Joint Q value modeling method based on surface data and seismic reflected waves
By combining the correlation analysis of surface data and seismic reflected waves and geological strata deduction, a whole-domain Q-value field is constructed and offset imaging is performed, which solves the problem of Q-value estimation error in complex environments of traditional geological exploration methods, and achieves higher precision Q-value estimation.
Patent Information
- Application Number
- CN202411966272.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-05-13
AI Technical Summary
Q-value estimation errors caused by seismic wave attenuation and propagation path complexity in traditional geological exploration methods in complex geological environments.
The combined Q-value modeling method based on surface data and seismic reflected waves is adopted to determine the Q-value estimation constraints through correlation analysis, and the Q-value model is preprocessed. The surface data is pre-processed to construct the Q-value model, the seismic reflected waves are received and the geological layer deduction is performed through ray path information, and the decomposition matrix field is determined. Combined with the surface Q-value model, attenuation relationship and decomposition matrix field, the entire domain Q-value field is constructed and offset imaging is performed to accurately determine the Q-value model.
The accuracy and reliability of Q-value estimation of underground geological layers are improved, and the Q-value estimation error problem of traditional methods in complex geological environments is solved.
Smart Images
Figure CN119986780A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and in particular to a joint Q-value modeling method based on surface data and seismic reflection waves. Background Art
[0002] With the increasing demand for geological exploration and underground resource development, accurate evaluation of underground rock characteristics and seismic wave propagation characteristics has become an important research topic in the field of geology and engineering exploration. Q value (quality factor), as a key parameter for measuring rock mass quality and seismic wave attenuation characteristics, is widely used in underground exploration, seismic wave propagation analysis and resource evaluation.
[0003] In traditional geological exploration methods, seismic reflection wave technology is usually used to detect underground geological layers, but this method has certain limitations, such as difficulty in directly obtaining accurate Q value information, complex data processing and low accuracy. Especially in complex geological environments, the Q value distribution of geological layers inferred by seismic reflection wave analysis is often limited by the attenuation characteristics of the reflection wave, wave velocity changes and geological heterogeneity. Summary of the invention
[0004] The present application provides a joint Q-value modeling method based on surface data and seismic reflection waves, which is used to solve the technical problem of Q-value estimation error caused by seismic wave attenuation and propagation path complexity in complex geological environments in traditional methods in the prior art.
[0005] The present application provides a joint Q-value modeling method based on surface data and seismic reflection waves, the method comprising: performing a correlation analysis on the surface data and the seismic reflection waves to determine the Q-value estimation constraints; preprocessing the surface data to construct a surface Q-value model, and establishing a surface relative attenuation relationship based on surface consistency, wherein the surface data at least includes geological features, topographic features, and lithological features; receiving seismic reflection waves, performing geological layer deduction through ray path information, and determining a decomposition matrix field based on Q-value decomposition parameters; combining the surface Q-value model, the relative attenuation relationship, and the decomposition matrix field to construct a global Q-value field, and performing offset imaging in combination with the Q-value estimation constraints to determine the Q-value model.
[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0007] The application provides a joint Q-value modeling method based on surface data and seismic reflection waves, which relates to the field of data processing technology. The Q-value estimation constraints are determined by correlation analysis, the surface data is preprocessed to construct a Q-value model and establish an attenuation relationship, seismic reflection waves are received and geological layers are deduced through ray path information, the decomposition matrix field is determined, and the surface Q-value model, attenuation relationship and decomposition matrix field are combined to construct a global Q-value field and perform offset imaging to accurately determine the Q-value model. The technical problem of Q-value estimation error caused by seismic wave attenuation and propagation path complexity in the traditional method in the prior art under complex geological environments is solved, and the technical effect of improving the accuracy and reliability of Q-value estimation of underground geological layers by jointly analyzing surface data and seismic reflection wave data is achieved. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0009] Figure 1 A schematic flow chart of a joint Q-value modeling method based on surface data and seismic reflection waves provided in an embodiment of the present application;
[0010] Figure 2 A schematic diagram of a flow chart of determining a decomposition matrix field based on Q-value decomposition parameters in a joint Q-value modeling method based on surface data and seismic reflection waves provided in an embodiment of the present application. DETAILED DESCRIPTION
[0011] The present application provides a joint Q-value modeling method based on surface data and seismic reflection waves, which is used to solve the technical problem of Q-value estimation error caused by seismic wave attenuation and propagation path complexity in complex geological environments in traditional methods in the prior art.
[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.
[0014] Embodiment 1, as Figure 1 As shown, the present application provides a joint Q value modeling method based on surface data and seismic reflection waves, the method comprising:
[0015] P10: Perform correlation analysis on surface data and seismic reflection waves to determine the Q value estimation constraints.
[0016] Specifically, a correlation analysis is performed on the surface data and seismic reflection waves to determine the Q-value estimation constraints. That is, by combining the characteristics of different data sources (surface data and seismic reflection waves), physical constraints suitable for Q-value estimation are derived.
[0017] Specifically, surface data and seismic reflection waves need to be preprocessed first to ensure the quality and availability of the data. Surface data usually include geological characteristics (such as lithology, soil type), terrain characteristics (such as slope, landform type) and lithological characteristics (such as soil moisture, rock thickness, etc.). The characteristics of these surface data are their high spatial resolution and convenient acquisition methods. Seismic reflection wave data is derived from the reflection information when seismic waves propagate through underground media, which can reflect the underground geological structure and medium characteristics. The amplitude attenuation information in the reflection wave is closely related to the Q value (attenuation coefficient) of the underground medium, and is the key data for studying rock mass quality.
[0018] Among them, the purpose of correlation analysis is to reveal the relationship between surface data and seismic reflection waves through mathematical models or statistical methods. Commonly used methods include Pearson correlation coefficient, partial correlation analysis, principal component analysis, etc. Through these methods, the correlation between surface data characteristics and seismic reflection waves can be quantified, and the main factors affecting Q value estimation can be identified. For example, the lithology characteristics in the geological layer have a strong correlation with the attenuation rate of seismic reflection waves, while the terrain characteristics may affect the propagation path of the reflection waves, thereby indirectly affecting the Q value.
[0019] Furthermore, the constraints for Q-value estimation are determined based on the results of the correlation analysis. For example, based on the characteristics of the surface data, certain constraints can be derived, such as the effects of rock type changes, soil moisture, terrain undulations, etc. on the attenuation of seismic reflection waves. For example, when the rocks in the geological layer change, there may be obvious reflections in the surface data, and this change is manifested in the seismic reflection wave as a change in the amplitude or frequency characteristics of the reflection wave attenuation. These characteristic changes will serve as constraints for the subsequent establishment of the Q-value model to ensure that the estimation of the Q-value can reflect the true underground medium characteristics.
[0020] In this process, numerical modeling and inversion algorithms are used to quantitatively model the results of the correlation analysis to ensure that the results of the analysis can be applied in practice. For example, by using the finite element method or wave equation inversion technology, combined with the geological information of the surface data, a propagation model of the reflected wave can be established to deduce the Q value distribution of different geological layers. In seismic exploration, the Q value is often used to describe the degree of energy loss in the underground medium, and through the correlation analysis of the surface data, important prior information and initial guesses can be provided for the inversion of the Q value.
[0021] Through the above steps, the correlation analysis of surface data and seismic reflection waves not only reveals the intrinsic connection between the two, but also provides precise constraints for Q value estimation. In this way, the subsequent modeling process will be carried out under a more rigorous physical framework, which can improve the accuracy and reliability of the Q value model.
[0022] P20: Preprocess the surface data, construct a surface Q value model, and establish a surface relative attenuation relationship based on surface consistency, wherein the surface data at least includes geological characteristics, topographic characteristics, and lithological characteristics.
[0023] Optionally, the surface data is preprocessed and a surface Q-value model is constructed. Based on the surface consistency, a surface relative attenuation relationship is established to provide an accurate and operational framework for subsequent Q-value modeling.
[0024] First, the preprocessing of surface data is a key step to ensure data quality and accuracy. Surface data usually includes geological features, topographic features and lithological features, which directly affect the estimation of the Q value of underground media. Among them, the geological features include soil type, rock type, groundwater level, etc., which affect the propagation speed and attenuation characteristics of seismic waves. Different types of rocks or soils have different attenuation effects on the propagation of seismic waves. The topographic features involve slope, landform type (such as mountains, plains, etc.), and surface cracks and faults. These factors directly affect the propagation path and attenuation of seismic waves. For example, complex terrain may cause changes in the propagation speed and direction of reflected waves, thereby affecting the distribution of Q values. The lithological characteristics include the mineral composition, porosity, water content, etc. of the rock. These characteristics determine the elasticity and energy loss characteristics of the rock, and directly affect the propagation and attenuation of seismic waves.
[0025] In the preprocessing stage, the data first need to be spatially interpolated, standardized and denoised to eliminate interference and inconsistency in the data. Common preprocessing techniques can be used, including spatial interpolation (such as Kriging interpolation, inverse distance weighted interpolation) and data smoothing (such as Gaussian filtering, sliding window method) to ensure high quality and consistency of the data.
[0026] Furthermore, the construction of the surface Q value model needs to rely on the processed surface data to establish a Q value distribution model that reflects the characteristics of the surface medium. The surface Q value can describe the attenuation of seismic waves in the surface medium, that is, the relative attenuation relationship, which is usually closely related to factors such as the lithology, humidity, and porosity of the surface, and the relative change relationship of the attenuation degree with depth or geological changes.
[0027] In this process, regression analysis or machine learning models can be used in combination to construct a Q-value model. Regression analysis can be used to quantify the linear or nonlinear relationship between geological and lithological characteristics and Q-values, while machine learning methods can capture more complex relationships between surface characteristics and Q-values by training models. Exemplarily, geological characteristics, topographic characteristics, and lithological characteristics are used as input variables, and the model is trained in combination with existing Q-value data. Since changes in geological layers will cause changes in the attenuation characteristics of seismic waves, the attenuation relationship of surface data needs to be modeled according to the consistency principle. By analyzing the relationship between the geological characteristics of the surface area and the measured attenuation data, the attenuation characteristics of different areas are derived. The surface consistency is a unified standard for measuring the degree of attenuation, because the attenuation of the surface area is usually high and is easily affected by external factors (such as hydrological conditions, temperature, etc.). Therefore, based on the consistent attenuation relationship, relatively accurate Q-value predictions can be established for different regions.
[0028] When establishing relative attenuation relationships, it is necessary to focus on the correlation between the physical properties of the surface layer and the Q value. For example, in areas with relatively uniform lithology, the Q value attenuation relationship may be relatively simple, while in areas with large lithology variations, more sophisticated modeling may be required. Through these attenuation relationships, the physical state of the geological layer can be further evaluated, providing an accurate reference for subsequent geological layer deduction.
[0029] P30: Receive seismic reflection waves, deduce geological layers through ray path information, and determine the decomposition matrix field based on Q-value decomposition parameters.
[0030] Further, such as Figure 2 As shown, step P30 of the embodiment of the present application also includes:
[0031] P31: Determine the ray path information and the decomposition parameter matrix based on the Q value for the seismic reflection wave by performing ray tracing; P32: Determine the path distribution characteristics based on the geological level based on the ray path information; P33: Determine the decomposition matrix field by performing reverse deduction based on the decomposition parameter matrix and the path distribution characteristics.
[0032] Among them, the decomposition parameter matrix is the Q value decomposition parameter of the sampling position, wherein the decomposition parameter matrix at least includes rock quality coefficient, joint group number, joint roughness coefficient, joint alteration coefficient, joint water reduction coefficient and stress reduction coefficient.
[0033] It should be understood that by receiving seismic reflection waves and using ray path information to perform geological layer deduction, the decomposition matrix field based on the Q-value decomposition parameters is determined. This process combines the propagation characteristics of seismic waves and the structure of underground geology, and derives the Q-value distribution of the underground medium by inverting the attenuation characteristics of seismic waves.
[0034] First, the propagation path of seismic reflection waves is analyzed using ray tracing technology. Ray tracing accurately tracks the movement of the wavefront by simulating the propagation path of seismic waves in different media. Based on the path information during the propagation of seismic waves, the Q-value decomposition parameter matrix of each sampling point can be determined. The Q-value decomposition parameter matrix is an important indicator for describing the quality of the rock mass based on the attenuation characteristics of wave propagation. Each element of the matrix reflects the influence of different physical properties in the geological layer on the seismic wave. Through the identification and analysis of wave signals, the Q-value decomposition parameters of the reflection wave are calculated. These parameters can characterize the quality of the rock, the distribution of cracks, and the influence of joints.
[0035] After determining the ray path information, it is necessary to analyze the geological layers and distribution characteristics of the seismic waves based on these path information. The path distribution characteristics include the source, path, geological layer interaction, etc. Among them, the source is the location of the earthquake source, which is usually inferred through the earthquake source model or the propagation time of the seismic wave. The path is the propagation path of the seismic wave, including the refraction, reflection and propagation time of the seismic wave in different geological layers. Through this process, the geological layers through which the wave passes can be identified, and the attenuation characteristics of the seismic wave during propagation can be calculated. The geological layer interaction can determine how many geological layers the seismic wave has passed through. The physical properties of each geological layer (such as lithology, porosity, water content, etc.) have a significant effect on the wave propagation characteristics, thereby affecting the estimation of the Q value. These path distribution characteristics are crucial for the subsequent Q value deduction because they directly affect the attenuation characteristics of the seismic wave and affect the distribution model of the Q value.
[0036] Furthermore, by combining the decomposition parameter matrix with the path distribution characteristics, reverse deduction can be performed to ultimately determine the decomposition matrix field. Reverse deduction refers to inferring the overall characteristics of the geological layer through model calculation based on the known decomposition parameters and path distribution characteristics. This process helps to establish a global decomposition matrix field that can more accurately reflect the physical properties of different geological layers and their impact on seismic wave propagation. The decomposition matrix field is a three-dimensional parameter field, in which each point represents the Q-value decomposition parameter at that location, which can reflect information such as the quality coefficient, joint characteristics, and stress state of the rock.
[0037] In this process, the Q-value decomposition parameter matrix involved includes several key factors, such as: rock quality coefficient: reflects the density and elastic properties of the rock itself; number of joint groups: describes the distribution of cracks and joints in the rock layer; joint roughness coefficient: measures the roughness of the joint surface and its impact on wave propagation; joint alteration coefficient: evaluates the corrosion or weathering degree of joints or fissures, affecting the attenuation characteristics of waves; joint water reduction coefficient: reflects the impact of water on the conductivity of the rock layer; stress reduction coefficient: describes the impact of the rock layer on wave propagation in the stress field. These decomposition parameter matrices can accurately describe the wave propagation characteristics of each geological layer, and derive the Q value of each position through numerical calculation, reflecting the energy loss and attenuation capacity of the formation.
[0038] Through the calculation and analysis of these decomposition parameter matrices, we can fully understand the characteristics of the geological layer and its impact on the propagation of seismic waves, and then provide an accurate Q value model for geological exploration. In this process, the accuracy of the reverse deduction and the accuracy of the decomposition parameters are the key, which can directly affect the construction of the final Q value model and the deduction results of the geological layer.
[0039] Furthermore, step P33 of the embodiment of the present application also includes:
[0040] P33-1: Based on the surface Q value model and the relative attenuation relationship of the surface, the decomposition parameter matrix is attenuated and the first position matrix is determined, wherein the first position is the surface boundary position; P33-2: Taking the first position matrix as the initial step, a linear inversion analysis is performed based on the ray path information to determine the source decomposition matrix, wherein the source decomposition matrix is the Q value decomposition parameter of the source position of the seismic reflection wave.
[0041] In a possible embodiment of the present application, in order to accurately derive the Q value of the geological layer at different depths underground and finally determine the source decomposition matrix, first, the attenuation deduction of the decomposition parameter matrix is performed using the pre-constructed surface Q value model and the relative attenuation relationship of the surface. Here, the surface Q value model is a model established by analyzing various characteristic data of the surface of the geological layer (such as lithology, geological characteristics, topographic characteristics, etc.), which can accurately reflect the attenuation characteristics of seismic waves in the surface geological environment. The relative attenuation relationship quantifies the attenuation rate of the wave in different surface materials based on the consistency characteristics of the surface. In this process, the core purpose of the attenuation deduction is to derive the first position matrix, which describes the Q value characteristics of the surface boundary position (i.e., the contact position between the surface rock layer and the adjacent layer). By deducing the surface boundary position, the relevant parameters of the bordering layer can be accurately determined, thereby providing a basis for further deducing deeper geological structures.
[0042] After obtaining the first position matrix, the next step is to use it as the initial state and perform linear inversion analysis based on the ray path information. The core principle of inversion analysis is to infer the physical properties of each layer through the laws of seismic wave propagation in different geological layers. Since geological layers have certain regularities in different regions (such as rock property changes, interlayer pressure, stress field and other factors), these laws can be used as the basis for inversion analysis. At the same time, these laws can be further explored with the help of historical seismic wave analysis data and used in the model to improve the accuracy of the inversion results.
[0043] Through linear inversion analysis, the source decomposition matrix is finally obtained. The source decomposition matrix is the Q-value decomposition parameter of the seismic reflection wave source position, which describes the attenuation effect of each geological layer during the propagation of the seismic wave source. The matrix contains the Q-value decomposition parameters of the wave source position, which reflect the attenuation and energy distribution of the energy emitted by the wave source when it propagates in different media.
[0044] Through the above steps, combined with the surface Q value model, attenuation deduction, ray path information and inversion analysis, the Q value distribution model of the entire geological layer can be gradually deduced, further optimizing the accuracy and efficiency of geological exploration.
[0045] Furthermore, step P33 of the embodiment of the present application also includes:
[0046] P33-3: Set a preset sampling frequency, sample seismic reflection waves, and determine the sampled reflection waves; P33-4: Perform deductive analysis on the sampled reflection waves to determine the sampling decomposition matrix, wherein each sampling decomposition matrix corresponds to a geological field location; P33-5: Relative spatial distribution of the source decomposition matrix and the sampling decomposition matrix to determine the decomposition matrix field.
[0047] Optionally, the sampling and deduction analysis process can be further refined.
[0048] Before conducting seismic wave analysis, an appropriate sampling frequency needs to be set. The sampling frequency refers to the acquisition rate of seismic reflection waves within a certain time interval, which is usually optimized based on the frequency characteristics of seismic waves and the complexity of geological layers. A reasonable sampling frequency can ensure that enough reflection wave data is collected to cover geological information at different depths and locations. During the sampling process, the collected reflection waves include the propagation path, velocity changes and reflection characteristics of the waves in different media. This information helps to further analyze the physical properties of each geological layer.
[0049] After obtaining the seismic reflection wave data, the deduction and analysis phase begins. By processing and deducing the sampled reflection waves, the sampling decomposition matrix associated with each sampling location can be identified. The sampling decomposition matrix is a mathematical tool that describes the propagation characteristics and attenuation laws of seismic waves in different geological layers, and it varies according to the sampling location. Each sampling decomposition matrix contains information such as the attenuation, velocity, and energy loss of the reflection wave, and can reflect the changes in the medium encountered by the seismic wave when it passes through different geological fields. For example, when a seismic wave propagates through different types of rock formations, its attenuation and velocity will change, and the sampling decomposition matrix can capture these changes and quantify them.
[0050] Finally, the source decomposition matrix and the sampling decomposition matrix are combined to analyze their spatial distribution relationship and determine the decomposition matrix field of the entire geological field. The decomposition matrix field is a comprehensive geological field model that describes the spatial distribution of Q values in different geological layers. By spatially matching and comparing the source decomposition matrix (i.e., the Q value decomposition parameters at the reflection wave source position) and the sampling decomposition matrix (i.e., the Q value decomposition parameters at the sampling position), the Q value change trend of each geological layer can be accurately determined, and the overall characteristics of seismic wave propagation in the entire region can be deduced.
[0051] Through the above-mentioned precise sampling, deduction and spatial distribution analysis, not only can the seismic wave attenuation information at different locations be obtained, but also basic data can be provided for further geological layer deduction and Q value modeling, helping to improve the accuracy and depth of geological exploration.
[0052] P40: Combine the surface Q value model, the relative attenuation relationship and the decomposition matrix field to construct a global Q value field, and combine the Q value estimation constraint to perform offset imaging to determine the Q value model.
[0053] Furthermore, before performing the offset imaging, the embodiment of the present application further includes step P40a, and step P40a further includes:
[0054] P41a: Identify the Q-value estimation constraint, perform offset check on the Q-value field, and determine a first compensation feature; P42a: Based on geological layer characteristics, perform offset compensation on the Q-value field and determine a second compensation feature; P43a: Based on the first compensation feature and the second compensation feature, perform offset imaging on the Q-value field.
[0055] Specifically, by combining the surface Q value model, relative attenuation relationship and decomposition matrix field, the global Q value field is constructed and offset imaging is performed. This process involves optimizing the Q value model through accurate geological layer deduction and inversion analysis, and ultimately achieving comprehensive modeling of the underground geological structure.
[0056] In order to improve the accuracy and reliability of imaging, the embodiment further introduces step P40a before performing offset imaging, and performs a series of compensation analysis and verification steps, which are as follows: First, it is necessary to identify the Q-value estimation constraint, which refers to the estimation range and conditions provided by seismic wave propagation characteristics, geological layer data, etc. during the Q-value modeling process. The role of the Q-value estimation constraint is to provide a certain constraint range for the deduction of the entire Q-value field to prevent overfitting or unreasonable deduction results during the calculation process. Usually, these constraint ranges may be empirical values calculated based on surface data, seismic wave propagation laws, or historical data. The purpose of the offset verification is to ensure that the preliminary modeling of the Q-value field does not deviate from the actual geological characteristics by verifying these estimation constraints.
[0057] Next, by deeply analyzing the characteristics of the geological layers, the offset compensation of the Q value field is performed according to the lithological characteristics, attenuation characteristics, etc. of different geological layers. The physical properties of different geological layers will affect the distribution of Q values, so local adjustments need to be made according to the characteristics within the layer. In this process, the amplitude and direction of the compensation are determined based on the physical parameters of the geological layer, and the second compensation feature is generated. This step ensures a more accurate distribution of Q values between different geological layers, especially for those transition zones and areas with complex geological structures.
[0058] After completing the offset compensation of the Q value field, the last step is to comprehensively analyze the first compensation feature and the second compensation feature to further optimize the overall structure of the Q value field. Based on these features, the Q value field is imaged by offset to obtain a more accurate Q value model. Offset imaging is to reconstruct the Q value distribution of the underground medium by calculating the wave propagation path and its attenuation characteristics. Through the compensation and imaging process, a more accurate global Q value field can be obtained, which can provide reliable support for seismic data analysis, geological exploration and other work.
[0059] By combining the Q-value estimation constraints with the characteristics of the geological layer, offset verification and compensation are performed to ensure the accuracy of the Q-value field. Finally, an accurate global Q-value model is constructed through offset imaging technology. This process ensures high-precision modeling of the Q-value field in complex geological environments, thereby improving the accuracy and reliability of subsequent seismic reflection wave analysis, underground structure detection and other work.
[0060] Furthermore, step P42a of the embodiment of the present application further includes:
[0061] P42-1a: Determine the geological layer of travel according to the ray path information; P42-2a: Determine the offset compensation characteristics by interacting with the geological layer characteristics of the geological layer of travel; P42-3a: Based on the offset compensation characteristics, calculate the Q value compensation amount based on the decomposition parameters, and perform offset compensation and imaging on the global Q value field.
[0062] Optionally, further offset compensation analysis is performed on the Q value field to improve the accuracy of the global Q value model.
[0063] First, it is necessary to determine the geological layer of the travel based on the ray path information, that is, the path of the seismic reflection wave propagation. The ray path information is obtained by analyzing the seismic reflection wave, and usually includes the source of the wave propagation, the propagation path and the geological layers it passes through. With this information, the various geological layers involved in the wave propagation process can be identified. Since the physical properties of different geological layers (such as density, elastic modulus, etc.) have different effects on the propagation of waves, accurate identification of each geological layer and its interface is crucial for subsequent compensation analysis.
[0064] Next, the offset compensation characteristics are determined by interactively analyzing the geological layer characteristics of the traveling geological layers. Each geological layer has its own specific physical characteristics, such as lithology, joint characteristics, porosity, stress distribution, etc. These characteristics directly affect the Q value (attenuation factor) because the Q value is essentially related to the energy loss of the medium. Therefore, by interactively analyzing the characteristics of different geological layers, the areas that need to be compensated can be effectively identified and the corresponding offset compensation characteristics can be determined. These compensation characteristics reflect the differences between the geological layers and help to make precise adjustments in subsequent calculations.
[0065] After identifying the offset compensation features, the next task is to calculate the Q value compensation based on these features. As a comprehensive indicator, it is complicated to directly obtain the compensation value of Q value, so it is necessary to use decomposition parameters (such as rock mass coefficient, number of joint groups, etc.) as an intermediary to calculate the compensation amount. The decomposition parameters provide basic physical property information of rocks or geological layers, helping to quantify the changes in Q value.
[0066] After calculating the Q-value compensation by decomposing the parameters, these compensation amounts are applied to the global Q-value field for offset compensation and imaging. Specifically, the compensation amount will be adjusted in different areas of the Q-value field to ensure that the compensated Q-value field can more accurately reflect the attenuation characteristics of the underground medium. After the compensation is completed, the Q-value field is finally reconstructed through imaging technology to obtain a more accurate underground Q-value distribution model. This compensation process ensures that the energy loss characteristics during seismic wave propagation can be accurately captured and can effectively handle the complex relationship between different geological layers. It ensures the precise adjustment of the Q-value field in a complex geological environment, thereby improving the accuracy of the subsequent analysis of seismic wave reflection data and providing more reliable technical support for geological exploration and resource development.
[0067] Further, after the Q value model is determined, the embodiment of the present application further includes step P50, and step P50 further includes:
[0068] P51: Obtain a first Q value, where the first Q value is located in a first geological layer; P52: Based on the geological layer characteristics of the first geological layer, divide the geological quality levels and determine multiple quality levels; P53: Traverse the multiple quality levels, match the first Q value, and determine the first quality level; P54: Based on the first quality level, identify the Q value model.
[0069] It should be understood that after the Q value model is determined, further analysis and classification of the Q value is performed to ensure that a Q value model that meets geological quality requirements is obtained.
[0070] First, obtain the first Q value, which is located in the first geological layer. The Q value is an indicator of the degree of attenuation of seismic waves by geological media. The larger the value, the less attenuation the medium has, and vice versa. In different geological layers, the Q value will be different due to differences in lithology, porosity, degree of joint development and other characteristics. The first geological layer can represent a shallow or near-surface geological unit with high attenuation characteristics. Obtaining the Q value of this layer is crucial to building an accurate underground model.
[0071] Next, the geological quality grade is divided based on the geological layer characteristics of the first geological layer. The quality requirements of each geological layer are different and may be affected by factors such as rock type, pore structure, and degree of joint development. Therefore, the geological layer needs to be analyzed in detail based on these factors. During the analysis process, the geological layer will be divided into multiple quality grades according to the properties and state of the materials in the layer. These grades may include excellent, good, medium, poor, etc., and each grade corresponds to different geological characteristics and bearing capacity.
[0072] After determining multiple quality levels of the geological layer, these levels are traversed and the first Q value is matched with these levels. Through the matching process, the first Q value can be attributed to a specific quality level. The basis for matching is usually the relationship between the attenuation characteristics of the geological layer and the Q value. For example, if the first Q value is high, it means that the attenuation of the geological layer is small, and it may be matched to a better quality level; if the Q value is low, it may correspond to a poorer quality level. Through this process, a more refined standard can be provided for the quality assessment of the geological layer.
[0073] Finally, the obtained Q value model is labeled according to the first quality level. The Q value model represents the distribution characteristics of energy loss in the geological body, and the Q value models at different quality levels have different representative meanings. Through labeling, each geological layer and its corresponding quality level can be clearly distinguished to ensure that in the subsequent geological exploration and resource development process, appropriate technical means can be selected according to the actual geological conditions. This labeling process helps to match the practical application of the Q value model with the actual quality requirements of the geological layer and improve the accuracy of prediction and analysis.
[0074] This process helps to accurately evaluate the quality of different geological layers, provide a more accurate geological basis for the exploration, development and utilization of underground resources, and enhance the practical application value of the Q-value model.
[0075] In summary, the embodiments of the present application have at least the following technical effects:
[0076] This application determines the Q-value estimation constraints through correlation analysis, pre-processes the surface data to build a Q-value model and establish an attenuation relationship. Receive seismic reflection waves and perform geological layer deduction through ray path information to determine the decomposition matrix field. Finally, combine the surface Q-value model, attenuation relationship and decomposition matrix field to construct a global Q-value field and perform offset imaging to accurately determine the Q-value model to support geological exploration and seismic assessment.
[0077] The technical effect of improving the accuracy and reliability of Q value estimation of underground geological layers is achieved by jointly analyzing surface data and seismic reflection wave data.
[0078] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
[0080] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
Claims
1. A joint Q-value modeling method based on surface data and seismic reflection waves, characterized in that: The method comprises: Conduct correlation analysis between surface data and seismic reflection waves to determine Q-value estimation constraints; Preprocessing surface data, constructing a surface Q value model, and establishing a surface relative attenuation relationship based on surface consistency, wherein the surface data at least includes geological features, topographic features, and lithological features; Receive seismic reflection waves, deduce geological layers through ray path information, and determine the decomposition matrix field based on Q-value decomposition parameters; The surface Q value model, the relative attenuation relationship and the decomposition matrix field are combined to construct a global Q value field, and offset imaging is performed in combination with the Q value estimation constraint to determine the Q value model.
2. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 1, characterized in that: Determine the decomposition matrix field based on the Q-value decomposition parameters, including: By performing ray tracing, for the seismic reflection wave, ray path information and a decomposition parameter matrix based on the Q value are determined; Determining path distribution characteristics based on geological levels according to the ray path information; The decomposition matrix field is determined by performing reverse deduction based on the decomposition parameter matrix and the path distribution characteristics.
3. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 2, characterized in that: The decomposition parameter matrix is the Q value decomposition parameter of the sampling position, wherein the decomposition parameter matrix at least includes rock quality coefficient, joint group number, joint roughness coefficient, joint alteration coefficient, joint water reduction coefficient and stress reduction coefficient.
4. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 3, characterized in that: Reverse deduction is performed based on the decomposition parameter matrix and the path distribution characteristics, including: Based on the surface Q value model and the relative attenuation relationship of the surface, the decomposition parameter matrix is attenuated to determine a first position matrix, wherein the first position is a surface boundary position; Taking the first position matrix as the initial step, a linear inversion analysis is performed based on the ray path information to determine a source decomposition matrix, wherein the source decomposition matrix is a Q-value decomposition parameter of the source position of the seismic reflection wave.
5. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 4, characterized in that: Determining the decomposition matrix field includes: Setting a preset sampling frequency, sampling seismic reflection waves, and determining the sampled reflection waves; Deducing and analyzing the sampled reflection waves to determine a sampling decomposition matrix, wherein each sampling decomposition matrix corresponds to a geological field location; The source decomposition matrix and the sampling decomposition matrix are relatively spatially distributed to determine the decomposition matrix field.
6. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 1, characterized in that: Before performing the migration imaging, the method includes: Identifying the Q-value estimation constraint, performing an offset check on the Q-value field, and determining a first compensation feature; Based on geological layer characteristics, offset compensation is performed on the Q value field to determine a second compensation feature; Based on the first compensation feature and the second compensation feature, the Q value field is offset imaged.
7. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 6, characterized in that: Based on the geological layer characteristics, the Q value field is offset compensated, including: Determining the geological layer of travel based on the ray path information; Interacting geological layer characteristics of the traveled geological layers to determine offset compensation characteristics; Based on the offset compensation feature, the Q value compensation amount is calculated based on the decomposition parameters, and the global Q value field is subjected to offset compensation and imaging.
8. A joint Q value modeling method based on surface data and seismic reflection waves as claimed in claim 1, characterized in that: After determining the Q value model, including: Obtaining a first Q value, wherein the first Q value is located in a first geological layer; Based on the geological layer characteristics of the first geological layer, geological quality grades are classified to determine multiple quality grades; Traversing the multiple quality levels, matching the first Q value, and determining a first quality level; The Q-value model is identified based on the first quality level.