Imaging profile evaluation method and device suitable for various observation systems

Abstract

The invention provides an imaging profile evaluation method and device suitable for multiple observation systems. The imaging profile evaluation method suitable for the multiple observation systems comprises the steps that the energy intensity similarity, the energy distribution contrast ratio and the structural similarity between a target imaging profile and at least one reference imaging profile are determined respectively; wherein the target imaging profile and the reference imaging profile are generated by seismic data measured by different observation systems; and evaluating the target imaging profile according to the energy intensity similarity, the energy distribution contrast, the structural similarity and the at least one reference imaging profile. According to the method, a reliable quantitative index is provided to analyze the change trend of imaging results of different observation systems, and the problem that subtle differences of imaging profile results cannot be distinguished visually is solved.

Description

Technical Field

[0001] This application belongs to the technical field of oil and gas exploration, especially the technical field of seismic data processing for oil and gas exploration. Specifically, it relates to an imaging profile evaluation method and device applicable to multiple observation systems. Background Art

[0002] In the process of oil and gas exploration and development, seismic data acquisition is the basis for seismic data processing and interpretation. During the seismic data acquisition process, the design of the three-dimensional observation system directly affects the imaging quality of the underground geological structure in the later stage. In the field of seismic exploration, the three-dimensional observation system is a technical system designed to obtain underground structure information. It utilizes the principle of seismic wave propagation underground. By arranging sensors at different positions, it records the propagation of seismic waves, and then analyzes the properties and characteristics of the underground structure using this data. The three-dimensional observation system usually includes the following main components:

[0003] Seismic source: It generates seismic signals by exciting seismic waves. The seismic source can be artificial, such as a seismograph or an explosive, or a natural seismic event.

[0004] Receiver (sensor) array: A series of sensors (also called seismic stations) are arranged on the surface or underground to record the vibration signals during the propagation of seismic waves. These sensors are usually seismographs or devices called seismic sensors.

[0005] Data acquisition system: It is responsible for collecting the seismic data recorded by the sensors and transmitting it to the data processing center. The data acquisition system usually includes an analog-to-digital converter (ADC), data storage devices, data transmission devices, etc.

[0006] Data processing and interpretation: The collected seismic data is imported into a computer system for processing steps such as seismic waveform processing, data analysis, and imaging reconstruction. By using information such as the propagation time, velocity, and amplitude of seismic wave signals, relevant information such as the structure, lithology, and porosity of the underground medium can be inferred.

[0007] With the development of oil and gas exploration towards complex reservoirs, higher requirements are put forward for the demonstration accuracy of the three-dimensional observation system. To improve the demonstration accuracy, currently, wave equation forward modeling is generally used to simulate different schemes during the design stage, and then migration imaging is performed to obtain the final simulated profile. The optimal scheme is selected by comparing the imaging profiles of different observation systems. However, this optimal selection method mainly relies on visual differences to analyze the trend of imaging results, has the disadvantages of strong subjectivity and poor resolution, and cannot give quantitative evaluation indicators for the imaging profile. Summary of the Invention

[0008] The present invention belongs to the technical field of seismic data processing. One object of the present invention is to provide a reliable quantitative index to analyze the subtle differences between different imaging profiles (imaging effects). The present invention has great application prospects in the design demonstration of seismic acquisition schemes and the field quality control process.

[0009] Another object of the present invention is to provide an imaging profile evaluation device applicable to multiple observation systems. Still another object of the present invention is to provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned imaging profile evaluation method applicable to multiple observation systems are implemented. Still another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned imaging profile evaluation method applicable to multiple observation systems are implemented.

[0010] To solve the technical problems in the background art of the present application, the present invention provides the following technical solutions:

[0011] In a first aspect, the present invention provides an imaging profile evaluation method applicable to multiple observation systems, including:

[0012] Determine the energy intensity similarity, energy distribution contrast, and structural similarity between a target imaging profile and at least one reference imaging profile respectively; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems;

[0013] Evaluate the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

[0014] In an embodiment of the present invention, the determining the energy intensity similarity between the target imaging profile and at least one reference imaging profile includes:

[0015] Calculate the average energy value of the target imaging profile according to the amplitude value of each sample point of the target imaging profile;

[0016] Calculate the average energy value of the reference imaging profile according to the amplitude value of each sample point of the reference imaging profile;

[0017] Determine the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile.

[0018] In an embodiment of the present invention, determining the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile includes:

[0019] Generate an energy intensity similarity function based on the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0020] Determine the energy intensity similarity according to the energy intensity similarity function.

[0021] In an embodiment of the present invention, the determination of the energy distribution contrast between the target imaging profile and at least one reference imaging profile includes:

[0022] Calculate the standard deviation of the amplitude values of the target imaging profile according to the amplitude values of each sample point of the target imaging profile;

[0023] Calculate the standard deviation of the amplitude values of the reference imaging profile according to the amplitude values of each sample point of the reference imaging profile;

[0024] Determine the energy distribution contrast according to the standard deviation of the amplitude values of the target imaging profile and the standard deviation of the amplitude values of the reference imaging profile.

[0025] In an embodiment of the present invention, the determination of the energy distribution contrast according to the standard deviation of the amplitude values of the target imaging profile and the standard deviation of the amplitude values of the reference imaging profile includes:

[0026] Generate an energy distribution contrast function according to the standard deviation of the amplitude values of the reference imaging profile and the standard deviation of the amplitude values of the reference imaging profile;

[0027] Determine the energy distribution contrast according to the energy distribution contrast function.

[0028] In an embodiment of the present invention, the determination of the structural similarity between the target imaging profile and at least one reference imaging profile includes:

[0029] Calculate the covariance between the target imaging profile and the reference imaging profile according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0030] Determine the structural similarity according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile.

[0031] In an embodiment of the present invention, the determination of the structural similarity according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile includes:

[0032] Generate a structural similarity function according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile;

[0033] Determine the structural similarity according to the structural similarity function.

[0034] In an embodiment of the present invention, evaluating the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile includes:

[0035] Generate a quantitative evaluation function according to the energy intensity similarity, the energy distribution contrast, and the structural similarity;

[0036] Evaluate the target imaging profile according to the quantitative evaluation function and the at least one reference imaging profile.

[0037] In a second aspect, the present invention provides an imaging profile evaluation device applicable to multiple observation systems, and the device includes:

[0038] A three-parameter determination module for respectively determining the energy intensity similarity, energy distribution contrast, and structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems;

[0039] A target imaging profile evaluation module for evaluating the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

[0040] In an embodiment of the present invention, the three-parameter determination module includes:

[0041] A first average energy value calculation unit for calculating the average energy value of the target imaging profile according to the amplitude value of each sample point of the target imaging profile;

[0042] A second average energy value calculation unit for calculating the average energy value of the reference imaging profile according to the amplitude value of each sample point of the reference imaging profile;

[0043] An energy intensity similarity determination unit for determining the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile.

[0044] In an embodiment of the present invention, the energy intensity similarity determination unit includes:

[0045] An energy intensity similarity function generation unit for generating an energy intensity similarity function according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0046] An energy intensity similarity determination subunit for determining the energy intensity similarity according to the energy intensity similarity function.

[0047] In an embodiment of the present invention, the three-parameter determination module further includes:

[0048] An amplitude standard deviation calculation first unit, configured to calculate the amplitude value standard deviation of the target imaging profile according to the amplitude value of each sample point of the target imaging profile;

[0049] An amplitude standard deviation calculation second unit, configured to calculate the amplitude value standard deviation of the reference imaging profile according to the amplitude value of each sample point of the reference imaging profile;

[0050] An energy distribution contrast determination unit, configured to determine the energy distribution contrast according to the amplitude value standard deviation of the target imaging profile and the amplitude value standard deviation of the reference imaging profile.

[0051] In an embodiment of the present invention, the energy distribution contrast determination unit includes:

[0052] An energy distribution contrast function generation unit, configured to generate an energy distribution contrast function according to the amplitude value standard deviation of the reference imaging profile and the amplitude value standard deviation of the reference imaging profile;

[0053] An energy distribution contrast determination subunit, configured to determine the energy distribution contrast according to the energy distribution contrast function.

[0054] In an embodiment of the present invention, the three-parameter determination module further includes:

[0055] A covariance calculation unit, configured to calculate the covariance between the target imaging profile and the reference imaging profile according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0056] A structural similarity determination unit, configured to determine the structural similarity according to the covariance, the amplitude value standard deviation of the target imaging profile, and the amplitude value standard deviation of the reference imaging profile.

[0057] In an embodiment of the present invention, the structural similarity determination unit includes:

[0058] A structural similarity function generation unit, configured to generate a structural similarity function according to the covariance, the amplitude value standard deviation of the target imaging profile, and the amplitude value standard deviation of the reference imaging profile;

[0059] A structural similarity determination subunit, configured to determine the structural similarity according to the structural similarity function.

[0060] In an embodiment of the present invention, the target imaging profile evaluation module includes:

[0061] A quantitative evaluation function generation unit, configured to generate a quantitative evaluation function according to the energy intensity similarity, the energy distribution contrast, and the structural similarity;

[0062] A target imaging profile evaluation unit, configured to evaluate the target imaging profile according to the quantitative evaluation function and the at least one reference imaging profile.

[0063] In a third aspect, the present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of an imaging profile evaluation method applicable to multiple observation systems are implemented.

[0064] In a fourth aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps of an imaging profile evaluation method applicable to multiple observation systems are implemented.

[0065] In a fifth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of an imaging profile evaluation method applicable to multiple observation systems are implemented.

[0066] As can be seen from the above description, the embodiments of the present invention provide an imaging profile evaluation method and device applicable to multiple observation systems. The corresponding imaging profile evaluation method applicable to multiple observation systems includes: first, respectively determining the energy intensity similarity, the energy distribution contrast, and the structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; then, evaluating the target imaging profile according to the energy intensity similarity, the energy distribution contrast, the structural similarity, and the at least one reference imaging profile.

[0067] The corresponding imaging profile evaluation device applicable to multiple observation systems includes: a three-parameter determination module, configured to respectively determine the energy intensity similarity, the energy distribution contrast, and the structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; a target imaging profile evaluation module, configured to evaluate the target imaging profile according to the energy intensity similarity, the energy distribution contrast, the structural similarity, and the at least one reference imaging profile.

[0068] The imaging profile evaluation method and device applicable to multiple observation systems provided by the embodiments of the present invention provide a reliable quantitative index to analyze the change trend of imaging results of different observation systems, and solve the problem that the subtle differences in imaging results cannot be visually distinguished. Description of the Drawings

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0070] Figure 1 It is a schematic flowchart of an imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0071] Figure 2 It is the first schematic flowchart of step 100 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0072] Figure 3 It is a schematic flowchart of step 103 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0073] Figure 4 It is the second schematic flowchart of step 100 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0074] Figure 5 It is a schematic flowchart of step 106 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0075] Figure 6 It is the third schematic flowchart of step 100 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0076] Figure 7 It is a schematic flowchart of step 108 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0077] Figure 8 It is a schematic flowchart of step 200 of the imaging profile evaluation method applicable to multiple observation systems in the embodiments of the present invention;

[0078] Figure 9 It is a schematic flowchart of the imaging profile evaluation method applicable to multiple observation systems in the specific embodiments of the present invention;

[0079] Figure 10 It is a schematic diagram of the imaging profile of the target observation system in the specific embodiments of the present invention;

[0080] Figure 11 It is a schematic diagram of the imaging profiles of 5 different observation systems in the specific embodiments of the present invention;

[0081] Figure 12 It is a schematic diagram of the similarity coefficient between the imaging profiles of 5 sets of different observation systems and the imaging profile of the target observation system in the specific implementation manner of the present invention;

[0082] Figure 13 It is a block diagram of an imaging profile evaluation device applicable to multiple observation systems in the specific implementation manner of the present invention;

[0083] Figure 14 It is a schematic structural diagram of an electronic device in the embodiment of the present invention. Specific implementation manner

[0084] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0085] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices. Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0087] In the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of laws and regulations.

[0088] Embodiment 1:

[0089] The embodiment of the present invention provides a specific implementation manner of an imaging profile evaluation method applicable to multiple observation systems. Refer to Figure 1, specifically including the following content:

[0090] Step 100: Determine the energy intensity similarity, energy distribution contrast, and structural similarity between the target imaging profile and at least one reference imaging profile respectively; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems.

[0091] Step 200: Evaluate the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

[0092] As can be seen from the above description, the embodiment of the present invention provides an imaging profile evaluation method applicable to multiple observation systems, including: first, determine the energy intensity similarity, energy distribution contrast, and structural similarity between the target imaging profile and at least one reference imaging profile respectively; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; then, evaluate the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and at least one reference imaging profile.

[0093] The imaging profile evaluation method applicable to multiple observation systems provided by the embodiment of the present invention provides a reliable quantitative index to analyze the change trend of imaging results of different observation systems, and solves the problem that the subtle differences in imaging results cannot be visually distinguished.

[0094] Embodiment 2:

[0095] It can be understood that the imaging profile in Step 100 is an image used to display the underground geological structure. Through data processing and interpretation techniques in seismic exploration, seismic wave data is converted into a cross-sectional view of the underground geological structure. The generation of the seismic imaging profile includes the following steps: arranging seismic instruments and sensors on the ground to record the propagation of seismic waves. Preprocessing the collected seismic wave data, including removing noise, correcting instrument responses, etc. Through inversion techniques, the seismic wave data is converted into a model of the underground geological structure. This usually involves complex mathematical calculations and model assumptions. Displaying the inverted underground geological structure model in the form of a cross-sectional view. This can be visually processed through computer software, presenting the underground structure as an image with different colors or grayscales.

[0096] In addition, it should be noted that the reference imaging profile here is used to compare with the target imaging profile and quantitatively evaluate the target imaging profile, and the target imaging profile and the reference imaging profile are generated from seismic data collected by different observation systems, and different reference imaging profiles are also generated from seismic data collected by different observation systems.

[0097] Regarding step 200, in seismic data processing, energy intensity similarity, energy distribution contrast, and structural similarity are metrics used to compare and evaluate the similarities and differences between seismic data. Specifically:

[0098] Energy intensity similarity is used to compare the intensity of energy in seismic data. In seismic data, energy is related to the amplitude of seismic waves. By calculating the similarity of energy intensity between different seismic data, the consistency and similarity of seismic data can be evaluated. Preferably, the energy intensity similarity metrics here include correlation coefficient, root mean square error, etc.

[0099] Energy distribution contrast is used to compare the energy distribution in seismic data. The energy distribution in seismic data is usually related to the changes in the underground geological structure. By calculating the contrast of energy distribution between different seismic data, the differences and changes in seismic data can be evaluated. Preferably, the energy distribution contrast metrics include cross-correlation function, spectrum comparison, etc. The spectrum represents the energy distribution of seismic data at different frequencies. By comparing the spectra, the similarities and differences of seismic data can be evaluated. Spectrum comparison includes:

[0100] By converting the seismic wave signal from the time domain to the frequency domain, the spectral representation of seismic data can be obtained. Fourier transform can decompose the seismic wave signal into components of different frequencies, thereby analyzing the spectral characteristics of seismic data.

[0101] For multiple seismic data, their average spectrum can be calculated and then compared. The average spectrum can eliminate the noise and fluctuations of individual data and more accurately reflect the spectral characteristics of seismic data.

[0102] Normalize the spectra of different seismic data and then compare them. Common normalization methods include amplitude normalization and energy normalization. The normalized spectra can better compare the spectral characteristics of different seismic data.

[0103] Structural similarity is used to compare the similarity of the underground geological structure in seismic data. The underground geological structure in seismic data can be obtained through inversion and imaging techniques. By calculating the similarity of the underground structure between different seismic data, the consistency and similarity of seismic data can be evaluated. Preferably, the similarity metrics include cross-correlation coefficient, mutual information, etc.

[0104] In some embodiments of the present invention, referring to Figure 2 , step 100 includes:

[0105] Step 101: Calculate the average energy value of the target imaging profile according to the amplitude value of each sample point of the target imaging profile;

[0106] Specifically, obtain the (absolute value) average energy value of the sample point values of the target imaging profile:

[0107]

[0108] μ x : The absolute value average energy value of the target imaging profile; x i : The amplitude value of each sample point on the target imaging profile, i = {1, 2, 3,..., N}; N: The total number of sample points on the target imaging profile.

[0109] It can be understood that the amplitude value of the seismic profile refers to the amplitude size of each data point on the seismic profile. The seismic profile is obtained by conducting seismic exploration surveys underground, recording the propagation of seismic waves underground, and then plotting these data into a profile diagram. The amplitude value represents the amplitude size of the seismic waves at different positions, reflecting the characteristics and variations of the underground geological structure.

[0110] The amplitude value of the seismic profile is usually expressed in a dimensionless form, and the commonly used units are Counts or amplitude units, which represent the size of the digital signal in the seismic record. The size of the amplitude value is related to the energy of the seismic wave. A large amplitude value indicates a stronger energy of the seismic wave, and a small amplitude value indicates a weaker energy of the seismic wave.

[0111] In seismic data processing and interpretation, the amplitude value of the seismic profile can be used to analyze the changes in the underground geological structure, identify seismic anomalies and seismic events, determine the location of oil and gas layers, etc.

[0112] Step 102: Calculate the average energy value of the reference imaging profile according to the amplitude value of each sample point of the reference imaging profile;

[0113] Specifically, obtain the (absolute value) average energy value of the sample point values of the reference imaging profile:

[0114]

[0115] μ y : The absolute value average energy value of the reference imaging profile; y i : The amplitude value of each sample point on the target imaging profile, i = {1, 2, 3,..., N}; N: The total number of sample points on the target imaging profile.

[0116] Step 103: Determine the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile.

[0117] In some embodiments of the present invention, refer to Figure 3 , Step 103 includes:

[0118] Step 1031: Generate an energy intensity similarity function based on the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0119] Specifically, construct an energy intensity similarity function using the absolute value average energy of the profile sample points:

[0120]

[0121] where: l(x,y): intensity similarity function; C 1 : constant.

[0122] Step 1032: Determine the energy intensity similarity according to the energy intensity similarity function.

[0123] In some embodiments of the present invention, referring to Figure 4 , step 100 further includes:

[0124] Step 104: Calculate the standard deviation of the amplitude values of each sample point of the target imaging profile;

[0125] Step 105: Calculate the standard deviation of the amplitude values of each sample point of the reference imaging profile;

[0126] It can be understood that the standard deviation is a statistic that describes the degree of dispersion or variation of a set of data. It measures the degree of dispersion of the data points in the data set relative to the mean. The calculation steps of the standard deviation are as follows: Calculate the mean (average value) of the data set. For each data point, calculate the difference between it and the mean. Square each difference. Calculate the average value of the squared differences. Take the square root of the average value to obtain the standard deviation.

[0127] The larger the standard deviation, the greater the degree of dispersion of the data points in the data set relative to the mean; the smaller the standard deviation, the smaller the degree of dispersion of the data points in the data set relative to the mean.

[0128] In step 104 and step 105, using the sample point value and the average amplitude as inputs, the standard deviation of the imaging profile is obtained. That is, the degree of severity of the amplitude energy change of the profile.

[0129] where:

[0130] Standard deviation of the amplitude values of the target imaging profile

[0131] Standard deviation of the amplitude values of the reference imaging profile

[0132] μ x μ y: They are the average energy values of the target imaging profile and the reference imaging profile respectively.

[0133] Step 106: Determine the energy distribution contrast based on the standard deviation of the amplitude values of the target imaging profile and the standard deviation of the amplitude values of the reference imaging profile.

[0134] In some embodiments of the present invention, referring to Figure 5 , step 106 includes:

[0135] Step 1061: Generate an energy distribution contrast function based on the standard deviation of the amplitude values of the reference imaging profile and the standard deviation of the amplitude values of the reference imaging profile;

[0136] Specifically, construct an energy distribution contrast function using the standard deviation:

[0137]

[0138] Where:

[0139] c(x,y): Energy distribution contrast function

[0140] C 2 : Constant

[0141] Step 1062: Determine the energy distribution contrast according to the energy distribution contrast function.

[0142] In some embodiments of the present invention, referring to Figure 6 , step 100 further includes:

[0143] Step 107: Calculate the covariance between the target imaging profile and the reference imaging profile based on the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0144] Covariance is a statistic used to measure the relationship between two variables. It describes whether the change trends of the two variables are consistent and their linear correlation. The calculation formula of covariance is as follows:

[0145]

[0146] Where xi and yi respectively represent each data point of the two variables, and respectively represent the means of the two variables, and n represents the number of data points.

[0147] The value of covariance can be positive, negative, or zero. A positive value indicates that two variables increase or decrease simultaneously. A negative value means that one variable increases while the other decreases. A value of zero indicates no linear relationship between the two variables. The larger the absolute value of the covariance, the stronger the relationship between the two variables. When the covariance is close to zero, it indicates a weak relationship between the two variables.

[0148] The value of covariance is affected by the units of the variables, making it difficult to compare the correlations between different datasets. Therefore, to eliminate the influence of units, the Pearson correlation coefficient can be used to measure the relationship between two variables. Specifically: The Pearson correlation coefficient is used to measure the linear correlation between two variables. Its value ranges from -1 to 1, and it can indicate the strength and direction of the relationship between the two variables. The calculation formula for the Pearson correlation coefficient is as follows:

[0149]

[0150] where xi and yi represent each data point of the two variables respectively, and represent the means of the two variables respectively.

[0151] When r = 1, it indicates a perfect positive linear relationship between the two variables, that is, perfect positive correlation. When r = -1, it indicates a perfect negative linear relationship between the two variables, that is, perfect negative correlation. When r = 0, it indicates no linear relationship between the two variables, that is, no correlation.

[0152] The Pearson correlation coefficient can also measure the strength between two variables. The closer the absolute value of r is to 1, the stronger the relationship between the two variables. It should be noted that the Pearson correlation coefficient measures linear relationships and cannot capture non-linear relationships. If there is a non-linear relationship between two variables, the Pearson correlation coefficient may yield a low value, while in fact, there is a certain association between them.

[0153] When implementing step 107, specifically: using the sample point value and the average energy as inputs, calculate the covariance between the target imaging profile and the reference imaging profile.

[0154]

[0155] where:

[0156] μ x : the absolute value average energy value of the target imaging profile;

[0157] μ y : the absolute value average energy value of the reference imaging profile.

[0158] Step 108: Determine the structural similarity according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile.

[0159] In some embodiments of the present invention, referring to Figure 7 , Step 108 includes:

[0160] Step 1081: Generate a structural similarity function according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile;

[0161] Specifically: Use cosine similarity to construct the structural similarity function.

[0162]

[0163] Wherein:

[0164] s(x,y): Structural similarity function

[0165] C 3 : Constant

[0166] Step 1082: Determine the structural similarity according to the structural similarity function.

[0167] In some embodiments of the present invention, referring to Figure 8 , Step 200 includes:

[0168] Step 201: Generate a quantitative evaluation function according to the energy intensity similarity, the energy distribution contrast, and the structural similarity;

[0169] Take the energy intensity similarity, distribution contrast, and structural similarity features of the imaging profile as three dimensions to construct a quantitative evaluation function:

[0170] M(x,y) = l(x,y)·c(x,y)·s(x,y)

[0171] Wherein:

[0172] l(x,y): Intensity similarity function;

[0173] c(x,y): Energy distribution contrast function;

[0174] s(x,y): Structural similarity function.

[0175] Substitute the intensity similarity function, energy distribution contrast function, and structural similarity function obtained in the above steps to obtain the final expression.

[0176]

[0177] Step 202: Evaluate the target imaging profile according to the quantitative evaluation function and the at least one reference imaging profile.

[0178] As can be seen from the above description, the embodiment of the present invention provides an imaging profile evaluation method applicable to multiple observation systems, including: first, respectively determining the energy intensity similarity, energy distribution contrast, and structural similarity between the target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; then, evaluating the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and at least one reference imaging profile.

[0179] Therefore, the present invention proposes a quantitative evaluation method for imaging results based on comprehensive similarity, which is used to provide a reliable quantitative index to analyze the subtle differences between different imaging effects. Therefore, the present invention is innovative and practical in the design and demonstration of seismic acquisition schemes and the field quality control process.

[0180] Embodiment III:

[0181] In a specific implementation manner, the present invention also provides a specific implementation manner of an imaging profile evaluation method applicable to multiple observation systems. Refer to Figure 9 , which specifically includes the following steps.

[0182] S1: Construct an energy intensity similarity function for the imaging profile;

[0183] Specifically, first calculate the absolute value average energy of the profile sample point values.

[0184]

[0185]

[0186] Wherein:

[0187] μ x : Absolute value average energy value of the target imaging profile;

[0188] μ y : Absolute value average energy value of the reference imaging profile;

[0189] x i y i : Amplitude value of each sample point on the profile, i = {1, 2, 3,..., N};

[0190] N: Total number of sample points on the profile;

[0191] Then, construct an energy intensity similarity function using the absolute value average energy of the profile sample point values.

[0192]

[0193] Among them:

[0194] l(x,y): intensity similarity function;

[0195] C 1 : constant;

[0196] S2: energy distribution contrast function for constructing the imaging profile.

[0197] First, using the sample point value and the average amplitude as inputs, calculate the standard deviation of the profile. That is, the degree of severity of the amplitude energy change of the profile.

[0198] Among them:

[0199] Standard deviation of the amplitude value of the target imaging profile

[0200] Standard deviation of the amplitude value of the reference imaging profile

[0201] μ x μ y : are the average energy values of the target imaging profile and 2 respectively

[0202] C 2 : constant

[0203] Next, use the standard deviation to construct the energy distribution contrast function.

[0204]

[0205] Among them:

[0206] c(x,y): energy distribution contrast function

[0207] C 2 : constant

[0208] S3: structural similarity function for constructing the imaging profile.

[0209] First, using the sample point value and the average energy as inputs, calculate the covariance of the two profiles.

[0210]

[0211] Among them:

[0212] μ x : absolute value average energy value of the target imaging profile

[0213] μ y : absolute value average energy value of the reference imaging profile

[0214] Next, the cosine similarity is used to construct a structural similarity function.

[0215]

[0216] Where:

[0217] s(x, y): Structural similarity function

[0218] C 3 : Constant

[0219] S4: Construct an objective function for quantitatively evaluating the imaging result by using the energy intensity, contrast, and structural similarity of the imaging profile as three dimensions.

[0220] Construct a quantitative evaluation function by using the energy intensity similarity, distribution contrast, and structural similarity features of the imaging profile as three dimensions.

[0221] M(x, y) = l(x, y)·c(x, y)·s(x, y)

[0222] Where:

[0223] l(x, y): Intensity similarity function

[0224] c(x, y): Energy distribution contrast function

[0225] s(x, y): Structural similarity function

[0226] The expression of the final quantitative evaluation function is:

[0227]

[0228] S5: Evaluate the target imaging profile according to the objective function for quantitatively evaluating the imaging result and at least one reference imaging profile.

[0229] The results show: See Figures 10 to 12 , which are respectively the schematic diagram of the imaging profile of the target observation system, the schematic diagrams of the imaging profiles of 5 different observation systems, and the schematic diagram of the similarity coefficients between the imaging profiles of 5 different observation systems and the imaging profile of the target observation system. It can be seen that the imaging profiles of 5 different observation systems have a high similarity with the imaging profile of the target observation system, that is, the imaging profile quality of the target observation system is high.

[0230] As can be seen from the above description, an imaging profile evaluation method applicable to multiple observation systems provided by an embodiment of the present invention includes: first, respectively determining the energy intensity similarity, energy distribution contrast, and structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; then, evaluating the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and at least one reference imaging profile.

[0231] The present invention provides a reliable quantitative index to analyze the change trend of imaging results of different observation systems, and solves the problem that the subtle differences in imaging results cannot be visually distinguished.

[0232] Embodiment 4:

[0233] Based on the same inventive concept, an embodiment of the present application further provides an imaging profile evaluation device applicable to multiple observation systems, which can be used to implement the method described in the above embodiment, as in the following embodiment. Since the principle of solving problems by the imaging profile evaluation device applicable to multiple observation systems is similar to that of the imaging profile evaluation method applicable to multiple observation systems, the implementation of the imaging profile evaluation device applicable to multiple observation systems can refer to the implementation of the imaging profile evaluation method applicable to multiple observation systems, and the repeated parts will not be described again. As used hereinafter, the term "unit" or "module" can be a combination of software and / or hardware that can implement a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0234] An embodiment of the present invention provides a specific implementation manner of an imaging profile evaluation device applicable to multiple observation systems that can implement the imaging profile evaluation method applicable to multiple observation systems. Refer to Figure 13 , an imaging profile evaluation device applicable to multiple observation systems includes:

[0235] A three-parameter determination module 10, configured to respectively determine the energy intensity similarity, energy distribution contrast, and structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems;

[0236] A target imaging profile evaluation module 20, configured to evaluate the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

[0237] In an embodiment of the present invention, the three-parameter determination module includes:

[0238] An average energy value calculation first unit, configured to calculate an average energy value of the target imaging profile according to amplitude values of each sample point of the target imaging profile;

[0239] An average energy value calculation second unit, configured to calculate an average energy value of the reference imaging profile according to amplitude values of each sample point of the reference imaging profile;

[0240] An energy intensity similarity determination unit, configured to determine the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile.

[0241] In an embodiment of the present invention, the energy intensity similarity determination unit includes:

[0242] An energy intensity similarity function generation unit, configured to generate an energy intensity similarity function according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0243] An energy intensity similarity determination subunit, configured to determine the energy intensity similarity according to the energy intensity similarity function.

[0244] In an embodiment of the present invention, the three-parameter determination module further includes:

[0245] An amplitude standard deviation calculation first unit, configured to calculate an amplitude standard deviation of the target imaging profile according to amplitude values of each sample point of the target imaging profile;

[0246] An amplitude standard deviation calculation second unit, configured to calculate an amplitude standard deviation of the reference imaging profile according to amplitude values of each sample point of the reference imaging profile;

[0247] An energy distribution contrast determination unit, configured to determine the energy distribution contrast according to the amplitude standard deviation of the target imaging profile and the amplitude standard deviation of the reference imaging profile.

[0248] In an embodiment of the present invention, the energy distribution contrast determination unit includes:

[0249] An energy distribution contrast function generation unit, configured to generate an energy distribution contrast function according to the amplitude standard deviation of the reference imaging profile and the amplitude standard deviation of the reference imaging profile;

[0250] An energy distribution contrast determination subunit, configured to determine the energy distribution contrast according to the energy distribution contrast function.

[0251] In an embodiment of the present invention, the three-parameter determination module further includes:

[0252] A covariance calculation unit, configured to calculate the covariance between the target imaging profile and the reference imaging profile according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile;

[0253] A structural similarity determination unit, configured to determine the structural similarity according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile.

[0254] In an embodiment of the present invention, the structural similarity determination unit includes:

[0255] A structural similarity function generation unit, configured to generate a structural similarity function according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile;

[0256] A structural similarity determination subunit, configured to determine the structural similarity according to the structural similarity function.

[0257] In an embodiment of the present invention, the target imaging profile evaluation module includes:

[0258] A quantitative evaluation function generation unit, configured to generate a quantitative evaluation function according to the energy intensity similarity, the energy distribution contrast, and the structural similarity;

[0259] A target imaging profile evaluation unit, configured to evaluate the target imaging profile according to the quantitative evaluation function and the at least one reference imaging profile.

[0260] As can be seen from the above description, the imaging profile evaluation device applicable to multiple observation systems provided by the embodiments of the present invention includes: a three-parameter determination module, configured to respectively determine the energy intensity similarity, the energy distribution contrast, and the structural similarity between the target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; a target imaging profile evaluation module, configured to evaluate the target imaging profile according to the energy intensity similarity, the energy distribution contrast, the structural similarity, and the at least one reference imaging profile.

[0261] The imaging profile evaluation device applicable to multiple observation systems provided by the embodiments of the present invention provides a reliable quantitative index to analyze the change trend of the imaging results of different observation systems, and solves the problem that the subtle differences in the imaging results cannot be visually distinguished.

[0262] Embodiment Five:

[0263] Embodiments of the present application also provide a specific implementation manner of an electronic device that can implement all steps in the imaging profile evaluation method applicable to multiple observation systems in the above embodiments. Refer to Figure 14 , the electronic device specifically includes the following:

[0264] A processor 1201, a memory 1202, a communication interface 1203, and a bus 1204;

[0265] Among them, the processor 1201, the memory 1202, and the communication interface 1203 communicate with each other through the bus 1204; the communication interface 1203 is used to implement information transmission between related devices such as server-side devices and client-side devices;

[0266] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, it implements all steps in the imaging profile evaluation method applicable to multiple observation systems in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0267] Respectively determine the energy intensity similarity, energy distribution contrast, and structural similarity between the target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems;

[0268] Evaluate the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

[0269] Embodiment Six:

[0270] Embodiments of the present application also provide a computer-readable storage medium that can implement all steps in the imaging profile evaluation method applicable to multiple observation systems in the above embodiments. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it implements all steps in the imaging profile evaluation method applicable to multiple observation systems in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0271] Respectively determine the energy intensity similarity, energy distribution contrast, and structural similarity between the target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems;

[0272] Evaluate the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

[0273] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the hardware + program type of embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant content.

[0274] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0275] Although this application provides method operation steps such as in the embodiments or flowcharts, based on routine or non-creative labor, there can be more or fewer operation steps. The order of steps listed in the embodiments is only one way among many execution orders of the steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in an environment of parallel processors or multithreaded processing).

[0276] For the convenience of description, when describing the above device, it is divided into various modules according to functions for separate description. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules implementing the same function can be realized by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0277] Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to implement the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0278] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0279] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0280] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0281] The above is only the embodiments of this specification and is not used to limit this specification. For those skilled in the art, various changes and modifications can be made to this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification should be included within the scope of the claims of this specification.

Claims

1. An imaging profile evaluation method applicable to multiple observation systems, characterized in that, it includes: respectively determining the energy intensity similarity, energy distribution contrast, and structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; evaluating the target imaging profile according to the energy intensity similarity, energy distribution contrast, structural similarity, and the at least one reference imaging profile.

2. The imaging profile evaluation method according to claim 1, characterized in that, the determining the energy intensity similarity between the target imaging profile and at least one reference imaging profile includes: calculating the average energy value of the target imaging profile according to the amplitude value of each sample point of the target imaging profile; calculating the average energy value of the reference imaging profile according to the amplitude value of each sample point of the reference imaging profile; determining the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile.

3. The imaging profile evaluation method according to claim 2, characterized in that, determining the energy intensity similarity according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile includes: generating an energy intensity similarity function according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile; determining the energy intensity similarity according to the energy intensity similarity function.

4. The imaging profile evaluation method according to claim 2, characterized in that, the determining the energy distribution contrast between the target imaging profile and at least one reference imaging profile includes: calculating the standard deviation of the amplitude value of the target imaging profile according to the amplitude value of each sample point of the target imaging profile; calculating the standard deviation of the amplitude value of the reference imaging profile according to the amplitude value of each sample point of the reference imaging profile; determining the energy distribution contrast according to the standard deviation of the amplitude value of the target imaging profile and the standard deviation of the amplitude value of the reference imaging profile.

5. The imaging profile evaluation method according to claim 4, characterized in that, determining the energy distribution contrast according to the standard deviation of the amplitude value of the target imaging profile and the standard deviation of the amplitude value of the reference imaging profile includes: generating an energy distribution contrast function according to the standard deviation of the amplitude value of the reference imaging profile and the standard deviation of the amplitude value of the reference imaging profile; determining the energy distribution contrast according to the energy distribution contrast function.

6. The imaging profile evaluation method according to claim 2, characterized in that, the determining the structural similarity between the target imaging profile and at least one reference imaging profile includes: calculating the covariance between the target imaging profile and the reference imaging profile according to the average energy value of the target imaging profile and the average energy value of the reference imaging profile; determining the structural similarity according to the covariance, the standard deviation of the amplitude value of the target imaging profile, and the standard deviation of the amplitude value of the reference imaging profile.

7. The imaging profile evaluation method according to claim 6, wherein, determining the structural similarity according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile includes: generating a structural similarity function according to the covariance, the standard deviation of the amplitude values of the target imaging profile, and the standard deviation of the amplitude values of the reference imaging profile; determining the structural similarity according to the structural similarity function.

8. The imaging profile evaluation method according to any one of claims 1 to 7, wherein, evaluating the target imaging profile according to the energy intensity similarity, the energy distribution contrast, the structural similarity, and the at least one reference imaging profile includes: generating a quantitative evaluation function according to the energy intensity similarity, the energy distribution contrast, and the structural similarity; evaluating the target imaging profile according to the quantitative evaluation function and the at least one reference imaging profile.

9. An imaging profile evaluation device applicable to multiple observation systems, wherein, comprising: a three-parameter determination module configured to respectively determine the energy intensity similarity, the energy distribution contrast, and the structural similarity between a target imaging profile and at least one reference imaging profile; wherein, the target imaging profile and the reference imaging profile are generated from seismic data measured by different observation systems; a target imaging profile evaluation module configured to evaluate the target imaging profile according to the energy intensity similarity, the energy distribution contrast, the structural similarity, and the at least one reference imaging profile.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, when the processor executes the program, the steps of an imaging profile evaluation method applicable to multiple observation systems according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of an imaging profile evaluation method applicable to multiple observation systems according to any one of claims 1 to 8 are implemented.

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