Method and device for determining construction parameters of a seismic source, storage medium and electronic device

Abstract

The present application provides a method and device for determining seismic source construction parameters, a storage medium and an electronic device. The method comprises: obtaining a first parameter set of a first area; determining a prediction model according to the first parameter set; obtaining a surface attribute parameter of a second area; inputting the surface attribute parameter into the prediction model to determine the seismic source construction parameters of the second area; wherein the second area is a to-be-constructed area, the first area is a completed area with the same surface type as the to-be-constructed area, and the first parameter set is a set of all parameters of the first area. The present application avoids the dependence on the construction experience of technicians, ensures the accuracy of the determined seismic source construction parameters, and avoids the situation that the construction process is affected by the high re-activation rate caused by unreasonable seismic source construction parameters, thereby improving the construction efficiency.

Description

Technical Field

[0001] This invention relates to the field of tunneling machine technology, and more specifically, to a method, apparatus, storage medium, and electronic device for determining seismic source construction parameters. Background Technology

[0002] In related technologies, before commencing field construction, technicians first determine the seismic source parameters based on past experience, then conduct excitation tests at the construction site, and make corresponding adjustments based on the test results. This approach may result in excessively long testing times due to insufficient experience of the technicians, affecting the normal commencement of construction, or it may lead to a persistently high recurrence rate during the construction process due to unreasonable seismic source parameters. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the related art.

[0004] Therefore, the first aspect of the present invention is to provide a method for determining the construction parameters of a seismic source.

[0005] The second aspect of the present invention is to provide a device for determining the construction parameters of a seismic source.

[0006] A third aspect of the present invention is to provide a device for determining the construction parameters of a seismic source.

[0007] A fourth aspect of the present invention is to provide a readable storage medium.

[0008] The fifth aspect of the present invention is to provide an electronic device.

[0009] In view of this, according to one aspect of the present invention, a method for determining source construction parameters is proposed, the method comprising: obtaining a first parameter set of a first region; determining a prediction model based on the first parameter set; obtaining surface attribute parameters of a second region; inputting the surface attribute parameters into the prediction model to determine the source construction parameters of the second region; wherein the second region is a region to be constructed, the first region is a completed region with the same surface type as the region to be constructed, and the first parameter set is a set of all parameters of the first region.

[0010] It should be noted that the execution subject of the method for determining the seismic source construction parameters proposed in this invention can be a device for determining the seismic source construction parameters. In order to more clearly explain the method for determining the seismic source construction parameters proposed in this invention, the following technical solution uses a device for determining the seismic source construction parameters as the execution subject of the method for determining the seismic source construction parameters for illustrative purposes.

[0011] In this technical solution, the aforementioned source construction parameters mainly include parameters such as average phase, peak phase, average distortion, peak distortion, average output, and peak output, which can be used for real-time analysis and quality control at the construction site; the aforementioned surface attribute parameters mainly include parameters such as stiffness, viscosity, elevation, and latitude and longitude; the aforementioned second region is the region to be constructed; the aforementioned first region is the region with the same surface type as the second region, and which has already been constructed; the aforementioned first parameter set is the set of parameters generated during the construction of the aforementioned first region and surface attribute parameters.

[0012] Specifically, the determining device first acquires a first parameter set for the first region, and then constructs a prediction model based on this first parameter set that can predict the source construction parameters based on surface attribute parameters. Specifically, the determining device can acquire the first parameter set by collecting data recorded during construction in the first region and the surface attribute parameters of that region.

[0013] Specifically, the determining device can analyze a first set of parameters to determine the correlation between source construction parameters and surface attribute parameters within a first region. Based on this correlation, a prediction model can be constructed. Therefore, the determining device can determine the prediction model based on the aforementioned first set of parameters. In particular, this prediction model can predict source construction parameters with high accuracy based on surface attribute parameters.

[0014] Furthermore, the device obtains the surface attribute parameters of the second region and inputs these surface attribute parameters into the prediction model to predict the source construction parameters of the second region.

[0015] Specifically, since the second region has not yet been constructed, it is impossible to obtain historical seismic source construction data for that region to construct and train a model that can be used to predict seismic source construction parameters. However, through big data analysis of seismic source construction data over the years, it has been found that seismic source construction parameters are closely related to surface attribute parameters within the construction area. Therefore, in the technical solution of this invention, a prediction model determined by the first parameter set in the first region, which has the same surface type as the second region, is used to predict the seismic source construction parameters in the second region.

[0016] In this technical solution, the determining device can construct a prediction model using a first parameter set from a first region with the same surface type as the second region (i.e., the region to be constructed). By inputting the obtained surface attribute parameters of the second region into the prediction model, the determining device can obtain accurate source construction parameters. This invention, through the above technical solution, avoids reliance on the construction experience of technical personnel, ensures the accuracy of the determined source construction parameters, and prevents situations where unreasonable source construction parameters lead to high repetition rates during construction, thus affecting construction efficiency.

[0017] Furthermore, the method for determining the seismic source construction parameters proposed according to the above-described technical solution of the present invention may also have the following additional technical features: In the above technical solution, the steps of determining the prediction model based on the first parameter set specifically include: preprocessing the first parameter set to determine the second parameter set; determining the surface attribute parameters in the second parameter set as the first variable and the source construction parameters in the second parameter set as the second variable; determining the first coefficient between the first variable and the second variable, wherein the first coefficient is used to indicate the degree of correlation between the first variable and the second variable; and determining the prediction model based on the first coefficient.

[0018] In this technical solution, the process by which the determining device constructs a prediction model based on the aforementioned first parameter set is as follows: The determining device first preprocesses the aforementioned first parameter set to determine the second parameter set. Specifically, the first parameter set may contain abnormal data and useless data, so the determining device needs to preprocess it to determine the second parameter set with higher reference value, so as to ensure that subsequent steps determine a prediction model with better prediction performance based on the second parameter set.

[0019] Furthermore, the determining device identifies the surface attribute parameters from the second parameter set as the first variable and the source construction parameters from the second parameter set as the second variable. Specifically, the first variable indicates the independent variable input into the prediction model, and the second variable indicates the dependent variable output by the prediction model.

[0020] Furthermore, the determining device determines a first coefficient between the first variable and the second variable, that is, it determines a first coefficient between the independent variable and the dependent variable. Specifically, the first coefficient is used to indicate the degree of correlation between the first variable and the second variable.

[0021] Specifically, the determining device can determine the first coefficient by using a comparison table or scatter plot between the parameters of the first and second variables.

[0022] Furthermore, the determining device determines the prediction model based on the aforementioned first coefficient. Specifically, the correlation between the first variable and the second variable can be analyzed based on the first coefficient. A linear regression equation can be constructed based on the correlation. The prediction model is obtained by solving and optimizing the linear regression equation using multiple first and second variables in the second parameter set.

[0023] In this technical solution, the determining device can preprocess the first parameter set to determine a second parameter set with higher reference value. Based on the first variables and their first coefficients in the second parameter set, the determining device can then determine a prediction model for predicting the source construction parameters in the second region. In this way, subsequent steps only require inputting the surface attribute parameters of the second region into the prediction model to obtain accurate source construction parameters. This avoids reliance on the construction experience of technical personnel, ensures the accuracy of the determined source construction parameters, and prevents situations where unreasonable source construction parameters lead to high repetition rates during construction, thus affecting construction efficiency.

[0024] In the above technical solution, the step of preprocessing the first parameter set to determine the second parameter set specifically includes: filtering the parameters in the first parameter set to determine the second parameter set.

[0025] In this technical solution, since there may be abnormal and useless data in the first parameter set, the determining device needs to screen it to determine the second parameter set with higher reference value, so as to ensure that subsequent steps determine the prediction model with better prediction effect based on the second parameter set.

[0026] In this technical solution, during the process of determining the prediction model based on the first parameter set, the determining device also needs to screen the data in the first parameter set, eliminate useless data, and select the second parameter set with higher reference value. This ensures that subsequent steps can determine a prediction model with better prediction effect based on the second parameter set.

[0027] In the above technical solution, the step of determining the first coefficient of the first variable and the second variable specifically includes: constructing a first graph of the first variable and the second variable using qualitative and quantitative analysis methods, wherein the first graph is a table or scatter plot used to indicate the degree of correlation between the first variable and the second variable; and determining the first coefficient based on the first graph.

[0028] In this technical solution, the first chart mentioned above is a table or scatter plot used to show the correlation between the first variable and the second variable.

[0029] Specifically, the determination device first uses qualitative and quantitative analysis to analyze the correlation between the first and second variables, and then statistically analyzes the results in tables or scatter plots.

[0030] Furthermore, the determining device calculates a first coefficient between the first variable and the second variable based on the aforementioned first graph. Specifically, the determining device can calculate the correlation coefficient between the first variable and the second variable using the first graph, and this coefficient is the aforementioned first coefficient.

[0031] In this technical solution, the determining device can use quantitative and qualitative analysis to construct a table or scatter plot showing the correlation between the first variable and the second variable, so as to intuitively and accurately calculate the first coefficient based on the first chart. This ensures the predictive effect of the prediction model determined based on the first coefficient in subsequent steps.

[0032] In the above technical solution, the steps of determining the prediction model based on the first coefficient specifically include: constructing a linear regression model based on the first coefficient; evaluating the linear regression model, and determining the linear regression model that passes the evaluation as the prediction model.

[0033] In this technical solution, the determining device first constructs a linear regression model based on the first coefficient mentioned above. Its mathematical expression is a linear regression equation. The second variable can be calculated based on the relationship between the variables and the first variable.

[0034] Furthermore, the determining device evaluates the aforementioned linear regression model, and if the linear regression model passes the evaluation, it is determined to be a predictive model. Specifically, evaluating the linear regression model refers to testing the linear regression model and calculating the prediction error. Only if the linear regression model passes the test can it be used as a predictive model.

[0035] In this technical solution, after the determining device constructs a linear regression model based on the first coefficient between the first and second variables, it also needs to evaluate the linear model. Only if the linear regression model passes the evaluation can it be used as a prediction model. This ensures the accuracy of the source construction parameters in the second region predicted by the prediction model.

[0036] In the above technical solution, the steps of evaluating the linear regression model and determining the evaluated linear regression model as the prediction model specifically include: using statistical testing to determine the prediction error of the linear regression model; if the prediction error is less than a preset threshold, determining that the linear regression model has passed the evaluation, and determining the evaluated linear regression model as the prediction model.

[0037] In this technical solution, the determining device can use statistical testing to calculate the prediction error of linear regression. Specifically, the prediction error can be one or more of MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), MSE (Mean Square Error), and RMSE (Root Mean Square Error).

[0038] Furthermore, the relationship between the prediction error calculated by the device and the preset threshold is determined, and if the prediction error is less than the preset threshold, the linear regression model is determined to pass the evaluation, and the linear regression model that passes the evaluation is determined as the prediction model.

[0039] Specifically, if the prediction error is less than the preset threshold, it indicates that the predicted values ​​of the seismic source construction parameters are in high agreement with the actual values. In this case, the determination device can confirm that the linear regression model has passed the evaluation.

[0040] Furthermore, the determining device can also evaluate the linear regression model by calculating the multiple correlation coefficient of the linear regression model. Specifically, the multiple correlation coefficient ranges from [0,1]. The closer it is to 1, the better the fitting effect of the linear regression model, that is, the more accurate the predicted value. Generally speaking, when the multiple correlation coefficient is greater than 0.5, it can be determined that the linear regression model has a good predictive effect.

[0041] Furthermore, the determination device can also assess the predictive performance of the linear retrospective model by constructing a scatter plot of the actual and predicted values ​​of the seismic source construction parameters.

[0042] In this technical solution, the determination device can calculate the prediction error of the linear regression model through statistical testing. Only when the prediction error is less than a preset threshold is the linear regression model determined to pass the evaluation and be identified as a prediction model. This ensures the accuracy of the source construction parameters in the second region predicted by the prediction model.

[0043] According to a second aspect of the present invention, an apparatus for determining source construction parameters is provided. The apparatus includes: an acquisition module for acquiring a first parameter set of a first region; a first processing module for determining a prediction model based on the first parameter set; the acquisition module is further configured to acquire surface attribute parameters of a second region; and a second processing module for inputting the surface attribute parameters into the prediction model to determine the source construction parameters of the second region.

[0044] In this technical solution, the aforementioned source construction parameters mainly include parameters such as average phase, peak phase, average distortion, peak distortion, average output, and peak output, which can be used for real-time analysis and quality control at the construction site; the aforementioned surface attribute parameters mainly include parameters such as stiffness, viscosity, elevation, and latitude and longitude; the aforementioned second region is the region to be constructed; the aforementioned first region is the region with the same surface type as the second region, and which has already been constructed; the aforementioned first parameter set is the set of parameters generated during the construction of the aforementioned first region and surface attribute parameters.

[0045] Specifically, the determining device first acquires a first parameter set for the first region, and then the first processing module constructs a prediction model based on the first parameter set that can predict the source construction parameters based on surface attribute parameters. Specifically, the acquisition module can acquire the first parameter set by collecting data recorded during construction in the first region and the surface attribute parameters of that region.

[0046] Specifically, the first processing module can determine the correlation between the source construction parameters and surface attribute parameters within the first region by analyzing the first parameter set. Based on this correlation, a prediction model can be constructed. Therefore, the first processing module can determine the prediction model based on the aforementioned first parameter set. In particular, this prediction model can predict the source construction parameters with high accuracy based on the surface attribute parameters.

[0047] Furthermore, the acquisition module is also used to acquire the surface attribute parameters of the second region, and the second processing module is used to input the surface attribute parameters into the prediction model to predict the source construction parameters in the second region.

[0048] Specifically, since the second region has not yet been constructed, it is impossible to obtain historical seismic source construction data for that region to construct and train a model that can be used to predict seismic source construction parameters. However, through big data analysis of seismic source construction data over the years, it has been found that seismic source construction parameters are closely related to surface attribute parameters within the construction area. Therefore, in the technical solution of this invention, a prediction model determined by the first parameter set in the first region, which has the same surface type as the second region, is used to predict the seismic source construction parameters in the second region.

[0049] In this technical solution, the first processing module can construct a prediction model using a set of first parameters from a first region with the same surface type as the second region (i.e., the region to be constructed). The second processing module can then input the obtained surface attribute parameters of the second region into the prediction model to obtain accurate source construction parameters. This invention, through the above technical solution, avoids reliance on the construction experience of technical personnel, ensures the accuracy of the determined source construction parameters, and prevents situations where unreasonable source construction parameters lead to high repetition rates during construction, thus affecting construction efficiency.

[0050] According to a third aspect of the present invention, an apparatus for determining seismic source construction parameters is provided. The apparatus includes: a memory storing a program or instructions; and a processor executing the program or instructions stored in the memory to implement the steps of the method for determining seismic source construction parameters as proposed in the above-described technical solution of the present invention. Therefore, it has all the beneficial technical effects of the method for determining seismic source construction parameters proposed in the above-described technical solution of the present invention, which will not be elaborated further here.

[0051] According to a fourth aspect of the present invention, a readable storage medium is provided on which a program or instructions are stored, which, when executed by a processor, implement the method for determining seismic source construction parameters as proposed in the above-described technical solution of the present invention. Therefore, this readable storage medium possesses all the beneficial effects of the method for determining seismic source construction parameters proposed in the above-described technical solution of the present invention, which will not be elaborated further here.

[0052] According to a fifth aspect of the present invention, an electronic device is provided, comprising a device for determining seismic source construction parameters as described in the above-described technical solution of the present invention, and / or a readable storage medium as described in the above-described technical solution of the present invention. Therefore, the electronic device possesses all the beneficial effects of the device for determining seismic source construction parameters as described in the above-described technical solution of the present invention and / or the readable storage medium as described in the above-described technical solution of the present invention, which will not be elaborated further here.

[0053] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0054] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic flowchart illustrating one of the methods for determining the construction parameters of the seismic source according to an embodiment of the present invention; Figure 2 This is a second schematic flowchart illustrating the method for determining the seismic source construction parameters according to an embodiment of the present invention; Figure 3 The third flowchart illustrates the method for determining the seismic source construction parameters according to an embodiment of the present invention. Figure 4 The fourth flowchart illustrates the method for determining the seismic source construction parameters according to an embodiment of the present invention. Figure 5 A scatter plot showing the correlation between surface property parameters and source parameters in an embodiment of the present invention is provided. Figure 6 The fifth flowchart illustrates the method for determining the seismic source construction parameters according to an embodiment of the present invention. Figure 7 This is a sixth flowchart illustrating the method for determining the seismic source construction parameters according to an embodiment of the present invention; Figure 8 One of the scatter plots showing the actual and predicted values ​​of the source construction parameters according to an embodiment of the present invention is shown. Figure 9 The second scatter plot shows the actual and predicted values ​​of the seismic source construction parameters according to an embodiment of the present invention. Figure 10The third scatter plot shows the actual and predicted values ​​of the seismic source construction parameters according to an embodiment of the present invention. Figure 11 One of the schematic block diagrams of a device for determining seismic source construction parameters according to an embodiment of the present invention is shown; Figure 12 The second schematic block diagram shows a device for determining the seismic source construction parameters according to an embodiment of the present invention. Detailed Implementation

[0055] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0057] The following is combined Figures 1 to 12 The present invention provides a detailed description of a method, apparatus, storage medium, and electronic device for determining seismic source construction parameters through specific embodiments and application scenarios.

[0058] Example 1: Figure 1 A schematic flowchart illustrating the determination of seismic source construction parameters according to an embodiment of the present invention is shown, wherein the determination method includes: S102, Obtain the first parameter set of the first region; S104, Determine the prediction model based on the first parameter set; S106, Obtain the surface attribute parameters of the second region; S108. Input the surface attribute parameters into the prediction model to determine the source construction parameters for the second region.

[0059] The second region is the area to be constructed, the first region is the completed area with the same surface type as the area to be constructed, and the first parameter set is the set of all parameters of the first region.

[0060] It should be noted that the execution subject of the method for determining the seismic source construction parameters proposed in this invention can be a device for determining the seismic source construction parameters. In order to more clearly explain the method for determining the seismic source construction parameters proposed in this invention, the following embodiments use a device for determining the seismic source construction parameters as the execution subject of the method for determining the seismic source construction parameters for illustrative purposes.

[0061] In this embodiment, the aforementioned source construction parameters mainly include parameters such as average phase, peak phase, average distortion, peak distortion, average output, and peak output, which can be used for real-time analysis and quality control at the construction site; the aforementioned surface attribute parameters mainly include parameters such as stiffness, viscosity, elevation, and latitude and longitude; the aforementioned second region is the region to be constructed; the aforementioned first region is the region with the same surface type as the second region, and which has already been constructed; the aforementioned first parameter set is a collection of parameters generated during the construction of the aforementioned first region and surface attribute parameters.

[0062] Specifically, the determining device first acquires a first parameter set for the first region, and then constructs a prediction model based on this first parameter set that can predict the source construction parameters based on surface attribute parameters. Specifically, the determining device can acquire the first parameter set by collecting data recorded during construction in the first region and the surface attribute parameters of that region.

[0063] Specifically, the determining device can analyze a first set of parameters to determine the correlation between source construction parameters and surface attribute parameters within a first region. Based on this correlation, a prediction model can be constructed. Therefore, the determining device can determine the prediction model based on the aforementioned first set of parameters. In particular, this prediction model can predict source construction parameters with high accuracy based on surface attribute parameters.

[0064] For example, the determining device can divide the first region into multiple region blocks as shown in Table 1 according to latitude and longitude, then correspond the parameters in the first parameter set to the region blocks respectively, determine the relationship between the parameters in each region block respectively, and finally determine the prediction model based on these relationships. This is beneficial to improving the prediction effect of the prediction model.

[0065] Table 1

[0066] Furthermore, the device obtains the surface attribute parameters of the second region and inputs these surface attribute parameters into the prediction model to predict the source construction parameters of the second region.

[0067] Specifically, since the second region has not yet been constructed, it is impossible to obtain historical seismic source construction data for that region to construct and train a model that can be used to predict seismic source construction parameters. However, through big data analysis of seismic source construction data over the years, it has been found that seismic source construction parameters are closely related to surface attribute parameters within the construction area. Therefore, in this embodiment of the invention, a prediction model determined by the first parameter set in the first region, which has the same surface type as the second region, is used to predict the seismic source construction parameters in the second region.

[0068] In this embodiment, the determining device can construct a prediction model using a first parameter set from a first region with the same surface type as the second region (i.e., the region to be constructed). By inputting the obtained surface attribute parameters of the second region into the prediction model, the determining device can obtain accurate source construction parameters. Through the above embodiment, this invention avoids reliance on the construction experience of technical personnel, ensures the accuracy of the determined source construction parameters, and prevents situations where unreasonable source construction parameters lead to high repetition rates during construction, thus affecting construction efficiency.

[0069] Figure 2 A schematic flowchart illustrating the determination of seismic source construction parameters according to an embodiment of the present invention is shown, wherein the determination method includes: S202, Obtain the first parameter set of the first region; S204, preprocess the first parameter set to determine the second parameter set; S206, the surface attribute parameters in the second parameter set are determined as the first variable, and the source construction parameters in the second parameter set are determined as the second variable; S208, Determine the first coefficient between the first variable and the second variable. The first coefficient is used to indicate the degree of correlation between the first variable and the second variable. S210, Determine the prediction model based on the first coefficient; S212, Obtain the surface attribute parameters of the second region; S214. Input the surface attribute parameters into the prediction model to determine the source construction parameters for the second region.

[0070] In this embodiment, the process by which the determining device constructs a prediction model based on the first parameter set is as follows: The determining device first preprocesses the first parameter set to determine a second parameter set. Specifically, the first parameter set may contain abnormal data and useless data, so the determining device needs to preprocess it to determine a second parameter set with higher reference value, so as to ensure that subsequent steps determine a prediction model with better prediction performance based on the second parameter set.

[0071] Furthermore, the determining device identifies the surface attribute parameters from the second parameter set as the first variable and the source construction parameters from the second parameter set as the second variable. Specifically, the first variable indicates the independent variable input into the prediction model, and the second variable indicates the dependent variable output by the prediction model.

[0072] Furthermore, the determining device determines a first coefficient between the first variable and the second variable, that is, it determines a first coefficient between the independent variable and the dependent variable. Specifically, the first coefficient is used to indicate the degree of correlation between the first variable and the second variable.

[0073] Specifically, the determining device can determine the first coefficient by using a comparison table or scatter plot between the parameters of the first and second variables.

[0074] Furthermore, the determining device determines the prediction model based on the aforementioned first coefficient. Specifically, the correlation between the first variable and the second variable can be analyzed based on the first coefficient. A linear regression equation can be constructed based on the correlation. The prediction model is obtained by solving and optimizing the linear regression equation using multiple first and second variables in the second parameter set.

[0075] In this embodiment, the determining device can preprocess the first parameter set to determine a second parameter set with higher reference value. The determining device can then determine a prediction model for predicting the source construction parameters in the second region based on the first variables and the first coefficients of the second variables determined in the second parameter set. Thus, in subsequent steps, only the surface attribute parameters of the second region need to be input into the prediction model to obtain accurate source construction parameters. This avoids reliance on the construction experience of technical personnel, ensures the accuracy of the determined source construction parameters, and prevents situations where unreasonable source construction parameters lead to high repetition rates during construction, affecting construction efficiency.

[0076] Figure 3 A schematic flowchart illustrating the determination of seismic source construction parameters according to an embodiment of the present invention is shown, wherein the determination method includes: S302, Obtain the first parameter set of the first region; S304, Filter the parameters in the first parameter set to determine the second parameter set; S306, the surface attribute parameters in the second parameter set are determined as the first variable, and the source construction parameters in the second parameter set are determined as the second variable; S308, Determine the first coefficient between the first variable and the second variable. The first coefficient is used to indicate the degree of correlation between the first variable and the second variable. S310, Determine the prediction model based on the first coefficient; S312, Obtain the surface attribute parameters of the second region; S314. Input the surface attribute parameters into the prediction model to determine the source construction parameters for the second region.

[0077] In this embodiment, since there may be abnormal and useless data in the first parameter set, the determining device needs to screen it to determine the second parameter set with higher reference value, so as to ensure that the subsequent steps determine the prediction model with better prediction effect based on the second parameter set.

[0078] In this embodiment, during the process of determining the prediction model based on the first parameter set, the determining device also needs to filter the data in the first parameter set, exclude useless data, and select the second parameter set with higher reference value. This ensures that subsequent steps can determine a prediction model with better prediction effect based on the second parameter set.

[0079] Figure 4 A schematic flowchart illustrating the determination of seismic source construction parameters according to an embodiment of the present invention is shown, wherein the determination method includes: S402, Obtain the first parameter set of the first region; S404, preprocess the first parameter set to determine the second parameter set; S406, the surface attribute parameters in the second parameter set are determined as the first variable, and the source construction parameters in the second parameter set are determined as the second variable; S408 uses qualitative and quantitative analysis to construct the first graph of the first and second variables; S410, Determine the first coefficient based on the first chart; S412, Determine the prediction model based on the first coefficient; S414, Obtain the surface attribute parameters of the second region; S416. Input the surface attribute parameters into the prediction model to determine the source construction parameters for the second region.

[0080] In this embodiment, the first chart is a table or scatter plot used to show the correlation between the first variable and the second variable.

[0081] Specifically, the determination device first uses qualitative and quantitative analysis to analyze the correlation between the first and second variables, and then statistically analyzes the results in tables or scatter plots.

[0082] For example, such as Figure 5 The figure shows a scatter plot representing the relationship between stiffness, a surface property parameter, and viscosity, a source parameter. The horizontal axis represents viscosity (Visc), and the vertical axis represents stiffness (Stif). The scatter plot shows a clear positive correlation between the two.

[0083] Furthermore, the determining device calculates a first coefficient between the first variable and the second variable based on the aforementioned first graph. Specifically, the determining device can calculate the correlation coefficient between the first variable and the second variable using the first graph, and this coefficient is the aforementioned first coefficient.

[0084] In this embodiment, the determining device can use quantitative and qualitative analysis to construct a table or scatter plot showing the correlation between the first variable and the second variable, so as to intuitively and accurately calculate the first coefficient based on the first chart. This ensures the predictive effect of the prediction model determined based on the first coefficient in subsequent steps.

[0085] Figure 6 A schematic flowchart illustrating the determination of seismic source construction parameters according to an embodiment of the present invention is shown, wherein the determination method includes: S602, Obtain the first parameter set of the first region; S604, preprocess the first parameter set to determine the second parameter set; S606, the surface attribute parameters in the second parameter set are determined as the first variable, and the source construction parameters in the second parameter set are determined as the second variable; S608, determine the first coefficient between the first variable and the second variable, the first coefficient is used to indicate the degree of correlation between the first variable and the second variable; S610, Construct a linear regression model based on the first coefficient; S612, Evaluate the linear regression model, and determine the linear regression model that passes the evaluation as the prediction model; S614, Obtain the surface attribute parameters of the second region; S616, input the surface attribute parameters into the prediction model to determine the source construction parameters of the second region.

[0086] In this embodiment, the determining device first constructs a linear regression model based on the first coefficient, which is mathematically expressed as a linear regression equation. The second variable can be calculated based on the relationship between the variables and the first variable.

[0087] For example, the expression for the linear regression equation is as follows: ; i = 1, ..., n; Among them, Y i Used to represent the second variable, X i Used to represent the first variable, Used to represent the error term variable. Used to represent linear coefficients, where P is any number from 1 to n.

[0088] Furthermore, the determining device evaluates the aforementioned linear regression model, and if the linear regression model passes the evaluation, it is determined to be a predictive model. Specifically, evaluating the linear regression model refers to testing the linear regression model and calculating the prediction error. Only if the linear regression model passes the test can it be used as a predictive model.

[0089] In this embodiment, after the determining device constructs a linear regression model based on the first coefficient between the first and second variables, it also needs to evaluate the linear model. Only if the linear regression model passes the evaluation can it be used as a prediction model. This ensures the accuracy of the source construction parameters in the second region predicted by the prediction model.

[0090] Figure 7 A schematic flowchart illustrating the determination of seismic source construction parameters according to an embodiment of the present invention is shown, wherein the determination method includes: S702, Obtain the first parameter set of the first region; S704, preprocess the first parameter set to determine the second parameter set; S706, the surface attribute parameters in the second parameter set are determined as the first variable, and the source construction parameters in the second parameter set are determined as the second variable; S708, Determine the first coefficient between the first variable and the second variable. The first coefficient is used to indicate the degree of correlation between the first variable and the second variable. S710, Construct a linear regression model based on the first coefficient; S712, use statistical tests to determine the prediction error of the linear regression model; S714, if the prediction error is less than a preset threshold, determine that the linear regression model has passed the evaluation, and determine the linear regression model that has passed the evaluation as the prediction model; S716, Obtain surface attribute parameters for the second region; S718. Input the surface attribute parameters into the prediction model to determine the source construction parameters for the second region.

[0091] In this embodiment, the determining device can use statistical tests to calculate the prediction error of the linear regression. Specifically, the prediction error can be one or more of MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), MSE (Mean Square Error), and RMSE (Root Mean Square Error).

[0092] For example, the formula for calculating MAE is shown in Equation 1, the formula for calculating MAPE is shown in Equation 2, the formula for calculating MSE is shown in Equation 3, and RMSE is equal to the square root of the value of MSE.

[0093] (Equation 1); (Equation 2); (Equation 3); in, Used to represent the second variable, y i Used to represent the first variable, that is It is y i The predicted value.

[0094] Furthermore, the relationship between the prediction error calculated by the device and the preset threshold is determined, and if the prediction error is less than the preset threshold, the linear regression model is determined to pass the evaluation, and the linear regression model that passes the evaluation is determined as the prediction model.

[0095] Specifically, if the prediction error is less than the preset threshold, it indicates that the predicted values ​​of the seismic source construction parameters are in high agreement with the actual values. In this case, the determination device can confirm that the linear regression model has passed the evaluation.

[0096] For example, the statistics of prediction errors are shown in Table 2: Table 2

[0097] Furthermore, the determining device can also evaluate the linear regression model by calculating the multiple correlation coefficient of the linear regression model. Specifically, the multiple correlation coefficient ranges from [0,1]. The closer it is to 1, the better the fitting effect of the linear regression model, that is, the more accurate the predicted value. Generally speaking, when the multiple correlation coefficient is greater than 0.5, it can be determined that the linear regression model has a good predictive effect.

[0098] Specifically, the formula for calculating the multiple correlation coefficient is as follows: ; Where R represents the multiple correlation coefficient. y is used to represent the second variable, and y is used to represent the first variable. Used to represent the average of multiple first variables.

[0099] Furthermore, the determination device can also assess the predictive performance of the linear retrospective model by constructing a scatter plot of the actual and predicted values ​​of the seismic source construction parameters.

[0100] For example, such as Figure 8 , Figure 9 and Figure 10 The figures shown are scatter plots comparing the actual and predicted values ​​of peak power, average power, and peak distortion in the source construction parameters. Figure 8 , Figure 9 and Figure 10In the diagram, from top to bottom, each row of scatter points indicates a multiple of 0.25, 0.50, and 0.75 of the predicted value, respectively, thus displaying the accuracy of the prediction results in segments. Furthermore, the smoothness of the curve formed by connecting scatter points at different multiples indicates the predictive performance of the linear retrospective model; understandably, a smoother curve indicates a better predictive performance and higher accuracy.

[0101] In this embodiment, the determining device can calculate the prediction error of the linear regression model through statistical testing. Only when the prediction error is less than a preset threshold is the linear regression model determined to pass the evaluation and be identified as a prediction model. This ensures the accuracy of the source construction parameters in the second region predicted by the prediction model.

[0102] Example 2: Figure 11 A schematic block diagram of a source construction parameter determination device 1100 according to an embodiment of the present invention is shown. The source construction parameter determination device 1100 includes an acquisition module 1102 for acquiring a first parameter set of a first region; a first processing module 1104 for determining a prediction model based on the first parameter set; the acquisition module 1102 is also used to acquire surface attribute parameters of a second region; and a second processing module 1106 for inputting the surface attribute parameters into the prediction model to determine the source construction parameters of the second region.

[0103] In this embodiment, the aforementioned source construction parameters mainly include parameters such as average phase, peak phase, average distortion, peak distortion, average output, and peak output, which can be used for real-time analysis and quality control at the construction site; the aforementioned surface attribute parameters mainly include parameters such as stiffness, viscosity, elevation, and latitude and longitude; the aforementioned second region is the region to be constructed; the aforementioned first region is the region with the same surface type as the second region, and which has already been constructed; the aforementioned first parameter set is a collection of parameters generated during the construction of the aforementioned first region and surface attribute parameters.

[0104] Specifically, the determining device first acquires a first parameter set for the first region, and then the first processing module 1104 constructs a prediction model based on the first parameter set that can predict the source construction parameters based on surface attribute parameters. Specifically, the acquisition module 1102 can acquire the first parameter set by collecting data recorded during the construction process in the first region and the surface attribute parameters of that region.

[0105] Specifically, the first processing module 1104 can determine the correlation between the source construction parameters and surface attribute parameters within the first region by analyzing the first parameter set. Based on this correlation, a prediction model can be constructed. Therefore, the first processing module 1104 can determine the prediction model based on the aforementioned first parameter set. In particular, this prediction model can predict source construction parameters with high accuracy based on surface attribute parameters.

[0106] Furthermore, the acquisition module 1102 is also used to acquire the surface attribute parameters of the second region, and the second processing module 1106 is used to input the surface attribute parameters into the prediction model to predict the source construction parameters in the second region.

[0107] Specifically, since the second region has not yet been constructed, it is impossible to obtain historical seismic source construction data for that region to construct and train a model that can be used to predict seismic source construction parameters. However, through big data analysis of seismic source construction data over the years, it has been found that seismic source construction parameters are closely related to surface attribute parameters within the construction area. Therefore, in this embodiment of the invention, a prediction model determined by the first parameter set in the first region, which has the same surface type as the second region, is used to predict the seismic source construction parameters in the second region.

[0108] In this embodiment, the first processing module 1104 can construct a prediction model using a first parameter set from a first region with the same surface type as the second region (i.e., the region to be constructed). The second processing module 1106 can obtain accurate source construction parameters by inputting the obtained surface attribute parameters of the second region into the prediction model. Through the above embodiment, this invention avoids reliance on the construction experience of technical personnel, ensures the accuracy of the determined source construction parameters, and prevents situations where unreasonable source construction parameters lead to high repetition rates during construction, thus affecting construction efficiency.

[0109] Furthermore, in this embodiment, the first processing module 1104 is also used to preprocess the first parameter set to determine the second parameter set; determine the surface attribute parameters in the second parameter set as the first variable, and determine the source construction parameters in the second parameter set as the second variable; determine the first coefficient between the first variable and the second variable, the first coefficient being used to indicate the degree of correlation between the first variable and the second variable; and determine the prediction model based on the first coefficient.

[0110] Furthermore, in this embodiment, the first processing module 1104 is also used to filter the parameters in the first parameter set to determine the second parameter set.

[0111] Furthermore, in this embodiment, the first processing module 1104 is also used to construct a first graph of the first variable and the second variable by means of qualitative and quantitative analysis. The first graph is a table or scatter plot used to indicate the degree of correlation between the first variable and the second variable; and to determine the first coefficient based on the first graph.

[0112] Furthermore, in this embodiment, the first processing module 1104 is also used to construct a linear regression model based on the first coefficient; evaluate the linear regression model, and determine the linear regression model that passes the evaluation as the prediction model.

[0113] Furthermore, in this embodiment, the first processing module 1104 is also used to determine the prediction error of the linear regression model by means of statistical testing; if the prediction error is less than a preset threshold, the linear regression model is determined to pass the evaluation, and the linear regression model that passes the evaluation is determined as the prediction model.

[0114] Example 3: Figure 12 A schematic block diagram of a device for determining seismic source construction parameters according to an embodiment of the present invention is shown. The device 1200 includes: a memory 1202 storing a program or instructions; and a processor 1204 executing the program or instructions stored in the memory 1202 to implement the steps of the method for determining seismic source construction parameters as proposed in the above embodiments of the present invention. Therefore, it has all the beneficial technical effects of the method for determining seismic source construction parameters proposed in the above embodiments of the present invention, and will not be described in detail here.

[0115] Example 4: According to a fourth embodiment of the present invention, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the method for determining seismic source construction parameters as proposed in the above embodiments of the present invention. Therefore, this readable storage medium possesses all the beneficial effects of the method for determining seismic source construction parameters proposed in the above embodiments of the present invention, and will not be elaborated further here.

[0116] Example 5: According to a fifth embodiment of the present invention, an electronic device is provided, including a device for determining seismic source construction parameters as proposed in the above embodiments of the present invention, and / or a readable storage medium as proposed in the above embodiments of the present invention. Therefore, the electronic device has all the beneficial effects of the device for determining seismic source construction parameters proposed in the above embodiments of the present invention and / or the readable storage medium proposed in the above embodiments of the present invention, which will not be repeated here.

[0117] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance, unless otherwise expressly specified and limited. The terms "connection," "installation," and "fixing," etc., should be interpreted broadly. For example, "connection" can mean a fixed connection, a detachable connection, or an integral connection; it can mean a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0118] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0119] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0120] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining seismic source construction parameters, characterized in that, include: Obtain the first parameter set of the first region; Determine the prediction model based on the first parameter set; Obtain the surface attribute parameters of the second region; The surface attribute parameters are input into the prediction model to determine the source construction parameters of the second region; Wherein, the second area is the area to be constructed, the first area is a completed area with the same surface type as the area to be constructed, and the first parameter set is the set of all parameters of the first area; Determining the prediction model based on the first parameter set specifically includes: The first parameter set is preprocessed to determine the second parameter set; The surface attribute parameters in the second parameter set are determined as the first variable, and the source construction parameters in the second parameter set are determined as the second variable; the first variable is used to indicate the independent variable input into the prediction model, and the second variable is used to indicate the dependent variable output by the prediction model. Determine a first coefficient between the first variable and the second variable, wherein the first coefficient is used to indicate the degree of correlation between the first variable and the second variable; The prediction model is determined based on the first coefficient; Determining the first coefficient between the first variable and the second variable specifically includes: Using qualitative and quantitative analysis, a first graph is constructed to represent the first variable and the second variable. The first graph is a table or scatter plot used to indicate the degree of correlation between the first variable and the second variable. Based on the first chart, determine the first coefficient; The step of determining the prediction model based on the first coefficient specifically includes: Construct a linear regression model based on the first coefficient; The linear regression model is evaluated, and the linear regression model that passes the evaluation is determined as the prediction model. The evaluation of the linear regression model, and the determination of the evaluated linear regression model as the prediction model, specifically includes: The prediction error of the linear regression model was determined using statistical tests. If the prediction error is less than a preset threshold, the linear regression model is determined to have passed the evaluation, and the linear regression model that has passed the evaluation is determined as the prediction model.

2. The method for determining the seismic source construction parameters according to claim 1, characterized in that, The step of preprocessing the first parameter set to determine the second parameter set specifically includes: The parameters in the first parameter set are filtered to determine the second parameter set.

3. A device for determining seismic source construction parameters, characterized in that, The method for determining the source construction parameters according to any one of claims 1 to 2 includes: The acquisition module is used to acquire the first parameter set of the first region; The first processing module is used to determine the prediction model based on the first parameter set; The acquisition module is also used to acquire surface attribute parameters of the second region; The second processing module is used to input the surface attribute parameters into the prediction model to determine the source construction parameters of the second region.

4. A device for determining seismic source construction parameters, characterized in that, include: A memory and a processor, the memory storing a program, the processor executing the program to implement the steps of the method for determining the source construction parameters as described in any one of claims 1 to 2.

5. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method for determining the source construction parameters as described in any one of claims 1 to 2.

6. An electronic device, characterized in that, include: The device for determining the construction parameters of the seismic source as described in claim 3 or 4; and / or The readable storage medium as described in claim 5.

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