High-precision detection method, system, equipment and storage medium for urban underground space

Abstract

The present application provides a method, system, equipment and storage medium for high-precision detection of urban underground space, which relates to the field of geological exploration technology. The method includes: obtaining reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data in the target area, and performing fine processing on the reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data to obtain multi-source multi-scale detection data; extracting constraints based on the multi-source multi-scale detection data to obtain multi-source constraints, and constructing a three-dimensional information model of the urban underground space based on the multi-source constraints and the multi-source multi-scale detection data; performing data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data. The above scheme can improve the accuracy of urban underground space detection.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and specifically to a high-precision detection method, system, equipment and storage medium for urban underground space. Background Art

[0002] With the continuous advancement of urbanization, the development and utilization of urban underground space has received increasing attention. In order to ensure the safety and sustainability of urban underground space development, it is necessary to conduct refined detection and characterization of underground space. At present, urban underground space detection mainly relies on a single geophysical exploration method, such as seismic exploration or electromagnetic exploration. Although these methods can obtain certain characteristic information of underground media, they are limited by factors such as data dimension, resolution and signal-to-noise ratio, and it is difficult to fully characterize the complex structure and attribute distribution of urban underground space.

[0003] For example, the traditional reflection seismic method can obtain underground velocity structure information, but its ability to distinguish low-velocity anomalies and thin-layer structures is limited. The electromagnetic exploration method is sensitive to electrical anomalies such as aquifers and mineralized bodies, but it is difficult to accurately depict the spatial distribution of geological structures. The information obtained by a single exploration method is often incomplete and difficult to meet the needs of refined detection and modeling of urban underground space. Summary of the invention

[0004] The present application provides a method, system, device and storage medium for high-precision detection of urban underground space, which can improve the accuracy of urban underground space detection.

[0005] In a first aspect, the present application provides a method for high-precision detection of urban underground space, the method comprising:

[0006] Acquire reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data in the target area, and perform fine processing on the reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data to obtain multi-source and multi-scale detection data;

[0007] Extract constraints based on multi-source and multi-scale detection data to obtain multi-source constraints, and construct a three-dimensional information model of urban underground space based on the multi-source constraints and multi-source and multi-scale detection data;

[0008] Data mining is performed based on the three-dimensional information model of urban underground space to obtain three-dimensional geological data.

[0009] By adopting the above technical solution, the high-precision detection method of urban underground space proposed by the present invention first obtains multi-source data such as reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and logging data in the target area, and performs fine processing on these data to obtain multi-source multi-scale detection data. Through fine processing, the signal-to-noise ratio and resolution of various types of data can be effectively improved, laying a good foundation for subsequent data fusion and modeling.

[0010] After obtaining high-quality multi-source and multi-scale detection data, this method further extracts constraints based on these data to obtain multi-source constraints. This step makes full use of the complementarity and correlation between different data, and forms multi-faceted constraints by extracting characteristic information such as velocity, resistivity, and dispersion from different data. These constraints reflect the physical parameters and structural characteristics of the underground medium from different angles, providing important prior information and boundary conditions for subsequent three-dimensional modeling.

[0011] Based on the constraints, this method uses multi-source constraints and multi-source multi-scale detection data to construct a three-dimensional information model of urban underground space. Through technical means such as collaborative inversion, data of different types and scales are fused and jointly inverted under a unified framework, overcoming the uncertainty and multiple solutions of single data inversion. The introduction of multi-source constraints further reduces the uncertainty of inversion and improves the reliability and resolution of the model. The constructed three-dimensional information model can comprehensively and finely depict the structure and attribute distribution of urban underground space.

[0012] Finally, this method performs data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data. By extracting features and interpreting attributes of information such as velocity, resistivity, and geological structure contained in the three-dimensional information model, rich geological attribute data can be obtained, including stratigraphic interfaces, lithology distribution, and structural morphology. These geological data are mutually verified with the three-dimensional structural model, further enhancing the understanding and interpretation of the underground space. Through data mining technology, this method can extract valuable geological information from massive model data, providing a more detailed basis for the planning and management of urban underground space.

[0013] In a second aspect of the present application, a high-precision detection method system for urban underground space is provided, comprising:

[0014] A data acquisition and processing module is used to acquire reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data in the target area, and to perform fine processing on the reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data to obtain multi-source and multi-scale detection data;

[0015] The three-dimensional information model construction module, Yonggu extracts constraints based on multi-source and multi-scale detection data, obtains multi-source constraints, and constructs a three-dimensional information model of the urban underground space based on the multi-source constraints and multi-source and multi-scale detection data;

[0016] The data mining module is used to perform data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data.

[0017] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above method steps.

[0018] In the fourth aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the above method.

[0019] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. This application obtains multi-source data such as reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and logging data in the target area, and processes these data in a refined manner to obtain multi-source and multi-scale detection data. Through refined processing, the signal-to-noise ratio and resolution of various types of data can be effectively improved, laying a good foundation for subsequent data fusion and modeling.

[0021] 2. Based on the constraints, this method uses multi-source constraints and multi-source multi-scale detection data to construct a three-dimensional information model of urban underground space. Through technical means such as collaborative inversion, data of different types and scales are fused and jointly inverted under a unified framework, overcoming the uncertainty and multi-solution problems of single data inversion. The introduction of multi-source constraints further reduces the uncertainty of inversion and improves the reliability and resolution of the model. The constructed three-dimensional information model can comprehensively and finely characterize the structure and attribute distribution of urban underground space.

[0022] 3. This application performs data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data. By extracting features and interpreting attributes of information such as velocity, resistivity, and geological structure contained in the three-dimensional information model, rich geological attribute data can be obtained, including stratigraphic interfaces, lithology distribution, structural morphology, etc. These geological data are mutually confirmed with the three-dimensional structural model, further enhancing the understanding and interpretation of the underground space. Through data mining technology, this method can extract valuable geological information from massive model data, providing a more detailed basis for the planning and management of urban underground space. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a process for high-precision detection of urban underground space provided in an embodiment of the present application;

[0024] Figure 2 An architectural diagram of a high-precision urban underground space detection system provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] In order to facilitate understanding of the method and system provided by the embodiments of the present application, before introducing the embodiments of the present application, the background of the embodiments of the present application is first introduced.

[0030] With the advancement of urbanization, the development and utilization of urban underground space has received more and more attention. In order to ensure the safety and sustainability of urban underground space development, it is urgent to conduct refined detection and characterization of underground space. At present, urban underground space detection mainly relies on a single geophysical exploration method, such as seismic exploration or electromagnetic exploration. Although these methods can obtain certain characteristic information of underground media, they are limited by factors such as data dimension, resolution and signal-to-noise ratio, making it difficult to fully characterize the complex structure and attribute distribution of urban underground space.

[0031] Taking the traditional reflection seismic method as an example, it can effectively obtain underground velocity structure information, but its ability to distinguish low-velocity anomalies and thin-layer structures is limited. The electromagnetic exploration method is more sensitive to electrical anomalies such as aquifers and mineralized bodies, but it is difficult to accurately depict the spatial distribution of geological structures. Since the information obtained by a single exploration method is often incomplete, it is difficult to meet the needs of refined detection and modeling of urban underground space.

[0032] After the background introduction of the above content, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0033] Based on the above background technology, please refer to Figure 1 , Figure 1 A flow chart of a high-precision detection method for urban underground space provided in an embodiment of the present application. The system can be implemented by a computer program or can be run as an independent tool application. Specifically, in an embodiment of the present application, the method can be applied on a server, but can also be applied to electronic devices such as servers. A high-precision detection method for urban underground space includes the following steps:

[0034] S101, acquiring reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data in the target area, and performing fine processing on the reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data to obtain multi-source and multi-scale detection data;

[0035] Specifically, reflection seismic data is obtained by exciting seismic waves on the surface and recording the reflected signals of the underground interface, and mainly provides underground velocity structure information; high-resolution electromagnetic response data is obtained by measuring the electrical conductivity and dielectric constant of the underground medium, and is sensitive to electrical anomalies such as aquifers and mineralized bodies; environmental noise data is obtained by passively receiving signals from natural or artificial noise sources, and can extract the velocity and attenuation characteristics of the underground medium; wellbore seismic data is obtained by exciting and receiving seismic waves in the borehole, and has a high vertical resolution; well logging data is obtained by continuously recording the physical properties of the formation in the borehole, and can provide accurate well position correction information.

[0036] After obtaining the above multi-source data, these data need to be processed in a refined manner. The purpose of refined processing is to improve data quality, including suppressing noise, enhancing signal-to-noise ratio, and improving resolution, so as to lay a good foundation for subsequent data fusion and modeling. For reflection seismic data, surface wave attenuation, offset imaging and other technologies are used to suppress surface wave noise and improve imaging quality; for high-resolution electromagnetic response data, reference model constrained inversion, anisotropy correction and other technologies are used to improve the resolution and reliability of resistivity models; for environmental noise data, spatial autocorrelation method, frequency-wavenumber domain filtering and other technologies are used to extract the dispersion characteristics of underground media from noise; for wellside seismic data, inter-well imaging, wave field separation and other technologies are used to obtain the fine structure of inter-well strata; for logging data, well-seismic combination, synthetic seismic recording and other technologies are used to achieve accurate registration of well location information and seismic data.

[0037] Based on the above embodiment, as an optional embodiment, the reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and logging data are processed in a refined manner to obtain multi-source and multi-scale detection data, including:

[0038] The reflection seismic data is processed using precise migration technology to obtain depth domain seismic image data;

[0039] Seismic tomography is used to process reflection seismic data to obtain geological velocity data;

[0040] Inversion processing is performed on high-resolution electromagnetic response data to obtain three-dimensional resistivity data;

[0041] The dispersion curve is extracted from the ambient noise data by using the surface wave dispersion curve extraction algorithm, and the S-wave velocity data is constructed based on the dispersion curve.

[0042] The wellside seismic data are inverted to obtain P- and S-wave velocity data, and the geological velocity data, depth domain seismic image data, three-dimensional resistivity data, S-wave velocity data, and P- and S-wave velocity data are used as multi-source and multi-scale detection data.

[0043] Specifically, first, the reflection seismic data is processed using precise migration technology to obtain depth domain seismic image data. Precision migration technology is a high-precision seismic migration imaging method that accurately calculates and corrects the propagation path of seismic waves by considering the lateral velocity changes of the underground medium, thereby obtaining a clearer and more accurate underground geological structure image. Compared with conventional time migration, precise migration can effectively suppress noise, improve the signal-to-noise ratio, and overcome the imaging distortion caused by lateral changes in underground velocity, making the imaging results closer to the actual geological situation. Depth domain seismic image data can intuitively display the spatial distribution and connectivity of underground strata, providing an important basis for subsequent geological interpretation.

[0044] Secondly, seismic tomography is used to process the reflected seismic data to obtain geological velocity data. Seismic tomography is a method of inverting the velocity structure of underground media using seismic travel time information. By measuring the travel time difference between different seismic traces and combining mathematical algorithms such as ray tracing, the velocity distribution of the underground medium can be reconstructed. Compared with simple velocity analysis, seismic tomography can obtain a more detailed and accurate velocity model, especially for complex geological bodies with lateral velocity changes. Tomography can characterize the internal velocity heterogeneity and provide a reliable velocity benchmark for subsequent seismic imaging and attribute analysis.

[0045] Thirdly, the high-resolution electromagnetic response data is inverted to obtain three-dimensional resistivity data. Electromagnetic inversion is a method of estimating the electrical structure of underground media using electromagnetic response data. By measuring the electromagnetic field intensity and phase at different frequencies and offsets, and combining electromagnetic field theory and inversion algorithms, the three-dimensional resistivity distribution of underground media can be reconstructed. Compared with conventional electrical and electromagnetic methods, high-resolution electromagnetic response data has a wider frequency range and denser spatial sampling. Therefore, the inverted resistivity model has a higher resolution and can accurately depict the spatial distribution and property characteristics of electrical anomalies such as underground aquifers and mineralized bodies.

[0046] Next, the dispersion curve is extracted from the environmental noise data through the surface wave dispersion curve extraction algorithm, and the S-wave velocity data is constructed based on the dispersion curve. Environmental noise surface waves are low-frequency surface waves generated by nature or human activities. Their propagation velocity shows a certain regularity with the change of frequency, reflecting the S-wave velocity structure of the near-surface medium. By performing time-frequency analysis and phase velocity picking on single or multiple records of environmental noise, the surface wave phase velocity at different frequencies can be extracted to form a dispersion curve. On this basis, the S-wave velocity distribution at different depths can be estimated using dispersion curve inversion algorithms, such as genetic algorithms and simulated annealing. The S-wave velocity is closely related to the physical properties of the underground medium, such as porosity and water saturation. Therefore, the S-wave velocity data constructed based on the dispersion curve can provide an important reference for lithology interpretation and fluid prediction.

[0047] Finally, the wellside seismic data is inverted to obtain P-wave and S-wave velocity data. Wellside seismic is a seismic record collected in a borehole. Since the source and the detector are close to the formation, it has a high vertical resolution and can obtain fine velocity information of the formation around the well. By performing travel time picking and first arrival wave tomography on the wellside seismic data, the P-wave velocity distribution of the formation around the well can be obtained. On this basis, the S-wave velocity distribution of the formation can also be estimated using wave equation inversion, elastic impedance inversion and other techniques. The P-wave and S-wave velocity ratio is an important parameter for evaluating the lithology and pore fluid of the formation. The acquisition of P-wave and S-wave velocity data provides important constraints for the subsequent comprehensive geological interpretation.

[0048] S102, extracting constraints based on the multi-source multi-scale detection data to obtain multi-source constraints, and constructing a three-dimensional information model of the urban underground space based on the multi-source constraints and the multi-source multi-scale detection data;

[0049] Specifically, constraint extraction is to analyze the characteristic information of different data, find the physical relationship between them, and then form constraint conditions that reflect the properties and structural characteristics of underground media. For example, seismic velocity constraints are extracted from reflection seismic data, resistivity constraints are extracted from high-resolution electromagnetic response data, dispersion characteristic constraints are extracted from environmental noise data, fine layer constraints are extracted from wellside seismic data, and well position correction constraints are extracted from logging data. These constraint conditions restrict the physical parameters and structural characteristics of underground media from different angles, making the subsequent three-dimensional modeling more in line with the actual geological conditions.

[0050] On the basis of extracting multi-source constraints and combining them with the optimized multi-source and multi-scale detection data, the construction of the three-dimensional information model of the urban underground space can be carried out. The three-dimensional modeling adopts a collaborative inversion strategy to fuse and jointly invert data of different types and scales under a unified framework. Among them, multi-source constraints are introduced into the inversion process as prior information and boundary conditions to constrain the model parameter space and reduce the uncertainty and multi-solution problems of the inversion process. At the same time, through the collaborative inversion of multiple parameters, multiple scales, and multiple attributes, the complementarity and linkage of different data can be fully utilized, thereby obtaining a more accurate and detailed three-dimensional underground space structure and attribute model.

[0051] Based on the above embodiment, as an optional embodiment, constraint conditions are extracted based on multi-source and multi-scale detection data to obtain multi-source constraint conditions, including:

[0052] S201, extracting seismic velocity constraints based on depth domain seismic image data and geological velocity data;

[0053] Specifically, the extraction of seismic velocity constraints is achieved by comprehensively analyzing the depth domain seismic image data and geological velocity data. First, the main stratigraphic interfaces and structural interfaces are identified from the depth domain seismic image data, and their geometric information such as depth and morphology are extracted. Since the depth domain seismic image has been precisely offset, the underground interface position it reflects is more accurate, so the extracted interface information can be used as an important constraint for the construction of a three-dimensional model. Secondly, the depth domain seismic image is corrected and converted using geological velocity data to establish a corresponding relationship between the seismic image and the actual geological depth. The geological velocity data is obtained through seismic tomography and reflects the actual velocity distribution of the underground medium. Therefore, the time coordinates of the seismic image can be converted into depth coordinates to make it consistent with the real stratigraphic interface.

[0054] After establishing the association between the depth domain seismic image and the geological velocity, the seismic velocity constraints can be extracted. Specifically, along the stratigraphic interface and structural interface identified by the seismic image, the seismic velocity values ​​at the corresponding positions are extracted to form velocity constraint points. These velocity constraint points reflect the velocity distribution characteristics at the stratigraphic interface and can control the velocity changes of the three-dimensional model at the interface, thereby ensuring that the model is consistent with the actual geological conditions. For areas with good seismic image quality and accurate interface identification, the number and distribution density of velocity constraint points can be appropriately increased; for areas with poor seismic image quality and difficult interface identification, the number of velocity constraint points can be appropriately reduced to avoid introducing erroneous constraint information.

[0055] Through the comprehensive analysis of deep-domain seismic image data and geological velocity data, seismic velocity constraints were extracted. These constraints have clear spatial position and velocity attributes, and can play a key control role in the construction of three-dimensional models. After the introduction of seismic velocity constraints, the multi-solution of the model in the velocity space can be effectively reduced, making the inverted velocity model more consistent with the actual geological conditions. At the same time, the velocity constraints can also guide the evolution of the model in the structural space, so that the interface morphology of the model is consistent with the seismic image recognition results, ensuring the structural accuracy of the model. In addition, the seismic velocity constraints can also provide a reference for the inversion of other attribute parameters, such as using empirical formulas to convert velocity into density, porosity, etc., thereby realizing the coordinated constraints of multiple parameters.

[0056] S202, extracting electrical characteristic constraints based on the three-dimensional resistivity data;

[0057] Specifically, the extraction of electrical characteristic constraints is achieved by analyzing three-dimensional resistivity data. Three-dimensional resistivity data is obtained by inversion using high-resolution electromagnetic response data. It has high accuracy and resolution and can accurately characterize the resistivity distribution of underground media. When extracting electrical characteristic constraints, it is first necessary to analyze and interpret the three-dimensional resistivity data to identify electrical anomalies and electrical stratification structures. Electrical anomalies usually appear as closed areas of high or low resistivity, which may correspond to special geological bodies such as underground mineralization, cavities, bedrock uplifts, etc.; electrical stratification structures appear as gradual changes in resistivity along the depth direction, reflecting the vertical heterogeneity of the underground medium.

[0058] After identifying the electrical anomalies and electrical layered structures, the corresponding electrical characteristic constraints can be extracted. For electrical anomalies, their spatial position, geometric shape, resistivity value and other characteristic parameters are mainly extracted to form discrete resistivity constraint points. These constraint points can control the position and properties of the anomalies in the three-dimensional model so that they are consistent with the resistivity data identification results. For the electrical layered structure, the resistivity values ​​at different depths are extracted to form a continuous resistivity constraint curve. This constraint curve can control the vertical resistivity change trend of the three-dimensional model so that it is consistent with the electrical heterogeneity of the actual formation.

[0059] It is worth noting that when extracting electrical characteristic constraints, the resolution and reliability of resistivity data need to be fully considered. For areas where resistivity changes dramatically and equivalent models may exist, the number of constraint points should be appropriately reduced to avoid errors introduced by overfitting; for areas where resistivity changes gently and the inversion results are stable and reliable, the number of constraint points can be appropriately increased to improve the accuracy of the model. At the same time, it is also necessary to comprehensively consider the compatibility of resistivity constraints with other types of constraints (such as seismic velocity constraints), and make necessary adjustments to the positions and attributes of constraint points to ensure the spatial coordination of various constraints.

[0060] S203, extracting S-wave velocity constraints based on the dispersion curve and S-wave velocity data;

[0061] Specifically, the extraction of S-wave velocity constraints is achieved by comprehensively analyzing dispersion curves and S-wave velocity data. The dispersion curve is extracted from the ambient noise data using the surface wave dispersion curve extraction algorithm, reflecting the propagation velocity characteristics of surface waves at different frequencies. Since the propagation velocity of surface waves is mainly controlled by the S-wave velocity of the shallow medium, the dispersion curve can be inverted to obtain the S-wave velocity structure near the surface. The S-wave velocity data is obtained by inverting the dispersion curve, reflecting the distribution characteristics of the S-wave velocity at different depths.

[0062] When extracting the S-wave velocity constraints, it is first necessary to analyze and interpret the dispersion curve and S-wave velocity data to identify the S-wave velocity anomaly and gradient change characteristics. The S-wave velocity anomaly usually appears as a "jump" or "inflection point" on the dispersion curve, corresponding to the sudden change area of ​​the S-wave velocity of the underground medium, which may indicate geological structures such as stratum interfaces and fault zones; the S-wave velocity gradient change appears as a gradual change in the dispersion curve morphology, corresponding to the continuous change trend of the S-wave velocity with depth, reflecting the vertical heterogeneity of the underground medium.

[0063] After identifying the S-wave velocity anomaly and gradient change characteristics, the corresponding S-wave velocity constraints can be extracted. For the S-wave velocity anomaly, the corresponding frequency value, phase velocity value and spatial position information are mainly extracted to form discrete S-wave velocity jump constraint points. These constraint points can control the mutation position and amplitude of the S-wave velocity in the three-dimensional model to make it consistent with the dispersion curve inversion results. For the S-wave velocity gradient change, the phase velocity values ​​at different frequencies are extracted to form a continuous S-wave velocity gradient constraint curve. This constraint curve can control the vertical change trend of the S-wave velocity of the three-dimensional model to make it consistent with the S-wave velocity heterogeneity of the actual formation.

[0064] S204, extracting P- and S-wave velocity constraints based on the P- and S-wave velocity data;

[0065] Specifically, the extraction of P-wave and S-wave velocity constraints is achieved by analyzing P-wave and S-wave velocity data. P-wave and S-wave velocity data usually come from high-resolution seismic detection methods such as cross-well / VSP seismic exploration and acoustic logging, which can accurately characterize the P-wave and S-wave velocity distribution characteristics of underground media. When extracting P-wave and S-wave velocity constraints, it is first necessary to analyze and interpret the P-wave and S-wave velocity data to identify the P-wave and S-wave velocity anomalies and velocity ratio change characteristics. P-wave and S-wave velocity anomalies usually manifest as obvious high or low value areas of P-wave and S-wave velocities at certain depths, which may indicate special geological bodies or fluid storage conditions; and the P-wave and S-wave velocity ratio changes reflect the relative relationship between the P-wave and S-wave velocities with depth, which is closely related to the Poisson's ratio, porosity and other properties of the medium.

[0066] After identifying the anomalies of P-wave and S-wave velocities and the characteristics of velocity ratio changes, the corresponding P-wave and S-wave velocity constraints can be extracted. For P-wave and S-wave velocity anomalies, the corresponding characteristic parameters such as depth position, P-wave velocity value, S-wave velocity value, etc. are mainly extracted to form discrete P-wave and S-wave velocity anomaly constraint points. These constraint points can control the value range of P-wave and S-wave velocities at specific positions in the three-dimensional model, so that it is consistent with the interpretation results of P-wave and S-wave velocity data. For the changes in the P-wave and S-wave velocity ratio, the P-wave and S-wave velocity ratios at different depths are extracted to form a continuous P-wave and S-wave velocity ratio constraint curve. This constraint curve can control the change trend of the P-wave and S-wave velocity ratio in the vertical direction of the three-dimensional model, so that it is consistent with the changes in the Poisson's ratio, porosity and other attributes of the actual formation.

[0067] When extracting P-wave and S-wave velocity constraints, attention should be paid to the representativeness and applicable scope of P-wave and S-wave velocity data. Since P-wave and S-wave velocity data usually come from local interwell / VSP seismic exploration or acoustic logging, the velocity information they reflect is highly localized and has limited spatial extrapolation capabilities. Therefore, when using P-wave and S-wave velocity constraints to constrain a three-dimensional model, attention should be paid to the velocity distribution in the vicinity of the constraint point, and for areas far from the constraint point, the weight of the constraint should be appropriately reduced to avoid errors introduced by excessive extrapolation. At the same time, it is also necessary to comprehensively consider the compatibility of P-wave and S-wave velocity constraints with other types of constraints (such as seismic velocity constraints, electrical characteristic constraints, and S-wave velocity constraints), and perform necessary smoothing on the positions and attributes of the constraint points to ensure the coordination and consistency of various constraints in space.

[0068] S205, taking the seismic velocity constraint condition, the electrical characteristic constraint condition, the S-wave velocity constraint condition and the longitudinal and transverse wave velocity constraint condition as multi-source constraint conditions.

[0069] Specifically, the process of using different types of constraints as multi-source constraints is achieved by weighting and spatially registering various types of constraints. First, different weight coefficients are assigned to different constraints according to factors such as data quality, resolution, and reliability. Generally speaking, constraints with high data quality, high resolution, and strong reliability should be given higher weights, while constraints with poor data quality, low resolution, and weak reliability should be appropriately reduced in weight. Through reasonable weight allocation, the contributions of constraints from different sources can be balanced to avoid excessive dominance of the model construction results by a certain type of constraint.

[0070] Secondly, since different types of constraints may differ in spatial position, sampling density, resolution scale, etc., it is necessary to spatially align various constraints to form a unified reference frame in three-dimensional space. Specific methods of spatial alignment include coordinate transformation, interpolation resampling, smoothing, etc. The purpose is to make constraints from different sources correspond to each other in space to avoid position offset or resolution mismatch. Through spatial alignment, seismic velocity constraints, electrical characteristic constraints, S-wave velocity constraints, and P- and S-wave velocity constraints can be organically combined to form a unified set of multi-source constraints.

[0071] Finally, the set of multi-source constraints after spatial registration is used as the prior information for the construction of the three-dimensional model and introduced into the objective function of the model parameter inversion. In the inversion process, the multi-source constraints play a regularization role, limiting the value range and change trend of the model parameters, so that the inversion results are consistent with the observed data and meet the constraints of the prior information. Through the comprehensive effect of multi-source constraints, the instability of the inversion can be effectively reduced and the stability and reliability of the model parameter estimation can be improved. At the same time, the multi-source constraints can also make up for the shortcomings of a single type of observation data and realize the comprehensive characterization of various properties of the underground medium.

[0072] Based on the above embodiment, as an optional embodiment, a three-dimensional information model of urban underground space is constructed based on multi-source constraint conditions and multi-source multi-scale detection data, including:

[0073] Based on multi-source constraints and multi-source and multi-scale detection data, collaborative inversion is performed to obtain a three-dimensional information model of the urban underground space.

[0074] Specifically, the process of collaborative inversion based on multi-source constraints and multi-source multi-scale detection data is to estimate the distribution of three-dimensional model parameters by constructing an objective function that comprehensively considers different types of observation data and prior information, and solving the minimum point of the objective function through an optimization algorithm. First, according to the type and characteristics of multi-source multi-scale detection data, a suitable forward simulation method is selected to establish a nonlinear relationship between observation data and model parameters. Common forward simulation methods include finite difference method, finite element method, integral equation method, etc., which are respectively applicable to different types of geophysical data, such as seismic wave field data, electromagnetic field data, gravity field data, etc.

[0075] Secondly, the multi-source constraints are introduced into the inversion objective function as regularization terms. The objective function generally consists of two parts: data fitting terms and regularization terms. The data fitting terms measure the error between the model parameters and the observed data, while the regularization terms impose a priori constraints on the model parameters so that they satisfy certain geological and geophysical laws. Common regularization terms include smoothness constraints, sparse constraints, reference model constraints, etc., which correspond to the smoothness assumptions, sparsity assumptions, and similarity assumptions with the prior geological model of the model parameters, respectively. By reasonably selecting the type and weight of the regularization term, a balance can be achieved between data fitting and a priori constraints to avoid overfitting or underfitting problems.

[0076] Finally, the optimization algorithm is used to solve the minimum point of the objective function to obtain the optimal three-dimensional model parameter estimation results. Commonly used optimization algorithms include least squares method, gradient descent method, conjugate gradient method, Newton method, etc. They have their own advantages and disadvantages in terms of convergence speed, computational efficiency, and sensitivity to noise. In order to improve the computational efficiency and stability of inversion, a multi-scale and multi-stage inversion strategy is usually adopted. Starting with low-frequency and long-wavelength detection data, an initial background model is established, and then high-frequency and short-wavelength detection data are gradually introduced to continuously correct and refine the model. At the same time, in order to overcome the non-uniqueness problem of inversion, a variety of regularization methods can be introduced, such as Bayesian regularization, total variation regularization, etc., to constrain the model parameters in a statistical sense.

[0077] S103, performing data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data.

[0078] Specifically, data mining is to obtain key information reflecting underground geological conditions by performing a series of data processing operations such as attribute analysis, feature extraction, and pattern recognition on the three-dimensional information model of urban underground space. For example, by statistically analyzing and spatially distributing the attribute parameters such as velocity, resistivity, and density in the model, the lithology, porosity, and saturation of the underground medium can be identified; by geometrically analyzing and topologically analyzing the structural information such as the structural morphology, fault location, and stratigraphic relationship in the model, the spatial distribution and connectivity of the underground geological body can be depicted; by performing spectral analysis and coherence analysis on wave dynamics parameters such as wave impedance and reflection coefficient in the model, the occurrence state and migration law of underground fluids can be predicted; by comprehensively analyzing and interpreting the model as a whole, the structural characteristics, physical property distribution, and evolution history of the urban underground space can be revealed.

[0079] Based on the above embodiment, as an optional embodiment, data mining is performed based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data, including:

[0080] S301, based on the velocity structure information in the three-dimensional information model of the urban underground space, extracting features of the velocity structure information by velocity tomography to obtain velocity distribution features of the underground medium;

[0081] Specifically, velocity tomography is a method based on the principle of seismic wave propagation that uses travel time data to invert the velocity structure of underground media. It establishes a nonlinear relationship between travel time data and medium velocity by analyzing the propagation time and path of seismic waves in underground media, and uses an inversion algorithm to solve the nonlinear problem, thereby obtaining the velocity distribution of underground media. When performing velocity tomography inversion, it is first necessary to extract velocity-related information from the three-dimensional information model of the urban underground space, such as seismic wave travel time data, initial velocity model, etc. This information is usually stored in the three-dimensional model in the form of discrete data points or grids, and needs to be extracted through appropriate data structures and query methods.

[0082] Secondly, the extracted velocity information is used to construct the objective function of velocity tomography inversion. The objective function generally consists of two parts: the travel time residual term and the regularization term. The travel time residual term measures the difference between the theoretical travel time and the observed travel time, and the regularization term imposes prior constraints such as smoothness and continuity on the velocity model. By minimizing the objective function, a velocity model that best matches the observed data and satisfies the prior constraints can be obtained. When constructing the objective function, it is necessary to reasonably select the measurement method of the residual term (such as the L1 norm, the L2 norm) and the type of regularization term (such as the Laplace operator, the total variation operator) to balance the relationship between data fitting and model constraints.

[0083] Finally, a suitable inversion algorithm is used to solve the minimum value of the objective function and obtain the optimal velocity model. Common velocity tomography inversion algorithms include algebraic reconstruction technique (ART), simultaneous iterative reconstruction technique (SIRT), least squares travel time method (LSQR), etc. These algorithms iteratively update the velocity model, continuously reducing the travel time residual and regularization term until the convergence condition is reached. In order to improve the efficiency and stability of the inversion, a multi-scale strategy can be adopted, starting with a low-resolution velocity model, and gradually refining and optimizing the model. At the same time, other types of data (such as well logging velocity data) can be introduced as constraints to further reduce the uncertainty of the inversion.

[0084] S302, based on the electrical structure information in the three-dimensional information model, extracting features of the electrical structure information by resistivity inversion method to obtain resistivity distribution features of the underground medium;

[0085] Specifically, the resistivity inversion method is a method based on electromagnetic field theory that uses observed apparent resistivity data to invert the true resistivity distribution of underground media. It establishes a nonlinear relationship between apparent resistivity and true resistivity by analyzing the conduction law of current in underground media, and uses an inversion algorithm to solve the nonlinear problem, thereby obtaining a three-dimensional resistivity model of the underground medium. When performing resistivity inversion, it is first necessary to extract electrical-related information from the three-dimensional information model of the urban underground space, such as apparent resistivity data, measurement point locations, electrode arrangement, etc. This information is usually stored in the three-dimensional model in the form of discrete data points or regular grids, and needs to be extracted through a suitable data format and reading interface.

[0086] Secondly, the objective function of resistivity inversion is constructed using the extracted electrical information. The objective function is generally composed of two parts: data fitting term and model constraint term. The data fitting term measures the error between the theoretical apparent resistivity and the observed apparent resistivity, while the model constraint term imposes prior constraints such as smoothness and reference model similarity on the resistivity model. By minimizing the objective function, a resistivity model that is consistent with the observed data and satisfies prior knowledge can be obtained. When constructing the objective function, it is necessary to reasonably select the metric norm of the data fitting term (such as L1 norm, L2 norm) and the regularization form of the model constraint term (such as Laplace regularization, reference model constraint) to balance the relationship between data error and model complexity.

[0087] Finally, a suitable inversion algorithm is used to solve the minimum value of the objective function and obtain the optimal three-dimensional resistivity model. Commonly used resistivity inversion algorithms include damped least squares method, conjugate gradient method, Newton method, etc. These algorithms iteratively update the resistivity model parameters and continuously reduce the objective function value until the convergence condition is reached. In order to improve the resolution and stability of the inversion, a multi-scale inversion strategy can be adopted, starting with a rough initial model, and gradually refining and optimizing the model. At the same time, other types of data (such as well logging resistivity data) can also be introduced as constraints to further reduce the multi-solution problem of inversion.

[0088] S303, based on the geological structure information in the three-dimensional information model, extracting features of the geological structure information through geological heuristic interpretation technology to obtain spatial distribution features of the geological structure;

[0089] Specifically, geological heuristic interpretation technology is a method for comprehensive analysis and interpretation of geological data based on geological principles and empirical knowledge. It extracts meaningful geological structural features from complex geological information by integrating multi-source and multi-scale geological data and combining geologists' prior knowledge and interpretation strategies. When performing geological heuristic interpretation, it is first necessary to extract information related to geological structures from the three-dimensional information model of urban underground space, such as stratigraphic interfaces, fault locations, fold morphology, etc. This information is usually stored in the three-dimensional model in the form of discrete vector elements such as points, lines, and surfaces, and needs to be extracted through appropriate data organization methods and topological relationships.

[0090] Secondly, the extracted geological structure information is used in combination with geological knowledge and empirical rules to identify the characteristics and interpret the attributes of the geological structure. This process usually adopts rule-based reasoning, case-based reasoning, statistical learning-based reasoning and other methods. By designing appropriate geological interpretation rules and reasoning strategies, the type, nature, occurrence and other attributes of the geological structure can be automatically or semi-automatically identified. For example, the geometric laws of the occurrence of the strata can be used to infer the type and activity of the fault; the morphological characteristics of the folds can be used to determine the cause and deformation age of the folds; the truncation relationship between the structure and the stratum can be used to determine the relative age of the structure. When interpreting geological structures, it is also necessary to fully consider the spatial relationship and evolutionary connection between different geological structures to form a complete and coherent structural interpretation framework.

[0091] Finally, based on the geological structural characteristics obtained through interpretation, the spatial distribution law is summarized and visualized. This process usually uses 3D geological modeling, spatial analysis and other technologies to transform discrete structural interpretation results into continuous 3D spatial models, and intuitively display the spatial distribution and mutual relationship of geological structures through 3D visualization methods. For example, 3D implicit modeling methods can be used to construct 3D geometric models of stratigraphic interfaces, fault planes, fold axes, etc. based on structural interpretation results; spatial interpolation methods can be used to generate continuous structural foliation models based on discrete structural occurrence data; 3D visualization technology can be used to display structural models from multiple perspectives and sections, and analyze the extension, intersection, deformation and other characteristics of the structure in space.

[0092] S304, in a unified spatial coordinate system, the extracted velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics are coupled and integrated to obtain three-dimensional geological data.

[0093] Specifically, the coupled integration process needs to be based on a unified spatial coordinate system, and data from different sources such as velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics need to be spatially registered and gridded. First, it is necessary to establish a unified three-dimensional spatial reference system and grid division scheme based on the actual scope and target resolution of the urban underground space. Commonly used three-dimensional spatial reference systems include Cartesian coordinate systems, geodetic coordinate systems, etc., and commonly used grid types include regular grids, unstructured grids, etc. After determining the spatial reference system and grid division scheme, it is necessary to convert the coordinates of different feature data into a unified reference system, and assign their attribute values ​​to the corresponding grid cells. This process can use spatial interpolation, Kriging and other methods to convert discrete feature data into continuous gridded data.

[0094] Secondly, under the unified grid framework, the attributes of different feature data need to be fused and coordinated. Since the velocity distribution characteristics, resistivity distribution characteristics and spatial distribution characteristics reflect the different physical properties and geological characteristics of the underground medium, there may be certain correlation and complementarity between them. Through attribute fusion, the properties of the underground medium can be described from multiple angles to improve the reliability of geological interpretation. Common attribute fusion methods include weighted average method, data assimilation method, machine learning method, etc. For example, the empirical relationship between velocity and resistivity can be used to jointly invert the two attributes to obtain a more accurate medium distribution model; the spatial distribution law of geological structure can be used to constrain the distribution form of velocity and resistivity to improve the geological rationality of the inversion results; machine learning methods can be used to extract implicit geological information from multiple attribute characteristics to achieve intelligent fusion of attributes.

[0095] Finally, after completing spatial registration and attribute fusion, the integrated three-dimensional geological data needs to be quality evaluated and supplemented with information. Quality evaluation mainly includes checking the integrity, consistency, accuracy and other aspects of the data to ensure that the integrated data meets the application requirements. Information supplementation mainly includes improving the attribute fields and metadata of the data to improve the availability and interpretability of the data. For example, the integrated velocity model can be analyzed for geological aspect ratio to evaluate its conformity with the actual geological conditions; the integrated resistivity model can be compared with the reference model to evaluate its consistency with the known geological information; the integrated structural model can be topologically checked to evaluate the rationality of its spatial logical relationship. At the same time, metadata such as data acquisition time, processing method, coordinate system, unit, and geological attributes such as lithology, age, and genesis can be supplemented to make the three-dimensional geological data more complete and standardized.

[0096] Based on the above embodiment, as an optional embodiment, in a unified spatial coordinate system, the extracted velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics are coupled and integrated to obtain three-dimensional geological data, including:

[0097] S401, correlating velocity distribution characteristics with stratum interfaces to obtain three-dimensional stratum structure framework data;

[0098] Specifically, the process of associating velocity distribution characteristics with formation interfaces requires the comprehensive use of seismic exploration data and geological survey data. First, it is necessary to extract formation interface information from seismic exploration data (such as reflection seismic profiles, tomography results, etc.). The formation interface is usually manifested as a continuous, strong reflection event on the reflection seismic profile, representing a sudden change in the physical properties of the formation. By identifying, comparing and tracking the reflection events, a series of discrete formation interface point data can be obtained. At the same time, it is also necessary to use geological survey data (such as drilling data, outcrop profiles, etc.) to constrain the position and occurrence of the formation interface. Geological survey data usually provide geometric attribute information such as the actual distribution position, dip, thickness, etc. of the formation, which can be used to correct and verify the seismic interpretation results.

[0099] After obtaining the discrete stratigraphic interface point data, it is necessary to use spatial interpolation and other methods to convert it into continuous stratigraphic interface facets. Common stratigraphic interface interpolation methods include krieging, radial basis function, discrete smooth interpolation, etc. By setting appropriate interpolation parameters and constraints, a continuous interface that is consistent with geological laws can be generated. In the process of interpolation, it is also necessary to make full use of the spatial topological relationship of the stratigraphic interface (such as up-down relationship, pinch-out relationship, etc.) to ensure the spatial logic rationality of the interface. In addition, for structural surfaces such as faults, special processing is required, such as the construction of fault planes and the connection of the two strata of the fault, in order to accurately express the spatial distribution of the structure.

[0100] After the continuous stratigraphic interface is constructed, it needs to be associated with the three-dimensional velocity distribution model. This process is mainly achieved through spatial superposition and attribute transfer. First, the stratigraphic interface is spatially aligned with the three-dimensional velocity model to keep the coordinate system and scale range of the two consistent. Then, spatial query and other methods are used to determine the stratigraphic unit to which each grid unit in the velocity model belongs. Finally, according to the geological attributes of the stratigraphic unit such as sequence, age, and lithology, the attribute table of the velocity model is expanded to establish a mapping relationship between velocity parameters and stratigraphic units. In this way, the velocity distribution characteristics are closely spatially associated with the stratigraphic interface, forming a unified three-dimensional stratigraphic structure velocity model.

[0101] By associating the velocity distribution characteristics with the formation interface, a three-dimensional structural framework data containing both velocity parameters and stratigraphic unit information can be obtained. This framework data not only reflects the spatial distribution law of formation velocity, but also reflects the spatial distribution morphology and topological relationship of stratigraphic units. It is the basis for geological modeling, seismic imaging, oil and gas reservoir characterization, etc. Based on the three-dimensional stratigraphic structural framework data, the lateral and vertical variation characteristics of velocity parameters can be analyzed more accurately, the geometric morphology of formation interfaces can be identified, and the spatial distribution trend of velocity parameters can be predicted, which provides important constraints for the subsequent association of resistivity distribution characteristics, spatial distribution characteristics, and oil and gas resource evaluation.

[0102] S402, correlating resistivity distribution characteristics with geological attributes to obtain three-dimensional geological attribute data;

[0103] Specifically, the process of associating resistivity distribution characteristics with geological properties requires the comprehensive use of logging data, core analysis data and geological interpretation results. First, it is necessary to extract resistivity values ​​from logging data (such as resistivity logging, sonic logging, density logging, etc.). Resistivity logging can continuously record the resistivity changes of the formation and is the main means of obtaining resistivity data. By segmenting and normalizing the logging curve, the resistivity values ​​at different depths can be obtained. At the same time, it is also necessary to collect core analysis data to obtain geological attribute parameters such as porosity, permeability, and water saturation of rock samples. Core analysis data usually comes from physical experimental tests of drilled cores and can provide a quantitative relationship between rock physical parameters and resistivity.

[0104] After obtaining the resistivity data and geological attribute parameters, it is necessary to establish an empirical relationship or theoretical model between resistivity and geological attributes through intersection analysis, regression fitting and other methods. Commonly used resistivity-porosity relationship models include Archie equation, Waxman-Smits equation, etc., and commonly used resistivity-water saturation relationship models include Indonesia equation, Simandoux equation, etc. By selecting a suitable relationship model and using core analysis data for parameter calibration, the corresponding relationship between resistivity and geological attributes can be quantitatively described. In the process of establishing the relationship model, it is also necessary to fully consider the influence of geological factors such as stratum lithology, mineral composition, pore structure, etc., and select different relationship models according to different geological conditions to improve the accuracy and applicability of the correlation results.

[0105] After establishing the relationship model between resistivity and geological attributes, it is necessary to apply it to the three-dimensional resistivity distribution model to obtain three-dimensional geological attribute data. This process is mainly achieved through attribute conversion and spatial assignment. First, the resistivity variable in the relationship model is replaced with the resistivity value in the three-dimensional resistivity distribution model, and the corresponding geological attribute parameters, such as porosity and water saturation, are calculated according to the relationship model. Then, the calculated geological attribute parameters are assigned to the grid cells of the three-dimensional resistivity model using spatial interpolation, Kriging and other methods to generate a continuous three-dimensional geological attribute model. When performing spatial assignment, it is also necessary to use structural surfaces such as stratum interfaces to constrain the distribution form of geological attributes to ensure that the generated geological attribute model is consistent with the stratum structure in space. In addition, for some complex geological conditions, such as rapid changes in lithology and fault development, special processing is required, and the parameters of the relationship model are locally adjusted or the partition modeling method is adopted to accurately reflect the spatial distribution characteristics of geological attributes.

[0106] S403, classifying the geological structures according to the spatial distribution characteristics to obtain different types of geological structure data, and spatially associating the different types of geological structure data based on the spatial distribution characteristics to obtain three-dimensional geological structure data;

[0107] Specifically, the process of classifying geological structures according to spatial distribution characteristics requires the comprehensive use of multi-source data such as geological mapping, seismic interpretation, and remote sensing images. First, it is necessary to extract the spatial position, occurrence, scale and other geometric parameters of structural elements such as faults, folds, and unconformities from geological mapping data. Geological mapping usually provides the outcrop distribution of structures and field occurrence measurement data, and is an important source for obtaining structural spatial information. At the same time, it is also necessary to interpret the seismic profile data, identify the structural reflection characteristics on the seismic profile, and extract structural attribute parameters such as the fault offset and the wing dip angle of the fold. Seismic data can reveal the spatial distribution of structures underground and is the key basis for structural classification and comparison. In addition, high-resolution remote sensing image data can also provide information such as the linear distribution and morphological characteristics of structures, which plays an important role in regional structural analysis.

[0108] After obtaining the parameters such as the spatial position, occurrence, and scale of the structural elements, it is necessary to classify and merge them according to the geometric and kinematic characteristics of the structure. Common structural classification schemes include genetic classification (such as tensional structure, compressional structure, strike-slip structure, etc.) and structural style classification (such as listric fault, positive flower structure, negative flower structure, etc.). By analyzing the genetic mechanism and spatial distribution style of the structure, the scattered structural elements can be divided into several types, and each structural type represents a specific structural combination style. In the process of structural classification, it is necessary to fully consider the spatial configuration relationship and combination mode of different structures, and use the spatial distribution law of the structural combination style to verify and optimize the structural type to ensure that the classification results can accurately reflect the characteristics of the regional structural pattern.

[0109] After obtaining different types of geological structure data, they need to be associated and combined in three-dimensional space to construct a complete geological structure model. This process is mainly achieved through methods such as spatial topological analysis and geometric modeling. First, the structure is explicitly expressed in three-dimensional space using geometric parameters such as the spatial position and occurrence of different types of structures to generate three-dimensional geometric facets representing the structure. Then, by performing spatial topological analysis on the three-dimensional structure facets, topological properties such as spatial adjacency and connectivity between different types of structures are extracted. Based on the spatial topological relationship of the structure, different types of structural data are associated and combined to generate a three-dimensional structural network model with a reasonable spatial combination relationship. In the process of constructing a three-dimensional structural model, it is also necessary to use the principles of dynamics to analyze the activity history and stress field conditions of the structure, and dynamically simulate its spatial distribution morphology according to the development and evolution characteristics of the structure, so as to accurately depict the three-dimensional spatial structure of the complex structural system.

[0110] S404, coupling and integrating the three-dimensional stratigraphic structure framework data, the three-dimensional geological attribute data and the three-dimensional geological structure data to obtain three-dimensional geological data.

[0111] Specifically, the process of classifying and associating geological structures according to spatial distribution characteristics requires the comprehensive use of multi-source data such as geological mapping, seismic interpretation, and well point data. First, it is necessary to extract spatial distribution information of different types of structures from geological mapping data, such as the strike, dip, and inclination of faults, the axial plane, hinge, and wing of folds, and the marker layer of unconformity surfaces. By identifying, digitizing, and annotating these structural elements, a structural distribution map on a two-dimensional plane can be obtained. Then, it is necessary to use seismic interpretation data to constrain the spatial morphology of the structure in three dimensions. Seismic interpretation data usually provides reflection characteristics of the structure on the seismic profile, such as reflection displacement, termination, and bending, which can be used to control the spatial distribution morphology of the structural surface. By combining the results of plane mapping and seismic profile interpretation, using spatial interpolation, geological modeling, and other methods, three-dimensional spatial models of different types of structures can be constructed. In the modeling process, it is also necessary to use well point data to calibrate and verify the structural model, such as using well deviation data to constrain the inclination of the fault plane, and using well section stratigraphic thickness data to constrain the tightness of the fold.

[0112] After obtaining the three-dimensional spatial models of different types of structures, it is necessary to further analyze the spatial correlation between them and establish a unified three-dimensional geological structure framework. This process is mainly achieved through topological analysis and logical rule establishment. First, it is necessary to analyze the spatial position relationship between different structures, such as intersection, cutting, contact, etc., and establish the spatial connection between them through topological operations. For example, for the relationship between faults and folds, it is necessary to determine whether the fault runs through the fold, whether it is parallel or oblique to the fold axis, and how the fault is distributed in different parts of the fold (such as the wing, hinge, etc.). Then, it is necessary to use the basic principles of geological mechanics and structural deformation to establish the formation sequence and kinematic connection between different structures. For example, for faults of different periods, it is necessary to determine the order of their formation and mutual constraints based on their intersection relationship, active age, stress field characteristics, etc. For another example, for the relationship between folds and thrust faults, it is necessary to analyze whether the folds are fault propagation folds or fault folds based on the geometric shape of the folds and the sliding direction of the faults. By analyzing the topological relationship and formation mechanism among these structures and integrating them spatially using computer 3D modeling technology, we can eventually obtain a 3D geological structure model that comprehensively reflects the spatial distribution characteristics of geological structures.

[0113] After obtaining the 3D stratigraphic structure framework data, 3D geological attribute data and 3D geological structure data, the last step is to couple and integrate them to obtain complete 3D geological data. The reason for coupling and integration is that the three types of data, strata, attributes and structures, reflect the spatial distribution and attribute characteristics of underground geological bodies from different perspectives. Only by integrating them can we truly build a complete, systematic and comprehensive 3D geological model. Through coupling and integration, the seamless connection and mutual constraint of stratigraphic interfaces, lithological attributes and structural elements can be achieved under a unified spatial coordinate framework, thereby obtaining high-precision 3D geological data with multi-dimensional information such as structure, attributes and structure.

[0114] Based on the above embodiment, as an optional embodiment, the three-dimensional stratigraphic structure framework data, the three-dimensional geological attribute data and the three-dimensional geological structure data are coupled and integrated to obtain three-dimensional geological data, including:

[0115] S501, using the three-dimensional stratigraphic structure framework data as a spatial reference, mapping the three-dimensional geological attribute data to corresponding stratigraphic units, and realizing spatial configuration of the geological attribute data within the three-dimensional stratigraphic framework;

[0116] Specifically, the process of mapping 3D geological attribute data to 3D stratigraphic structure framework requires grid cells as the basic mapping object. The 3D stratigraphic structure framework is usually composed of a series of triangular grid cells, each of which corresponds to a specific stratigraphic unit. Therefore, the key to attribute data mapping is to establish the correspondence between geological attribute data and grid cells. First, the geological attribute data needs to be converted into a data structure form that is compatible with the grid cells. For example, for the porosity parameters expressed in the form of discrete point data, it is necessary to convert them into a parameter table that corresponds to the grid cells one by one through spatial interpolation. Then, the converted attribute data is assigned to the corresponding grid cells using the geometric topological relationship of the grid cells. In the assignment process, for some grid cells that lack attribute data, the attribute values ​​of adjacent cells can be used for reasonable estimation and supplementation. In addition, since the data characteristics and spatial resolution of different attribute parameters may be different, necessary data resampling and normalization processing are also required during attribute mapping to ensure the consistency of different attribute parameters in space.

[0117] By mapping the three-dimensional geological attribute data into the three-dimensional stratigraphic structure framework, a stratigraphic structure model enriched with attribute information can be obtained. This model not only describes the spatial distribution of the stratigraphic interface, but also reflects the spatial distribution law of rock physical parameters. It is an important basis for quantitative analysis of oil and gas geological conditions and evaluation of reservoir quality. Based on the stratigraphic structure model filled with attribute information, the lithological combination characteristics of different stratigraphic units can be analyzed from a macroscopic perspective, and the development and distribution law of high-quality reservoirs can be predicted; the physical parameters of a single stratigraphic unit can also be characterized at a microscopic scale, and the pore structure characteristics, seepage capacity, etc. can be quantified, providing a fine parameter model for reservoir description and dynamic monitoring. This method of coupling and mapping the structural framework with the attribute parameters not only ensures the spatial consistency of the geological attribute data and the structural model, but also makes maximum use of the existing geological survey data, greatly improving the accuracy and geological reliability of the modeling. The coupled three-dimensional stratigraphic structure-attribute model is an important basis for the comprehensive characterization and quantitative evaluation of oil and gas reservoirs, and is of great significance for guiding reservoir description and improving oil and gas recovery.

[0118] S502, identifying the spatial relationship between the three-dimensional geological structure data and the three-dimensional stratigraphic structure framework data, and extracting the cutting information and deformation information of the geological structure on the stratigraphic unit based on the spatial relationship;

[0119] Specifically, identifying the spatial relationship between geological structures and stratigraphic structures requires the comprehensive use of topological analysis and spatial intersection methods. First, the three-dimensional geological structure data and the three-dimensional stratigraphic structure framework data are unified into the same coordinate system so that they overlap in space. Then, the positional relationship between each structural surface element and the stratigraphic unit is determined by topological analysis, which is mainly divided into several types such as intersection, tangency, and separation. For the intersection relationship, it is necessary to further calculate the geometric parameters such as the spatial position and length of the intersection line. On this basis, the spatial intersection algorithm is used to obtain the intersection line between the structural surface element and the stratigraphic unit to generate a new boundary line unit. By analyzing the intersection line unit, the cutting information of the tectonic action on the stratigraphic unit can be obtained, including the cutting position, cutting area, and integrity of the stratigraphic unit. The extraction of cutting information usually needs to be carried out under the condition of meeting a certain cutting threshold to balance the calculation efficiency and accuracy.

[0120] For the identified intersection relationship, it is necessary to further analyze the stratigraphic deformation information caused by tectonic action. This is mainly achieved by comparing the geometric morphology of stratigraphic units before and after tectonic action. For example, for fold structures, the geometric parameters such as the tightness and symmetry of the folds can be evaluated by analyzing the changes in the inclination of the stratigraphic units on the wings of the folds and the degree of closure of the stratigraphic units on the axis. For another example, for fault structures, the deformation parameters such as the vertical fault throw and horizontal displacement of the fault can be estimated by analyzing the offset distance of the stratigraphic units on both sides of the fault and the inclination of the fault plane. In the process of analyzing tectonic deformation, it is usually necessary to refer to the characteristics of the regional tectonic stress field and fully consider the influence of rock mechanical properties. For some complex structural styles, it is also necessary to use technical means such as numerical simulation of tectonic dynamics to quantitatively analyze the stress-strain conditions and kinematic mechanisms of tectonic deformation.

[0121] S503, modifying and updating the three-dimensional stratum structure framework data according to the cutting information and the deformation information to obtain optimized three-dimensional stratum structure framework data;

[0122] Specifically, identifying the spatial relationship between geological structures and stratigraphic structures requires the comprehensive use of topological analysis and spatial intersection methods. First, the three-dimensional geological structure data and the three-dimensional stratigraphic structure framework data are unified into the same coordinate system so that they overlap in space. Then, the positional relationship between each structural surface element and the stratigraphic unit is determined by topological analysis, which is mainly divided into several types such as intersection, tangency, and separation. For the intersection relationship, it is necessary to further calculate the geometric parameters such as the spatial position and length of the intersection line. On this basis, the spatial intersection algorithm is used to obtain the intersection line between the structural surface element and the stratigraphic unit to generate a new boundary line unit. By analyzing the intersection line unit, the cutting information of the tectonic action on the stratigraphic unit can be obtained, including the cutting position, cutting area, and integrity of the stratigraphic unit. The extraction of cutting information usually needs to be carried out under the condition of meeting a certain cutting threshold to balance the calculation efficiency and accuracy.

[0123] For the identified intersection relationship, it is necessary to further analyze the stratigraphic deformation information caused by tectonic action. This is mainly achieved by comparing the geometric morphology of stratigraphic units before and after tectonic action. For example, for fold structures, the geometric parameters such as the tightness and symmetry of the folds can be evaluated by analyzing the changes in the inclination of the stratigraphic units on the wings of the folds and the degree of closure of the stratigraphic units on the axis. For another example, for fault structures, the deformation parameters such as the vertical fault throw and horizontal displacement of the fault can be estimated by analyzing the offset distance of the stratigraphic units on both sides of the fault and the inclination of the fault plane. In the process of analyzing tectonic deformation, it is usually necessary to refer to the characteristics of the regional tectonic stress field and fully consider the influence of rock mechanical properties. For some complex structural styles, it is also necessary to use technical means such as numerical simulation of tectonic dynamics to quantitatively analyze the stress-strain conditions and kinematic mechanisms of tectonic deformation.

[0124] S504, spatially reorganizing the three-dimensional geological structure data under the constraint of optimizing the three-dimensional stratigraphic structure framework data to obtain optimized three-dimensional geological structure data;

[0125] Specifically, the spatial reorganization of the three-dimensional geological structure data under the constraints of the optimized three-dimensional stratigraphic framework data requires the comprehensive use of spatial coordinate transformation, surface constraint interpolation and other methods. First, the optimized three-dimensional stratigraphic structure surface data is used as the reference surface to establish the spatial correspondence between the three-dimensional geological structure surface and the stratigraphic structure surface. By analyzing the topological structure of the two, the key control points that need to be adjusted on the structural surface are identified. Then, with the optimized stratigraphic structure surface as the constraint condition, the coordinate positions of these key control points are adjusted using spatial coordinate transformation methods (such as translation, rotation, scaling, etc.) to meet the requirements of fitting with the stratigraphic structure surface. On this basis, with the adjusted control points as the skeleton, the spatial morphology of the entire structural surface is reshaped through surface constraint interpolation methods (such as Kriging interpolation, spline function interpolation, etc.). In the interpolation process, it is necessary to make full use of the optimized stratigraphic structure surface information and use it as the boundary condition and morphological constraint for the generation of the structural surface to ensure that the interpolated structural surface is completely consistent with the stratigraphic structure surface in space. After the adjustment and interpolation of all key structural surfaces are completed, an optimized three-dimensional geological structure model can be obtained. The model inherits the basic characteristics of the original geological structure interpretation, but its spatial form and position have been matched with the optimized stratigraphic structure framework, reflecting the dynamic coordination relationship between tectonic action and stratigraphic deformation. The optimized three-dimensional geological structure data is obtained by spatially reorganizing the three-dimensional geological structure data under the constraints of the optimized three-dimensional stratigraphic structure framework data.

[0126] S505, integrating the optimized three-dimensional geological structure data, the three-dimensional geological attribute data and the optimized three-dimensional stratigraphic structure framework data to obtain three-dimensional geological data.

[0127] Specifically, the process of integrating optimized 3D geological structure data, 3D geological attribute data and optimized 3D stratigraphic structure framework data requires the seamless integration of different types of data under a unified spatial reference system. First, the optimized 3D stratigraphic structure framework model is used as the skeleton and as the basic grid of the 3D geological space. On this basis, the optimized 3D geological structure surface data is embedded into the stratigraphic structure framework so that the two fit each other in space. By checking the topological relationship, it is ensured that the structural surface and the stratigraphic structure surface completely overlap at the intersection position, and there are no problems such as intersection and suspension. Then, with the combination of the structural surface and the stratigraphic structure surface as a constraint, the 3D geological attribute data obtained by the previous mapping is introduced into the framework. Using spatial interpolation, voxel modeling and other technologies, the corresponding attribute values ​​are assigned to each grid unit or voxel unit in the framework to achieve continuous filling of attributes in the 3D space. For the units located near the structural surface, the constraint effect of the structural surface needs to be considered to ensure that the interpolated attribute values ​​meet the characteristics of mutation on both sides of the structural surface. In the process of data integration, it is also necessary to perform necessary quality checks and corrections on data from different sources to improve the accuracy and consistency of the data.

[0128] See also Figure 2 , Figure 2 An architecture diagram of a high-precision urban underground space detection system provided in an embodiment of the present application, the high-precision urban underground space detection system may include:

[0129] A data acquisition and processing module is used to acquire reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data in the target area, and to perform fine processing on the reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data to obtain multi-source and multi-scale detection data;

[0130] The three-dimensional information model construction module, Yonggu extracts constraints based on multi-source and multi-scale detection data, obtains multi-source constraints, and constructs a three-dimensional information model of the urban underground space based on the multi-source constraints and multi-source and multi-scale detection data;

[0131] The data mining module is used to perform data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data.

[0132] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0133] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302 or end-to-end wireless communication.

[0134] The communication bus 302 is used to realize the connection and communication between these components.

[0135] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0136] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0137] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field~Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0138] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read~Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage system located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a high-precision detection method for urban underground space.

[0139] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of the high-precision detection method of the urban underground space stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application. In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0140] In the several implementations provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, indirect coupling or communication connection of systems or modules, which can be electrical or other forms.

[0141] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1 The high-precision detection method for urban underground space in the embodiment shown in the figure can be found in the specific implementation process. Figure 1 The specific description of the illustrated embodiment will not be repeated here.

[0143] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0144] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0145] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0146] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A high-precision detection method for urban underground space, characterized in that: The method comprises: Acquire reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data of the target area, and perform fine processing on the reflection seismic data, the high-resolution electromagnetic response data, the environmental noise data, the wellside seismic data and the well logging data to obtain multi-source and multi-scale detection data; Extracting constraints based on the multi-source multi-scale detection data to obtain multi-source constraints, and constructing a three-dimensional information model of urban underground space based on the multi-source constraints and the multi-source multi-scale detection data; Performing data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data; performing data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data includes: Based on the velocity structure information in the three-dimensional information model of the urban underground space, feature extraction is performed on the velocity structure information by velocity tomography to obtain velocity distribution characteristics of the underground medium; Based on the electrical structure information in the three-dimensional information model, the electrical structure information is subjected to feature extraction by resistivity inversion method to obtain resistivity distribution characteristics of the underground medium; Based on the geological structure information in the three-dimensional information model, feature extraction is performed on the geological structure information through geological heuristic interpretation technology to obtain spatial distribution features of the geological structure; In a unified spatial coordinate system, the extracted velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics are coupled and integrated to obtain the three-dimensional geological data; in a unified spatial coordinate system, the extracted velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics are coupled and integrated to obtain the three-dimensional geological data, including: Correlating the velocity distribution characteristics with the stratum interface to obtain three-dimensional stratum structure framework data; Correlating the resistivity distribution characteristics with geological attributes to obtain three-dimensional geological attribute data; Classifying geological structures according to the spatial distribution characteristics to obtain different types of geological structure data, and spatially associating the different types of geological structure data based on the spatial distribution characteristics to obtain three-dimensional geological structure data; The three-dimensional stratigraphic structure framework data, the three-dimensional geological attribute data and the three-dimensional geological structure data are coupled and integrated to obtain the three-dimensional geological data; the three-dimensional stratigraphic structure framework data, the three-dimensional geological attribute data and the three-dimensional geological structure data are coupled and integrated to obtain the three-dimensional geological data, including: Using the three-dimensional stratigraphic structure framework data as a spatial reference, the three-dimensional geological attribute data is mapped to the corresponding stratigraphic unit to achieve spatial configuration of the geological attribute data within the three-dimensional stratigraphic framework; Identify the spatial relationship between the three-dimensional geological structure data and the three-dimensional stratigraphic structure framework data, and extract the cutting information and deformation information of the geological structure on the stratigraphic unit based on the spatial relationship; Modifying and updating the three-dimensional stratum structure framework data according to the cutting information and the deformation information to obtain optimized three-dimensional stratum structure framework data; Under the constraint of the optimized three-dimensional stratigraphic structure framework data, the three-dimensional geological structure data is spatially reorganized to obtain optimized three-dimensional geological structure data; The optimized three-dimensional geological structure data, the three-dimensional geological attribute data and the optimized three-dimensional stratigraphic structure framework data are integrated to obtain three-dimensional geological data.

2. The method according to claim 1, characterized in that: The step of finely processing the reflection seismic data, the high-resolution electromagnetic response data, the environmental noise data, the wellside seismic data and the well logging data to obtain multi-source multi-scale detection data includes: Processing the reflected seismic data using a precise migration technique to obtain depth domain seismic image data; Processing the reflected seismic data using seismic tomography technology to obtain geological velocity data; Performing inversion processing on the high-resolution electromagnetic response data to obtain three-dimensional resistivity data; Extracting a dispersion curve from the environmental noise data by using a surface wave dispersion curve extraction algorithm, and constructing S-wave velocity data based on the dispersion curve; The wellside seismic data is inverted to obtain P- and S-wave velocity data, and the geological velocity data, the depth domain seismic image data, the three-dimensional resistivity data, the S-wave velocity data, and the P- and S-wave velocity data are used as the multi-source multi-scale detection data.

3. The method according to claim 2, characterized in that The extracting of constraints based on the multi-source and multi-scale detection data to obtain multi-source constraints includes: Extracting seismic velocity constraints based on the depth-domain seismic image data and the geological velocity data; Extracting electrical characteristic constraints based on the three-dimensional resistivity data; extracting S-wave velocity constraints based on the dispersion curve and the S-wave velocity data; Extracting the P- and S-wave velocity constraints based on the P- and S-wave velocity data; The seismic velocity constraint condition, the electrical characteristic constraint condition, the S-wave velocity constraint condition and the longitudinal and transverse wave velocity constraint condition are used as the multi-source constraint conditions.

4. The method according to claim 1, characterized in that The constructing of the urban underground space three-dimensional information model based on the multi-source constraint conditions and the multi-source multi-scale detection data includes: Based on the multi-source constraint conditions and the multi-source multi-scale detection data, collaborative inversion is performed to obtain a three-dimensional information model of the urban underground space.

5. A high-precision detection system for urban underground space, characterized in that: The system comprises: A data acquisition processing module is used to acquire reflection seismic data, high-resolution electromagnetic response data, environmental noise data, wellside seismic data and well logging data in the target area, and to perform fine processing on the reflection seismic data, the high-resolution electromagnetic response data, the environmental noise data, the wellside seismic data and the well logging data to obtain multi-source and multi-scale detection data; A three-dimensional information model construction module, which extracts constraints based on the multi-source multi-scale detection data to obtain multi-source constraints, and constructs a three-dimensional information model of the urban underground space based on the multi-source constraints and the multi-source multi-scale detection data; A data mining module is used to perform data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data; the data mining based on the three-dimensional information model of the urban underground space to obtain three-dimensional geological data includes: Based on the velocity structure information in the three-dimensional information model of the urban underground space, feature extraction is performed on the velocity structure information by velocity tomography to obtain velocity distribution characteristics of the underground medium; Based on the electrical structure information in the three-dimensional information model, the electrical structure information is subjected to feature extraction by resistivity inversion method to obtain resistivity distribution characteristics of the underground medium; Based on the geological structure information in the three-dimensional information model, feature extraction is performed on the geological structure information through geological heuristic interpretation technology to obtain spatial distribution features of the geological structure; In a unified spatial coordinate system, the extracted velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics are coupled and integrated to obtain the three-dimensional geological data; in a unified spatial coordinate system, the extracted velocity distribution characteristics, resistivity distribution characteristics, and spatial distribution characteristics are coupled and integrated to obtain the three-dimensional geological data, including: Correlating the velocity distribution characteristics with the stratum interface to obtain three-dimensional stratum structure framework data; Correlating the resistivity distribution characteristics with geological attributes to obtain three-dimensional geological attribute data; Classifying geological structures according to the spatial distribution characteristics to obtain different types of geological structure data, and spatially associating the different types of geological structure data based on the spatial distribution characteristics to obtain three-dimensional geological structure data; The three-dimensional stratigraphic structure framework data, the three-dimensional geological attribute data and the three-dimensional geological structure data are coupled and integrated to obtain the three-dimensional geological data; the three-dimensional stratigraphic structure framework data, the three-dimensional geological attribute data and the three-dimensional geological structure data are coupled and integrated to obtain the three-dimensional geological data, including: Using the three-dimensional stratigraphic structure framework data as a spatial reference, the three-dimensional geological attribute data is mapped to the corresponding stratigraphic unit to achieve spatial configuration of the geological attribute data within the three-dimensional stratigraphic framework; Identify the spatial relationship between the three-dimensional geological structure data and the three-dimensional stratigraphic structure framework data, and extract the cutting information and deformation information of the geological structure on the stratigraphic unit based on the spatial relationship; Modifying and updating the three-dimensional stratum structure framework data according to the cutting information and the deformation information to obtain optimized three-dimensional stratum structure framework data; Under the constraint of the optimized three-dimensional stratigraphic structure framework data, the three-dimensional geological structure data is spatially reorganized to obtain optimized three-dimensional geological structure data; The optimized three-dimensional geological structure data, the three-dimensional geological attribute data and the optimized three-dimensional stratigraphic structure framework data are integrated to obtain three-dimensional geological data.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 4.

7. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 4.

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