Polymorphic seismic exploration method and system

Abstract

The invention relates to the technical field of geophysical exploration, in particular to a polymorphic seismic exploration method and system, and the method comprises the steps: obtaining common midpoint discretized seismic collection data; based on a quantum state superposition theory, constructing wave function expression of a seismic wave field, and representing the seismic wave field as superposition of a reflection wave state and a scattering wave state; carrying out fusion processing on the acquired data under a weak constraint condition to obtain fused wave field data; carrying out wave state decomposition on the fused wave field data, and decomposing the fused wave field data into reflected wave field data and scattered wave field data; generating an underground interface structure image based on the reflected wave field data; the invention further provides a corresponding system, the system comprises a polymorphic acquisition subsystem, a quantum state wave field processing subsystem, a weak constraint fusion subsystem and a probability domain interpretation subsystem, the resolution limit of traditional reflected wave seismic exploration is broken through, and the probability distribution image of underground medium scattering characteristics is generated based on scattered wave field data. The problem of non-uniform and ultra-small-scale medium imaging is solved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a polymorphic seismic exploration method and system thereof, which are used for high-precision seismic exploration imaging, and are particularly suitable for imaging of non-uniform and ultra-small-scale media under complex geological conditions. Background Art

[0002] Traditional reflection wave seismic exploration technology is based on ray and particle models, focusing primarily on the physical characteristics of seismic wave propagation in idealized media and its industrial application. This traditional approach has significant limitations when dealing with complex geological bodies. Its main problems include:

[0003] First, traditional reflection wave seismic exploration technology focuses on the utilization of interface reflection information, while treating scattered waves as interference signals or noise and suppressing them. This results in a large amount of scattered wave data containing underground small-scale medium information being discarded, resulting in insufficient information utilization.

[0004] Secondly, traditional seismic exploration uses a regularly arranged common midpoint (CMP) acquisition method. Although this method is conducive to the collection and processing of reflected waves, it has low efficiency in capturing scattered waves and makes it difficult to obtain complete wave field information.

[0005] Third, traditional seismic data processing usually adopts strong constraints, requiring that acquisition equipment, construction parameters and natural environment remain highly consistent, which greatly limits the ability to integrate multi-source data, especially when it is necessary to integrate data collected at different times or by different observation systems.

[0006] Finally, traditional seismic interpretation methods pursue the deterministic position and attributes of geological bodies. This deterministic interpretation method has limited accuracy when faced with small-scale, heterogeneous media and cannot effectively express the uncertainty of imaging results.

[0007] As exploration targets shift towards complex tectonic areas and small-scale geological bodies, the limitations of traditional reflection wave seismic exploration technology have become increasingly prominent, and there is an urgent need to develop new seismic exploration methods to break through these technical bottlenecks. Summary of the Invention

[0008] The purpose of the present invention is to provide a polymorphic seismic exploration method and system thereof. By introducing the theory of quantum state superposition, combined with common center point discretization acquisition technology, weak constraint fusion processing and probability domain interpretation methods, high-precision acquisition, processing and interpretation of seismic wave fields under complex geological conditions can be achieved, thereby breaking through the resolution limit of traditional reflection wave seismic exploration and solving the problem of imaging in non-uniform and ultra-small scale media.

[0009] The present invention proposes a multi-state seismic exploration method, comprising:

[0010] Acquiring common-center-point discretized seismic acquisition data, wherein the common-center-point discretized seismic acquisition data includes reflected wave data and scattered wave data;

[0011] Based on the quantum state superposition theory, a wave function expression of the seismic wave field is constructed, wherein the wave function expression represents the seismic wave field as a superposition of the reflected wave state and the scattered wave state;

[0012] fusing the common center point discretized seismic acquisition data under weak constraint conditions to obtain fused wave field data;

[0013] performing wave state decomposition on the fused wavefield data to decompose the fused wavefield data into reflection wavefield data and scattering wavefield data;

[0014] generating an underground interface structure image based on the reflected wavefield data; and

[0015] Based on the scattered wavefield data, a probability distribution image of the underground medium scattering characteristics is generated.

[0016] Preferably, the obtaining of common-center-point discretized seismic acquisition data comprises:

[0017] Divide the exploration area into multiple non-uniformly sized regions;

[0018] Setting randomly disturbed shot and receiver point positions for each of the bins;

[0019] Using multiple observation systems to collect data in phases over the same exploration area; and

[0020] The data collected by the multiple observation systems are standardized to obtain ergodic common center point discretized seismic acquisition data.

[0021] Preferably, the acquisition parameters of the multiple observation systems are different, including:

[0022] The first observation system optimized for reflected waves uses a regularly arranged excitation and reception method, suitable for detecting underground interface structures; and

[0023] The second observation system optimized for scattered waves adopts an excitation and reception method with small facets, small track spacing, small offset distance and near-high coverage, which is suitable for detecting the characteristics of small-scale underground media.

[0024] Preferably, the wave function expression of the seismic wave field constructed based on the quantum state superposition theory includes:

[0025] Construct the wave field state space and define the reflected wave state and scattered wave state as the base state;

[0026] The seismic wave field is expressed as , where Ψ(x,t) is the total wave field function, and Represent the wave functions of the reflected and scattered wave states, respectively, with α and β being complex coefficients satisfying |α|²+|β|²=1; and

[0027] A multi-scale analysis framework is constructed to decompose the wavefield function at different scales.

[0028] Preferably, the fusing process of the common center point discretized seismic acquisition data under weak constraint conditions comprises:

[0029] Identify hard constraints that must be maintained and soft constraints that can be relaxed;

[0030] performing coordinate system unification, time base alignment, amplitude normalization and spectrum uniformity processing on the common center point discretized seismic acquisition data;

[0031] constructing a pixel space and mapping seismic data from different sources to the pixel space; and

[0032] Local fusion processing and global consistency optimization are performed in the pixel space to obtain fused wave field data.

[0033] Preferably, performing wave state decomposition on the fused wavefield data comprises:

[0034] Constructing wavefield models containing polymorphic components;

[0035] Design a decomposition operator to decompose the composite wave field into elementary wave states;

[0036] Introducing regularization constraints to ensure decomposition stability; and

[0037] The fused wavefield data is decomposed into reflection wavefield data and scattered wavefield data through an iterative decomposition process.

[0038] Preferably, generating a probability distribution image of underground medium scattering characteristics based on the scattered wavefield data includes:

[0039] Construct a priori probability model based on geological knowledge;

[0040] designing a likelihood function based on the scattered wavefield data;

[0041] Calculating a posterior probability distribution by combining the prior probability model and the likelihood function; and

[0042] A probability distribution image of the scattering characteristics of the underground medium is generated according to the posterior probability distribution, wherein small-scale geological bodies are represented by occurrence probabilities rather than definite positions.

[0043] As an option, it also includes:

[0044] quantifying the uncertainty range of the imaging result according to the probability distribution image of the scattering characteristics of the underground medium;

[0045] Identify the factors that have the greatest impact on uncertainty;

[0046] Translating uncertainty into risk indicators; and

[0047] Providing exploration decision recommendations based on the risk indicators.

[0048] Preferably, after generating the underground interface structure image based on the reflection wavefield data and generating the probability distribution image of the underground medium scattering characteristics based on the scattering wavefield data, the method further includes:

[0049] Joint interpretation of underground interface structure images and probability distribution images of underground medium scattering characteristics in multi-scale space;

[0050] Extract multi-scale and multi-dimensional attribute features;

[0051] Identify the lithology, physical properties and fluid content of the underground geological body based on the attribute characteristics; and

[0052] Establish a comprehensive geological model to provide a basis for oil and gas exploration, geothermal resource exploration, carbon sequestration monitoring or geological disaster early warning.

[0053] Multi-state seismic exploration system, including:

[0054] A polymorphic acquisition subsystem for acquiring common-center discretized seismic acquisition data, wherein the common-center discretized seismic acquisition data includes reflected wave data and scattered wave data;

[0055] A quantum state wave field processing subsystem is used to construct a wave function expression of the seismic wave field based on the quantum state superposition theory, wherein the wave function expression represents the seismic wave field as a superposition of the reflected wave state and the scattered wave state;

[0056] a weak constraint fusion subsystem, configured to fuse the common center point discretized seismic acquisition data under weak constraint conditions to obtain fused wavefield data, and perform wave state decomposition on the fused wavefield data to decompose the fused wavefield data into reflection wavefield data and scattering wavefield data; and

[0057] The probability domain interpretation subsystem is used to generate an underground interface structure image based on the reflection wavefield data, and to generate a probability distribution image of the underground medium scattering characteristics based on the scattering wavefield data.

[0058] The beneficial effects of the present invention include:

[0059] 1. By introducing the quantum superposition theory to describe the seismic wave field, seismic waves are represented as the superposition of reflected wave states and scattered wave states, which can capture wave field information more comprehensively and lay the theoretical foundation for high-precision seismic imaging.

[0060] 2. Adopting the common center point discretization acquisition technology, through non-uniform surface element division and random perturbation layout, the acquisition efficiency of scattered wave information is improved, and polymorphic seismic data with ergodicity is obtained.

[0061] 3. Data fusion processing based on weak constraints reduces the strict requirements for data consistency, achieves efficient fusion of multi-source heterogeneous data, and separates the reflected wavefield and scattered wavefield information through wave state decomposition technology.

[0062] 4. The introduction of the probability domain interpretation method represents small-scale geological bodies as spatial probability distributions rather than definite locations, which more accurately characterizes the characteristics of complex media and provides uncertainty quantification and risk assessment capabilities.

[0063] 5. In practical applications, the present invention can significantly improve the imaging accuracy of complex structural areas, enhance the ability to identify small-scale geological bodies, reduce exploration risks, and provide more reliable technical support for oil and gas exploration, geothermal resource exploration, carbon sequestration monitoring, and geological disaster early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a flow chart of the polymorphic seismic exploration method of the present invention;

[0065] Figure 2 Schematic diagram of the common center point discretization acquisition technology of the present invention;

[0066] Figure 3 This is a schematic diagram of the theoretical framework of the quantum state superposition seismic wave field of the present invention;

[0067] Figure 4 This is a flowchart of the weak constraint fusion process of the present invention;

[0068] Figure 5 Schematic diagram of wave state decomposition of the present invention;

[0069] Figure 6 Schematic diagram of the probability domain interpretation method of the present invention;

[0070] Figure 7 This is the architecture diagram of the polymorphic seismic exploration system of the present invention. DETAILED DESCRIPTION

[0071] Please refer to Figure 1 - Figure 7 , the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0072] Reference Figure 1The present invention provides a multi-state seismic exploration method, comprising the following steps:

[0073] First, common-center-point discretized seismic acquisition data are acquired, wherein the common-center-point discretized seismic acquisition data include reflected wave data and scattered wave data.

[0074] Next, based on the quantum state superposition theory, a wave function expression of the seismic wave field is constructed, where the wave function expression represents the seismic wave field as a superposition of a reflected wave state and a scattered wave state.

[0075] Then, the common center point discretized seismic acquisition data are fused under weak constraint conditions to obtain fused wave field data.

[0076] Afterwards, wave state decomposition is performed on the fused wavefield data to decompose the fused wavefield data into reflection wavefield data and scattering wavefield data.

[0077] Next, a subsurface interface structure image is generated based on the reflection wavefield data.

[0078] Finally, a probability distribution image of the scattering characteristics of the underground medium is generated based on the scattered wavefield data.

[0079] In a preferred embodiment of the present invention, the process of obtaining common-center-point discretized seismic acquisition data includes the following steps:

[0080] like Figure 2 As shown, the exploration area is first divided into multiple bins of non-uniform size. Preferably, the bin size is determined based on the characteristic scale of the target geological body. For example, for small faults (typically 5-10 meters wide) to be identified, the corresponding bin size can be set to 2-5 meters; while for large structures (such as anticlines, which range from hundreds to thousands of meters in size), the bin size can be set to 50-100 meters. This non-uniform binning allows for the acquisition of both large-scale structures and small-scale features.

[0081] Next, randomly perturbed shot and receiver locations are set for each bin. Unlike the traditional regularly arranged common center point (CMP) acquisition method, this method employs a random perturbation approach to the placement of shot and receiver locations, with the perturbation range typically controlled within 10% to 30% of the bin size. For example, for a 5m x 5m bin, the random perturbation range of shot and receiver locations is 0.5 to 1.5 meters. This random perturbation placement significantly improves the efficiency of scattered wave acquisition, particularly the quality of backscattered waves.

[0082] Next, multiple observation systems are used to collect data in phases over the same exploration area. In practical applications, two main types of observation systems are commonly used: a conventional observation system optimized for reflected waves, employing a regularly arranged excitation-receive pattern suitable for detecting subsurface interface structures; and a special observation system optimized for scattered waves, employing an excitation-receive pattern with small bins, small track spacing, small offsets, and near-height coverage suitable for detecting small-scale subsurface medium properties.

[0083] Furthermore, the acquisition parameters of the two observation systems differ significantly. Observation systems optimized for reflected waves typically use larger shot spacing (e.g., 50-100 meters) and receiver spacing (e.g., 25-50 meters), with coverage set at 30-60 times. Observation systems optimized for scattered waves, on the other hand, use smaller shot spacing (e.g., 10-20 meters) and receiver spacing (e.g., 5-10 meters), with coverage increased to 60-120 times, to capture more small-scale scattering information.

[0084] Finally, the data collected by these multiple observation systems is standardized to obtain ergodic, common-center discretized seismic data. This standardization process includes coordinate unification, time alignment, amplitude normalization, and spectrum adjustment to ensure the effective integration of data from different sources.

[0085] Preferably, the quantum state superposition theory framework of the present invention is as follows Figure 3 As shown in the figure, constructing the wave function expression of the seismic wave field based on the quantum state superposition theory includes the following steps:

[0086] First, the wavefield state space is constructed, defining the reflected wave state and the scattered wave state as the basis state. This step establishes the Hilbert space describing the seismic wavefield, in which the reflected wave state and the scattered wave state form a set of orthogonal bases.

[0087] Next, the seismic wave field is expressed as:

[0088] ,

[0089] in: For spatial location and time The total wave field function at ; For spatial location and time The reflected wave state wave function at ; For spatial location and time The scattered wave state wave function at ; and is a complex coefficient, which represents the projection amplitude of the wave field on each state and satisfies the normalization condition ,in express The square of the modulus, express This expression regards the seismic wave field as a quantum superposition of the reflected wave state and the scattered wave state, where and They represent the probability of the wave field being in the reflected wave state and the scattered wave state respectively.

[0090] In practical applications, the coefficient and The value of is closely related to geological conditions. For geological environments dominated by layered media, , the typical value is , For geological environments dominated by heterogeneous media, The typical value is , .

[0091] Finally, a multi-scale analysis framework is constructed to decompose the wave field function at different scales. Decompose into components of different scales:

[0092] ,

[0093] in: Indicates the scale index, ranging from 1 to , is the maximum scale level; It represents the position index and characterizes the spatial position; is the scale function, representing the scale and location The scale basis function at ; is the wavelet function, representing the maximum scale and location Wavelet basis function at ; is the scale coefficient, which indicates that the wave field is in the scale function projection on; is the wavelet coefficient, which indicates the wave field in the wavelet function The projection on . The first summation symbol For all scales from 1 to The accumulation of the second summation symbol For all positions Through this decomposition, we can.

[0094] like Figure 4 As shown, the process of fusing common center point discretized seismic acquisition data under weak constraint conditions includes the following steps:

[0095] First, identify the hard constraints that must be maintained and the soft constraints that can be relaxed. Hard constraints typically include the spatial coordinates of the data and the sampling interval, which must be strictly aligned for effective fusion. Soft constraints include amplitude range, spectral characteristics, and phase characteristics, which can be adjusted within a certain range to meet fusion requirements.

[0096] Next, the discretized seismic data from the common center point undergoes coordinate system unification, time base alignment, amplitude normalization, and spectral harmonization. Coordinate system unification ensures that all data are in the same spatial reference system. Time base alignment ensures accurate matching of time series, typically requiring time synchronization accuracy better than 1 / 10 of the sampling interval. For example, for data with a 1ms sampling interval, the time synchronization accuracy should be within 0.1ms. Amplitude normalization adjusts the amplitude ranges of data from different sources to a unified standard, typically using a normalization method with a mean of 0 and a standard deviation of 1. Spectral harmonization adjusts the spectral characteristics of each data set to maximize compatibility. A common approach is to adjust the spectral ranges of all data sets to the intersection of the spectral ranges of all data sets.

[0097] Next, a pixel space is constructed, into which seismic data from various sources are mapped. A pixel is the basic unit of fusion processing, and its size is typically set to 1 / 2 to 1 times the grid size of the highest-resolution data. For example, for data with a minimum shot check spacing of 10 meters, the pixel size can be set to 5 to 10 meters. Each pixel contains multidimensional attribute information such as position, time, amplitude, and phase.

[0098] Finally, local fusion processing and global consistency optimization are performed within the pixel space to obtain the fused wavefield data. Local fusion processing is performed based on neighborhood information, and the fusion range is usually limited to 3×3 to 5×5 pixels. Global consistency optimization ensures that the fusion results maintain logical coherence throughout the entire data volume, avoiding discontinuities that may be caused by local fusion.

[0099] like Figure 5 As shown, the process of performing wave state decomposition on fused wave field data in the present invention includes the following steps:

[0100] First, a wavefield model containing polymorphic components is constructed. This model comprehensively considers the characteristics of reflected and scattered waves and contains multi-dimensional information such as wavefield amplitude, phase, frequency, and propagation direction.

[0101] Secondly, a decomposition operator is designed to decompose the composite wave field into basic wave states. Acting on fused wavefield data , to achieve wave state decomposition:

[0102] ,

[0103] in: is the wave state decomposition operator, which is a linear operator; To fuse wavefield data, a composite wavefield containing multiple wave states is represented; is the reflected wave field data obtained by decomposition; is the scattered wave field data obtained by decomposition. This formula represents the decomposition operator Acting on the fusion wave field Then, it is decomposed into the reflected wave field and scattered wave fields Two parts.

[0104] Decomposition operator The construction of is based on the various differences in wave field characteristics. There are obvious differences between reflected waves and scattered waves in terms of directionality, coherence, and frequency characteristics: reflected waves have strong directionality and coherence, and the frequency components are relatively concentrated; while scattered waves have omnidirectionality, weak coherence, and more dispersed frequency components. Based on these differences, the decomposition operator It can be expressed as a combination of multiple sub-operators:

[0105] ,

[0106] in: It is a sub-operator based on directionality, used to identify the directional characteristics of the wave field; It is a coherence-based sub-operator used to identify the coherence characteristics of the wave field; It is a sub-operator based on frequency characteristics, used to identify the spectrum characteristics of the wave field; It is a sub-operator based on amplitude characteristics, used to identify the amplitude characteristics of the wave field; 、 、 and is the corresponding weight coefficient, which indicates the importance of each sub-operator in the overall decomposition and satisfies , ensuring the normalization of the operator. In practical applications, these weight coefficients need to be optimized according to geological conditions and exploration objectives, and the typical value is , , , .

[0107] Next, regularization constraints are introduced to ensure the stability of decomposition. Regularization constraints are usually adopted Norm or Norm, to prevent overfitting or instability during the decomposition process. The regularization constraint can be expressed as:

[0108] ,

[0109] in: Represents a variable and Find the minimum value; is the data fitting term, which represents the sum of the fused wave field and the decomposition result The squared norm is used to measure how well the decomposition results match the original data; For the reflected wave field Norm, which makes the reflected wave field sparse; For the scattered wave field The square of the norm makes the scattered wave field smooth; and is the regularization parameter, which controls the strength of the regularization term and is usually set to , The goal of this optimization problem is to find the best and , so that the objective function value is minimized.

[0110] Finally, the fused wavefield data is decomposed into reflected wavefield data and scattered wavefield data through an iterative decomposition process. The iterative process typically uses the alternating direction method of multipliers (ADMM) or coordinate descent method. The number of iterations is determined based on convergence, typically ranging from 50 to 200.

[0111] like Figure 6 As shown, the process of generating a probability distribution image of underground medium scattering characteristics based on scattered wave field data in the present invention includes the following steps:

[0112] First, a priori probability model is constructed based on geological knowledge. The priori probability model contains an initial understanding of the distribution of underground media and can be derived from drilling data, geostatistical analysis, or expert experience. The priori probability can be expressed as:

[0113] ,

[0114] in: is the geological model parameter The prior probability of It means directly proportional to, which means the two sides of the equation are in proportional relationship; Represents geological model parameters, such as velocity, density and other physical parameters; is the expected value of the prior model parameter, indicating the parameter The a priori best estimate of ; is the prior uncertainty, which indicates the uncertainty of the parameter the degree of uncertainty in the a priori estimate; represents the natural exponential function. This formula describes the geological model parameters The prior probability distribution of adopts the form of Gaussian distribution, and the probability density is inversely proportional to the degree to which the parameter deviates from the prior expected value.

[0115] Secondly, a likelihood function is designed based on the scattered wavefield data. The likelihood function describes the probability of observing specific scattered wavefield data under given geological model conditions and can be expressed as:

[0116] ,

[0117] in: For a given model parameter Observation data under conditions The likelihood probability of ; is the observed scattered wave field data; is the forward model operator, which represents the given model parameters The calculated theoretical response; The difference between the observed data and the theoretical response Norm squared, which measures the degree of data fit; is the data uncertainty, which represents the noise level of the observation data; Represents the natural exponential function. This formula describes the degree of match between observed data and theoretical predictions. The higher the match, the greater the likelihood probability.

[0118] Then, the posterior probability distribution is calculated by combining the prior probability model and the likelihood function. According to Bayes' theorem, the posterior probability distribution can be expressed as:

[0119] ,

[0120] in: For a given observation data Conditional model parameters The posterior probability of is the likelihood function, which represents the given model parameters Observation data under conditions probability; is the prior probability, which represents the model parameters This formula is an application of Bayes' theorem, combining prior information with observational data to obtain an updated model understanding. The posterior probability distribution contains the geological model information updated based on the observational data and is the basis for generating the probability distribution image.

[0121] Finally, a probability distribution image of the subsurface scattering properties is generated based on the posterior probability distribution. Small-scale geological bodies are represented by their probability of occurrence rather than their exact locations. Unlike traditional deterministic interpretations, this probability distribution image directly reflects the spatial uncertainty of geological features and is more consistent with the actual situation under complex geological conditions.

[0122] In another embodiment of the present invention, an uncertainty analysis and evaluation step is further included, specifically including:

[0123] Based on the probability distribution image of the underground medium scattering characteristics, the uncertainty range of the imaging result is quantified. Uncertainty quantification usually uses statistical indicators such as information entropy or variance. For example, information entropy can be expressed as:

[0124] ,

[0125] in: is the geological model parameter The information entropy of is used to measure the uncertainty of the parameters; Indicates all possible parameter values Perform summation; is the geological model parameter The value is probability; represents the natural logarithm. Higher entropy values ​​indicate greater uncertainty. This formula calculates the entropy of a probability distribution and quantifies the degree of uncertainty in the distribution.

[0126] In addition, the factors that have the greatest impact on uncertainty are identified. Through sensitivity analysis, it is determined which parameters contribute the most to the uncertainty of the imaging results, providing guidance for further improvement. Sensitivity analysis can be achieved by calculating the partial derivatives of uncertainty with respect to each parameter:

[0127] ,

[0128] in: For parameters The sensitivity index of the parameter Towards uncertainty the extent of the impact; Expressing uncertainty Parameters The partial derivative of describes the rate of change of uncertainty with small changes in parameters. The larger the value, the more significant the impact of the parameter on the uncertainty. This formula quantifies the contribution of each parameter to the uncertainty by calculating the partial derivative of the uncertainty with respect to the parameter.

[0129] Next, uncertainty is converted into risk indicators. Risk indicators combine uncertainty and potential impact to provide a quantitative basis for decision-making. Risk indicators can be expressed as:

[0130] ,

[0131] in: It is a risk indicator that quantifies the size of potential risk; Adverse events The probability of occurrence is based on the uncertainty analysis results; For events The impact of an event is typically assessed based on economic, safety, or environmental factors. This formula multiplies the probability of an event occurring by its impact to produce a comprehensive risk assessment.

[0132] Finally, exploration decision recommendations are provided based on the risk indicators. The decision recommendations take into account risk tolerance and potential returns to provide guidance for exploration activities.

[0133] In another embodiment of the present invention, after generating a subsurface interface structure image based on the reflection wavefield data and generating a probability distribution image of the subsurface medium scattering characteristics based on the scattering wavefield data, a comprehensive interpretation step is further included:

[0134] First, a joint interpretation of the subsurface interface structure image and the probability distribution image of the subsurface medium scattering properties is performed in a multi-scale space. Multi-scale interpretation considers all scales of information, from macroscopic structures to microscopic features, ensuring the integrity and consistency of the interpretation results.

[0135] Secondly, multi-scale and multi-dimensional attribute features are extracted. Attribute feature extraction is based on the various physical properties of wavefield data, including amplitude, phase, frequency, AVO characteristics, etc. These attributes together form the basis for geological body feature identification.

[0136] Next, the lithology, physical properties, and fluid content of the underground geological body are identified based on the attribute characteristics. The identification process uses a multi-attribute joint analysis method, combining probability theory and pattern recognition technology to improve the accuracy and reliability of the identification.

[0137] Finally, a comprehensive geological model is developed to provide a basis for oil and gas exploration, geothermal resource exploration, carbon sequestration monitoring, or geological disaster early warning. A comprehensive geological model integrates information from multiple sources, comprehensively reflecting underground geological conditions and providing a crucial support for scientific decision-making.

[0138] Reference Figure 7 The present invention also provides a multi-state seismic exploration system, comprising:

[0139] The multi-state acquisition subsystem 10 is used to acquire common-center discretized seismic acquisition data, wherein the common-center discretized seismic acquisition data includes reflected wave data and scattered wave data;

[0140] The quantum state wave field processing subsystem 20 is used to construct a wave function expression of the seismic wave field based on the quantum state superposition theory, wherein the wave function expression represents the seismic wave field as a superposition of a reflected wave state and a scattered wave state;

[0141] a weak constraint fusion subsystem 30 for fusing the common center point discretized seismic acquisition data under weak constraint conditions to obtain fused wavefield data, and performing wave state decomposition on the fused wavefield data to decompose the fused wavefield data into reflection wavefield data and scattering wavefield data; and

[0142] The probability domain interpretation subsystem 40 is configured to generate a subsurface interface structure image based on the reflection wavefield data, and to generate a probability distribution image of the scattering characteristics of the subsurface medium based on the scattering wavefield data.

[0143] Preferably, the polymorphic acquisition subsystem 10 includes an acquisition design module 11, a multi-observation system acquisition module 12, and a data standardization module 13. The acquisition design module 11 is responsible for binning the exploration area and placing shot checkpoints; the multi-observation system acquisition module 12 is responsible for performing phased acquisition using different observation systems; and the data standardization module 13 is responsible for unified processing of the acquired data to ensure data integration.

[0144] The quantum state wavefield processing subsystem 20 includes a wave state space construction module 21, a wave function expression module 22, and a multi-scale analysis module 23. The wave state space construction module 21 is responsible for establishing a state space describing the seismic wavefield; the wave function expression module 22 is responsible for representing the wavefield as a superposition of reflected and scattered wave states; and the multi-scale analysis module 23 is responsible for decomposing the wavefield function at different scales.

[0145] The weakly constrained fusion subsystem 30 includes a constraint identification module 31, a data preprocessing module 32, a pixel space construction module 33, a fusion processing module 34, and a wave state decomposition module 35. The constraint identification module 31 is responsible for distinguishing between hard and soft constraints; the data preprocessing module 32 is responsible for data standardization; the pixel space construction module 33 is responsible for establishing the basic unit space for fusion processing; the fusion processing module 34 is responsible for performing local fusion and global optimization; and the wave state decomposition module 35 is responsible for decomposing the fused wavefield into the reflected wavefield and the scattered wavefield.

[0146] The probability domain interpretation subsystem 40 includes a probability model construction module 41, a probability calculation module 42, an image generation module 43, and an interpretation and analysis module 44. The probability model construction module 41 is responsible for establishing the prior probability model and likelihood function; the probability calculation module 42 is responsible for calculating the posterior probability distribution; the image generation module 43 is responsible for generating the probability distribution image; and the interpretation and analysis module 44 is responsible for performing comprehensive interpretation and uncertainty assessment.

[0147] Each subsystem exchanges information through standardized data interfaces, forming a complete multi-modal seismic exploration system. The overall system adopts a modular design, with each module functioning independently and collaboratively, facilitating system maintenance and functional expansion.

[0148] In practical applications, the polymorphic seismic exploration system of this invention can be deployed in field exploration equipment or post-processing centers, supporting high-precision seismic exploration in complex geological conditions. The hardware resources required for system operation include a high-performance computing cluster, a large-capacity storage system, and a professional visualization workstation. The software environment includes data acquisition and control software, a wavefield processing engine, an imaging reconstruction module, and an interpretation and analysis platform.

[0149] The polymorphic seismic exploration method and system of the present invention are not only suitable for conventional oil and gas exploration, but can also be widely used in unconventional resource exploration, geothermal resource assessment, carbon sequestration monitoring and geological disaster early warning, and have broad application prospects.

[0150] The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A multi-state seismic exploration method, characterized in that: include: Acquiring common-center-point discretized seismic acquisition data, wherein the common-center-point discretized seismic acquisition data includes reflected wave data and scattered wave data; Based on the quantum state superposition theory, a wave function expression of the seismic wave field is constructed, wherein the wave function expression represents the seismic wave field as a superposition of the reflected wave state and the scattered wave state; fusing the common center point discretized seismic acquisition data under weak constraint conditions to obtain fused wave field data; performing wave state decomposition on the fused wavefield data to decompose the fused wavefield data into reflection wavefield data and scattering wavefield data; generating an underground interface structure image based on the reflected wavefield data; and Based on the scattered wavefield data, a probability distribution image of the underground medium scattering characteristics is generated.

2. The multi-state seismic exploration method according to claim 1, characterized in that: The obtaining of common center point discretized seismic acquisition data comprises: Divide the exploration area into multiple non-uniformly sized regions; Setting randomly disturbed shot and receiver point positions for each of the bins; Using multiple observation systems to collect data in phases over the same exploration area; and The data collected by the multiple observation systems are standardized to obtain ergodic common center point discretized seismic acquisition data.

3. The multi-state seismic exploration method according to claim 2, characterized in that: The acquisition parameters of the various observation systems are different, including: The first observation system optimized for reflected waves uses a regularly arranged excitation and reception method, suitable for detecting underground interface structures; and The second observation system optimized for scattered waves adopts an excitation and reception method with small facets, small track spacing, small offset distance and near-high coverage, which is suitable for detecting the characteristics of small-scale underground media.

4. The multi-state seismic exploration method according to claim 1, wherein: The wave function expression of the seismic wave field constructed based on the quantum state superposition theory includes: Construct the wave field state space and define the reflected wave state and scattered wave state as the base state; Express the seismic wavefield as Ψ(x, t) = α·ΨR(x, t) + β·ΨS(x, t), where Ψ(x, t) is the total wavefield function, ΨR(x, t) and ΨS(x, t) are the wavefunctions of the reflected and scattered wave states, respectively, and α and β are complex coefficients satisfying |α|² + |β|² = 1; and A multi-scale analysis framework is constructed to decompose the wavefield function at different scales.

5. The multi-state seismic exploration method according to claim 1, characterized in that: The fusing process of the common center point discretized seismic acquisition data under weak constraint conditions includes: Identify hard constraints that must be maintained and soft constraints that can be relaxed; performing coordinate system unification, time base alignment, amplitude normalization and spectrum uniformity processing on the common center point discretized seismic acquisition data; constructing a pixel space and mapping seismic data from different sources to the pixel space; and Local fusion processing and global consistency optimization are performed in the pixel space to obtain fused wave field data.

6. The multi-state seismic exploration method according to claim 1, characterized in that: The performing wave state decomposition on the fused wave field data includes: Constructing wavefield models containing polymorphic components; Design a decomposition operator to decompose the composite wave field into elementary wave states; Introducing regularization constraints to ensure decomposition stability; and The fused wavefield data is decomposed into reflection wavefield data and scattered wavefield data through an iterative decomposition process.

7. The multi-state seismic exploration method according to claim 1, wherein: Generating a probability distribution image of underground medium scattering characteristics based on the scattered wavefield data includes: Construct a priori probability model based on geological knowledge; designing a likelihood function based on the scattered wavefield data; Calculating a posterior probability distribution by combining the prior probability model and the likelihood function; and A probability distribution image of the scattering characteristics of the underground medium is generated according to the posterior probability distribution, wherein small-scale geological bodies are represented by occurrence probabilities rather than definite positions.

8. The multi-state seismic exploration method according to claim 7, characterized in that: Also includes: quantifying the uncertainty range of the imaging result according to the probability distribution image of the scattering characteristics of the underground medium; Identify the factors that have the greatest impact on uncertainty; Translating uncertainty into risk indicators; and Providing exploration decision recommendations based on the risk indicators.

9. The multi-state seismic exploration method according to claim 1, characterized in that: After generating the underground interface structure image based on the reflection wavefield data and generating the probability distribution image of the underground medium scattering characteristics based on the scattering wavefield data, the method further includes: Joint interpretation of underground interface structure images and probability distribution images of underground medium scattering characteristics in multi-scale space; Extract multi-scale and multi-dimensional attribute features; Identify the lithology, physical properties and fluid content of the underground geological body based on the attribute characteristics; and Establish a comprehensive geological model to provide a basis for oil and gas exploration, geothermal resource exploration, carbon sequestration monitoring or geological disaster early warning.

10. A multi-state seismic exploration system, using the method according to any one of claims 1 to 9, characterized in that: include: A polymorphic acquisition subsystem for acquiring common-center discretized seismic acquisition data, wherein the common-center discretized seismic acquisition data includes reflected wave data and scattered wave data; A quantum state wave field processing subsystem is used to construct a wave function expression of the seismic wave field based on the quantum state superposition theory, wherein the wave function expression represents the seismic wave field as a superposition of the reflected wave state and the scattered wave state; a weak constraint fusion subsystem, configured to fuse the common center point discretized seismic acquisition data under weak constraint conditions to obtain fused wavefield data, and perform wave state decomposition on the fused wavefield data to decompose the fused wavefield data into reflection wavefield data and scattering wavefield data; as well as The probability domain interpretation subsystem is used to generate an underground interface structure image based on the reflection wavefield data, and to generate a probability distribution image of the underground medium scattering characteristics based on the scattering wavefield data.

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