A method, system, terminal, and readable storage medium for predicting multiple seismic sources and shot points.

Abstract

This application discloses a multi-source shot point prediction method, system, terminal, and readable storage medium. The positioning method includes: acquiring observation data collected by data acquisition devices on the exploration vessel and each source in the source array; preprocessing the observation data to remove gross errors and perform time synchronization; calculating the coordinates of each source in the source array at the current time based on the preprocessed observation data; inputting the coordinates of the current source as observation vectors into a Kalman filter-based source navigation and positioning model to obtain the spatial state information of each source; and determining the design shot point and the next shot source corresponding to the next shot based on the spatial state information of each source and the location of the design shot point. This application can improve the requirements for shot point prediction in marine seismic exploration.

Description

Technical Field

[0001] This application belongs to the field of marine seismic exploration, specifically involving a multi-source shot point prediction method, system, terminal, and readable storage medium. Background Technology

[0002] The process of marine seismic exploration involves an exploration vessel towing a source array along a designed survey line (pre-drawn survey line) and generating seismic waves at the designed shot points. Detectors receive the seismic waves reflected or refracted through seabed rock interfaces. By processing and interpreting the seismic wave data, seabed seismic structures can be determined, thereby achieving the goal of detecting marine oil and gas resources. The closer the actual shot location is to the designed shot point location, the higher the accuracy of seismic data acquisition. Therefore, a high-precision and highly stable shot point prediction method is needed. Current marine seismic exploration shot point prediction methods use polynomial models, constant velocity models, and constant acceleration models to estimate the motion state of the exploration vessel and the source array, based on the scenario of the exploration vessel traveling at a constant speed along the survey line. However, these models are too idealized and cannot be applied to the navigation and positioning of the source array in complex sea conditions. Meanwhile, in the process of shot point prediction, the source array is treated as a rigid body and its geometric center point (MGP, Mean Gun Position) is used for shot point prediction. However, in actual operation, there is no hardware connection between the sources and the source array will tilt due to the influence of wind and waves. Therefore, the method of using the MGP as the shot reference point for shot point prediction will affect the accuracy of seismic data acquisition.

[0003] Therefore, there is an urgent need for a shot point prediction method that can overcome the above-mentioned technical defects. Summary of the Invention

[0004] The purpose of this application is to provide a multi-source shot point prediction method, system, terminal, and readable storage medium that can improve the accuracy of shot point prediction in marine seismic exploration.

[0005] The technical solution provided in this application is as follows:

[0006] Firstly, this application provides a method for predicting shot points from multiple seismic sources, comprising the following steps:

[0007] Step 1: Acquire observation data from the data acquisition devices on each seismic source in the exploration vessel and seismic source array;

[0008] Step 2: Preprocess the observation data, remove gross errors, and synchronize the time.

[0009] Step 3: Calculate the coordinates of each source in the source array at the current moment based on the preprocessed observation data; input the coordinates of the current source as observation vectors into the Kalman filter-based source navigation and positioning model to obtain the spatial state information of each source; wherein, the Kalman filter-based source navigation and positioning model has the function of calculating the spatial state information of the corresponding source based on the input observation vectors; the spatial state information of the source includes the coordinates of the source;

[0010] Step 4: Based on the spatial state information of each seismic source and the location of the design shot point, determine the design shot point and seismic source corresponding to the next shot.

[0011] In one possible implementation, in step 1, the data acquisition devices on the exploration vessel and each seismic source in the source array include: a Regional Global Navigation Satellite System (RGNSS) base station on the exploration vessel, a Differential Global Navigation Satellite System (DGNSS) and an electric compass on the exploration vessel, and an RGNSS rover on each seismic source in the source array;

[0012] Step 2 includes: detecting gross errors in the observation data based on geometric constraints and mathematical statistics, and removing gross error data;

[0013] Step 2 further includes: for each RGNSS rover station at a seismic source, if there is no valid observation data at the current time, then the observation data is synchronized with historical observation data to obtain the observation data at the current time; wherein, the process of synchronizing the observation data with historical observation data to obtain the observation data at the current time includes:

[0014] If valid observation data exists on both sides at the current time, then the linear interpolation method is used to obtain the observation data at the current time.

[0015] If there is no valid observation data to the left or right of the current moment, the observation data for the current moment is obtained by extrapolating from historical observation data using polynomial fitting. The polynomial fitting formula is as follows:

[0016]

[0017] In the formula: for The observation data corresponding to the time, For a moment, For polynomial coefficients, The order of the polynomial;

[0018] By performing polynomial fitting on historical observation data, the fitting relationship between time and observation data is obtained, and the polynomial coefficients in the polynomial fitting formula are determined; then, the current time is substituted to obtain the observation data at the current time.

[0019] In one possible implementation, step 3, calculating the coordinates of each source in the source array at the current moment based on the preprocessed observation data, includes:

[0020]

[0021]

[0022] In the formula: The coordinates of the RGNSS base station on the exploration vessel. The DGNSS coordinates on the exploration vessel; The azimuth of the exploration vessel is obtained by the gyrocompass. The relative offset between the RGNSS base station on the exploration vessel and the DGNSS on the exploration vessel; for The coordinates of the epicenter at that moment. and These represent the azimuth and slant distance between the RGNSS base station and the RGNSS rover station at the epicenter, respectively.

[0023] In one possible implementation, step 3, the source navigation and localization model based on Kalman filtering, includes:

[0024]

[0025]

[0026]

[0027]

[0028] In the formula, for The state vector at time t, i.e. Spatial state information of the earthquake source at any given moment; for The state vector at any given time; and They are respectively and The covariance matrix; for The time-observation vector is calculated using the observation data preprocessed in step 2. The coordinates of the epicenter at that moment. for Always Predicted state vector value at time step; for Always The state transition matrix at time t; for The covariance matrix, for The covariance matrix of the process noise matrix at time step; for Gain matrix at time step ; It is a coefficient matrix; It is the identity matrix; for Observation noise matrix at time step The covariance matrix.

[0029] In one possible implementation, the method further includes:

[0030] right Observation noise matrix at time step covariance matrix Adjustments were made to obtain Then As Substitute into In the calculation formula, the spatial state information of each earthquake source is then solved;

[0031] Among them, for Observation noise matrix at time step covariance matrix Adjustments were made to obtain The calculation formula is:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] In the formula, For the adjusted Observation noise matrix at time step The covariance matrix; and These are the innovation of the Kalman filter and its covariance matrix, respectively. For the first Standardized residuals of each observation data; For the first The weighting factor corresponding to each observation data point and For the corresponding critical value, It is a local minimum.

[0038] In one possible implementation, for the multi-source ship described in the above embodiments, the positions of each source in the source array and the designed firing point are first determined based on their relative positions. The shot number of the nearest next design shot point (i.e., the number of the nearest design shot point along the design survey line) is used. Then, based on the relationship between the shot numbers of the next next design shot points closest to each seismic source and the shot number increment of the previously drawn design shot points (the change in the number of the design shot points along the design survey line), the design shot point (i.e., the predicted shot point) and the seismic source of the next shot are determined. The determination method is as follows:

[0039] Assuming the earthquake source The closest gun number for the next design firing point is ,in , This represents the number of seismic sources in the seismic source array; if the shot number increment is greater than 0, then find... The minimum value in the range is the design firing point for the next shot, and the seismic source corresponding to this minimum value is the seismic source for the next shot. If the increment of the firing number is less than 0, then find... The maximum value in the range is the design firing point for the next firing, and the seismic source corresponding to the maximum value is the seismic source for the next firing.

[0040] In one possible implementation, step 3, the spatial state information of the seismic source, also includes the velocity of each seismic source in the seismic source array;

[0041] The method further includes: Step 5: Calculate the remaining firing distance based on the relative positional relationship between the design firing point corresponding to the next firing and the seismic source of the next firing, and the velocity of the seismic source of the next firing. and remaining firing time It is used by gun control systems to determine whether to trigger seismic waves based on the remaining firing time.

[0042] In one possible implementation, in step 5, the remaining firing distance is calculated based on the relative positional relationship between the design firing point corresponding to the next firing and the seismic source of the next firing. and remaining firing time ,include:

[0043] Calculate the distance along the survey line (DA) between the design firing point corresponding to the next firing and the starting point of the survey line. ;

[0044]

[0045] In the formula, and These represent the distance and azimuth of the line connecting the design firing point and the starting point of the survey line for the next firing shot. The azimuth of the survey line;

[0046] Calculate the distance along the survey line from the source of the next artillery strike to the starting point of the survey line. :

[0047]

[0048] In the formula, and These are the distance and azimuth of the line connecting the next seismic source and the starting point of the survey line, respectively, calculated based on the spatial state information of each seismic source and the position information of the starting point of the survey line obtained in step 3. The azimuth of the survey line;

[0049] based on and Calculate the remaining firing distance and remaining firing time And send it to the gun control system, including:

[0050]

[0051]

[0052] In the formula: The velocity of the seismic source along the survey line for the next firing is obtained based on the spatial state information of each seismic source obtained in step 3.

[0053] Secondly, this application provides a multi-source shot point prediction system, comprising:

[0054] The observation data acquisition module is used to acquire observation data collected by the data acquisition devices on each seismic source in the exploration vessel and the seismic source array.

[0055] The data preprocessing module is used to preprocess the observation data, remove gross errors, and perform time synchronization.

[0056] The source spatial state information calculation module is used to calculate the coordinates of each source in the source array at the current moment based on preprocessed observation data; the coordinates of the sources at the current moment are used as observation vectors and input into the source navigation and positioning model based on Kalman filtering to obtain the spatial state information of each source; wherein, the source navigation and positioning model based on Kalman filtering has the function of calculating the spatial state information of the corresponding source based on the input observation vector; the spatial state information of the source includes the coordinates of the source;

[0057] The shot point prediction module is used to determine the design shot point and the seismic source for the next shot based on the spatial state information of each seismic source and the location of the design shot point.

[0058] In one possible implementation, the shot point prediction module is further configured to calculate the remaining shot distance based on the relative positional relationship between the designed shot point corresponding to the next shot and the seismic source of the next shot. and remaining firing time .

[0059] Thirdly, this application provides a terminal, including: a memory and a processor;

[0060] The memory is used to store computer programs;

[0061] The processor is used to call the computer program to execute the multi-source shot point prediction method as described above.

[0062] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on an electronic device, enables the electronic device to implement the multi-source shot point prediction method as described above.

[0063] Fifthly, this application provides a computer program product, including a computer program that, when run on an electronic device, enables the electronic device to implement the multi-source shot point prediction method as described above.

[0064] The specific implementation methods of the second to fifth aspects of this application can refer to the implementation methods of the first aspect, and will not be elaborated here.

[0065] Beneficial effects:

[0066] 1. The multi-source shot point prediction method provided in this application, compared with the prior art, has a source navigation and positioning model based on Kalman filtering that can more realistically describe the actual motion trajectory of the source, and can resist the influence of gross errors on the accuracy of shot point prediction.

[0067] 2. This application adopts the method of independent prediction of left and right seismic sources. Compared with the traditional method of predicting shot points using the geometric center of the seismic source array, it can better meet the needs of marine seismic exploration and ensure the accuracy requirements of shot point prediction in marine seismic exploration.

[0068] 3. This application uses a gross error detection method that combines geometric constraints and mathematical statistics, which can provide "cleaner" data for shot point prediction. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of multi-source shot point prediction;

[0070] Figure 2 This is a flowchart provided in an embodiment of this application.

[0071] Figure 3 This is a comparison diagram of the accuracy of shot point prediction between the embodiments of this application and traditional polynomial fitting. Detailed Implementation

[0072] The present application will be further described below with reference to the embodiments.

[0073] The multi-source shot point prediction method provided in this application includes: RGNSS data preprocessing, including gross error detection and observation data time synchronization; a source navigation and positioning model based on Kalman filtering; and multi-source shot point prediction using an independent multi-source prediction method based on the spatial location information of the source array obtained by filtering. The gross error detection method combines the geometric constraints of the source array with the statistical characteristics of the observation data, while the observation data time synchronization method is based on polynomial fitting. When using this method for shot point prediction, this application can greatly reduce the impact of gross errors in the observation data on the accuracy of shot point prediction. Furthermore, the independent prediction method for left and right source shot points better meets the needs of multi-source shot point prediction in marine seismic exploration.

[0074] The embodiments of this application are described in detail below.

[0075] This application provides a method for predicting shot points from multiple seismic sources, including the following steps:

[0076] Step 1: Acquire observation data;

[0077] The observation data includes data collected by the DGNSS, RGNSS base station, gyrocompass, and depth sounder on the exploration vessel, as well as data collected by the RGNSS rover stations on each source in the source array. Specifically, the DGNSS on the exploration vessel can collect DGNSS coordinate data; the RGNSS base station can collect the relative offset between the RGNSS base station and the DGNSS on the exploration vessel; and the RGNSS rover stations on each source in the source array can collect distance and azimuth data between the RGNSS base station and the RGNSS rover stations.

[0078] Step 2: Preprocess the observation data to remove gross errors;

[0079] In some embodiments, the observation data is preprocessed, including: detecting gross errors in the observation data based on geometric constraints and mathematical statistical properties, and removing data containing gross errors; and synchronizing the observation data in time based on a polynomial fitting method.

[0080] Among them, gross error detection of observation data based on geometric constraints and mathematical statistical properties includes:

[0081] 1) Calculate the source array based on the observation data. Coordinates of the RGNSS rover;

[0082] 2) Calculate the actual distance between each RGNSS rover and other RGNSS rover stations on the same source array. and design distance ;

[0083] 3) If If the absolute value of the difference between the actual distance and the design distance is greater than the set threshold, it indicates that the observation data provided by the RGNSS rover contains gross errors. Therefore, the observation data provided by the RGNSS rover is discarded and not used for subsequent calculation of the earthquake source location.

[0084] Since the design distance between RGNSS rover stations is known, and the source array moves under the towing of the exploration vessel during marine exploration operations, the actual distance between RGNSS rover stations will not deviate significantly from the design distance. The difference between the two will fluctuate within a small range. Based on this characteristic, the above method can be used to detect gross errors in the observation data and remove abnormal data.

[0085] Among them, gross error detection based on mathematical and statistical characteristics of observation data includes: based on the characteristics of observation data, a combination of statistical analysis and the rate of change method is used for gross error detection; the rate of change method is to detect gross errors by comparing the relationship between the rate of change of observation data at adjacent time points and a manually set rate of change threshold.

[0086] The above data preprocessing method only utilizes the mathematical and statistical characteristics of the positioning data and fully considers the prior information of the geometric structure of the RGNSS rover (positioning sensor), which can effectively reduce the impact of gross errors on the accuracy of shot point prediction.

[0087] In some embodiments, preprocessing the observation data further includes: for a certain RGNSS rover station, if there is no valid observation data at the current time (e.g., data removed due to gross errors), then the observation data is synchronized with historical observation data to obtain the observation data at the current time. Specifically, this includes: if there is valid observation data on both sides of the current time, then the observation data at the current time is obtained using a linear interpolation method; if there is no valid observation data on the left or right side of the current time, then the observation data at the current time is obtained by extrapolating the historical observation data using polynomial fitting, where the polynomial fitting formula is:

[0088]

[0089] In the formula: for The observation data corresponding to the time, For a moment, For polynomial coefficients, Let be the order of the polynomial.

[0090] By performing polynomial fitting on historical observation data, the fitting relationship between time and observation data can be obtained, and the polynomial coefficients in the above polynomial fitting formula can be determined; then, by substituting the current time, the observation data at the current time can be obtained.

[0091] Before performing source array localization calculations, it is necessary to obtain observational data at the time of the shot detonation. However, the observational data provided by the marine seismic exploration positioning sensors are not synchronized with the time of the shot detonation; therefore, time synchronization of the observational data is required. The observational data at the time of the shot detonation can be obtained using the method described above.

[0092] Step 3: Based on the preprocessed observation data and the Kalman filter-based source navigation and positioning model with robust Kalman filtering, obtain the spatial state information of each source;

[0093] Based on the motion characteristics of an exploration vessel towing a seismic source array for seismic data acquisition, traveling at a constant speed along the designed survey line, a robust Kalman filter-based seismic source navigation and positioning model is established. The specific process is as follows:

[0094] In some embodiments, the current time can be calculated based on the observation data preprocessed in step 2, i.e. Coordinates of the epicenter at that moment:

[0095]

[0096]

[0097] In the formula: The coordinates of the RGNSS base station on the exploration vessel. The DGNSS coordinates on the exploration vessel; The azimuth of the exploration vessel is obtained by the gyrocompass. The relative offset between the RGNSS base station on the exploration vessel and the DGNSS on the exploration vessel; for The coordinates of the epicenter at that moment. and These represent the azimuth and slant distance between the RGNSS base station and the RGNSS rover station at the epicenter, respectively.

[0098] In some embodiments, the coordinates of the earthquake source can be used as observation data to construct observation equations and state equations:

[0099]

[0100]

[0101] In the formula: for The state vector at time t, i.e. The spatial state information of the earthquake source at any given time, including the coordinates and velocity of the source; for The state vector at time t, for The time-observation vector is calculated using the observation data preprocessed in step 2. The coordinates of the epicenter at that moment. for The observation noise matrix at time t, for The process noise matrix at each time step; The coefficient matrix, Let be the state transition matrix.

[0102] In some embodiments, the coordinates of the seismic source include direction and The components in both directions, and correspondingly, the velocity of the source array also includes... direction and The two components of the direction; where The direction can be set along the survey line, and the Y direction can be set perpendicular to the survey line.

[0103] Therefore, based on the state equation, it can be utilized State vector at time step and its covariance matrix ,predict The state vector and its covariance matrix at time t:

[0104]

[0105]

[0106] In the formula, for Always The predicted value of the state vector at time step; during the initial prediction process, it can be set as follows: ; for Always The state transition matrix at time t; for The covariance matrix, for The covariance matrix of the process noise matrix at time step 1. It needs to be determined based on the actual sea conditions (such as wind and wave levels);

[0107] Furthermore, the estimation method based on Kalman filtering can be used to calculate... State vector at time step and its covariance matrix :

[0108]

[0109]

[0110] In the formula: Gain matrix at time step , for Observation noise matrix at time step The covariance matrix, Determined based on the prior accuracy of observations provided by the RGNSS positioning sensor; It is an identity matrix.

[0111] According to the constant velocity model, it can be... , and Initialize to:

[0112]

[0113]

[0114]

[0115] The standard Kalman filter estimation can update the spatial state information of the seismic source in real time. However, due to the influence of sea waves and currents, there is a certain deviation between the actual situation and the theoretical filtering model. Observational noise and process noise parameters are difficult to estimate accurately, leading to deviations in the calculation of the prediction variance-covariance matrix, which can cause standard Kalman filter divergence in severe cases. To avoid filter divergence, the residuals and their covariance matrix are represented by the innovation and its covariance, thus achieving robust estimation in one step. This weakens the influence of gross errors in the observation data on the filtering model and eliminates the need for multiple robust filtering steps. (Innovation in Kalman filtering) and its covariance matrix It can be calculated based on the predicted state and the observation data at the current moment:

[0116]

[0117]

[0118] Then the first Standardized residuals of each observation data for:

[0119]

[0120] In the formula, The innovation vector of the Kalman filter The first in One element, For matrix The first on the diagonal The elements are respectively the nth element and the nth element. The information and corresponding variance values ​​of each observation data;

[0121] In some embodiments, the weights of the observation data can be adjusted based on the IGG-III model and standardized residuals:

[0122]

[0123]

[0124] In the formula, For the first The weighting factor corresponding to each observation data point and For the corresponding critical value; The value is generally taken as 1.0 to 2.5. The value is generally between 2.5 and 8.0. For the adjusted Observation noise matrix at time step The covariance matrix, It is a local minimum (1e-8). , In the gain matrix and the gain matrix after weight adjustment The first on the diagonal Each element represents Time of the first The covariance of the observed data and its weighted variance.

[0125] Will As To calculate This allows us to obtain the robust state vector. and its covariance matrix .

[0126] In some embodiments, the exploration vessel is a multi-source vessel, meaning it tows a source array composed of multiple sources along a designed survey line to fire shots in real time. The closer the actual firing point is to the designed firing point, the higher the accuracy of the seismic data acquired by the geophones in real time. Therefore, it is necessary to predict in real time the time it takes for the source to reach the next designed firing point based on the spatial location and velocity of the source, in order to control the source to generate seismic waves. The traditional method is to treat the source array as a rigid body and use its geometric center point (MGP) for firing point prediction. However, in actual operations, there is no hardware connection between the sources, and the source array will tilt due to the influence of wind and waves. Therefore, using the MGP as a firing reference point for firing point prediction will affect the accuracy of seismic data acquisition.

[0127] To improve the accuracy and stability of shot point prediction, this application uses multiple seismic sources on the exploration vessel as independent shot reference points for shot point prediction, i.e., performs independent seismic source prediction.

[0128] In the specific implementation, the spatial state information of multiple seismic sources in the seismic source array is first obtained based on the Kalman filter-based source navigation and localization model. For example, for a dual-source ship, the spatial state information of the left-hand source is obtained. Spatial state information of the right-side hypocenter ,in, , The coordinates of the left and right seismic sources are shown. , The velocities of the left and right hypocenters are, and The axis direction can be directly set to the direction along the survey line and perpendicular to the survey line, or it can be set to other directions. After obtaining the spatial state information, the velocity is then projected onto the direction along the survey line and perpendicular to the survey line.

[0129] Step 4: Based on the spatial state information of each seismic source and the location of the design shot point, determine the design shot point and the seismic source for the next shot.

[0130] For the multi-source ship in the above embodiments, the positions of each source in the source array and the designed firing point are first determined according to their relative positions. The shot number of the nearest next design shot point (i.e., the number of the nearest design shot point advancing along the design survey line) is used as the basis for determining the design shot point and the seismic source corresponding to the next shot, based on the relationship between the shot numbers of the next next design shot points closest to each seismic source and the previously drawn shot number increments of the design shot points (the changes in the design shot point numbers along the design survey line). The determination method is as follows:

[0131] Assuming the earthquake source The closest gun number for the next design firing point is ,in , This represents the number of seismic sources in the seismic source array; if the shot number increment is greater than 0, then find... The minimum value in the range is the design firing point for the next shot, and the seismic source corresponding to this minimum value is the seismic source for the next shot. If the increment of the firing number is less than 0, then find... The maximum value in the range is the design firing point for the next firing, and the seismic source corresponding to the maximum value is the seismic source for the next firing.

[0132] For example, for the dual-source ship in the above embodiment, the gun number of the next design gun point closest to the left and right sources is found according to the positional relationship between the sources and the design gun points (i.e., the number of the design gun point closest to the source along the design survey line direction). , ,according to , Based on the size relationship and the increment of the design shot number given in the previous drawing, the design shot number corresponding to the next shot and the seismic source of the next shot are determined, including:

[0133] If the design firing point increases the firing caliber :if The design firing point number corresponding to the next firing is: The next firing shot will be from the right-side seismic source; that is, the next firing shot will be from the right-side seismic source. The design firing point number corresponding to the next firing is: The next cannon shot will be fired from the left-side hypocenter; the next cannon shot will be fired from the left-side hypocenter. :if The design firing point number corresponding to the next firing is: The next firing shot will be fired from the left-side hypocenter; the next firing shot will be fired from the left-side hypocenter. The design firing point number corresponding to the next firing is: The next firing of the cannon will be from the right-side seismic source, meaning the seismic source for the next firing of the cannon will be the right-side seismic source.

[0134] In some embodiments, the above method further includes:

[0135] Step 5: Calculate the remaining firing distance based on the seismic source of the next firing and its relative position to the predicted firing point. and remaining firing time The gun control system determines whether to trigger seismic waves (or fire the cannon) based on the remaining firing time.

[0136] In some embodiments, the relative positional relationship between the design firing point corresponding to the next firing and the starting point of the survey line can be calculated based on the design survey line information, including the distance along the survey line direction of the design firing point corresponding to the next firing relative to the starting point of the survey line. ;

[0137]

[0138] In the formula, and These are the distance and azimuth of the line connecting the design firing point and the starting point of the survey line corresponding to the next firing shot. They can be obtained through inverse coordinate calculation using the coordinates of the design firing point and the starting point of the survey line. The azimuth of the survey line is known.

[0139] The relative positional relationship between the design firing point corresponding to the next firing and the starting point of the survey line, including the distance along the survey line from the design firing point corresponding to the next firing relative to the starting point of the survey line. Distance across the perpendicular survey line (DC) ; In this embodiment, only the following steps are needed. Calculate the firing distance and remaining firing time.

[0140] Calculate the relative positional relationship between the seismic source of the next artillery strike and the starting point of the survey line, including the distance along the survey line from the seismic source of the next artillery strike to the starting point of the survey line. Distance in the direction of perpendicular survey line :

[0141]

[0142] In the formula, and These represent the distance and azimuth of the line connecting the epicenter of the next artillery barrage to the starting point of the survey line. The azimuth of the survey line; It can be calculated based on the spatial state information of the seismic source obtained in step 3 and the location information of the starting point of the survey line.

[0143] The relative positional relationship between the seismic source of the next artillery strike and the starting point of the survey line, including the distance along the survey line from the seismic source of the next artillery strike to the starting point of the survey line. Distance in the direction of perpendicular survey line , In this embodiment, only the following steps are needed. Calculate the firing distance and remaining firing time.

[0144] Calculate the remaining firing distance based on the relative position of the next firing source and the predicted firing point. and remaining firing time And send it to the gun control system, which determines whether to trigger seismic waves based on the remaining firing time, including:

[0145]

[0146]

[0147] In the formula: The velocity of the seismic source along the survey line for the next artillery barrage.

[0148] This application also provides a multi-source shot point prediction system, including:

[0149] The observation data acquisition module is used to acquire observation data collected by the data acquisition devices on each seismic source in the exploration vessel and the seismic source array.

[0150] The data preprocessing module is used to preprocess the observation data, remove gross errors, and perform time synchronization.

[0151] The source spatial state information calculation module is used to calculate the coordinates of each source in the source array at the current moment based on preprocessed observation data; the coordinates of the sources at the current moment are used as observation vectors and input into the source navigation and positioning model based on Kalman filtering to obtain the spatial state information of each source; wherein, the source navigation and positioning model based on Kalman filtering has the function of calculating the spatial state information of the corresponding source based on the input observation vector; the spatial state information of the source includes the coordinates of the source;

[0152] The shot point prediction module is used to determine the design shot point and the seismic source for the next shot based on the spatial state information of each seismic source and the location of the design shot point.

[0153] In some embodiments, the shot point prediction module is further configured to calculate the remaining shot distance based on the relative positional relationship between the design shot point corresponding to the next shot and the seismic source of the next shot. and remaining firing time .

[0154] This application also provides a terminal, including: a memory and a processor;

[0155] The memory is used to store computer programs;

[0156] The processor is used to call the computer program to execute the multi-source shot point prediction method as described above.

[0157] This application also provides a computer-readable storage medium storing a computer program. When the computer program is run on an electronic device, it enables the electronic device to implement the multi-source shot point prediction method as described above.

[0158] This application also provides a computer program product, including a computer program that, when run on an electronic device, enables the electronic device to implement the multi-source shot point prediction method as described above.

[0159] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the controller described in any of the foregoing embodiments, such as the controller's hard drive or memory. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both the controller's internal storage unit and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0160] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0161] The specific implementation methods of the multi-source shot point prediction system, terminal, computer-readable storage medium, and computer program product provided in this application embodiment can be referred to the specific embodiments of the multi-source shot point prediction method described above, and will not be repeated here.

[0162] It should be emphasized that the examples described in this application are illustrative and not limiting. Therefore, this application is not limited to the examples described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solutions of this application, which do not depart from the spirit and scope of this application, whether modifications or substitutions, are also within the protection scope of this application.

Claims

1. A method for predicting shot points from multiple seismic sources, characterized in that, Includes the following steps: Step 1: Acquire observation data from the data acquisition devices on each seismic source in the exploration vessel and seismic source array; Step 2: Preprocess the observation data, remove gross errors, and synchronize the time. Step 3: Calculate the coordinates of each source in the source array at the current moment based on the preprocessed observation data; The coordinates of the earthquake source at the current moment are used as observation vectors and input into a Kalman filter-based earthquake source navigation and positioning model to obtain the spatial state information of each earthquake source. The Kalman filter-based earthquake source navigation and positioning model has the function of calculating the spatial state information of the corresponding earthquake source based on the input observation vectors. The spatial state information of the earthquake source includes the coordinates of the earthquake source. Step 4: Based on the spatial state information of each seismic source and the location of the design shot point, determine the design shot point and seismic source corresponding to the next shot.

2. The method according to claim 1, characterized in that, In step 1, the data acquisition devices on the exploration vessel and each source in the source array include: the RGNSS base station, DGNSS and gyrocompass on the exploration vessel, and the RGNSS rover on each source in the source array. Step 2 includes: detecting gross errors in the observation data based on geometric constraints and mathematical statistics, and removing gross error data; Step 2 further includes: for each RGNSS rover station at a seismic source, if there is no valid observation data at the current time, then the observation data is synchronized with historical observation data to obtain the observation data at the current time; wherein, the process of synchronizing the observation data with historical observation data to obtain the observation data at the current time includes: If valid observation data exists on both sides at the current time, then the linear interpolation method is used to obtain the observation data at the current time. If there is no valid observation data to the left or right of the current moment, the observation data for the current moment is obtained by extrapolating from historical observation data using polynomial fitting. The polynomial fitting formula is as follows: ； In the formula: for The observation data corresponding to the time, For a moment, For polynomial coefficients, The order of the polynomial; By performing polynomial fitting on historical observation data, the fitting relationship between time and observation data is obtained, and the polynomial coefficients in the polynomial fitting formula are determined; then, the current time is substituted to obtain the observation data at the current time.

3. The method according to claim 1, characterized in that, In step 3, the source navigation and localization model based on Kalman filtering includes: ； ； ； ； In the formula, for The state vector at time t, i.e. Spatial state information of the earthquake source at any given moment; for The state vector at any given time; and They are respectively and The covariance matrix; for The time-observation vector is calculated using the observation data preprocessed in step 2. The coordinates of the epicenter at that moment. for Always Predicted state vector value at time step; for Always The state transition matrix at time t; for The covariance matrix, for The covariance matrix of the process noise matrix at time step; for Gain matrix at time step ; It is a coefficient matrix; It is the identity matrix; for Observation noise matrix at time step The covariance matrix.

4. The method according to claim 3, characterized in that, The method further includes: right Observation noise matrix at time step covariance matrix Adjustments were made to obtain Then As Substitute into In the calculation formula, the spatial state information of each earthquake source is then solved; Among them, for Observation noise matrix at time step covariance matrix Adjustments were made to obtain The calculation formula is: ； ； ； ； ； In the formula, For the adjusted Observation noise matrix at time step The covariance matrix; and These are the innovation of the Kalman filter and its covariance matrix, respectively. The innovation vector of the Kalman filter The first in One element, For matrix The first on the diagonal One element; For the first Standardized residuals of each observation data; For the first The weighting factor corresponding to each observation data point and For the corresponding critical value, It is the minimum value; and They are respectively and The first on the diagonal Each element.

5. The method according to claim 1, characterized in that, For multi-source ships, first, based on the positional relationship between each source in the source array and the design firing point, find the firing number of the next design firing point closest to each source. Then, based on the magnitude relationship of the firing numbers of the next design firing points closest to each source and the increment of the firing number of the design firing point given in the design, determine the design firing point and the source of the next firing. The determination method is as follows: Assuming the earthquake source The closest gun number for the next design firing point is ,in , This represents the number of seismic sources in the seismic source array; if the shot number increment is greater than 0, then find... The minimum value in the range is the design firing point for the next shot, and the seismic source corresponding to this minimum value is the seismic source for the next shot. If the increment of the firing number is less than 0, then find... The maximum value in the range is the design firing point for the next firing, and the seismic source corresponding to the maximum value is the seismic source for the next firing.

6. The method according to claim 1, characterized in that, In step 3, the spatial state information of the seismic source also includes the velocity of each seismic source in the seismic source array; The method further includes: Step 5: Calculate the remaining firing distance based on the relative positional relationship between the design firing point corresponding to the next firing and the seismic source of the next firing, and the velocity of the seismic source of the next firing. and remaining firing time It is used by gun control systems to determine whether to trigger seismic waves based on the remaining firing time.

7. A multi-source shot point prediction system, characterized in that, include: The observation data acquisition module is used to acquire observation data collected by the data acquisition devices on each seismic source in the exploration vessel and the seismic source array. The data preprocessing module is used to preprocess the observation data, remove gross errors, and perform time synchronization. The source spatial state information calculation module is used to calculate the coordinates of each source in the source array at the current moment based on the preprocessed observation data. The coordinates of the earthquake source at the current moment are used as observation vectors and input into a Kalman filter-based earthquake source navigation and positioning model to obtain the spatial state information of each earthquake source. The Kalman filter-based earthquake source navigation and positioning model has the function of calculating the spatial state information of the corresponding earthquake source based on the input observation vectors. The spatial state information of the earthquake source includes the coordinates of the earthquake source. The shot point prediction module is used to determine the design shot point and the seismic source for the next shot based on the spatial state information of each seismic source and the location of the design shot point.

8. A terminal, characterized in that, include: Memory and processor; The memory is used to store computer programs; The processor is configured to invoke the computer program to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed on an electronic device, causes the electronic device to perform the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is run on an electronic device, it causes the electronic device to perform the method as described in any one of claims 1 to 6.

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