Polymorphic seismic exploration method and system

Abstract

The present application relates to the technical field of geophysical exploration, in particular to a multi-state seismic exploration method and system, the method comprising: obtaining common center point discretization seismic acquisition data; constructing a wave function expression of a seismic wave field based on quantum state superposition theory, representing the seismic wave field as a superposition of reflection wave states and scattering wave states; performing fusion processing on the acquisition data under weak constraint conditions to obtain fused wave field data; performing wave state decomposition on the fused wave field data to decompose it into reflection wave field data and scattering wave field data; generating an underground interface structure image based on the reflection wave field data; and generating a probability distribution image of the scattering characteristics of the underground medium based on the scattering wave field data, the present application also provides a corresponding system, comprising a multi-state acquisition subsystem, a quantum state wave field processing subsystem, a weak constraint fusion subsystem and a probability domain interpretation subsystem, which breaks through the resolution limit of traditional reflection wave seismic exploration and solves the imaging problem of non-uniform and very small scale media.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a multi-mode seismic exploration method and system for high-precision seismic exploration imaging, especially suitable for imaging non-uniform, ultra-small-scale media under complex geological conditions. Background Technology

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

[0003] First, traditional reflection wave seismic exploration technology focuses on utilizing 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 information about small-scale underground media being discarded, leading to insufficient utilization of information.

[0004] Secondly, traditional seismic exploration uses a regularly arranged common center point (CMP) acquisition method. While this method is beneficial for the acquisition and processing of reflected waves, it has low efficiency in capturing scattered waves and makes it difficult to obtain complete wavefield information.

[0005] Third, traditional seismic data processing typically employs strong constraints, requiring a high degree of consistency between acquisition equipment, construction parameters, and the natural environment. This significantly limits the ability to integrate multi-source data, especially when it is necessary to integrate data acquired from different periods or different observation systems.

[0006] Finally, traditional seismic interpretation methods seek the definite location and properties of geological bodies. However, this deterministic interpretation method has limited accuracy when dealing with small-scale, heterogeneous media and cannot effectively express the uncertainty of imaging results.

[0007] As exploration targets shift towards complex tectonic zones and small-scale geological bodies, the limitations of traditional reflection wave seismic exploration technology are becoming increasingly apparent, necessitating the development of new seismic exploration methods to overcome these technical bottlenecks. Summary of the Invention

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

[0009] This invention proposes a multi-mode seismic exploration method, including:

[0010] Acquire discretized seismic acquisition data with common midpoint, wherein the discretized seismic acquisition data with common midpoint includes reflected wave data and scattered wave data;

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

[0012] The discretized seismic acquisition data at the common center point are fused under weak constraints to obtain fused wavefield data.

[0013] Wave state decomposition is performed on the fused wave field data to decompose the fused wave field data into reflected wave field data and scattered wave field data;

[0014] Based on the reflected wavefield data, an image of the subsurface interface structure is generated; and

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

[0016] Preferably, the acquisition of discretized seismic acquisition data with common center points includes:

[0017] The exploration area is divided into multiple non-uniformly sized surface elements;

[0018] For each surface element, randomly perturbed shot and receiver positions are set;

[0019] Multiple observation systems were used to collect data in stages in the same exploration area; and

[0020] The data collected by the various observation systems are standardized to obtain discretized seismic acquisition data with ergodicity and common center point.

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

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

[0023] The second observation system, optimized for scattered waves, employs an excitation and reception method with small area elements, small channel spacing, small offset distance, and near-high coverage, making it suitable for detecting the characteristics of small-scale underground media.

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

[0025] Construct a wave field state space, and define the reflected wave state and the scattered wave state as the ground state;

[0026] The seismic wave field is represented as Where Ψ(x,t) is the total wave field function, and Let α and β represent the wave functions of the reflected and scattered wave states, respectively, where α and β are complex coefficients and satisfy |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 fusion processing of the discretized seismic acquisition data with common midpoint under weak constraints includes:

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

[0030] The discretized seismic acquisition data with the common center point are processed by coordinate system unification, time reference alignment, amplitude normalization, and spectrum consistency.

[0031] Construct a cell space and map seismic data from different sources to the cell space; and

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

[0033] Preferably, the wave state decomposition of the fused wave field data includes:

[0034] Construct a wavefield model that includes polymorphic components;

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

[0036] Introducing regularization constraints ensures decomposition stability; and

[0037] Through an iterative decomposition process, the fused wavefield data is decomposed into reflected wavefield data and scattered wavefield data.

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

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

[0040] Design a likelihood function based on the scattered wave field data;

[0041] The posterior probability distribution is calculated by combining the prior probability model and the likelihood function; and

[0042] A probability distribution image of the scattering characteristics of the subsurface medium is generated based on the posterior probability distribution, wherein small-scale geological bodies are represented by their probability of occurrence rather than their definite location.

[0043] As a preferred option, it also includes:

[0044] Based on the probability distribution image of the scattering characteristics of the underground medium, the uncertainty range of the imaging results is quantified;

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

[0046] Transforming uncertainty into risk indicators; and

[0047] Exploration decision recommendations are provided based on the aforementioned risk indicators.

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

[0049] Joint interpretation of probability distribution images of subsurface interface structure and subsurface medium scattering properties in a multi-scale spatial environment;

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

[0051] Based on the aforementioned attribute characteristics, the lithology, physical properties, and fluid-bearing properties of underground geological bodies are identified; 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-modal seismic exploration systems include:

[0054] A multi-mode acquisition subsystem 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;

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

[0056] A weakly constrained fusion subsystem is used to fuse the discretized seismic acquisition data at the common midpoint under weak constraints to obtain fused wavefield data, and to perform wavestate decomposition on the fused wavefield data, decomposing it into reflected wavefield data and scattered wavefield data; and

[0057] The probability domain interpretation subsystem is used to generate an image of the subsurface interface structure based on the reflected wave field data, and to generate a probability distribution image of the scattering characteristics of the subsurface medium based on the scattered wave field data.

[0058] The beneficial effects of this invention include:

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

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

[0061] 3. Data fusion processing based on weak constraints reduces the strict requirements for data consistency, realizes efficient fusion of multi-source heterogeneous data, and separates reflected wave field and scattered wave field 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 properties of complex media and provides the ability to quantify uncertainty and assess risk.

[0063] 5. In practical applications, this 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 fields such as oil and gas exploration, geothermal resource exploration, carbon sequestration monitoring, and geological disaster early warning. Attached Figure Description

[0064] Figure 1 This is a flowchart of the multi-mode seismic exploration method of the present invention;

[0065] Figure 2 This is a 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 quantum state superposition seismic wave field of the present invention;

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

[0068] Figure 5 This is a schematic diagram of wave state decomposition according to the present invention;

[0069] Figure 6 This is a schematic diagram of the probability domain interpretation method of the present invention;

[0070] Figure 7 This is a diagram of the architecture of the multi-mode seismic exploration system of the present invention. Detailed Implementation

[0071] Please refer to Figure 1 - Figure 7 The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

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

[0073] First, discretized seismic acquisition data with a common midpoint is acquired, wherein the discretized seismic acquisition data with a common midpoint includes reflected wave data and scattered wave data.

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

[0075] Then, under weak constraints, the discretized seismic acquisition data of the common center point are fused to obtain fused wavefield data.

[0076] Subsequently, wave state decomposition is performed on the fused wave field data, decomposing the fused wave field data into reflected wave field data and scattered wave field data.

[0077] Next, based on the reflected wave field data, an image of the underground interface structure is generated.

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

[0079] In a preferred embodiment of the present invention, the process of acquiring discretized seismic acquisition data with common midpoints includes the following steps:

[0080] like Figure 2 As shown, the exploration area is first divided into multiple non-uniformly sized facets. Preferably, the facet size is determined according to the characteristic scale of the target geological body. For example, for small faults to be identified (typically 5-10 meters wide), the corresponding facet size can be set to 2-5 meters; while for large structures (such as anticlines, with a scale of hundreds to thousands of meters), the facet size can be set to 50-100 meters. This non-uniform facet division can simultaneously meet the needs of acquiring large-scale structures and small-scale features.

[0081] Secondly, for each surface element, the positions of the shot point and receiver are randomly perturbed. Unlike the traditional regularly arranged common center point (CMP) acquisition method, this invention uses a random perturbation method to deploy the shot point and receiver, with the perturbation range typically controlled within 10% to 30% of the surface element size. For example, for a 5m × 5m surface element, the random perturbation range of the shot point and receiver is 0.5 to 1.5 meters. This random perturbation deployment method can significantly improve the acquisition efficiency of scattered waves, especially the acquisition quality of backscattered waves.

[0082] Next, multiple observation systems are used to collect data in stages on the same exploration area. In practical applications, two main observation systems are typically used: one is a conventional observation system optimized for reflected waves, which uses a regularly arranged excitation and reception method and is suitable for detecting subsurface interface structures; the other is a special observation system optimized for scattered waves, which uses a small area, small trace spacing, small offset distance and near-high coverage excitation and reception method and is suitable for detecting the characteristics of small-scale subsurface media.

[0083] Furthermore, the acquisition parameters of the two observation systems differ significantly. Observation systems optimized for reflected waves typically employ larger shot-to-shot spacing (e.g., 50–100 meters) and receiver-to-memory spacing (e.g., 25–50 meters), with a coverage of 30–60 times. In contrast, observation systems optimized for scattered waves employ smaller shot-to-shot spacing (e.g., 10–20 meters) and receiver-to-memory spacing (e.g., 5–10 meters), increasing the coverage to 60–120 times, in order to capture more small-scale scattering information.

[0084] Finally, the data collected by the various observation systems are standardized to obtain discretized seismic acquisition data with ergodicity and a common center point. The standardization process includes steps such as coordinate unification, time alignment, amplitude normalization, and spectrum adjustment to ensure effective fusion of data from different sources.

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

[0086] First, a wavefield state space is constructed, defining the reflected and scattered wave states as the ground states. This step establishes a Hilbert space describing the seismic wavefield, where the reflected and scattered wave states form an orthogonal basis.

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

[0088] ,

[0089] in: Spatial location and time The total wave field function at that location; Spatial location and time The reflected wave state wave function at the location; Spatial location and time The scattered wave state wave function at the location; and The complex coefficients represent the projected amplitudes of the wave field in each state and satisfy the normalization condition. ,in express The square of the modulus, express The square of the modulus. This expression treats the seismic wave field as a quantum superposition of reflected and scattered wave states, where and These represent the probabilities of the wave field being in the reflected wave state and the scattered wave state, respectively.

[0090] In practical applications, coefficient and The value of is closely related to geological conditions. For geological environments dominated by layered media, typically... Typical value , However, in geological environments dominated by heterogeneous media, the following may occur: In this case, the typical value is , .

[0091] Finally, a multi-scale analysis framework is constructed to decompose the wavefield function at different scales. This invention uses wavelet transform to achieve multi-scale analysis, decomposing the wavefield function... Decomposed into components of different scales:

[0092] ,

[0093] in: This represents the scaling index, with values ​​ranging from 1 to... , The maximum scale level; This represents a location index, indicating spatial location; The scaling function represents the scale. and location The scaling basis function at the location; The wavelet function represents the maximum scale. and location Wavelet basis functions at; The scaling factor represents the wave field at the scaling function. Projection on; The wavelet coefficients represent the wavefield in the wavelet function. The projection onto the surface. The first summation symbol. This indicates that for all scales from 1 to The summation, the second summation symbol Indicates all positions The accumulation of. Through this decomposition, it is possible.

[0094] like Figure 4 As shown, the process of fusing discretized seismic acquisition data with common center points under weak constraints according to the present invention 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 time interval; these parameters must be strictly aligned for effective fusion. Soft constraints include amplitude range, spectral characteristics, and phase features; these parameters can be adjusted within a certain range to meet the fusion requirements.

[0096] Next, the discretized seismic acquisition data with the common center point are subjected to coordinate system unification, time reference alignment, amplitude normalization, and spectrum unification. Coordinate system unification ensures that all data are in the same spatial reference frame; time reference 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 range of data from different sources to a unified standard, usually using a normalization method with a mean of 0 and a standard deviation of 1; spectrum unification adjusts the spectral characteristics of each data to maximize compatibility, a common method being to adjust the spectral range of all data to the intersection of the spectral ranges of all datasets.

[0097] Next, a cell space is constructed, mapping seismic data from different sources to this cell space. A cell is the basic unit of the fusion process, and its size is typically set to 1 / 2 to 1 times the size of the highest resolution data grid. For example, for data with a minimum shot-receiver spacing of 10 meters, the cell size can be set to 5–10 meters. Each cell contains multi-dimensional attribute information such as location, time, amplitude, and phase.

[0098] Finally, local fusion processing and global consistency optimization are performed within the pixel space to obtain fused wavefield data. Local fusion processing is 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 result maintains logical coherence throughout the entire data body, avoiding discontinuity problems that may be caused by local fusion.

[0099] like Figure 5 As shown, the process of wave state decomposition of fused wavefield data in this invention includes the following steps:

[0100] First, a wavefield model incorporating multi-state components is constructed. This model comprehensively considers the characteristics of reflected and scattered waves, including 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 fundamental wave states. Decomposition Operator Effect on fused wavefield data To achieve wave state decomposition:

[0102] ,

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

[0104] Decomposition Operator The construction is based on the differences in various characteristics of wave fields. Reflected and scattered waves differ significantly in directionality, coherence, and frequency characteristics: reflected waves have strong directionality and coherence, with relatively concentrated frequency components; while scattered waves are omnidirectional, have weaker coherence, and more dispersed frequency components. Based on these differences, the decomposition operator... It can be represented as a combination of multiple suboperators:

[0105] ,

[0106] in: It is a directionality-based suboperator used to identify the directional characteristics of the wave field; It is a coherence-based sub-operator used to identify the coherence characteristics of wave fields; It is a frequency-based sub-operator used to identify the spectral characteristics of wave fields; It is a sub-operator based on amplitude characteristics, used to identify the amplitude features of the wave field; , , and Let be the corresponding weight coefficients, representing the importance of each sub-operator in the overall decomposition, and satisfying . This ensures operator normalization. In practical applications, these weighting coefficients need to be optimized based on geological conditions and exploration objectives; typical values ​​are... , , , .

[0107] Next, regularization constraints are introduced to ensure decomposition stability. Regularization constraints typically employ... norm or The norm prevents overfitting or instability during the decomposition process. Regularization constraints can be expressed as:

[0108] ,

[0109] in: Represents the variable and Find the minimum value; The data fitting term represents the sum of the fused wavefield and the decomposition result. Norm squared is used to measure the degree of matching between the decomposition result and the original data; For the reflected wave field The norm contributes to the sparsity of the reflected wave field; For the scattered wave field The square norm contributes to the smoothness of the scattered wave field; and This is a regularization parameter that controls the strength of the regularization term; it is usually set to [value]. , The goal of this optimization problem is to find the optimal... and This minimizes the objective function value.

[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 employs the Alternating Direction Multiplier Method (ADMM) or the coordinate descent method, with the number of iterations 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 the scattering characteristics of subsurface media based on scattered wavefield data according to the present invention includes the following steps:

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

[0113] ,

[0114] in: Geological model parameters The prior probability; The expression "directly proportional" indicates that both sides of the equation are proportional. These represent geological model parameters, such as physical properties like velocity and density. Let be the expected value of the prior model parameters, representing the parameter... The prior best estimate; The prior uncertainty represents the uncertainty about the parameters. The degree of uncertainty of the prior estimate; This represents the natural exponential function. This formula describes the parameters of the geological model. The prior probability distribution adopts a Gaussian distribution form, 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 wave field data. The likelihood function describes the probability of observing specific scattered wave field data under given geological model conditions, and can be expressed as:

[0116] ,

[0117] in: Given model parameters Observational data under conditions The likelihood probability; The observed scattered wave field data; Forward model operators represent the operators for given model parameters. The theoretical response obtained from the calculation; The difference between observed data and theoretical response Norm squared measures the goodness of fit to data; This represents the noise level in the observed data, indicating data uncertainty. This represents the natural exponential function. This formula describes the degree of matching between observed data and theoretical predictions; the higher the degree of matching, the greater the likelihood probability.

[0118] Next, 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: Given observation data Model parameters under conditions The posterior probability; Let be the likelihood function, representing the likelihood given the model parameters. Observational data under conditions The probability of; Let be the prior probability, representing the probability of model parameters. The prior knowledge is used. 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 information about the geological model updated based on the observational data and is the basis for generating the probability distribution image.

[0121] Finally, a probability distribution image of the scattering characteristics of the subsurface medium is generated based on the aforementioned posterior probability distribution, where small-scale geological bodies are represented by their probability of occurrence rather than their definite location. Unlike traditional deterministic interpretations, the probability distribution image directly reveals the spatial uncertainty of geological features, which 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 scattering characteristics of the subsurface medium, the uncertainty range of the imaging results is quantified. Uncertainty quantification typically employs statistical indicators such as information entropy or variance. For example, information entropy can be expressed as:

[0124] ,

[0125] in: Geological model parameters Information entropy measures the magnitude of parameter uncertainty. This represents all possible parameter values. Perform summation; Geological model parameters Values The probability of; This represents the natural logarithm. A higher information entropy value indicates greater uncertainty. This formula calculates the information entropy of a probability distribution, quantifying the degree of uncertainty in the distribution.

[0126] Furthermore, the factors that have the greatest impact on uncertainty are identified. Sensitivity analysis determines which parameters contribute most to the uncertainty of the imaging results, providing guidance for further improvements. Sensitivity analysis can be achieved by calculating the partial derivatives of uncertainty with respect to each parameter:

[0127] ,

[0128] in: For parameters Sensitivity index, representing the parameter uncertainty The extent of the impact; Indicating uncertainty For parameters The partial derivatives describe the rate of change of uncertainty with small changes in parameters. A larger value indicates a more significant impact of the parameter on uncertainty. This formula quantifies the contribution of each parameter to uncertainty by calculating the partial derivative of the uncertainty with respect to the parameter.

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

[0130] ,

[0131] in: As a risk indicator, it quantifies the magnitude of potential risks; For adverse events The probability of occurrence is based on the results of uncertainty analysis; For the event The extent of an event's impact is typically assessed based on economic, security, or environmental factors. This formula multiplies the probability of an event occurring by its magnitude of impact to arrive at a comprehensive risk assessment.

[0132] Finally, exploration decision recommendations are provided based on the aforementioned risk indicators. These recommendations consider risk tolerance and potential returns, providing guidance for exploration activities.

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

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

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

[0136] Next, the lithology, physical properties, and fluidity of the underground geological bodies are identified based on the aforementioned attribute characteristics. The identification process employs a multi-attribute joint analysis method, combining probability theory and pattern recognition techniques to improve the accuracy and reliability of the identification.

[0137] Finally, a comprehensive geological model is established to provide a basis for oil and gas exploration, geothermal resource exploration, carbon sequestration monitoring, and geological hazard early warning. The comprehensive geological model integrates multi-source information, comprehensively reflects underground geological conditions, and is an important support for scientific decision-making.

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

[0139] The multi-mode acquisition subsystem 10 is used to acquire common midpoint discretized seismic acquisition data, wherein the common midpoint 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 the wave function expression of the seismic wave field based on the quantum state superposition theory. The wave function expression represents the seismic wave field as a superposition of reflected wave state and scattered wave state.

[0141] The weakly constrained fusion subsystem 30 is used to fuse the discretized seismic acquisition data at the common midpoint under weak constraints to obtain fused wavefield data, and to perform wavestate decomposition on the fused wavefield data, decomposing the fused wavefield data into reflected wavefield data and scattered wavefield data; and

[0142] The probability domain interpretation subsystem 40 is used to generate an image of the subsurface interface structure based on the reflected wave field data, and to generate a probability distribution image of the scattering characteristics of the subsurface medium based on the scattered wave field 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 the area division of the exploration area and the layout of shot and receiver points; the multi-observation system acquisition module 12 is responsible for phased acquisition using different observation systems; and the data standardization module 13 is responsible for uniformly processing the acquired data to ensure data fusion.

[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 the 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 cell 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 cell 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 wave field into a reflected wave field and a scattered wave field.

[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 the 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] The subsystems exchange information through standardized data interfaces, forming a complete multi-modal seismic exploration system. The system as a whole adopts a modular design, with each module functioning independently yet collaboratively, facilitating system maintenance and functional expansion.

[0148] In practical applications, the multi-mode seismic exploration system of this invention can be deployed at field exploration equipment or post-processing centers, supporting high-precision seismic exploration under complex geological conditions. The hardware resources required for system operation include high-performance computing clusters, large-capacity storage systems, and professional visualization workstations, while 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 multi-mode seismic exploration method and system of the present invention are not only applicable to 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, etc., and have broad application prospects.

[0150] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-mode seismic exploration method, characterized in that, include: Acquire discretized seismic acquisition data with common midpoint, wherein the discretized seismic acquisition data with common midpoint includes reflected wave data and scattered wave data; Based on the quantum superposition theory, a wave function expression for the seismic wave field is constructed, which represents the seismic wave field as a superposition of reflected wave state and scattered wave state; The discretized seismic acquisition data at the common center point are fused under weak constraints to obtain fused wavefield data. Wave state decomposition is performed on the fused wave field data to decompose the fused wave field data into reflected wave field data and scattered wave field data; Based on the reflected wavefield data, an image of the subsurface interface structure is generated; and Based on the scattered wave field data, a probability distribution image of the scattering characteristics of the subsurface medium is generated; The wave state decomposition of the fused wave field data includes: Construct a wavefield model that includes polymorphic components; Design a decomposition operator to decompose a composite wave field into basic wave states; Introducing regularization constraints ensures decomposition stability; and Through an iterative decomposition process, the fused wavefield data is decomposed into reflected wavefield data and scattered wavefield data.

2. The multi-mode seismic exploration method according to claim 1, characterized in that, The acquisition of discretized seismic acquisition data with common midpoints includes: The exploration area is divided into multiple non-uniformly sized surface elements; For each surface element, randomly perturbed shot and receiver positions are set; Multiple observation systems were used to collect data in stages in the same exploration area; and The data collected by the various observation systems are standardized to obtain discretized seismic acquisition data with ergodicity and common center point.

3. The multi-mode seismic exploration method according to claim 2, characterized in that, The various observation systems have different acquisition parameters, including: The first observation system, optimized for reflected waves, employs a regularly arranged excitation and reception method, suitable for detecting subsurface interface structures; and The second observation system, optimized for scattered waves, employs an excitation and reception method with small area elements, small channel spacing, small offset distance, and near-high coverage, making it suitable for detecting the characteristics of small-scale underground media.

4. The multi-mode seismic exploration method according to claim 1, characterized in that, The wave function expression of the seismic wavefield constructed based on the quantum superposition theory includes: Construct a wave field state space, and define the reflected wave state and the scattered wave state as the ground state; The seismic wavefield is expressed 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) represent the wave functions 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-mode seismic exploration method according to claim 1, characterized in that, The fusion processing of the discretized seismic acquisition data with common midpoint under weak constraints includes: Identify the hard constraints that must be maintained and the soft constraints that can be relaxed; The discretized seismic acquisition data with the common center point are processed by coordinate system unification, time reference alignment, amplitude normalization, and spectrum consistency. Construct a cell space and map seismic data from different sources to the cell space; and Local fusion processing and global consistency optimization are performed within the pixel space to obtain fused wavefield data.

6. The multi-mode seismic exploration method according to claim 1, characterized in that, The probability distribution image of the scattering characteristics of the subsurface medium generated based on the scattered wavefield data includes: Constructing a priori probability model based on geological knowledge; Design a likelihood function based on the scattered wave field data; The posterior probability distribution is calculated by combining the prior probability model and the likelihood function; and A probability distribution image of the scattering characteristics of the subsurface medium is generated based on the posterior probability distribution, wherein small-scale geological bodies are represented by their probability of occurrence rather than their definite location.

7. The multi-mode seismic exploration method according to claim 6, characterized in that, Also includes: Based on the probability distribution image of the scattering characteristics of the underground medium, the uncertainty range of the imaging results is quantified; Identify the factors that have the greatest impact on uncertainty; Transforming uncertainty into risk indicators; and Exploration decision recommendations are provided based on the aforementioned risk indicators.

8. The multi-mode seismic exploration method according to claim 1, characterized in that, After generating the subsurface interface structure image based on the reflected wavefield data and the probability distribution image of the subsurface medium scattering characteristics based on the scattered wavefield data, the method further includes: Joint interpretation of probability distribution images of subsurface interface structure and subsurface medium scattering properties in a multi-scale spatial environment; Extract multi-scale, multi-dimensional attribute features; Based on the aforementioned attribute characteristics, the lithology, physical properties, and fluid-bearing properties of underground geological bodies are identified; 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.

9. A multi-mode seismic exploration system, employing the method described in any one of claims 1-8, characterized in that, include: A multi-mode acquisition subsystem 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; A quantum state wave field processing subsystem is used to construct a wave function expression of a seismic wave field based on the quantum state superposition theory. The wave function expression represents the seismic wave field as a superposition of reflected wave states and scattered wave states. The weakly constrained fusion subsystem is used to fuse the discretized seismic acquisition data of the common center point under weak constraints to obtain fused wavefield data, and to perform wave state decomposition on the fused wavefield data to decompose the fused wavefield data into reflected wavefield data and scattered wavefield data. as well as The probability domain interpretation subsystem is used to generate an image of the subsurface interface structure based on the reflected wave field data, and to generate a probability distribution image of the scattering characteristics of the subsurface medium based on the scattered wave field data.

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