CN114488286A - Amplitude weighting-based streamer and ocean bottom seismic data joint waveform inversion method - Google Patents
Amplitude weighting-based streamer and ocean bottom seismic data joint waveform inversion method Download PDFInfo
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Abstract
The invention relates to an amplitude weighting-based streamer and seabed seismic data joint waveform inversion method, and belongs to the technical field of underground medium velocity model inversion. The invention comprises the following steps: acquiring observation data, denoising, and extracting seismic wavelets; generating seismic data by utilizing a given initial velocity model and the forward modeling of the extracted seismic wavelets; calculating fitting errors of the towing cables and the ocean bottom seismic data; carrying out amplitude weighting on the fitting error; taking the fitting error after amplitude weighting as a backward propagation seismic source, and calculating a backward propagation wave field; calculating the updating direction and step length of the speed model; calculating an objective function value; updating the speed model; and judging whether the updated speed model meets the requirement. The problem that the conventional full waveform inversion method is difficult to effectively invert the speed of the middle and deep stratum due to weak energy of the stratum reflected waves below a strong wave impedance interface is solved.
Description
Technical Field
The invention relates to an amplitude weighting-based streamer and seabed seismic data joint waveform inversion method, and belongs to the technical field of underground medium velocity model inversion.
Background
The full waveform inversion method is one of the methods with the highest speed modeling precision theoretically at present. The method gradually fits the observed seismic data and the simulated seismic data by updating the initial velocity model through multiple iterations. For the area with a simple geological structure, the energy of the reflected wave of the middle and deep stratum is strong, the weight of the corresponding reflected wave record in the full waveform inversion target function is large, and the speed of the middle and deep stratum can be effectively inverted by minimizing the target function. However, in a region with a complex geological structure and a strong wave impedance interface, the energy of the reflected wave of the middle and deep stratum is weak, the weight of the corresponding reflected wave recorded in the objective function is small, and it is difficult to obtain the accurate speed of the middle and deep stratum by minimizing the objective function. In order to obtain accurate velocities for mid-depth formations, a targeted full waveform inversion method is required.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an amplitude-weighting-based streamer and seabed seismic data joint waveform inversion method, which solves the problem that the accurate velocity value of the stratum below a strong wave impedance interface is difficult to obtain by a conventional full waveform inversion method due to weak energy of the reflected wave of the deep stratum in a strong wave impedance area.
The invention is realized by adopting the following technical scheme: the invention relates to an amplitude weighting-based streamer and ocean bottom seismic data joint waveform inversion method, which comprises the following steps:
the method comprises the following steps: acquiring observation data: taking the artificial seismic source wave fields recorded by the field hydrophones and the ocean bottom seismograph as observation seismic data; for a theoretical model, observing seismic data is obtained by forward modeling by adopting a time 2-order space 12-order precision sound wave equation according to a real velocity model;
step two: denoising the observed seismic data to obtain denoised seismic data;
step three: extracting seismic wavelets by a high-order statistical method by using the denoised seismic data;
step four: performing forward modeling on a 12-order acoustic wave equation in a time 2-order space by using a given initial velocity model and the extracted seismic wavelets to obtain simulated seismic data;
step five: calculating fitting errors of the towing cable and the ocean bottom seismic data; the fitting error calculation formula of the streamer seismic data is as follows:
wherein, the upper labelTSRepresenting the seismic data of the streamer,d err the fitting error of the seismic data is represented,d cal representing simulated seismic dataThe raw materials are mixed and stirred evenly,d obs representing observed seismic data; the fitting error calculation formula of the ocean bottom seismic data is as follows:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,d err the fitting error of the seismic data is represented,d cal representing the simulated seismic data and representing the seismic data,d obs representing observed seismic data;
step six: calculating fitting errors of the streamer and the ocean bottom seismic data after amplitude weighting; the formula for amplitude weighting the fitting error of the streamer seismic data is as follows:
wherein, the upper labelTSRepresenting the seismic data of the streamer,D err representing the fitting error of the amplitude weighted seismic data,wwhich represents the amplitude-weighting factor, is,d err representing the fitting error of the seismic data; the amplitude weighting coefficients for the streamer seismic data are calculated using the following equation:
wherein, the upper labelTSRepresenting the seismic data of the streamer cable,wwhich represents the amplitude weighting factor, is,σthe standard deviation of the seismic data is represented,εa small amount is indicated to prevent the denominator from being zero; the formula for amplitude weighting the fitting error of ocean bottom seismic data is as follows:
wherein, onSign boardOBSRepresenting the ocean bottom seismic data and,D err representing the fitting error of the amplitude weighted seismic data,wwhich represents the amplitude-weighting factor, is,d err representing the fitting error of the seismic data; the amplitude weighting coefficient of the ocean bottom seismic data is calculated by adopting the following formula:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,wwhich represents the amplitude weighting factor, is,σthe standard deviation of the seismic data is represented,εa small amount is indicated to prevent the denominator from being zero;
step seven: calculating the updating direction of the velocity model according to the fitting error of the streamer and the ocean bottom seismic data after amplitude weighting;
step eight: calculating the updating step length of the speed model according to a linear search method;
step nine: calculating an objective function value;
step ten: calculating the correction quantity of the model according to the step length and the updating direction, and updating the speed model;
step eleven: judging whether the updated speed model meets the requirements: if yes, outputting a result; if not, the updated speed model is used as a new initial speed model, and the step four is returned.
The invention has the beneficial effects that: by adopting the amplitude weighting-based streamer and seabed seismic data joint waveform inversion method, the fitting error of the seismic data is subjected to amplitude weighting processing, so that the weight of weak reflected waves recorded in a target function is improved, the corresponding updating amount of a middle and deep stratum is large, the speed of the middle and deep stratum can be correctly inverted, and the problem that the conventional full waveform inversion method is difficult to effectively invert the speed of the stratum below a strong wave impedance interface is solved; the method is simple in calculation, easy to implement, high in adaptability and high in reliability of inversion results.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a true velocity model of the present invention;
FIG. 3 is a diagram of an initial velocity model of the present invention;
FIG. 4 is a graph of a velocity model inverted by a conventional full waveform method;
FIG. 5 is a diagram of an inversion velocity model according to the present invention.
Detailed Description
In order to make the purpose and technical solution of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. The flow chart of the present invention, as shown in fig. 1, includes the following steps:
the method comprises the following steps: acquiring observation data: taking the artificial seismic source wave fields recorded by the field hydrophones and the ocean bottom seismograph as observation seismic data; for a theoretical model, observing seismic data is obtained by forward modeling by adopting a time 2-order space 12-order precision sound wave equation according to a real velocity model;
step two: denoising the observed seismic data to obtain denoised seismic data;
step three: extracting seismic wavelets by a high-order statistical method by using the denoised seismic data;
step four: performing forward modeling on a 12-order acoustic wave equation in a time 2-order space by using a given initial velocity model and the extracted seismic wavelets to obtain simulated seismic data;
step five: calculating fitting errors of the towing cables and the ocean bottom seismic data; the fitting error calculation formula of the streamer seismic data is as follows:
wherein, the upper labelTSRepresenting the seismic data of the streamer,d err the fitting error of the seismic data is represented,d cal representing the simulated seismic data and representing the seismic data,d obs representing observed seismic data; the fitting error calculation formula of the ocean bottom seismic data is as follows:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,d err the fitting error of the seismic data is represented,d cal representing the simulated seismic data and representing the seismic data,d obs representing observed seismic data;
step six: calculating fitting errors of the streamer and the ocean bottom seismic data after amplitude weighting; the formula for amplitude weighting the fitting error of the streamer seismic data is as follows:
wherein, the upper labelTSRepresenting the seismic data of the streamer,D err representing the fitting error of the amplitude weighted seismic data,wwhich represents the amplitude weighting factor, is,d err representing the fitting error of the seismic data; the amplitude weighting coefficients for the streamer seismic data are calculated using the following equation:
wherein, the upper labelTSRepresenting the seismic data of the streamer,wwhich represents the amplitude weighting factor, is,σthe standard deviation of the seismic data is represented,εa small amount is indicated to prevent the denominator from being zero; the formula for amplitude weighting the fitting error of ocean bottom seismic data is as follows:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,D err representing the fitting error of the amplitude weighted seismic data,wwhich represents the amplitude weighting factor, is,d err representing the fitting error of the seismic data; the amplitude weighting coefficient of the ocean bottom seismic data is calculated by adopting the following formula:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,wwhich represents the amplitude weighting factor, is,σthe standard deviation of the seismic data is represented,εa small amount is indicated to prevent the denominator from being zero;
step seven: calculating the updating direction of the velocity model according to the fitting error of the streamer and the ocean bottom seismic data after amplitude weighting;
step eight: calculating the updating step length of the speed model according to a linear search method;
step nine: calculating a target function value;
step ten: calculating the correction quantity of the model according to the step length and the updating direction, and updating the speed model;
step eleven: judging whether the updated speed model meets the requirements: if yes, outputting a result; if not, the updated speed model is used as a new initial speed model, and the step four is returned.
The first embodiment is as follows:
the theoretical model test of the present invention is explained and illustrated below with reference to specific embodiments.
In order to further explain the realization idea and the realization process of the method and prove the effectiveness of the method, a model containing a strong speed difference interface is used for testing and is compared with the result of the conventional full-waveform inversion.
And S1, taking the model (see the detailed figure 2) containing the interface with the strong speed difference as a real speed model. The width of the real speed model is 25km, and the depth is 6.25 km. Only the middle portion of the model is shown due to the lack of sufficient illumination on both sides of the model. And a square grid is adopted for dispersion, and the grid size is 12.5 m.
S2 observation system: the sources are laid out uniformly at a pitch of 100m to the rightmost side of the model starting at 7.5km in the X direction. 600 hydrophones are uniformly distributed on the left side of the seismic source at a distance of 12.5 m. The hydrophones move synchronously with the movement of the seismic source. The minimum offset distance is 12.5m, and the maximum offset distance is 7.5 km. 63 ocean bottom seismographs are uniformly arranged on the ocean bottom at intervals of 400 m. The sampling time of the seismic data is 6s, and the sampling interval is 1 ms.
And S3, forward simulating by a real velocity model and a Rake wavelet with a seismic source function of 15Hz through a regular grid acoustic wave equation with the precision of time 2 order and space 12 order and by adopting a boundary condition of a complete matching layer to obtain the towing cable and the submarine seismic data, and using the towing cable and the submarine seismic data as the observed towing cable and the submarine seismic data.
And S4, obtaining simulated streamer and seabed seismic data by using the same forward modeling method through an initial velocity model (see figure 3 in detail) and a Rake wavelet with a source function of 15 Hz.
And S5, calculating fitting errors of the streamer and the ocean bottom seismic data respectively.
S6, amplitude weighting is performed on the fitting errors of the streamer and ocean bottom seismic data respectively. Small quantities in equations (4) and (6) when amplitude weighting is performedεSet to 0.01.
And S7, calculating a backward wave field by taking the fitting error of the amplitude weighted streamer and the ocean bottom seismic data as a backward seismic source function. And calculating the updating direction of the velocity model by adopting a conjugate gradient method according to the back propagation wave field.
And S8, calculating the updating step size of the speed model by using a linear search method.
And S9, calculating the objective function value.
And S10, updating the speed model by using the updating direction and the updating step length calculated in the steps 7 and 8.
S11: judging whether the updated speed model meets the requirement, if not, returning to the step 4; if so, outputting the result. The final inverted velocity model is shown in FIG. 5.
Fig. 4 is the inversion result of a conventional full waveform without amplitude weighting. As is apparent from fig. 4, the velocities of the formations below the depth of about 0.6km are greatly different from the velocities of the corresponding formations in the real velocity model. However, the method effectively improves the weight of the medium-depth weak reflected wave recorded in the target function by carrying out amplitude weighting on the fitting error of the seismic data, and finally makes the inversion result closer to a real velocity model.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, but rather the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A streamer and ocean bottom seismic data joint waveform inversion method based on amplitude weighting is characterized by comprising the following steps:
the method comprises the following steps: acquiring observation data: taking the artificial seismic source wave fields recorded by the field hydrophones and the ocean bottom seismograph as observed seismic data;
step two: denoising the observed seismic data;
step three: extracting seismic wavelets by a high-order statistical method by using the denoised seismic data;
step four: performing forward modeling on a 12-order acoustic wave equation in a time 2-order space by using a given initial velocity model and the extracted seismic wavelets to obtain simulated seismic data;
step five: calculating fitting errors of the towing cables and the ocean bottom seismic data; the fitting error calculation formula of the streamer seismic data is as follows:
wherein, the upper labelTSRepresenting the seismic data of the streamer,d err the fitting error of the seismic data is represented,d cal representing the simulated seismic data and representing the seismic data,d obs presentation viewMeasuring seismic data; the fitting error calculation formula of the ocean bottom seismic data is as follows:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,d err the fitting error of the seismic data is represented,d cal representing the simulated seismic data and representing the seismic data,d obs representing observed seismic data;
step six: calculating fitting errors of the streamer and the ocean bottom seismic data after amplitude weighting; the calculation formula for carrying out amplitude weighting on the fitting error of the towing cable seismic data is as follows:
wherein, the upper labelTSRepresenting the seismic data of the streamer,D err representing the fitting error of the amplitude weighted seismic data,wwhich represents the amplitude weighting factor, is,d err representing the fitting error of the seismic data; the amplitude weighting coefficients for the streamer seismic data are calculated using the following equation:
wherein, the upper labelTSRepresenting the seismic data of the streamer,wwhich represents the amplitude weighting factor, is,σthe standard deviation of the seismic data is represented,εa small amount is indicated to prevent the denominator from being zero; the calculation formula for amplitude weighting of the fitting error of the ocean bottom seismic data is as follows:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,D err representing the fitting error of the amplitude weighted seismic data,wwhich represents the amplitude weighting factor, is,d err representing the fitting error of the seismic data; the amplitude weighting coefficient of the ocean bottom seismic data is calculated by adopting the following formula:
wherein, the upper labelOBSRepresenting the ocean bottom seismic data and,wwhich represents the amplitude weighting factor, is,σthe standard deviation of the seismic data is represented,εa small amount is indicated to prevent the denominator from being zero;
step seven: calculating the updating direction of the velocity model according to the fitting error of the streamer and the ocean bottom seismic data after amplitude weighting;
step eight: calculating the updating step length of the speed model according to a linear search method;
step nine: calculating an objective function value;
step ten: calculating the correction quantity of the model according to the updating direction calculated in the step seven and the step length calculated in the step eight, and updating the speed model;
step eleven: judging whether the updated speed model meets the requirements: if yes, outputting a result; if not, the updated speed model is used as a new initial speed model, and the step four is returned.
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