CN112526601A

CN112526601A - Multi-source data inversion method, device, equipment and storage medium

Info

Publication number: CN112526601A
Application number: CN202011241805.7A
Authority: CN
Inventors: 孙红林; 刘铁华; 刘铁; 化希瑞; 张邦; 卞友艳; 肖立锋; 陈支兴; 刘伟; 柳青; 赵晓博; 李凯; 段圣龙; 陈应君; 杨正国
Original assignee: China Railway Siyuan Survey and Design Group Co Ltd
Current assignee: China Railway Siyuan Survey and Design Group Co Ltd
Priority date: 2020-11-09
Filing date: 2020-11-09
Publication date: 2021-03-19
Anticipated expiration: 2040-11-09
Also published as: CN112526601B

Abstract

The embodiment of the invention discloses a multi-source data inversion method, a device, equipment and a storage medium, wherein the method is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component; obtaining vertical component data through a single component detector; the vertical component data comprises active source data and passive source data; obtaining an active source frequency dispersion curve according to the active source data; determining a simulated frequency dispersion curve based on the active source frequency dispersion curve; obtaining a passive source frequency dispersion curve according to the passive source data; determining a target function of joint inversion of active source data and passive source data based on the simulated frequency dispersion curve, the passive source frequency dispersion curve and the active source frequency dispersion curve; the target function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to a target function and the active source frequency dispersion curve; the target velocity model characterizes the depth and velocity correspondence of the formation.

Description

Multi-source data inversion method, device, equipment and storage medium

Technical Field

The invention relates to the technical field of seismic exploration and detection, in particular to a multi-source data inversion method, a device, equipment and a storage medium.

Background

When the linear arrangement type device is adopted, the device can collect the active source surface wave and can also synchronously collect the passive source surface wave data, and has the following characteristics:

1) surface wave speed difference obtained by active source surface wave and passive source surface wave of linear observation device

When the 'embedded triangle' device is adopted, due to the fact that measuring points are evenly distributed in the direction and the multilayer nested design of the 'embedded triangle' device, signals obtained by the device have uniformity of the direction and the frequency range, the stable random process theory is met, the obtained speed of a dispersion curve can be regarded as the real surface wave speed, and the speed is consistent with the speed obtained by the active source surface wave. In the case of the linear array type device, the velocity obtained is only the apparent surface wave velocity due to the unicity of the direction, and is generally higher than the true surface wave velocity, and the degree of difference between the frequency velocities is uniform.

2) The information frequency bands obtained by the active source surface wave and the passive source surface wave are different

Because the active source surface wave adopts an artificial excitation seismic source, the obtained signal mainly takes high-frequency energy and is sensitive to the speed reaction of the shallow surface stratum. The passive source surface wave depends on the frequency composition of natural micro-motion signals, the frequency band of the obtained signals is generally lower than that of the active source surface wave signals, and the corresponding passive source surface wave is sensitive to the speed response of the deep stratum.

In actual work, the requirement for the convenience of one-time exploration and operation at different depths is eliminated, the combined exploration of the active source and the passive source of the linear observation device becomes a choice for more people, and the speed difference extracted by the active source and the passive source in application brings difficulty to actual production work. However, no effective solution is available for this problem.

Disclosure of Invention

In view of the above, embodiments of the present invention are intended to provide a multi-source data inversion method, apparatus, device and storage medium.

The technical embodiment of the invention is realized as follows:

the embodiment of the invention provides a multisource data inversion method, which is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component; the method comprises the following steps:

obtaining vertical component data by the single component detector; the vertical component data comprises active source data and passive source data; the active source data is obtained based on an artificial seismic source;

obtaining an active source frequency dispersion curve according to the active source data; the active source frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the first surface wave;

determining a simulated dispersion curve based on the active source dispersion curve; the simulated frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the surface wave;

obtaining a passive source frequency dispersion curve according to the passive source data; the passive source frequency dispersion curve represents the corresponding relation between the phase speed and the frequency of the second surface wave;

determining a target function of the joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve;

determining a target speed model according to the target function and the active source frequency dispersion curve; the target velocity model represents the corresponding relationship between the depth and the velocity of the stratum.

In the foregoing solution, the determining a simulated dispersion curve based on the active source dispersion curve includes:

obtaining an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum;

and forward modeling the initial velocity model to obtain a simulated frequency dispersion curve.

In the foregoing solution, the determining an objective function for joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve, and the active source dispersion curve includes:

determining a first objective function according to the simulated frequency dispersion curve and the passive source frequency dispersion curve; the first objective function represents the correlation of the passive source dispersion curve and the simulated dispersion curve;

determining a second objective function according to the simulated frequency dispersion curve and the active source frequency dispersion curve; the second objective function represents the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve;

and obtaining an objective function of the joint inversion of the active source data and the passive source data based on the first objective function and the second objective function.

In the foregoing solution, the determining a first objective function according to the simulated dispersion curve and the passive source dispersion curve includes:

obtaining a correlation coefficient function of the simulated frequency dispersion curve and the passive source frequency dispersion curve;

and determining a first target function according to the correlation coefficient function and a preset threshold value.

In the foregoing solution, the determining a second objective function according to the simulated dispersion curve and the active source dispersion curve includes:

obtaining a similarity coefficient function of the simulated frequency dispersion curve and the active source frequency dispersion curve;

and taking the similarity coefficient function as a second objective function.

In the above scheme, the determining a target velocity model according to the objective function and the active source dispersion curve includes:

and correcting the initial speed model according to the objective function to obtain an objective speed model.

The embodiment of the invention provides a multi-source data inversion device, which is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component; the device comprises: an obtaining unit and a determining unit, wherein:

the obtaining unit is used for obtaining vertical component data through the single component detector; the vertical component data comprises active source data and passive source data; the active source data is obtained based on an artificial seismic source; obtaining an active source frequency dispersion curve according to the active source data; the active source frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the first surface wave;

the determining unit is configured to determine a simulated frequency dispersion curve based on the active source frequency dispersion curve obtained by the obtaining unit; the simulated frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the surface wave;

the obtaining unit is further configured to obtain a passive source dispersion curve according to the passive source data; the passive source frequency dispersion curve represents the corresponding relation between the phase speed and the frequency of the second surface wave;

the determining unit is further configured to determine a target function of joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve obtained by the obtaining unit, and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve;

the determining unit is further configured to determine a target speed model according to the target function and the active source dispersion curve obtained by the obtaining unit; the target velocity model represents the corresponding relationship between the depth and the velocity of the stratum.

In the foregoing scheme, the obtaining unit is further configured to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and forward modeling the initial velocity model to obtain a simulated frequency dispersion curve.

In the above solution, the determining unit comprises a first determining module, a second determining module and an obtaining module, wherein,

the first determining module is configured to determine a first objective function according to the simulated dispersion curve and the passive source dispersion curve; the first objective function represents the correlation of the passive source dispersion curve and the simulated dispersion curve;

the second determining module is configured to determine a second objective function according to the simulated dispersion curve and the active source dispersion curve; the second objective function represents the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve;

the obtaining module is configured to obtain an objective function of joint inversion of the active source data and the passive source data based on the first objective function determined by the first determining module and the second objective function determined by the second determining module.

In the foregoing solution, the first determining module is further configured to obtain a correlation coefficient function between the simulated dispersion curve and the passive source dispersion curve; and determining a first target function according to the correlation coefficient function and a preset threshold value.

In the foregoing solution, the second determining module is further configured to obtain a similarity coefficient function between the simulated frequency dispersion curve and the active source frequency dispersion curve; and taking the similarity coefficient function as a second objective function.

In the foregoing solution, the determining unit is further configured to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and correcting the initial speed model according to the objective function to obtain an objective speed model.

The embodiment of the invention provides multi-source data inversion equipment which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor realizes any step of the method when executing the program.

Embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements any of the steps of the above-mentioned method.

The embodiment of the invention provides a multi-source data inversion method, a device, equipment and a storage medium, wherein the method comprises the following steps: obtaining vertical component data by the single component detector; the vertical component data comprises active source data and passive source data; the active source data is obtained based on an artificial seismic source; obtaining an active source frequency dispersion curve according to the active source data; the active source frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the first surface wave; determining a simulated dispersion curve based on the active source dispersion curve; the simulated frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the surface wave; obtaining a passive source frequency dispersion curve according to the passive source data; the passive source frequency dispersion curve represents the corresponding relation between the phase speed and the frequency of the second surface wave; determining a target function of the joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to the target function and the active source frequency dispersion curve; the target velocity model represents the corresponding relationship between the depth and the velocity of the stratum. By adopting the technical scheme of the embodiment of the invention, the target function of the joint inversion of the active source data and the passive source data is determined based on the simulated frequency dispersion curve, the passive source frequency dispersion curve and the active source frequency dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to the target function and the active source frequency dispersion curve; the target speed model represents the corresponding relation between the depth and the speed of the stratum; the problem of difference of frequency dispersion linear velocities of the active source and the passive source is solved by changing a target function during the joint inversion of the active source and the passive source, so that the joint inversion of the real surface wave velocity is realized, and the difference of surface waves extracted by active source data and passive source data is greatly reduced.

Drawings

FIG. 1 is a schematic diagram of an implementation flow of a multi-source data inversion method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of active source data in a multi-source data inversion method according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of passive source data in a multi-source data inversion method according to an embodiment of the invention;

FIG. 4 is a schematic diagram of an active source frequency dispersion spectrum in a multi-source data inversion method according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of an active source dispersion curve in a multi-source data inversion method according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a simulated dispersion curve in a multi-source data inversion method according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of a passive source dispersion spectrum in a multi-source data inversion method according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of a passive source dispersion curve in a multi-source data inversion method according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of a target velocity model in a multi-source data inversion method according to an embodiment of the invention;

FIG. 10 is a schematic diagram of an initial velocity model in a multi-source data inversion method according to an embodiment of the invention;

FIG. 11 is a schematic diagram of a component structure of a multi-source data inversion apparatus according to an embodiment of the present invention;

fig. 12 is a schematic diagram of a hardware entity structure of a multi-source data inversion apparatus according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following describes specific technical solutions of the present invention in further detail with reference to the accompanying drawings in the embodiments of the present invention. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

The embodiment provides a multisource data inversion method, which is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component.

It should be noted that the linear observation device may be a device including a plurality of single component detectors, where the plurality of single component detectors are arranged on a straight line at preset intervals, and the preset intervals may be determined according to actual situations, and are not limited herein, and as an example, the preset intervals may be set to be 5 m. The number of the single component detectors is generally greater than or equal to 2, and the specific number may be determined according to actual conditions, which is not limited herein. Each single component detector may correspond to a measurement point.

Fig. 1 is a schematic view of an implementation flow of a multi-source data inversion method according to an embodiment of the present invention, as shown in fig. 1, the method includes:

step S101: obtaining vertical component data by the single component detector; the vertical component data comprises active source data and passive source data; the active source data is obtained based on an artificial seismic source.

It should be noted that, an artificial seismic source may be excited at a preset distance from a linear observation device, and vertical component data is collected by using a single-component detector in the linear observation device, where the vertical component data includes active source data and passive source data. The active source data may be data obtained by using an artificial seismic source for excitation, and the passive source data may be data obtained by using a natural micro seismic source. Here, the artificially excited seismic source is mainly used, and the signal is mainly high-frequency energy, so that the time for transmitting the signal to the linear observation device is far shorter than the time for transmitting the signal of the artificially-excited seismic source to the linear observation device. Wherein the signal of the seismic source without artificial excitation can be a natural micro-motion signal. The active source data excited by the artificial seismic source and the passive source data obtained by the natural micro seismic source are obtained through the single-component detector.

In practical application, after the single-component detectors in the linear observation device are arranged according to each measuring point, an artificial seismic source can be excited at a place with a certain distance from the linear observation device, the detectors at each measuring point start to record data, the vertical component data of all measuring points in a short time (usually less than 1 second) after the artificial seismic source is excited are active source data, and the vertical component data of all measuring points which are acquired for a long time and do not contain seismic source signals are passive source data.

For convenience of understanding, schematic diagrams of active source data and passive source data are respectively illustrated here, and fig. 2 is a schematic diagram of active source data in a multi-source data inversion method according to an embodiment of the present invention; as shown in FIG. 2, the abscissa is the data trace and the ordinate is time; FIG. 3 is a schematic diagram of passive source data in a multi-source data inversion method according to an embodiment of the invention; as shown in FIG. 3, the abscissa is time and the ordinate is data track.

Step S102: obtaining an active source frequency dispersion curve according to the active source data; and the active source frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the first surface wave.

Here, obtaining the active source dispersion curve from the active source data may be obtaining an active source dispersion spectrum from the active source data, and determining the active source dispersion curve based on the active source dispersion spectrum. Obtaining an active source frequency dispersion spectrum according to the active source data may be to perform frequency dispersion spectrum calculation on the active source data to obtain an active source frequency dispersion spectrum; determining an active source dispersion curve based on the active source dispersion spectrum may be picking up an active source dispersion curve based on the active source dispersion spectrum.

The active source frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the first surface wave; the first surface wave can be an elastic surface wave generated by artificial seismic source excitation. As an example, the elastic surface wave may be a seismic surface wave.

For convenience of understanding, a schematic diagram of an active source frequency dispersion spectrum in a multi-source data inversion method is illustrated here, and fig. 4 is a schematic diagram of an active source frequency dispersion spectrum in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 4, the abscissa is frequency and the ordinate is velocity.

For convenience of understanding, a schematic diagram of an active source dispersion curve in a multi-source data inversion method is illustrated here, and fig. 5 is a schematic diagram of an active source dispersion curve in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 5. In fig. 5, the abscissa is the frequency of the first surface wave and the ordinate is the phase velocity, also referred to as velocity, of the first surface wave.

Step S103: determining a simulated dispersion curve based on the active source dispersion curve; and the simulated frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the surface wave.

Determining a simulated dispersion curve based on the active source dispersion curve may be to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and determining a simulated frequency dispersion curve according to the initial velocity model. Obtaining an initial velocity model based on the active source dispersion curve may be calculating an initial velocity model based on the active source dispersion curve according to an empirical formula; as an example, the empirical formula may be that the frequency-velocity relationship corresponding to the active source dispersion curve is converted according to the principle that a quarter wave field corresponding to a wave field is the depth of the velocity model corresponding to the wave field, that is, the "frequency-velocity" relationship of the dispersion curve becomes the "depth-velocity" relationship of the model. In other words, the empirical formula is: the depth H is the wavelength/4 is the velocity Vel/(4 frequency f), i.e. the signal corresponds to a depth of the geologic volume of one quarter of the corresponding wavefield. Determining a simulated dispersion curve according to the initial velocity model may be to forward the initial velocity model to obtain a simulated dispersion curve. The simulated dispersion curve represents a corresponding relationship between a phase velocity and a frequency of a surface wave, and the surface wave may be any type of surface wave, which is not limited herein. In practical applications, the simulated dispersion curve may also be referred to as a theoretical dispersion curve.

For convenience of understanding, a schematic diagram of a simulated dispersion curve in a multi-source data inversion method is illustrated here, and fig. 6 is a schematic diagram of a simulated dispersion curve in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 6. In fig. 6, the abscissa represents the frequency of the surface wave, and the ordinate represents the phase velocity of the surface wave. The phase velocity is also referred to as velocity.

Step S104: obtaining a passive source frequency dispersion curve according to the passive source data; and the passive source frequency dispersion curve represents the corresponding relation between the phase speed and the frequency of the second surface wave.

Here, the obtaining of the passive source dispersion curve from the passive source data may be obtaining a passive source dispersion spectrum from the passive source data, and determining the passive source dispersion curve based on the passive source dispersion spectrum. Obtaining a passive source frequency dispersion spectrum according to the passive source data can be used for performing frequency dispersion spectrum calculation on the passive source data to obtain a passive source frequency dispersion spectrum; determining a passive source dispersion curve based on the passive source dispersion spectrum may be picking up a passive source dispersion curve based on the passive source dispersion spectrum.

The passive source frequency dispersion curve represents the corresponding relation between the phase speed and the frequency of the second surface wave; wherein the second surface wave can be a natural micro-motion surface wave.

For convenience of understanding, a schematic diagram of a passive source dispersion spectrum in a multi-source data inversion method is illustrated here, and fig. 7 is a schematic diagram of a passive source dispersion spectrum in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 7, the abscissa represents frequency, and the ordinate represents velocity.

For convenience of understanding, a schematic diagram of a passive source dispersion curve in a multi-source data inversion method is illustrated here, and fig. 8 is a schematic diagram of a passive source dispersion curve in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 8. In fig. 8, the abscissa is the frequency of the second surface wave, and the ordinate is the phase velocity, also referred to as velocity, of the second surface wave.

Step S105: determining a target function of the joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve and the active source dispersion curve; the objective function represents the correlation between the passive source dispersion curve and the simulated dispersion curve and the similarity between the active source dispersion curve and the simulated dispersion curve.

In this embodiment, the objective function represents a correlation between the passive source dispersion curve and the simulated dispersion curve and a similarity between the active source dispersion curve and the simulated dispersion curve; wherein the correlation is understood as the degree of correlation between the passive source dispersion curve and the simulated dispersion curve characterized by the objective function; the similarity can be understood as the similarity degree of the active source dispersion curve and the simulated dispersion curve.

Determining a target function of the joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve and the active source dispersion curve may be determining a first target function according to the simulated dispersion curve and the passive source dispersion curve; the first objective function represents the correlation of the passive source dispersion curve and the simulated dispersion curve; determining a second objective function according to the simulated frequency dispersion curve and the active source frequency dispersion curve; the second objective function represents the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; and obtaining an objective function of the joint inversion of the active source data and the passive source data based on the first objective function and the second objective function.

The first objective function representing the correlation between the passive source dispersion curve and the simulated dispersion curve can be understood as determining the first objective function representing the correlation between the passive source dispersion curve and the simulated dispersion curve based on the characteristics that the velocity in the passive source dispersion curve is higher than the real surface wave velocity and the difference degree of each frequency velocity is consistent.

The similarity between the active source dispersion curve and the simulated dispersion curve, which is characterized by the second objective function, can be understood as the similarity between the active source dispersion curve and the simulated dispersion curve.

In this embodiment, when the passive source dispersion curve speed is higher than the active dispersion curve speed, if the same inversion target function is adopted, both dispersion curves cannot be completely inverted and converged, and the problem of speed difference between the active source data and the passive source data dispersion curves is solved by changing the target function during the joint inversion of the active source data and the passive source data, so that the joint inversion of the real surface wave speed is realized.

Step S106: determining a target speed model according to the target function and the active source frequency dispersion curve; the target velocity model represents the corresponding relationship between the depth and the velocity of the stratum.

In this embodiment, determining the target velocity model according to the target function and the active source dispersion curve may be: obtaining an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and correcting the initial speed model according to the objective function to obtain an objective speed model.

For convenience of understanding, a schematic diagram of a target velocity model in a multi-source data inversion method is illustrated here, and fig. 9 is a schematic diagram of a target velocity model in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 9. In fig. 9, the abscissa is the velocity and the ordinate is the depth of the formation. The target velocity model represents the corresponding relationship between the depth and the velocity of the stratum.

According to the multi-source data inversion method provided by the embodiment of the invention, by adopting the technical scheme of the embodiment of the invention, the target function of joint inversion of the active source data and the passive source data is determined based on the simulated dispersion curve, the passive source dispersion curve and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to the target function and the active source frequency dispersion curve; the target speed model represents the corresponding relation between the depth and the speed of the stratum; the problem of difference of frequency dispersion linear velocities of the active source and the passive source is solved by changing a target function during the joint inversion of the active source and the passive source, so that the joint inversion of the real surface wave velocity is realized, and the difference of surface waves extracted by active source data and passive source data is greatly reduced.

In an optional embodiment of the present invention, the determining a simulated dispersion curve based on the active source dispersion curve may include: obtaining an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and forward modeling the initial velocity model to obtain a simulated frequency dispersion curve.

In this embodiment, obtaining the initial velocity model based on the active source dispersion curve may be calculating the initial velocity model based on the active source dispersion curve according to an empirical formula; as an example, the empirical formula may be that the frequency-velocity relationship corresponding to the active source dispersion curve is converted according to the principle that a quarter wave field corresponding to a wave field is the depth of the velocity model corresponding to the wave field, that is, the "frequency-velocity" relationship of the dispersion curve becomes the "depth-velocity" relationship of the model. In other words, the empirical formula is: the depth H is the wavelength/4 is the velocity Vel/(4 frequency f), i.e. the signal corresponds to a depth of the geologic volume of one quarter of the corresponding wavefield.

For convenience of understanding, an example is illustrated here, and fig. 10 is a schematic diagram of an initial velocity model in a multi-source data inversion method according to an embodiment of the present invention; as shown in fig. 10, the correspondence of the depth of the formation on the ordinate and the velocity on the abscissa is illustrated.

In an optional embodiment of the present invention, the determining an objective function of joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve, and the active source dispersion curve may include: determining a first objective function according to the simulated frequency dispersion curve and the passive source frequency dispersion curve; the first objective function represents the correlation of the passive source dispersion curve and the simulated dispersion curve; determining a second objective function according to the simulated frequency dispersion curve and the active source frequency dispersion curve; the second objective function represents the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; and obtaining an objective function of the joint inversion of the active source data and the passive source data based on the first objective function and the second objective function.

In this embodiment, the determining the first objective function according to the simulated frequency dispersion curve and the passive source frequency dispersion curve may be determining a correlation coefficient function of the simulated frequency dispersion curve and the passive source frequency dispersion curve according to the simulated frequency dispersion curve and the passive source frequency dispersion curve, and then determining the first objective function according to the correlation coefficient function. The correlation coefficient function is used for measuring the closeness degree of the relation between the simulation frequency dispersion curve and the passive source frequency dispersion curve.

As an example, determining a first objective function according to the correlation coefficient function determines a first objective function according to the correlation coefficient function and a preset threshold; the preset threshold may be determined according to an actual situation, and is not limited herein, and as an example, the preset threshold may be 1. As an example, the first objective function may be that the correlation coefficient of the analog dispersion curve and the passive source dispersion curve is different from 1.

And determining a second objective function according to the simulated frequency dispersion curve and the active source frequency dispersion curve, namely determining a similar coefficient function of the simulated frequency dispersion curve and the active source frequency dispersion curve according to the simulated frequency dispersion curve and the active source frequency dispersion curve, and taking the similar coefficient function as the second objective function. The similarity coefficient function is used for measuring the similarity degree of the simulated frequency dispersion curve and the active source frequency dispersion curve. The similarity coefficient function may be determined according to actual conditions, and is not limited herein. As an example, the similarity coefficient function may be a mean square error of the analog dispersion curve and the active source dispersion curve.

Obtaining the target function for joint inversion of the active source data and the passive source data based on the first target function and the second target function may be summing the first target function and the second target function to obtain the target function for joint inversion of the active source data and the passive source data, that is, the target function is the first target function + the second target function.

As an example, when the first objective function is the difference between the correlation coefficient of the simulated dispersion curve and the passive dispersion curve and 1, and the second objective function is the mean square error of the simulated dispersion curve and the active dispersion curve, the objective function is the difference between the correlation coefficient of the simulated dispersion curve and the passive dispersion curve and 1 + the mean square error of the simulated dispersion curve and the active dispersion curve. In practical application, since the correlation coefficient of the analog dispersion curve and the passive source dispersion curve is between (0-1), the absolute value of the difference between the correlation coefficient of the analog dispersion curve and the passive source dispersion curve and 1 is also between (0-1), which is a very small amount. The mean square error of the simulated dispersion curve and the active source dispersion curve is a minimum, i.e., the second objective function is also a minimum. The objective function is that the sum of the difference between the correlation coefficient of the analog dispersion curve and the passive source dispersion curve and 1 and the mean square error of the analog dispersion curve and the active source dispersion curve is also a very small amount.

In the embodiment of the invention, aiming at the speed difference of the extraction of the active source data and the passive source data of the linear arrangement type device, the inversion method based on the difference characteristics is adopted, so that the problem that the inversion can not be completely converged due to the high speed of the frequency dispersion curve of the linear device can be solved, and the automatic joint inversion of the active source and the passive source is realized. In the implementation and application, the dispersion curves of the surface waves of the active source and the passive source do not need to be spliced manually by a user, and the working efficiency and the exploration precision are improved.

In an optional embodiment of the present invention, the determining a first objective function according to the analog dispersion curve and the passive source dispersion curve may include: obtaining a correlation coefficient function of the simulated frequency dispersion curve and the passive source frequency dispersion curve; and determining a first target function according to the correlation coefficient function and a preset threshold value.

In this embodiment, a correlation coefficient function of the simulated dispersion curve and the passive source dispersion curve is obtained; and the correlation coefficient function represents a statistical index of the degree of closeness of correlation among the variables. For convenience of understanding, in this example, assuming that the analog dispersion curve is defined as X and the passive source dispersion curve is defined as Y, the correlation coefficient function ρ is_XYCan be as follows:

in formula (1), Cov (X, Y) is the covariance of X and Y; d (X), D (Y) are the variances of X and Y, respectively.

Determining a first objective function according to the correlation coefficient function and a preset threshold may be determining the first objective function by subtracting the correlation coefficient function from the preset threshold; the preset threshold may be determined according to an actual situation, and is not limited herein, and as an example, the preset threshold may be 1. As an example, the first objective function may be a difference between a correlation coefficient of the analog dispersion curve and the passive source dispersion curve and 1. In practical applications, the first objective function may also be an absolute value of a correlation coefficient and a 1 difference between the simulated dispersion curve and the passive source dispersion curve.

In practical applications, since the correlation coefficient of the analog dispersion curve and the passive source dispersion curve is between (0-1), the preset threshold is generally 1, and the absolute value of the difference between the correlation coefficient and 1 of the analog dispersion curve and the passive source dispersion curve is also between (0-1), which is a very small amount.

In an optional embodiment of the present invention, the determining a second objective function according to the simulated dispersion curve and the active source dispersion curve may include: obtaining a similarity coefficient function of the simulated frequency dispersion curve and the active source frequency dispersion curve; and taking the similarity coefficient function as a second objective function.

The obtaining of the similarity coefficient function of the simulated frequency dispersion curve and the active source frequency dispersion curve may be obtaining a mean square error of the simulated frequency dispersion curve and the active source frequency dispersion curve, and the mean square error is recorded as the similarity coefficient function of the simulated frequency dispersion curve and the active source frequency dispersion curve; the mean square error, or "standard deviation", is the arithmetic square root of the arithmetic mean squared off the mean.

Taking the similarity coefficient function as a second objective function may be to take the mean square error of the simulated frequency dispersion curve and the active source frequency dispersion curve as the second objective function, that is, the second objective function is the mean square error of the simulated frequency dispersion curve and the active source frequency dispersion curve. In practical applications, the mean square error of the simulated dispersion curve and the active source dispersion curve is a very small amount, i.e., the second objective function is a very small amount.

In an optional embodiment of the present invention, the determining a target velocity model according to the objective function and the active source dispersion curve may include: obtaining an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and correcting the initial speed model according to the objective function to obtain an objective speed model.

In this embodiment, the initial velocity model is modified according to the objective function, and the target velocity model may be obtained by repeatedly updating parameters of the initial velocity model according to the value of the objective function when the value of the objective function does not satisfy the preset range, and obtaining the updated initial velocity model, and taking the updated initial velocity model as the target velocity model when the value of the objective function satisfies the preset range. The preset range may be determined according to an actual situation, and is not limited herein. As an example, the preset range may be 0 to 0.00001, and the 0.000001 is determined according to actual conditions, for example, an empirical value.

In practical application, an initial velocity model is obtained based on the active source frequency dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; the initial velocity model is corrected according to the objective function, and the process of obtaining the target velocity model is a cyclic process, which can be understood as that the initial velocity model is corrected according to the objective function to obtain a corrected initial velocity model under the condition that an inversion cutoff condition is met, and the corrected initial velocity model is used as the target velocity model. The inversion cut-off condition can be that the target function meets a preset range or the inversion times reach a preset threshold value. The preset range can be determined according to actual conditions, and is not limited herein; as an example, the preset range may be 0 to 0.00001. The preset threshold value can be determined according to actual conditions, and is not limited herein; as an example, the preset threshold may be 5.

For convenience of understanding, a multi-source data inversion method in practical application is illustrated, and the method is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component.

Firstly, obtaining vertical component data through the single component detector; the vertical component data comprises active source data and passive source data; the active source data is obtained based on an artificial seismic source.

And secondly, respectively carrying out frequency dispersion spectrum calculation on the active source data and the passive source data to obtain an active source frequency dispersion spectrum and a passive source frequency dispersion spectrum.

Thirdly, picking up an active source frequency dispersion curve based on the active source frequency dispersion spectrum; and picking up a passive source frequency curve based on the passive source frequency dispersion spectrum.

And fourthly, calculating an initial velocity model according to an empirical formula based on the active source frequency dispersion curve.

And fifthly, calculating a simulated dispersion curve based on the initial velocity model.

Sixthly, determining a target function of joint inversion of the active source data and the passive source data based on the simulated frequency dispersion curve, the passive source frequency dispersion curve and the active source frequency dispersion curve; and the target function is sum of the difference between the correlation coefficient of the simulated frequency dispersion curve and the passive source frequency dispersion curve and 1 and the mean square error of the simulated frequency dispersion curve and the active source frequency dispersion curve.

And seventhly, correcting the initial speed model based on the objective function to obtain a corrected initial speed model.

Eighthly, repeating the fifth, sixth and seventh steps until the inversion cutoff condition is met, and outputting an inversion result; and the inversion cut-off condition is that the target function meets a preset range or the inversion times reach a preset threshold value.

The explanation of this embodiment is the same as the description of the related content before this embodiment, and reference may be made to the description of the related content before this embodiment, which is not described herein again.

According to the multi-source data inversion method provided by the embodiment of the invention, a target function of joint inversion of the active source data and the passive source data is determined based on the simulated dispersion curve, the passive source dispersion curve and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to the target function and the active source frequency dispersion curve; the target speed model represents the corresponding relation between the depth and the speed of the stratum; the problem of difference of frequency dispersion linear velocities of the active source and the passive source is solved by changing a target function during the joint inversion of the active source and the passive source, so that the joint inversion of the real surface wave velocity is realized, and the difference of surface waves extracted by active source data and passive source data is greatly reduced.

The embodiment provides a multi-source data inversion device, which is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component; fig. 11 is a schematic structural diagram of a multi-source data inversion apparatus according to an embodiment of the present invention, and as shown in fig. 8, the apparatus 200 includes: an obtaining unit 201 and a determining unit 202, wherein:

the obtaining unit 201 is configured to obtain vertical component data by the single component detector; the vertical component data comprises active source data and passive source data; the active source data is obtained based on an artificial seismic source; obtaining an active source frequency dispersion curve according to the active source data; and the active source frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the first surface wave.

The determining unit 202 is configured to determine a simulated dispersion curve based on the active source dispersion curve obtained by the obtaining unit 201; and the simulated frequency dispersion curve represents the corresponding relation between the phase velocity and the frequency of the surface wave.

The obtaining unit 201 is further configured to obtain a passive source dispersion curve according to the passive source data; and the passive source frequency dispersion curve represents the corresponding relation between the phase speed and the frequency of the second surface wave.

The determining unit 202 is further configured to determine an objective function of joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve obtained by the obtaining unit 201, and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to the target function and the active source frequency dispersion curve obtained by the obtaining unit; the target velocity model represents the corresponding relationship between the depth and the velocity of the stratum.

In other embodiments, the obtaining unit 201 is further configured to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and forward modeling the initial velocity model to obtain a simulated frequency dispersion curve.

In other embodiments, the determining unit 202 comprises a first determining module, a second determining module, and an obtaining module, wherein,

In other embodiments, the first determining module is further configured to obtain a correlation coefficient function between the simulated dispersion curve and the passive source dispersion curve; and determining a first target function according to the correlation coefficient function and a preset threshold value.

In other embodiments, the second determining module is further configured to obtain a similarity coefficient function of the simulated dispersion curve and the active source dispersion curve; and taking the similarity coefficient function as a second objective function.

In other embodiments, the determining unit 202 is further configured to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and correcting the initial speed model according to the objective function to obtain an objective speed model.

The above description of the apparatus embodiments, similar to the above description of the method embodiments, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus according to the invention, reference is made to the description of the embodiments of the method according to the invention for understanding.

It should be noted that, in the embodiment of the present invention, if the above-mentioned multi-source data inversion method is implemented in the form of a software functional module, and is sold or used as a stand-alone product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a multi-source data inversion apparatus (which may be a personal computer, a server, or a network device) to perform all or part of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.

Correspondingly, an embodiment of the present invention provides a multi-source data inversion apparatus, including a memory and a processor, where the memory stores a computer program that can be run on the processor, and the processor implements the steps in the multi-source data inversion method provided in the above embodiment when executing the program.

Correspondingly, the embodiment of the invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps in the multi-source data inversion method provided by the above embodiment.

Here, it should be noted that: the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and the apparatus according to the invention, reference is made to the description of the embodiments of the method according to the invention.

It should be noted that fig. 12 is a schematic diagram of a hardware entity structure of a multi-source data inversion apparatus in an embodiment of the present invention, as shown in fig. 12, the hardware entity of the multi-source data inversion apparatus 300 includes: a processor 301 and a memory 303, optionally the multi-source data inversion apparatus 300 may further comprise a communication interface 302.

It will be appreciated that the memory 303 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 303 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The method disclosed in the above embodiments of the present invention may be applied to the processor 301, or implemented by the processor 301. The processor 301 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 301. The Processor 301 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 301 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium located in the memory 303, and the processor 301 reads the information in the memory 303 and performs the steps of the aforementioned methods in conjunction with its hardware.

In an exemplary embodiment, the multi-source data inversion Device may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.

In the embodiments provided in the present invention, it should be understood that the disclosed method and apparatus can be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another observation, or some features may be omitted, or not performed. In addition, the communication connections between the components shown or discussed may be through interfaces, indirect couplings or communication connections of devices or units, and may be electrical, mechanical or other.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.

Alternatively, the integrated unit according to the embodiment of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the technical embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a multi-source data inversion apparatus (which may be a personal computer, a server, or a network device) to perform all or part of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.

The multi-source data-based inversion method, apparatus and storage medium described in the embodiments of the present invention are only examples of the embodiments of the present invention, but are not limited thereto, and the multi-source data-based inversion method, apparatus and storage medium are within the scope of the present invention.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and all such changes or substitutions are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims

1. A multi-source data inversion method is characterized in that the method is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component; the method comprises the following steps:

2. The method of claim 1, wherein determining an analog dispersion curve based on the active source dispersion curve comprises:

3. The method of claim 1, wherein determining an objective function for joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve, and the active source dispersion curve comprises:

4. The method of claim 3, wherein determining a first objective function from the simulated dispersion curve and the passive source dispersion curve comprises:

5. The method of claim 3, wherein determining a second objective function from the simulated dispersion curve and the active source dispersion curve comprises:

and taking the similarity coefficient function as a second objective function.

6. The method of claim 1, wherein determining a target velocity model from the objective function and the active source dispersion curve comprises:

7. A multi-source data inversion device is characterized in that the device is applied to a linear observation device; the linear observation device comprises a single-component detector for acquiring a vertical component; the device comprises: an obtaining unit and a determining unit, wherein:

the determining unit is further configured to determine a target function of joint inversion of the active source data and the passive source data based on the simulated dispersion curve, the passive source dispersion curve obtained by the obtaining unit, and the active source dispersion curve; the objective function represents the correlation between the passive source frequency dispersion curve and the simulated frequency dispersion curve and the similarity between the active source frequency dispersion curve and the simulated frequency dispersion curve; determining a target speed model according to the target function and the active source frequency dispersion curve obtained by the obtaining unit; the target velocity model represents the corresponding relationship between the depth and the velocity of the stratum.

8. The apparatus of claim 7, wherein the obtaining unit is further configured to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and forward modeling the initial velocity model to obtain a simulated frequency dispersion curve.

9. The apparatus of claim 7, wherein the determination unit comprises a first determination module, a second determination module, and an obtaining module, wherein,

10. The apparatus of claim 9, wherein the first determining module is further configured to obtain a correlation coefficient function of the simulated dispersion curve and the passive source dispersion curve; and determining a first target function according to the correlation coefficient function and a preset threshold value.

11. The apparatus of claim 9, wherein the second determining module is further configured to obtain a similarity coefficient function of the simulated dispersion curve and the active source dispersion curve; and taking the similarity coefficient function as a second objective function.

12. The apparatus of claim 7, wherein the determining unit is further configured to obtain an initial velocity model based on the active source dispersion curve; the initial velocity model represents the corresponding relation between the depth and the velocity of the stratum; and correcting the initial speed model according to the objective function to obtain an objective speed model.

13. A multi-source data inversion apparatus comprising a memory and a processor, the memory storing a computer program executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the program.

14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.