Multi-component seismic data fusion and interpretation method

Abstract

The invention provides a multi-component seismic data fusion and interpretation method, and the method comprises the steps: obtaining original multi-component seismic data, and carrying out the time-space registration and polarization correction processing, and obtaining a preprocessing data body; performing multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field; performing elastic parameter joint inversion processing on the fusion feature field based on geological constraints to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field comprises P-wave speed, S-wave speed and density; and knowledge graph-driven semantic modeling processing is carried out on the three-dimensional physical property parameter field, a geological interpretation model is obtained, and the geological interpretation model comprises at least one of a fault system, a stratum interface and a lithologic boundary topological structure. By adopting the method, various seismic data information can be comprehensively utilized, and the accuracy of geological interpretation under complex geological conditions is improved.

Description

Technical Field

[0001] The present invention belongs to the field of geographic information science, and particularly relates to a multi-component seismic data fusion and interpretation method. Background Art

[0002] The interpretation methods of traditional seismic data mainly rely on single longitudinal wave (P-wave) data, and it is difficult to distinguish the anisotropic characteristics of complex geological bodies. For example, the geological structure in Shandong area is complex, with problems such as multi-stage fault systems, subtle reservoirs, and volcanic rock shielding. The existing technologies usually have the following problems in multi-component data fusion: on the one hand, the spatial registration error between shear wave (S-wave) and converted wave (PS-wave) data is large, resulting in low accuracy of the geographical basis; on the other hand, the multi-component feature fusion relies on artificial experience and lacks physical constraints, and the resolution of deep structure inversion is insufficient, affecting the accuracy of geological interpretation. Summary of the Invention

[0003] Based on this, it is necessary to provide a multi-component seismic data fusion and interpretation method for the above technical problems, which can comprehensively utilize various seismic data information and improve the accuracy of geological interpretation under complex geological conditions.

[0004] In a first aspect, the present application provides a multi-component seismic data fusion and interpretation method, including: Obtaining the original multi-component seismic data and performing spatio-temporal registration and polarization correction processing to obtain a preprocessed data volume; Performing multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field; Performing elastic parameter joint inversion processing on the fusion feature field based on geological constraints to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field includes P-wave velocity, S-wave velocity, and density; Performing knowledge graph-driven semantic modeling processing on the three-dimensional physical property parameter field to obtain a geological interpretation model, and the geological interpretation model includes at least one of a fault system, a stratigraphic interface, and a lithological boundary topological structure.

[0005] Further, obtaining the original multi-component seismic data and performing spatio-temporal registration and polarization correction processing to obtain a preprocessed data volume includes: Performing quantum computing optimization processing on the original multi-component seismic data to obtain multi-component data with sub-wavelength alignment, wherein the quantum computing optimization processing includes encoding spatial displacement parameters into qubit states and performing parameter search through quantum gate operations; Performing polarization direction tensor decomposition processing on the S-wave data in the multi-component data to obtain S-wave data with polarization distortion eliminated; Performing matching filtering processing on the PS-wave data in the multi-component data to obtain PS-wave data with azimuth difference corrected; Perform spatio-temporal domain weighted fusion processing on multi-component data, S-wave data, and PS-wave data to obtain a preprocessed data volume.

[0006] Further, perform multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field, including: Perform tensor integration processing on the P-wave amplitude envelope, S-wave polarization ellipticity, PS-wave phase gradient, and acoustic impedance parameters in the preprocessed data volume to obtain a four-dimensional feature tensor; Perform cross-component attention weight calculation processing on the four-dimensional feature tensor to obtain a correlation weight matrix of P-wave and S-wave features; Perform multi-scale convolutional neural network processing on the correlation weight matrix to obtain a fusion feature field with optimized resolution.

[0007] Further, perform elastic parameter joint inversion processing on the fusion feature field based on geological constraints to obtain a three-dimensional physical property parameter field, including: Construct an elastic wave full waveform inversion objective function based on the fusion feature field, and the objective function includes a data fitting term and a geological prior constraint term; Perform alternating direction multiplier method solution processing on the objective function to obtain a three-dimensional physical property parameter field of P-wave velocity field, S-wave velocity field, and density field; Perform shear wave splitting parameter calculation processing on the shear wave velocity field to obtain a fracture density field, and the fracture density field is used to correct the anisotropic characteristics in the three-dimensional physical property parameter field. The calculation formula of the shear wave splitting parameter is as follows: ; Where, is the shear wave splitting parameter, is the fast shear wave velocity, is the slow shear wave velocity, is the background shear wave velocity.

[0008] Further, perform knowledge graph-driven semantic modeling processing on the three-dimensional physical property parameter field to obtain a geological interpretation model, including: Use the following formula to perform radial basis function interpolation processing on the three-dimensional physical property parameter field to generate an implicit geological interface function: ; Where, is the implicit geological interface function, is the weight coefficient, is the influence radius; Perform level set extraction processing on the implicit geological interface function to generate an initial geological interface model; Perform confidence weighted correction processing on the initial geological interface model with drilling data constraints to generate an error-corrected geological interpretation model.

[0009] Further, perform quantum computing optimization processing on the original multi-component seismic data to obtain multi-component data aligned at the sub-wavelength level, including: Perform quantum bit superposition state mapping processing on the spatial displacement parameter to generate an initial quantum state; Perform Hamiltonian-driven quantum evolution processing on the initial quantum state, where the Hamiltonian includes a data alignment error term and a spatial smoothing regularization term; Perform projective measurement processing on the evolved quantum state to obtain an optimal spatial displacement parameter, and the optimal spatial displacement parameter is used to perform spatial displacement correction on the original multi-component seismic data to generate multi-component data.

[0010] Further, perform confidence-weighted correction processing on the initial geological interface model with drilling data constraints to generate a geological interpretation model after error correction, including: Perform confidence calculation processing on the geological interpretation model after error correction based on fuzzy Petri net rules for faults. Specifically, determine the fault confidence based on the weighted minimum of the longitudinal wave velocity gradient confidence and the shear wave splitting parameter confidence; Perform topological consistency verification processing on the geological interpretation model after error correction. By analyzing the spatial geometric relationship between the formation dip angle and the fault strike, generate a final model that conforms to the formation contact relationship; When the interface position error of the final model exceeds a preset threshold, trigger adversarial generation network reconstruction processing, and the reconstruction processing generates a geological interface model consistent with the three-dimensional physical property parameter field gradient distribution.

[0011] In a second aspect, the present application also provides a multi-component seismic data fusion and interpretation device, characterized in that the device includes: A data preprocessing module for acquiring the original multi-component seismic data and performing spatio-temporal registration and polarization correction processing to obtain a preprocessed data volume; A data fusion module for performing multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field; A three-dimensional parameter generation module for performing joint inversion processing of elastic parameters on the fusion feature field based on geological constraints to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field includes P-wave velocity, S-wave velocity, and density; A geological interpretation module for performing knowledge graph-driven semantic modeling processing on the three-dimensional physical property parameter field to obtain a geological interpretation model, and the geological interpretation model includes at least one of a fault system, a formation interface, and a lithology boundary topological structure.

[0012] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements the above multi-component seismic data fusion and interpretation method.

[0013] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above multi-component seismic data fusion and interpretation method is implemented.

[0014] For the above multi-component seismic data fusion and interpretation method, spatial alignment and signal correction of the original multi-component seismic data are performed through spatio-temporal registration and polarization correction processing, eliminating the phase difference and polarization distortion between different component data, and forming a preprocessed data volume with spatio-temporal consistency; based on multi-physical feature fusion processing, cross-component feature extraction and non-linear interaction are performed on the corrected P-wave, S-wave and PS-wave data to construct a fusion feature field containing multi-dimensional attributes such as amplitude, polarization and phase, breaking through the representation limitation of a single data attribute; through elastic parameter joint inversion processing, the three-dimensional distribution fields of longitudinal wave velocity, transverse wave velocity and density parameters are synchronously solved to realize the coupled constraint inversion of multi-physical fields; and combined with knowledge graph-driven semantic modeling processing, the geological rule base is deeply fused with the gradient features of the physical property parameter field to generate a three-dimensional topological model containing fault systems, stratigraphic interfaces and lithologic boundaries. This method improves the structural recognition accuracy and reservoir prediction reliability of complex geological bodies such as continental faulted basins through a cross-component data collaboration mechanism and a geological knowledge embedding strategy, and improves the accuracy of geological interpretation under complex geological conditions. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a multi-component seismic data fusion and interpretation method provided by an embodiment of the present invention; Figure 2 It is a schematic structural diagram of an interactive advertising system based on multi-modal perception and AI dynamic generation provided by an embodiment of the present invention. Detailed Embodiments

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] First, a brief introduction is made to the nouns involved in the embodiments of the present application.

[0019] Seismic data refers to various information related to earthquakes collected by seismic monitoring equipment such as seismographs, including the waveforms, amplitudes, frequencies, propagation times, etc. of seismic waves. These data record the vibration conditions of the Earth's internal medium during an earthquake, reflecting characteristics such as the intensity, location, and occurrence time of the earthquake, and are important bases for studying the laws of earthquake activities, the internal structure of the Earth, as well as for earthquake early warning, disaster assessment, and other work.

[0020] A knowledge graph is a graph-based data structure composed of nodes (entities) and edges (relationships), used to describe concepts, entities, and their mutual relationships in the objective world in a structured way. It integrates and correlates knowledge in various fields, connecting originally scattered information into an organic knowledge network. Through the knowledge graph, a computer can understand and process human knowledge, enabling various applications such as intelligent search, knowledge answering, and recommendation systems, providing people with more accurate and comprehensive information services, and helping people better understand and utilize knowledge.

[0021] Quantum computing optimization is a method that uses the principles of quantum mechanics to process and optimize problems. In the processing of original multi-component seismic data, it encodes spatial displacement parameters into qubit states, and utilizes the characteristics of qubit superposition and entanglement, and performs efficient searches on these parameters through quantum gate operations to find the optimal solution or an approximate optimal solution, so as to achieve the optimized processing of multi-component seismic data and obtain multi-component data aligned at the sub-wavelength level. Compared with traditional computing methods, quantum computing optimization can process complex computing tasks faster and more accurately.

[0022] According to the above noun explanations, the implementation environment of the multi-component seismic data fusion and interpretation method provided in the embodiments of this application is described. Schematically, this implementation environment includes: a processor and a terminal. Among them, the processor and the terminal are connected through a network signal; the terminal is deployed with a data collector to obtain original multi-component seismic data; the processor includes, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), an artificial intelligence chip, etc., which are not limited here.

[0023] Combined with the above noun explanations and implementation environment, the application scenarios of the embodiments of this application are described. The multi-component seismic data fusion and interpretation method provided in the embodiments of this application can be applied to the following scenarios, including but not limited to: Under complex fault geological conditions, such as in continental faulted basins, in such areas, due to the multi-stage development of the fault system, it is difficult for traditional methods to accurately identify small faults and fractures. Multi-component data fusion can combine the amplitude information of P-waves and the polarization characteristics of S-waves to improve the accuracy of depicting the spatial distribution of faults.

[0024] In the exploration of hidden reservoirs in volcanic rock shielding areas, through the joint analysis of PS converted wave phase characteristics and P-wave attenuation attributes, it is possible to penetrate the high-speed volcanic rock cap layer and identify the top and bottom interfaces of the underlying sand bodies, improving the prediction accuracy of reservoir thickness in low signal-to-noise ratio areas.

[0025] In the exploration of salt dome structures, the tomography technology with multi-component velocity field joint constraints can effectively solve the imaging distortion problem caused by the sudden change of velocity at the salt body boundary and achieve the accurate restoration of the subsalt structure.

[0026] Schematically, the multi-component seismic data fusion and interpretation method provided by the embodiments of the present application can also be applied to other application scenarios. Only examples are given here, and the specific application scenarios are not limited.

[0027] In an exemplary embodiment, as Figure 1 shown, a multi-component seismic data fusion and interpretation method is provided. In this embodiment, the algorithm is applied to the terminal in the foregoing implementation environment as an example. It can be understood that the system can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 101 to step 104: Step 101, obtain the original multi-component seismic data and perform spatio-temporal registration and polarization correction processing to obtain a preprocessed data volume.

[0028] Specifically, the original multi-component seismic data can be obtained through a high-performance computing platform, and spatio-temporal registration processing is performed on the original multi-component seismic data. The cross-correlation algorithm is used to calculate the time difference and spatial displacement of different component data, and the polarization direction distortion of the S wave is corrected through the polarization analysis algorithm to eliminate the signal phase inconsistency problem caused by the acquisition azimuth difference, generating a preprocessed data volume with a unified spatio-temporal reference.

[0029] Step 102, perform multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field.

[0030] Specifically, various physical features can be extracted from the preprocessed data volume, such as the amplitude, frequency, phase, etc. of seismic waves. Analysis methods such as weighted average and principal component analysis are used to fuse different seismic features. For example, the amplitude feature and the frequency feature are combined according to a certain weight to generate a fusion feature field to comprehensively reflect the various characteristics of seismic data.

[0031] Step 103, perform elastic parameter joint inversion processing on the fusion feature field based on geological constraints to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field includes P-wave velocity, S-wave velocity, and density.

[0032] Exemplarily, geological data of the target area can be collected, such as information on known stratigraphic structures, rock type distributions, etc., as geological constraint conditions. The fused characteristic field data is substituted into the elastic parameter joint inversion algorithm, and combined with geological constraints, iterative calculations are continuously performed to solve parameters such as P-wave velocity, S-wave velocity, and density that can reflect the characteristics of underground media, construct a three-dimensional physical property parameter field, and present the distribution of the physical properties of underground media in three-dimensional space.

[0033] Step 104: Perform knowledge graph-driven semantic modeling processing on the three-dimensional physical property parameter field to obtain a geological interpretation model, where the geological interpretation model includes at least one of a fault system, a stratigraphic interface, and a lithological boundary topological structure.

[0034] Specifically, a knowledge graph containing geological domain knowledge can be constructed. The graph covers relevant concepts such as faults, strata, and lithology and the relationships between them. The data of the three-dimensional physical property parameter field is associated and matched with the knowledge graph, and semantic modeling technology is used to judge the position of the fault system, identify the stratigraphic interface, and divide the lithological boundary topological structure according to the parameter characteristics, generating a geological interpretation model.

[0035] The above multi-component seismic data fusion and interpretation method performs spatial alignment and signal correction on the original multi-component seismic data through spatio-temporal registration and polarization correction processing, eliminates the phase difference and polarization distortion between different component data, and forms a preprocessed data volume with spatio-temporal consistency; based on multi-physical feature fusion processing, cross-component feature extraction and non-linear interaction are performed on the corrected P-wave, S-wave, and PS-wave data to construct a fusion feature field containing multi-dimensional attributes such as amplitude, polarization, and phase, breaking through the representation limitation of a single data attribute; through elastic parameter joint inversion processing, the three-dimensional distribution fields of longitudinal wave velocity, transverse wave velocity, and density parameters are synchronously solved to achieve coupled constrained inversion of multi-physical fields; and combined with knowledge graph-driven semantic modeling processing, the geological rule base is deeply fused with the gradient characteristics of the physical property parameter field to generate a three-dimensional topological model containing a fault system, a stratigraphic interface, and a lithological boundary. This method improves the structural recognition accuracy and reservoir prediction reliability of complex geological bodies such as continental faulted basins through a cross-component data collaboration mechanism and a geological knowledge embedding strategy, and improves the accuracy of geological interpretation under complex geological conditions.

[0036] In a possible embodiment, obtaining the original multi-component seismic data and performing spatio-temporal registration and polarization correction processing to obtain the preprocessed data volume may include: Step 201: Perform quantum computing optimization processing on the original multi-component seismic data to obtain multi-component data aligned at the sub-wavelength level, where the quantum computing optimization processing includes encoding the spatial displacement parameters into quantum bit states and performing parameter search through quantum gate operations.

[0037] Specifically, the spatial displacement parameters (including the horizontal displacement amounts Δx, Δy, and the vertical displacement amount Δz) in the original multi-component seismic data can be encoded into the superposition state of qubits, a quantum circuit for parameter search can be constructed through quantum gate operations, and the objective function can be optimized based on the quantum annealing algorithm. By measuring the quantum state, the optimal displacement parameters can be obtained, realizing sub-wavelength spatial alignment of P-wave, S-wave, and PS-wave data. Through the quantum parallel computing feature, the registration efficiency and accuracy of multi-component data in complex structural areas can be improved.

[0038] Step 202: Perform polarization direction tensor decomposition processing on the S-wave data in the multi-component data to obtain S-wave data with polarization distortion eliminated.

[0039] Specifically, the polarization direction tensor matrix can be calculated for the spatially aligned S-wave data, the principal polarization direction vector corresponding to the maximum eigenvalue can be extracted through eigenvalue decomposition, and a polarization correction operator can be constructed to perform direction reprojection on the original S-wave data, eliminating the polarization distortion caused by formation anisotropy or acquisition azimuth deviation to ensure the reliability of the shear wave splitting parameters in subsequent inversion.

[0040] Step 203: Perform matched filtering processing on the PS-wave data in the multi-component data to obtain PS-wave data with azimuth difference corrected.

[0041] Specifically, based on the propagation path difference between the PS-wave and the P-wave, an azimuth-related matched filter can be designed. Through convolution operation, the wavefield phase distortion under different incident angles can be compensated, and the least squares algorithm can be used to optimize the filter coefficients to reduce the azimuth amplitude difference of the PS-wave data and improve the consistency of multi-component data fusion.

[0042] Step 204: Perform spatio-temporal domain weighted fusion processing on the multi-component data, S-wave data, and PS-wave data to obtain a preprocessed data volume.

[0043] Specifically, by performing weighted superposition fusion on the sub-wavelength aligned P-wave data, S-wave data with polarization distortion eliminated, and azimuth-corrected PS-wave data in the spatio-temporal domain, a preprocessed data volume with a unified spatio-temporal reference can be generated.

[0044] Furthermore, perform multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field, which may include: Step 301: Perform tensor integration processing on the P-wave amplitude envelope, S-wave polarization ellipticity, PS-wave phase gradient, and acoustic impedance parameters in the preprocessed data volume to obtain a four-dimensional feature tensor.

[0045] Exemplarily, the P-wave amplitude envelope is extracted from the preprocessed data volume to characterize the formation impedance interface, the S-wave polarization ellipticity is extracted to reflect the anisotropic characteristics, the PS-wave phase gradient is extracted to indicate the thin layer tuning effect, and the acoustic impedance parameter is extracted to correlate with the rock physical properties. The four types of parameters are aligned according to the spatial coordinates and stacked along the feature dimension. Specifically, a four-dimensional feature tensor with dimensions (X, Y, Z, 4) can be constructed, where X and Y are spatial coordinates, Z is the time / depth axis, and 4 is the number of feature channels. Through the joint characterization of multiple parameters, the cross-dimensional correlation of different physical properties is realized, and the data utilization rate is improved.

[0046] Step 302: Perform cross-component attention weight calculation processing on the four-dimensional feature tensor to obtain the correlation weight matrix of P-wave and S-wave features.

[0047] Specifically, a cross-component attention mechanism can be designed. The P-wave amplitude envelope feature is used as the query vector, and the S-wave polarization ellipticity feature is used as the key vector. The attention weight is calculated through the following formula: ; where is the query vector, is the key vector, is the scaling factor consistent with the feature dimension. The above steps enhance the cooperative response of P-wave and S-wave features at geological targets such as faults and fractures through adaptive weight allocation, improve the accuracy of fusion, and contribute to more accurate analysis of anisotropic and other characteristics of the underground geological structure.

[0048] Step 303: Perform multi-scale convolutional neural network processing on the correlation weight matrix to obtain a fusion feature field with optimized resolution.

[0049] Specifically, a multi-scale convolutional neural network model can be built. The model contains convolutional layers with different sizes of convolutional kernels. The correlation weight matrix is used as the input data and input into the first layer of the convolutional neural network. The convolutional kernels of different sizes slide on the correlation weight matrix to perform convolutional operations. Among them, small convolutional kernels focus on local detailed features, and large convolutional kernels focus on global features. Through convolutional operations, the information in the correlation weight matrix is extracted and fused. After processing through multiple convolutional layers and possibly pooling layers, activation function layers, etc., the network outputs a new feature matrix, which is the fusion feature field with optimized resolution. In this process, the convolutional neural network automatically learns the relationship between P-wave and S-wave features at different scales, fuses and optimizes the features, and improves the reliability of the entire seismic data processing process and the accuracy of geological interpretation.

[0050] Furthermore, based on geological constraints, elastic parameter joint inversion processing is performed on the fusion feature field to obtain a three-dimensional physical property parameter field, which may include: Step 401: Construct an elastic wave full waveform inversion objective function based on the fused feature field. The objective function includes a data fitting term and a geological prior constraint term.

[0051] Specifically, the data fitting term ensures the consistency between the inversion result and the actual seismic data, and can accurately reflect the seismic response generated by the underground geological structure. The geological prior constraint term uses existing geological knowledge to limit the range of the inversion result, avoid the multi-solution problem that does not conform to the geological reality, and improve the reliability and accuracy of the inversion result.

[0052] Step 402: Solve the objective function by the alternating direction method of multipliers to obtain the three-dimensional physical property parameter fields of the P-wave velocity field, S-wave velocity field, and density field.

[0053] Specifically, the initial model parameters of the P-wave velocity field, S-wave velocity field, and density field can be set according to the preliminary geological understanding or previous data. The alternating direction method of multipliers (ADMM) is used to iteratively solve the objective function. In each iteration, the S-wave velocity field and density field are fixed, and the partial derivative of the objective function with respect to the P-wave velocity field is calculated. The P-wave velocity field is updated according to the ADMM iteration formula. Then, the P-wave velocity field and density field are fixed, and the S-wave velocity field is updated. Finally, the P-wave velocity field and S-wave velocity field are fixed, and the density field is updated. This cycle of iteration continues until the objective function converges, that is, the change in the objective function value is less than the preset threshold after multiple iterations. At this time, the three-dimensional physical property parameter fields composed of the P-wave velocity field, S-wave velocity field, and density field are obtained.

[0054] Step 403: Calculate the shear wave splitting parameters for the S-wave velocity field to obtain the fracture density field. The fracture density field is used to correct the anisotropic characteristics in the three-dimensional physical property parameter fields. The calculation formula for the shear wave splitting parameters is as follows: ; where, is the shear wave splitting parameter, is the fast shear wave velocity, is the slow shear wave velocity, is the background shear wave velocity.

[0055] Specifically, the fracture density field can be obtained by calculating the shear wave splitting parameter to quantitatively describe the development status of underground fractures. Fractures are the key cause of anisotropy in underground media. By using the fracture density field to correct the three-dimensional physical property parameter field, the physical property differences in different directions of the underground media can be accurately reflected, and the anisotropic characteristics of the underground geological structure can be described more precisely, providing a high-quality geological model for geological interpretation, oil and gas exploration, etc. The above method supplements and improves the anisotropic information of the three-dimensional physical property parameter field, making the physical property parameter field more complete and accurate, making up for the deficiencies in the anisotropic description of the traditional physical property parameter field, and improving the overall quality of the seismic data processing and geological analysis process.

[0056] Furthermore, semantic modeling processing driven by a knowledge graph is performed on the three-dimensional physical property parameter field to obtain a geological interpretation model, which may include: Step 501: Use the following formula to perform radial basis function interpolation processing on the three-dimensional physical property parameter field to generate an implicit geological interface function: ; where is the implicit geological interface function, is the weight coefficient, is the influence radius.

[0057] Specifically, radial basis function interpolation can flexibly perform interpolation according to the spatial distribution of physical property parameters, effectively capturing the complex shape of the geological interface; by adjusting the weight coefficient and the influence radius, it can adapt to the interface characteristics under different geological conditions. Compared with traditional interpolation methods, the above method can better handle non-uniform data and can accurately interpolate in areas where physical property parameters change drastically, providing a more accurate basis for subsequent extraction of geological interfaces and helping to discover hidden geological structures and interfaces.

[0058] Step 502: Perform level set extraction processing on the implicit geological interface function to generate an initial geological interface model.

[0059] Specifically, level set extraction processing has strong topological adaptability and can automatically handle complex topological changes such as the merging and splitting of geological interfaces. Compared with other methods that directly construct interfaces based on data points, the geological interfaces generated by level set extraction are smoother and more continuous, conforming to the natural shape of geological bodies, providing a more reliable basic model for subsequent geological interpretation.

[0060] Step 503: Perform confidence-weighted correction processing with drilling data constraints on the initial geological interface model to generate an error-corrected geological interpretation model.

[0061] Specifically, through confidence-weighted correction, the high-precision advantage of drilling data is fully utilized, effectively correcting the errors in the initial geological interface model and improving the accuracy and practicality of the model.

[0062] Furthermore, performing quantum computing optimization processing on the original multi-component seismic data to obtain sub-wavelength aligned multi-component data may include: Step 601: Perform a quantum bit superposition state mapping process on the spatial displacement parameters to generate an initial quantum state.

[0063] Specifically, by utilizing the superposition property of quantum bits, it is possible to simultaneously encode the possible values of multiple spatial displacement parameters in a single quantum state. Compared with traditional computing methods, it improves the information storage and processing efficiency. In traditional methods, it may be necessary to sequentially traverse each parameter value, while the quantum bit superposition state can simultaneously process a large number of parameter combinations, providing a rich parallel processing basis for subsequent quantum evolution operations, shortening the parameter search time, and improving the overall processing speed.

[0064] Step 602: Perform a Hamiltonian-driven quantum evolution process on the initial quantum state, where the Hamiltonian includes a data alignment error term and a spatial smoothing regularization term.

[0065] Specifically, the data alignment error term in the Hamiltonian directly guides the quantum state to evolve in the direction that meets the data alignment requirements, ensuring that the finally obtained spatial displacement parameters can effectively correct the spatial displacement of the original multi-component seismic data and improving the data alignment accuracy. The spatial smoothing regularization term, starting from the actual geological situation, ensures the rationality of the spatial displacement parameters and avoids abnormal parameters that do not conform to geological laws. Based on the Hamiltonian-based quantum evolution method, by fully utilizing the principles of quantum mechanics, it can efficiently search for the optimal solution in a complex high-dimensional parameter space. Compared with traditional optimization algorithms, it can converge to a better parameter combination more quickly, improving the accuracy and efficiency of data processing.

[0066] Step 603: Perform a projection measurement process on the evolved quantum state to obtain the optimal spatial displacement parameters, and the optimal spatial displacement parameters are used to perform spatial displacement correction on the original multi-component seismic data to generate multi-component data.

[0067] Specifically, perform a projection measurement on the evolved quantum state to obtain the classical measurement values of each quantum bit, decode to obtain the optimal spatial displacement parameters, and apply the optimal spatial displacement parameters to the three-dimensional spatial resampling of the original P-wave and S-wave data to eliminate the spatial misalignment caused by acquisition geometry differences, generate multi-component data, and improve the quality of multi-component seismic data.

[0068] Furthermore, performing a confidence-weighted correction process with drilling data constraints on the initial geological interface model to generate an error-corrected geological interpretation model may include: Step 701: Perform confidence calculation processing on the fault of the geologic interpretation model after error correction based on fuzzy Petri net rules. Specifically, determine the fault confidence based on the weighted minimum of the longitudinal wave velocity gradient confidence and the shear wave splitting parameter confidence.

[0069] Exemplarily, the fuzzy Petri net rule is a rule system that combines the graphical modeling ability of the Petri net and the advantage of fuzzy logic in processing uncertain information. Based on elements such as places and transitions of the Petri net, it represents the degree of uncertainty of information by assigning fuzzy values to places, and the transitions are triggered according to fuzzy rules. Specifically, in the geological field, when dealing with fault confidence calculation, for example, fuzzy information such as the longitudinal wave velocity gradient confidence and the shear wave splitting parameter confidence can be used as inputs. By setting appropriate fuzzy rules such as "when the longitudinal wave velocity gradient confidence is high and the shear wave splitting parameter confidence is high, the fault confidence is high; when one of the confidences is low, the fault confidence takes the weighted minimum of the two", the result is output through fuzzy inference operations, so as to effectively process the uncertainty and fuzziness commonly existing in geological data and make the analysis and judgment of geological phenomena more in line with the actual complex situation.

[0070] Step 702: Perform topological consistency verification processing on the geologic interpretation model after error correction. After passing the verification, analyze the spatial geometric relationship between the formation dip angle and the fault strike to generate a final model that conforms to the formation contact relationship.

[0071] Specifically, the topological consistency verification ensures that the geologic interpretation model conforms to geological laws in terms of the overall structure. There are inherent spatial geometric constraint relationships between formations and faults in geological structures. A model that does not conform to this relationship cannot accurately reflect the underground geological structure. Through topological consistency verification, possible unreasonable structures in the model can be discovered, avoiding incorrect interpretations of geological structures caused by model construction errors. The generated final model that conforms to the formation contact relationship has higher reliability in geological interpretation and subsequent geological research.

[0072] Step 703: When the interface position error of the final model exceeds the preset threshold, trigger the adversarial generation network reconstruction processing. The reconstruction processing generates a geological interface model consistent with the three-dimensional physical property parameter field gradient distribution.

[0073] Specifically, the adversarial generation network reconstruction process provides an effective method for solving the problem of excessive interface position errors in geological interpretation models. Traditional methods often have poor effects when dealing with models with large errors, while GAN can make full use of the gradient distribution information of the three-dimensional physical property parameter field to generate a geological interface model consistent with the characteristics of the original data. Through the adversarial training of the generator and discriminator, the model is continuously optimized, so that the reconstructed model not only satisfies the geological and physical laws, but also improves the accuracy of the interface position. The accuracy of the geological interpretation model is improved, and the adaptability of the model to complex geological conditions is enhanced, providing a more accurate geological model for geological exploration and research.

[0074] In summary, for the multi-component seismic data fusion and interpretation method provided by the embodiments of the present application, the quantum optimization algorithm is used to achieve sub-wavelength level spatial registration and polarization correction, generating a preprocessed data volume with high consistency; the cross-component attention mechanism is used to fuse multi-physical characteristics such as P-wave amplitude, S-wave polarization, and PS-wave phase to construct a fusion feature field with enhanced representation; further combined with geological prior constraints, elastic parameter joint inversion is performed on the fusion feature field to simultaneously solve the three-dimensional distribution fields of P-wave velocity, S-wave velocity, and density parameters, and the anisotropic characteristics are corrected based on the shear wave splitting parameters; through knowledge graph-driven semantic modeling, the physical property parameter field is deeply fused with the geological rule base, and the three-dimensional geological interpretation model is generated by using radial basis function interpolation, level set extraction, and confidence weighted correction, accurately depicting the topological structures of fault systems, stratigraphic interfaces, and lithological boundaries. The above technical solutions can solve the problems of insufficient data utilization and strong interpretive non-uniqueness under complex geological conditions, and can comprehensively utilize various seismic data information to improve the accuracy of geological interpretation under complex geological conditions.

[0075] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps is not strictly restricted by order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0076] Based on the same inventive concept, an embodiment of the present application further provides a multi-component seismic data fusion and interpretation device for implementing the multi-component seismic data fusion and interpretation method involved above. The implementation solutions provided by this device to solve problems are similar to those recorded in the above method. Therefore, the specific limitations in one or more embodiments of the multi-component seismic data fusion and interpretation device provided below can refer to the limitations on the multi-component seismic data fusion and interpretation method in the above text, and will not be elaborated here.

[0077] In an exemplary embodiment, as Figure 2 shown, a multi-component seismic data fusion and interpretation device 20 is provided, including: A data preprocessing module 21, configured to obtain original multi-component seismic data and perform spatio-temporal registration and polarization correction processing to obtain a preprocessed data volume.

[0078] A data fusion module 22, configured to perform multi-physical feature fusion processing on the preprocessed data volume to obtain a fusion feature field.

[0079] A three-dimensional parameter generation module 23, configured to perform joint inversion processing of elastic parameters on the fusion feature field based on geological constraints to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field includes P-wave velocity, S-wave velocity, and density.

[0080] A geological interpretation module 24, configured to perform knowledge graph-driven semantic modeling processing on the three-dimensional physical property parameter field to obtain a geological interpretation model, and the geological interpretation model includes at least one of a fault system, a stratigraphic interface, and a lithological boundary topological structure.

[0081] Furthermore, the data preprocessing module 21 may include: A quantum computing optimization unit 211, configured to perform quantum computing optimization processing on the original multi-component seismic data to obtain sub-wavelength aligned multi-component data, where the quantum computing optimization processing includes encoding spatial displacement parameters into qubit states and performing parameter search through quantum gate operations.

[0082] An S-wave distortion elimination unit 212, configured to perform polarization direction tensor decomposition processing on the S-wave data in the multi-component data to obtain S-wave data with eliminated polarization distortion.

[0083] A PS-wave correction unit 213, configured to perform matching filtering processing on the PS-wave data in the multi-component data to obtain azimuth difference corrected PS-wave data.

[0084] A data fusion unit 214, configured to perform spatio-temporal domain weighted fusion processing on the multi-component data, S-wave data, and PS-wave data to obtain a preprocessed data volume.

[0085] Furthermore, the data fusion module 22 may include: A tensor integration unit 221 is configured to perform tensor integration processing on the P-wave amplitude envelope, S-wave polarization ellipticity, PS-wave phase gradient, and acoustic impedance parameters in the preprocessed data volume to obtain a four-dimensional feature tensor.

[0086] An attention weight calculation unit 222 is configured to perform cross-component attention weight calculation processing on the four-dimensional feature tensor to obtain a correlation weight matrix of P-wave and S-wave features.

[0087] A convolutional neural network unit 223 is configured to perform multi-scale convolutional neural network processing on the correlation weight matrix to obtain a fusion feature field with optimized resolution.

[0088] Furthermore, the three-dimensional parameter generation module 23 may include: A constrained inversion unit 231 is configured to construct an elastic wave full waveform inversion objective function based on the fusion feature field, and the objective function includes a data fitting term and a geological prior constraint term.

[0089] An ADMM unit 232 is configured to perform alternating direction method of multipliers (ADMM) solution processing on the objective function to obtain a three-dimensional physical property parameter field of the P-wave velocity field, S-wave velocity field, and density field.

[0090] A shear wave splitting parameter calculation unit 233 is configured to perform shear wave splitting parameter calculation processing on the S-wave velocity field to obtain a fracture density field, and the fracture density field is used to correct the anisotropic characteristics in the three-dimensional physical property parameter field. The calculation formula for the shear wave splitting parameter is as follows: ; Wherein, is the shear wave splitting parameter, is the fast shear wave velocity, is the slow shear wave velocity, is the background shear wave velocity.

[0091] Furthermore, the geological interpretation module 24 may include: An interpolation unit 241 is configured to perform radial basis function interpolation processing on the three-dimensional physical property parameter field using the following formula to generate an implicit geological interface function: ; Wherein, is the implicit geological interface function, is the weight coefficient, is the influence radius.

[0092] An initial geological interface generation unit 242 is configured to perform level set extraction processing on the implicit geological interface function to generate an initial geological interface model.

[0093] An error correction unit 243 is configured to perform confidence weighted correction processing on the initial geological interface model with drilling data constraints to generate a geologic interpretation model after error correction.

[0094] Further, the quantum computing optimization unit 211 may include: An initial quantum state subunit 2111 is configured to perform quantum bit superposition state mapping processing on the spatial displacement parameters to generate an initial quantum state.

[0095] A quantum evolution subunit 2112 is configured to perform Hamiltonian-driven quantum evolution processing on the initial quantum state, where the Hamiltonian includes a data alignment error term and a spatial smoothing regularization term.

[0096] A projection measurement subunit 2113 is configured to perform projection measurement processing on the evolved quantum state to obtain optimal spatial displacement parameters, and the optimal spatial displacement parameters are used to perform spatial displacement correction on the original multi-component seismic data to generate multi-component data.

[0097] Further, the error correction unit 243 may include: A fuzzy Petri net rule subunit 2431 is configured to perform confidence calculation processing on the fault of the geologic interpretation model after error correction based on fuzzy Petri net rules. Specifically, the fault confidence is determined based on the weighted minimum value of the longitudinal wave velocity gradient confidence and the shear wave splitting parameter confidence.

[0098] A topology check subunit 2432 is configured to perform topology consistency verification processing on the geologic interpretation model after error correction. By analyzing the spatial geometric relationship between the formation dip angle and the fault strike, a final model that conforms to the formation contact relationship is generated.

[0099] An adversarial generation network subunit 2433 is configured to trigger adversarial generation network reconstruction processing when the interface position error of the final model exceeds a preset threshold, and the reconstruction processing generates a geologic interface model that is consistent with the three-dimensional physical property parameter field gradient distribution.

[0100] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of a multi-component seismic data fusion and interpretation method as described above are implemented.

[0101] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0102] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0103] The above embodiments only represent several implementation manners of the embodiments of the present application. The descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A multi-component seismic data fusion and interpretation method, characterized in that: The method comprises: Acquire original multi-component seismic data and perform time-space registration and polarization correction processing to obtain a preprocessed data volume; Performing multi-physical feature fusion processing on the pre-processed data volume to obtain a fused feature field; Based on geological constraints, elastic parameter joint inversion processing is performed on the fused characteristic field to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field includes P-wave velocity, S-wave velocity and density; The three-dimensional physical property parameter field is subjected to knowledge graph-driven semantic modeling to obtain a geological interpretation model, wherein the geological interpretation model includes at least one of a fault system, a stratigraphic interface, and a lithologic boundary topological structure.

2. The method according to claim 1, characterized in that The original multi-component seismic data is obtained and processed for time-space registration and polarization correction to obtain the pre-processed data volume including: Performing quantum computing optimization processing on the original multi-component seismic data to obtain sub-wavelength aligned multi-component data, wherein the quantum computing optimization processing includes encoding spatial displacement parameters into quantum bit states and performing parameter search through quantum gate operations; Performing polarization direction tensor decomposition processing on S-wave data in the multi-component data to obtain S-wave data with polarization distortion eliminated; Performing matched filtering processing on the PS wave data in the multi-component data to obtain PS wave data after azimuth difference correction; The multi-component data, the S-wave data and the PS-wave data are subjected to weighted fusion processing in the time and space domain to obtain the pre-processed data volume.

3. The method according to claim 2, characterized in that The performing multi-physical feature fusion processing on the pre-processed data volume to obtain a fused feature field includes: Performing tensor integration processing on the P-wave amplitude envelope, S-wave polarization ellipticity, PS-wave phase gradient and acoustic impedance parameters in the preprocessed data volume to obtain a four-dimensional feature tensor; Performing cross-component attention weight calculation processing on the four-dimensional feature tensor to obtain a correlation weight matrix of P-wave and S-wave features; The association weight matrix is ​​processed by a multi-scale convolutional neural network to obtain a fused feature field with optimized resolution.

4. The method according to claim 1, characterized in that: The elastic parameter joint inversion processing is performed on the fused characteristic field based on geological constraints to obtain a three-dimensional physical property parameter field, including: Constructing an elastic wave full waveform inversion objective function based on the fused characteristic field, wherein the objective function includes a data fitting term and a geological priori constraint term; Solving the objective function using an alternating direction multiplier method to obtain the three-dimensional physical property parameter fields of the longitudinal wave velocity field, the shear wave velocity field and the density field; The shear wave velocity field is subjected to shear wave splitting parameter calculation processing to obtain a fracture density field, and the fracture density field is used to correct the anisotropic characteristics in the three-dimensional physical property parameter field, wherein the calculation formula of the shear wave splitting parameter is as follows: ； in, is the shear wave splitting parameter, is the fast shear wave speed, is the slow shear wave velocity, is the background shear wave velocity.

5. The method according to claim 1, characterized in that The three-dimensional physical property parameter field is subjected to knowledge graph-driven semantic modeling processing to obtain a geological interpretation model, including: The following formula is used to perform radial basis function interpolation processing on the three-dimensional physical property parameter field to generate an implicit geological interface function: ; in, is the implicit geological interface function, is the weight coefficient, is the influence radius; Performing level set extraction processing on the implicit geological interface function to generate an initial geological interface model; The initial geological interface model is subjected to confidence weighted correction processing constrained by drilling data to generate a geological interpretation model after error correction.

6. The method according to claim 2, characterized in that The step of performing quantum computing optimization processing on the original multi-component seismic data to obtain sub-wavelength-level aligned multi-component data includes: Performing quantum bit superposition state mapping processing on the spatial displacement parameter to generate an initial quantum state; Performing a Hamiltonian-driven quantum evolution process on the initial quantum state, wherein the Hamiltonian includes a data alignment error term and a spatial smoothing regularization term; The evolved quantum state is subjected to projection measurement processing to obtain an optimal spatial displacement parameter, and the optimal spatial displacement parameter is used to perform spatial displacement correction on the original multi-component seismic data to generate the multi-component data.

7. The method according to claim 5, characterized in that The step of performing a confidence weighted correction process on the initial geological interface model constrained by drilling data to generate a geological interpretation model after error correction includes: The error-corrected geological interpretation model is subjected to fault confidence calculation based on fuzzy Petri net rules, specifically, the fault confidence is determined based on the weighted minimum value of the P-wave velocity gradient confidence and the S-wave splitting parameter confidence; Performing a topological consistency check on the error-corrected geological interpretation model, wherein the check generates a final model that conforms to the stratum contact relationship by analyzing the spatial geometric relationship between the stratum dip angle and the fault strike; When the interface position error of the final model exceeds a preset threshold, a generative adversarial network reconstruction process is triggered, and the reconstruction process generates a geological interface model consistent with the gradient distribution of the three-dimensional physical property parameter field.

8. A multi-component seismic data fusion and interpretation device, characterized in that: The device comprises: A data preprocessing module is used to obtain the original multi-component seismic data and perform time-space registration and polarization correction processing to obtain a preprocessed data volume; A data fusion module, used for performing multi-physical feature fusion processing on the pre-processed data volume to obtain a fused feature field; A three-dimensional parameter generation module is used to perform elastic parameter joint inversion processing on the fused characteristic field based on geological constraints to obtain a three-dimensional physical property parameter field; the three-dimensional physical property parameter field includes P-wave velocity, S-wave velocity and density; The geological interpretation module is used to perform knowledge graph-driven semantic modeling processing on the three-dimensional physical parameter field to obtain a geological interpretation model, which includes at least one of a fault system, a stratigraphic interface, and a lithologic boundary topological structure.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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