave and acoustic wave data are converted using scale conversion coefficients to adjust the values of the seismic wave and acoustic wave data in the same parameter range. By comparing the propagation characteristics of the two waves, the consistency of the parameters in the fusion is ensured. The contributions of the seismic wave and acoustic wave data in the final model are controlled by setting weight coefficients (a and b) and to ensure reasonable fusion of the two data. Furthermore, the cascaded fusion model can be calculated by the following weighted average method:

[0075]

[0076] wherein, is the seismic wave velocity; is the acoustic wave velocity; and is the weight coefficient, corresponding to the macroscopic attribute weight provided by the seismic wave, corresponding to the local attribute weight provided by the acoustic wave, both of which are set according to the data reliability and satisfy to ensure that the fusion result takes into account the macroscopic coverage of the seismic wave and the local high resolution of the acoustic wave, and improves the accuracy of the geological unit attribute representation. In the above weight setting process, the seismic wave data (corresponding to ) is scored from the coverage integrity, signal-to-noise ratio, and inversion error; the acoustic wave data (corresponding to ) is scored from the logging depth coverage, data stability, and stratum matching degree, and the average scores , are obtained respectively; the above weight coefficients are calculated according to the formula , .

[0077] Step S3, based on the geological attribute change rate, the target area is divided into different scale grids; wherein the address attribute change rate includes porosity, density, etc. In the division process, in the area with rapid attribute change, the grid division is finer to improve the calculation accuracy; while in the area with slow attribute change, the grid division is coarser to improve the calculation efficiency. The formula is:

[0078]

[0079] In the formula, represents the attribute change rate. Then, the seismic wave and acoustic wave data are fused into the grid using the weighted average method. The seismic wave and acoustic wave data are preliminarily fused into the grid using the weighted average method, so as to provide basic data for subsequent attribute prediction and refined fusion in each grid unit, 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 representation map can accurately reflect the geological characteristics of the entire area.

[0080] The specific assignment can adopt the following weighted formula:

[0081]

[0082] wherein Attribute represents different geological attributes (such as density, porosity, etc.), and are weight coefficients, which are set according to data reliability and satisfy .

[0083] Further, based on the divided grid, the converted seismic wave data and acoustic wave data are input into a cascaded fusion model to realize attribute fusion of the seismic wave data and the acoustic wave data; wherein the converted seismic wave data is taken as the basis of a macro model by the cascaded fusion model, and the macro model refers to a large-scale model describing geological features of the target region constructed by the seismic wave data. The converted acoustic wave data is taken as a local fine constraint, and attribute fusion of the seismic wave data and the acoustic wave data is realized through a multi-scale coupling algorithm; wherein the multi-scale coupling algorithm is specifically as follows:

[0084]

[0085] wherein, is the fused attribute value; is a macro attribute provided by the seismic wave; is a local attribute provided by the acoustic wave; and are weight coefficients, which are set according to data reliability and satisfy .

[0086] The acoustic wave data is subjected to three-dimensional interpolation, and the interpolation result is superimposed into the seismic wave model. The acoustic wave data distributed at the drilling position is interpolated into the entire target region by three-dimensional interpolation. Since acoustic logging is usually performed only at the drilling position, the local data (drilling acoustic wave data) is expanded to the entire region by three-dimensional interpolation, which makes up for the insufficient resolution of the seismic wave data and realizes estimation of the global attribute. Subsequently, the interpolated acoustic wave data is combined with the seismic wave model through a superposition operation to obtain a more comprehensive and accurate geological attribute representation model.

[0087] In step S4, the fused data is input into an attribute prediction algorithm to predict the attribute of the un-drilled region, and a geological unit attribute representation map is obtained.

[0088] In step S4, the attribute prediction algorithm adopts the Kriging interpolation method, which is an optimal linear unbiased estimation method based on statistics. It predicts the value of an unknown point through the spatial position relationship of known points, and considers the variability and correlation of spatial data. Its core formula is as follows:

[0089] Prediction value formula:

[0090]

[0091] where, is the predicted value of the unknown point ; is the observed value of the known point ; is the weight coefficient, representing the contribution of each known point to the predicted value; n is the number of known data points.

[0092] Weight coefficient calculation: determination of the weight is based on the semi-variogram function, aiming to make the estimated value have optimal linear unbiasedness. The specific calculation is as follows:

[0093] Semi-variogram formula: the semi-variogram describes the relationship between the values of geological attributes with spatial distance:

[0094]

[0095] where, is the semi-variogram value when the spatial interval is h; is the number of point pairs with a spatial interval of h; is the attribute value at the i-th position.

[0096] Kriging system equation: build the Kriging system equation through the semi-variogram function, and solve the weight coefficient :

[0097]

[0098] where, is the semi-variogram value between known points; is the semi-variogram value between the known point and the predicted point; is the Lagrange multiplier, used to constrain the sum of weights to be 1.

[0099] By solving the above linear equation system, the weight corresponding to each known point is obtained, and the interpolation result of the target point can be obtained by substituting the weight into the predicted value formula.

[0100] Finally, the attribute prediction of the un-drilled area is realized, and the geological unit attribute characterization map showing the key physical parameters such as porosity and density is generated. Use GIS software (such as ArcGIS or QGIS) to perform three-dimensional visualization of geological attributes, generate an isosurface of a certain physical parameter (such as porosity), and show its distribution in three-dimensional space.

[0101] Example Two

[0102] The embodiment discloses a geological unit attribute characterization system based on multi-scale cascade fusion.

[0103] As shown in the figure, the geological unit attribute characterization system based on multi-scale cascade fusion comprises: Figure 2

[0104] The data acquisition module is configured to acquire seismic wave data and acoustic wave data of a target area and perform preprocessing.

[0105] The consistency conversion module is configured to perform parameter consistency conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient.

[0106] The data fusion module is configured to divide the target area into grids of different scales based on a geological attribute change rate; input the converted seismic wave data and acoustic wave data into a cascade fusion model based on the divided grids to realize attribute fusion of the seismic wave data and the acoustic wave data; wherein the cascade fusion model takes the converted seismic wave data as a basis of a macro model and takes the converted acoustic wave data as a local fine constraint, and realizes attribute fusion of the seismic wave data and the acoustic wave data by a multi-scale coupling algorithm.

[0107] The geological unit attribute characterization module is configured to input the fused data into an attribute prediction algorithm to perform attribute prediction on an undrilled area and obtain a geological unit attribute characterization map.

[0108] Embodiment three

[0109] The purpose of the embodiment is to provide a computer-readable storage medium.

[0110] A computer-readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the steps in the geological unit attribute characterization method based on multi-scale cascade fusion as described in embodiment 1.

[0111] Embodiment four

[0112] The purpose of the embodiment is to provide an electronic device.

[0113] An electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor implements the steps in the geological unit attribute characterization method based on multi-scale cascade fusion as described in embodiment 1 when executing the program.

[0114] ​The steps involved in the apparatuses of the above embodiments two, three and four correspond to the method of embodiment one, and the specific implementation can refer to the relevant description of embodiment one. The term "computer readable storage medium" should be understood as including a single medium or multiple media of one or more instruction sets; it should also be understood as including any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and causing the processor to perform any of the methods in the present application.

[0115] Those skilled in the art should understand that each module or step of the present application described above can be realized by a general computer device, alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device for execution by a computing device, or they can be respectively manufactured into each integrated circuit module, or a plurality of modules or steps among them can be manufactured into a single integrated circuit module to realize. The present application is not limited to any specific combination of hardware and software.

[0116] Although the specific embodiments of the present application are described above in combination with the drawings, the description is not a limitation on the scope of protection of the present application, and those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A method for representing geological unit property based on multi-scale cascading fusion, characterized in that, The method comprises the following steps: acquiring seismic wave data and acoustic wave data of a target area and performing preprocessing; performing parameter consistency conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient; Specifically, the propagation process of seismic waves and acoustic waves is numerically simulated based on wave equations to obtain the propagation characteristics of the two kinds of waves in different strata; a proportional relationship between the seismic waves and the acoustic waves is established to obtain a scale conversion coefficient; the scale conversion coefficient includes a spatial scale conversion coefficient and a time scale conversion coefficient . wherein, Vzis the longitudinal wave velocity, Vtis the transverse wave velocity, and T is the travel time; performing conversion on the seismic wave data and the acoustic wave data by using the scale conversion coefficient, and adjusting the numerical values of the seismic wave data and the acoustic wave data in the same parameter range; dividing the target area into grids of different scales based on a geological attribute change rate; inputting the converted seismic wave data and acoustic wave data into a cascaded fusion model based on the divided grids, and realizing attribute fusion of the seismic wave data and the acoustic wave data; wherein the cascaded fusion model takes the converted seismic wave data as a macro model basis and the converted acoustic wave data as a local fine constraint, and realizes attribute fusion of the seismic wave data and the acoustic wave data by using a multi-scale coupling algorithm. wherein, is the fused attribute value; is the macro attribute provided by the seismic wave; is the local attribute provided by the acoustic wave; and are weights, which are set according to the reliability of the data, and satisfy + = 1; the acoustic wave data is three-dimensionally interpolated, and the interpolation result is superimposed into the seismic wave model; inputting the fused data into an attribute prediction algorithm to predict the attributes of the un-drilled area, and obtaining a geological unit attribute characterization map.

2. The method for representing geological unit property based on multi-scale cascaded fusion according to claim 1, characterized in that, In the process of acquiring the seismic wave data of the target area, the underground structure of the target area is also obtained by inversion based on the acquired seismic wave data; wherein the inversion formula is: wherein is the travel time of the wave propagation, is the P-wave velocity, is the position of each location of the seismic wave propagation path.

3. The method for representing geological unit property based on multi-scale cascaded fusion according to claim 1, wherein, In the process of acquiring the acoustic wave data of the target area, the acoustic logging method is used to emit acoustic waves from the drill hole of the target area and receive reflected waves; the acoustic velocity of each layer is calculated based on the time of acoustic wave propagation and the drill hole depth; The acoustic impedance is obtained by measuring the acoustic velocity and the formation density, and the acoustic impedance reflects the elastic properties of the formation.

4. The method of claim 1, wherein, The process of dividing the target area into grids of different scales based on the geological attribute change rate comprises the following steps: For areas with rapid attribute changes, high-density grid division is performed to improve the calculation accuracy; For areas with slow attribute changes, low-density grid division is performed to improve the calculation efficiency.

5. The method for representing geological unit property based on multi-scale cascaded fusion according to claim 1, wherein, The attribute prediction algorithm uses the Kriging interpolation method, and the formula is: where, is the predicted value for the unknown point ; is the observed value for the known point ; is the weight coefficient representing the contribution of each known point to the predicted value ; is the number of known data points.

6. A system for representing attributes of a geological unit based on multi-scale cascading fusion, characterized in that, The method comprises the following steps: a data acquisition module configured to acquire seismic wave data and acoustic wave data of a target area and perform preprocessing; The consistency conversion module is configured to perform parameter consistency conversion on the seismic wave data and the acoustic wave data by using scale conversion coefficients; specifically, numerical simulation is performed on propagation processes of the seismic wave and the acoustic wave based on a wave equation to obtain propagation characteristics of the two waves in different strata; a proportional relationship between the seismic wave and the acoustic wave is established to obtain the scale conversion coefficients; the scale conversion coefficients include a spatial scale conversion coefficient and a time scale conversion coefficient ​​ wherein, Vp is the longitudinal wave velocity, Vt is the transverse wave velocity, and T is the travel time; performing conversion on the seismic wave data and the acoustic wave data by using a scale conversion coefficient, and adjusting the numerical values of the seismic wave data and the acoustic wave data in the same parameter range; a data fusion module configured to divide the target area into grids of different scales based on a geological attribute change rate; input the converted seismic wave data and acoustic wave data into a cascaded fusion model based on the divided grids, and realize attribute fusion of the seismic wave data and the acoustic wave data; wherein the cascaded fusion model takes the converted seismic wave data as a macro model basis and the converted acoustic wave data as a local fine constraint, and realizes attribute fusion of the seismic wave data and the acoustic wave data by using a multi-scale coupling algorithm. Wherein, is the fused attribute value; is the macro attribute provided by the seismic wave; is the local attribute provided by the acoustic wave; and is the weight, which is set according to the credibility of the data, and satisfies + =1; the acoustic wave data is three-dimensionally interpolated, and the interpolation result is superimposed into the seismic wave model; a geological unit attribute characterization module configured to input the fused data into an attribute prediction algorithm to predict the attributes of the un-drilled area, and obtain a geological unit attribute characterization map.

7. A computer-readable storage medium having stored thereon a program, characterized in that, The program is executed by the processor to realize the steps in the geological unit attribute characterization method based on multi-scale cascaded fusion according to any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized by The processor executes the program to realize the steps in the geological unit attribute characterization method based on multi-scale cascaded fusion according to any one of claims 1-5.

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