CN107589455B

CN107589455B - A kind of method and device of land and water detector seismic data merging treatment

Info

Publication number: CN107589455B
Application number: CN201710565184.XA
Authority: CN
Inventors: 高少武; 赵波; 钱忠平; 黄少卿; 祝树云; 刘增强
Original assignee: China National Petroleum Corp; BGP Inc
Current assignee: China National Petroleum Corp; BGP Inc
Priority date: 2017-07-12
Filing date: 2017-07-12
Publication date: 2019-03-15
Anticipated expiration: 2037-07-12
Also published as: CN107589455A

Abstract

The embodiment of the present application discloses a kind of method and device of land and water detector seismic data merging treatment.The described method includes: carrying out Phase Processing to land detector seismic data；The amplitude processing is carried out to detector seismic data in water and to the land detector seismic data after Phase Processing；Fourier transformation is carried out respectively to detector seismic data in the water after the amplitude processing and land detector seismic data；Calibration factor is calculated according to detector seismic data in the water after the amplitude processing and land detector seismic data, and according to detector seismic data in the water after Fourier transformation and land detector seismic data, calculates sea water advanced and bottom reflection coefficient；Based on sea water advanced, mirror image connector is determined；Mirror image combined treatment is carried out to detector seismic data in the water of target area and land detector seismic data, obtains land and water wave detector merging data.The accuracy of land and water detector seismic data merging treatment can be improved in embodiment provided by the present application.

Description

Method and device for merging and processing seismic data of amphibious detector

Technical Field

The application relates to the technical field of oil exploration, exploitation and development, in particular to a method and a device for merging and processing seismic data of an amphibious detector.

Background

With the development of seismic exploration technology, the difficulty and depth of offshore oil and gas exploration are increased, and the requirements on the signal-to-noise ratio and the resolution of seismic data are increased. OBC (Ocean Bottom Cable), a combined marine and land seismic data acquisition technology, fixes a geophone on the Ocean floor to obtain high-resolution three-dimensional seismic data. In OBC data acquisition, at least three vessels: the seismic source ship only drags the air gun seismic source arrangement to carry out seismic wave excitation; a receiving vessel, which is stationary, is connected to the submarine cable and receives seismic waves; a vessel or several vessels, laying and recovering the submarine cables. During OBC data acquisition, the seabed and the sea surface are stronger reflecting interfaces. As a seismic wavelet excited by the source reaches the seafloor from the source location, or a reflected seismic wavelet reaches the seafloor from the subsurface, a detector in the seafloor cable senses and records the reflected seismic wavelet. The reflected wavelet continues to advance upwards to reach the sea surface, is reflected by the sea surface, then changes direction and propagates downwards to reach the sea bottom. The geophone in the submarine cable again senses and records this seismic wavelet. Meanwhile, the seismic wavelet is reflected by the seabed, changes direction to propagate upwards to reach the sea surface, is reflected by the sea surface, and then changes direction to propagate downwards to reach the seabed. This cycle is repeated. The undesirable secondary and subsequent arrival of these primary reflected seismic wavelets is the marine seismic multiples (reverberation). The sea-water-seismic multiples are the largest noise disturbances in marine seismic survey data. The method is the most important step in the processing of marine seismic data.

The data collected by the OBC provides two kinds of data of hydrophone seismic data and land geophone seismic data at the same position. Both data were recorded using a hydrophone and a land-detector, respectively. The hydrophone is a pressure geophone, and records pressure changes generated by seismic waves; the land geophone is a particle velocity geophone that records particle velocity changes. The two detectors have different recording mechanisms, and show different characteristics for the interference of the multiple waves of the water ringing at the same position. The sea ringing multiples interference recorded by the land geophone exhibits a difference in polarity and amplitude characteristics compared to the sea ringing multiples interference recorded by the hydrophone. The two detectors record the interference of multiple waves of the marine seismic, the polarities of the interference are opposite, the amplitudes of the interference are different, and the difference is a constant which is proportional to the sea bottom reflection coefficient, and the constant value is a calibration factor. Therefore, by utilizing the amplitude and polarity characteristic difference, the interference of the multiple waves of the seawater ringing can be effectively eliminated. The step of eliminating the interference of the multiple waves of the sea water ringing comprises the following steps: (1) recording two kinds of data of seismic data of a hydrophone and seismic data of a land geophone at the position of each receiving point; (2) adjusting the amplitude of the land geophone seismic data to match the amplitude of the hydrophone seismic data using the sensor sensitivities (propagation constants) of the two geophones; (3) calculating and determining parameters of a calibration factor, seawater depth and a seabed reflection coefficient; (4) calibrating the land geophone seismic data after amplitude adjustment by using a calibration factor; (5) adding the calibrated land geophone seismic data with the corresponding hydrophone seismic data to obtain land geophone calibration data; (6) the interference of multiple waves of sea water ringing is eliminated by using the sea water depth and the sea bottom reflection coefficient parameters. Thus, parameters such as a calibration factor, the depth of seawater, the sea bottom reflection coefficient and the like are calculated, and a fundamental method and key steps of the method for eliminating the interference of the multiple waves of the seawater ringing are formed in the indoor marine seismic data processing.

And in a conventional processing method, a scanning method is adopted to calculate and determine the calibration factor. A calibration factor range value and a scanning step length are preset, a series of calibration factor values are given by a scanning method, then the sum of data of seismic data of a hydrophone and data of seismic data of a land geophone is calculated, then an autocorrelation function is calculated for the sum of the data, the maximum variance module is calculated by the autocorrelation function, and finally the value of the calibration factor is determined by the maximum variance module value. This method requires a large number of autocorrelation calculations and maximum variance module calculations, and therefore the calculations are very time consuming. The conventional processing method adopts a cross-correlation method of data of an up-going wave field and a down-going wave field to calculate and determine the depth of the seawater. Calculating up-going wave field and down-going wave field data by using hydrophone seismic data and land geophone seismic data; and then, performing cross-correlation on the data of the uplink wave field and the data of the downlink wave field, and determining the depth value of the seawater according to the maximum value of a cross-correlation function. Various noise interferences are included in the actual hydrophone seismic data and the land geophone seismic data. In particular, land and water test data have different effective frequency ranges, and therefore have different low-frequency noise (such as surface waves) and different useless high-frequency distributions. In addition, the prestack land and water inspection data also comprises strong amplitude energy interference. The cross-correlation function thus calculated using hydrophone seismic data and land-geophone seismic data also contains various noise components. Therefore, the sea water depth determined by using the cross-correlation function containing the noise has a large error, and the actual data processing requirements are difficult to meet. And (3) calculating and determining the sea bottom reflection coefficient by adopting a scanning method in a conventional processing method. The method comprises the steps of presetting a sea bottom reflection coefficient range value and a scanning step length, giving a series of reflection coefficient values by a scanning method, calculating a series of Backus sea water ringing multiple inverse filter operators for a series of sea bottom reflection coefficients, multiplying frequency domain water and land detection uplink wave field data (namely the sum of water and land detector seismic data) by the Backus sea water ringing multiple inverse filter operators to obtain filtered uplink wave field data, transforming the uplink wave field data to a time domain by using inverse Fourier transform, and calculating a series of wave field data energy in the time domain, wherein the sea bottom reflection coefficient corresponding to the minimum energy is the optimum sea bottom reflection coefficient. This method requires a large number of autocorrelation calculations and maximum variance module calculations, and therefore the calculations are very time consuming.

Therefore, how to accurately and time-saving calculate and determine the calibration factor, the seawater depth and the seabed reflection coefficient, so that the combination processing of the seismic data of the hydrophone and the land geophone is rapidly and accurately realized, the interference influence of the multiple waves of the marine seismic in the seismic data is eliminated, and the signal-to-noise ratio and the resolution ratio of the seismic data are improved.

Disclosure of Invention

The embodiment of the application aims to provide a method and a device for merging and processing seismic data of a hydrophone and a geophone, so that the seismic data of the hydrophone and the seismic data of the geophone are quickly and accurately merged, the influence of interference of multiple waves of sea water singing in the seismic data is eliminated, and the signal-to-noise ratio and the resolution ratio of the seismic data are improved.

In order to solve the above technical problems, an embodiment of the present application provides a method and a device for merging and processing seismic data of an amphibious detector, which are implemented as follows:

a method of combined processing of hydrophone and geophone seismic data, providing hydrophone seismic data and geophone seismic data for a target area, the method comprising:

carrying out phase processing on the land geophone seismic data to obtain land geophone seismic data after phase processing;

performing first amplitude processing on the hydrophone seismic data to obtain hydrophone seismic data after amplitude processing, and performing second amplitude processing on the land geophone seismic data after phase processing to obtain land geophone seismic data after amplitude processing;

performing Fourier transform on the hydrophone seismic data and the land detector seismic data after amplitude processing respectively to obtain Fourier transformed hydrophone seismic data and Fourier transformed land detector seismic data;

calculating a calibration factor according to the hydrophone seismic data after amplitude processing and the land geophone seismic data after amplitude processing, and calculating the sea depth and the sea bottom reflection coefficient of the target area according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform;

determining a mirror image combination factor based on the sea water depth;

and carrying out mirror image combination processing on the hydrophone seismic data of the target area and the land geophone seismic data of the target area based on the calibration factor, the seawater depth, the seabed reflection coefficient and the mirror image combination factor to obtain the land geophone merged data of the target area.

In a preferred scheme, the mirror image combination treatment is carried out by adopting the following formula:

S＝O_H[n]*H+O_G[n]*G

wherein S represents the hydrophone merged data, H represents hydrophone seismic data for the target region, and G represents land geophone seismic data for the target region; represents a convolution operator; o is_H[n]Representing water detection data combination operators, O_G[n]Representing a land survey data combination operator, n representing a mirror image combination operator sequence number, n-NOp, -NOp +1, -NOp +2,. multidot., -1,0, 1.. multidot.,. NOp-2, NOp-1, NOp, (2NOp +1) representing a mirror image combination operator length,. DELTA.t representing a seismic data time sampling interval, α representing an optimal calibration factor, A representing an expected absolute value amplitude of the merged data, A_HRepresenting the mean absolute amplitude, A, of the hydrophone gather data_GRepresents the mean absolute amplitude of the land detector gather data; r_sRepresenting the water surface reflection coefficient; r represents the optimal sea bottom reflection coefficient; z represents a delay operator, (1-Z) and (1+ Z) represent the mirror combining factor; l is an imaginary unit, and²1, omega tableThe angular frequency is shown, D represents the depth of the sea, V represents the velocity of the sea, and phi represents the phase correction factor obtained when the seismic data of the land geophone is subjected to phase processing.

In a preferred scheme, the hydrophone seismic data is subjected to first amplitude processing by using the following formula:

wherein,representing said amplitude-processed hydrophone seismic data, H_i,jRepresenting hydrophone seismic data, A_HThe method comprises the steps of representing the average absolute value amplitude of data of a hydrophone gather, i representing the sequence number of a time window reference channel, i being 1, 2.

In a preferred embodiment, the phase-processed geophone seismic data is subjected to a second amplitude processing using the following formula:

wherein,representing said amplitude-processed geophone seismic data, G_i,jRepresenting said phase-processed geophone seismic data, A_GThe amplitude of the average absolute value of the land detector trace set data is represented, i represents the sequence number of a time window reference trace, i is 1,2, the.

In a preferred embodiment, the calculating a calibration factor according to the amplitude-processed hydrophone seismic data and the amplitude-processed land geophone seismic data includes:

calculating an autocorrelation function of the amplitude processed hydrophone seismic data and an autocorrelation function of the amplitude processed land geophone seismic data, respectively, and a cross-correlation function associated with the amplitude processed hydrophone seismic data and the amplitude processed land geophone seismic data;

calculating a maximum variance modulus equation coefficient according to the autocorrelation function and the cross-correlation function of the hydrophone seismic data and the land geophone seismic data after amplitude processing;

calculating a calibration factor characteristic equation coefficient according to the maximum variance module equation coefficient;

constructing a calibration factor characteristic equation according to the calibration factor characteristic equation coefficient, and solving the calibration factor characteristic equation to obtain a calibration factor characteristic equation root;

and calculating a maximum variance model according to the maximum variance model equation coefficient and the calibration factor characteristic equation root, and determining an optimal calibration factor according to the maximum variance model.

In a preferred embodiment, the calculating a maximum variance module according to the maximum variance module equation coefficient and the calibration factor characteristic equation root, and determining an optimal calibration factor according to the maximum variance module includes:

the following formula is used to calculate the maximum square difference mode:

wherein, Q (α)_n) Representing said maximum square difference mode, P_mRepresents the maximum variance modulus equation coefficient, m is 0,1,2_nRepresenting a characteristic equation root of the calibration factor, wherein n is 1, 2.

Determining the maximum value of the maximum square difference model by adopting the following formula, and determining the optimal calibration factor according to the maximum square difference model:

wherein, Q (α)_best) Represents the maximum of the maximum square difference mode, α_bestRepresenting the optimal scaling factor corresponding to the maximum of said maximum variance model.

In a preferred embodiment, the calculating the depth of the sea water in the target area according to the hydrophone seismic data after fourier transform and the land geophone seismic data after fourier transform includes:

calculating an average cross-correlation spectrum of the geophone seismic data according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform;

calculating an average cross-correlation function of the seismic data of the land and water detectors according to the average cross-correlation spectrum of the seismic data of the land and water detectors;

and calculating the sea water depth of the target area according to the average cross-correlation function of the seismic data of the land and water detectors.

In a preferred embodiment, the calculating the submarine reflection coefficient of the target area according to the fourier-transformed hydrophone seismic data and the fourier-transformed land geophone seismic data includes:

calculating first wave field combination data, second wave field combination data and third wave field combination data according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform;

calculating a sea-bottom reflection coefficient characteristic equation coefficient according to the first wave field combination data, the second wave field combination data and the third wave field combination data;

constructing a submarine reflection coefficient characteristic equation according to the submarine reflection coefficient characteristic equation coefficients, and solving the submarine reflection coefficient characteristic equation to obtain a plurality of submarine reflection coefficient values;

wave field data energy respectively corresponding to the seabed reflection coefficient values is respectively calculated according to the seabed reflection coefficient values, and the optimal seabed reflection coefficient in the plurality of seabed reflection coefficient values is determined according to the wave field data energy.

In a preferred embodiment, the calculating the first wave field combination data, the second wave field combination data, and the third wave field combination data according to the fourier transformed hydrophone seismic data and the fourier transformed land geophone seismic data includes:

calculating uplink wave field data, first-order delay downlink wave field data, second-order delay uplink wave field data, second-order delay downlink wave field data and third-order delay downlink wave field data according to the hydrophone seismic data after Fourier transform and the land seismic data after Fourier transform;

computing the first wavefield combination data based on the up-going wavefield data and the first-order delayed down-going wavefield data;

computing the second wavefield combination data based on the first order delayed up-going wavefield data and the second order delayed down-going wavefield data;

calculating the third wavefield combination data based on the second order delayed upgoing wavefield data and the third order delayed downgoing wavefield data.

An apparatus for combined processing of hydrophone seismic data and geophone seismic data for a target area, the apparatus comprising: the device comprises a phase processing module, an amplitude processing module, a Fourier transform module, a parameter calculation module, a mirror image combination factor determination module and a water and land inspection data merging module; wherein,

the phase processing module is used for carrying out phase processing on the land geophone seismic data to obtain land geophone seismic data after the phase processing;

the amplitude processing module is used for carrying out first amplitude processing on the hydrophone seismic data to obtain hydrophone seismic data after amplitude processing, and carrying out second amplitude processing on the land geophone seismic data after phase processing to obtain land geophone seismic data after amplitude processing;

the Fourier transform module is used for respectively carrying out Fourier transform on the hydrophone seismic data and the land geophone seismic data after the amplitude processing to obtain Fourier transformed hydrophone seismic data and Fourier transformed land geophone seismic data;

the parameter calculation module is used for calculating a calibration factor according to the hydrophone seismic data after amplitude processing and the land geophone seismic data after amplitude processing, and calculating the sea depth and the sea bottom reflection coefficient of the target area according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform;

the mirror image combination factor determining module is used for determining a mirror image combination factor based on the seawater depth;

the land and water detection data merging module is used for carrying out mirror image combination processing on the hydrophone seismic data of the target area and the land geophone seismic data of the target area based on the calibration factor, the sea water depth, the sea bottom reflection coefficient and the mirror image combination factor to obtain land and water geophone merging data of the target area.

The embodiment of the application provides a method and a device for merging and processing seismic data of an amphibious detector, which do not need to preset corresponding parameter range values and scanning step lengths, but directly calculate optimal calibration factors and optimal submarine reflection coefficients by adopting a correlation function method, and have the advantages of small calculation amount and high calculation speed; meanwhile, through the phase processing of land geophone seismic data and the amplitude processing of land geophone seismic data in advance, low-frequency noise is reduced, and amplitude energy interference is reduced, so that the calculation accuracy of the sea water depth is improved. According to the embodiment of the application, the mirror image combination factor is introduced according to the calculated calibration factor, the sea water depth and the sea bottom reflection coefficient, and the combination processing of the seismic data of the hydrophone and the seismic data of the land geophone is realized quickly and accurately, so that the influence of the interference of the multiple waves of the sea water singing in the seismic data is effectively eliminated, and the signal-to-noise ratio and the resolution ratio of the seismic data are improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a flow chart of an embodiment of a method of the present application for hydrophone seismic data consolidation;

FIG. 2(a) is a schematic diagram of water surface inspection and water surface inspection data combined with common inspection wave channel water collection inspection according to an embodiment of the present application;

FIG. 2(b) is a land survey data schematic diagram of a merged co-survey point gather for land and water survey according to an embodiment of the present application;

FIG. 2(c) is a schematic diagram of the present combined water-land survey and the merged data of the common survey point gather;

FIG. 2(d) is a schematic diagram of the merged data of the water-land inspection merged common-detector gather according to an embodiment of the present application;

FIG. 3(a) is a schematic diagram of a frequency spectrum of collected water detection data of a water-land detection combined common detection wave channel according to an embodiment of the present application;

FIG. 3(b) is a schematic diagram of land survey data spectrum of a merged co-detection point gather for land and water detection according to an embodiment of the present application;

FIG. 3(c) is a schematic diagram of the combined data spectrum of the existing water-land detection combined common-detector gather water-land detection;

FIG. 3(d) is a schematic diagram of a combined data spectrum of an amphibious detection combined common-detector gather according to an embodiment of the present application;

fig. 4(a) is a schematic diagram of water surface inspection and water surface inspection data combined with shot point and trace catchment inspection data according to an embodiment of the present application;

FIG. 4(b) is a schematic diagram of land survey data of a merged common shot gather for land and water test according to an embodiment of the present application;

FIG. 4(c) is a schematic diagram of the current water-land inspection and shot-shared gather water-land inspection and data;

FIG. 4(d) is a schematic diagram of the water-land inspection and shot-shared gather water-land inspection and data merging according to an embodiment of the present application;

fig. 5(a) is a schematic frequency spectrum diagram of water surface inspection and water surface inspection combined common shot point channel water collection inspection data according to an embodiment of the application;

FIG. 5(b) is a schematic diagram of land survey data frequency spectrum of a merged common shot gather for land and water detection according to an embodiment of the present application;

FIG. 5(c) is a schematic diagram of the existing water-land detection and shot-shared gather water-land detection and data frequency spectrum;

FIG. 5(d) is a schematic diagram of a frequency spectrum of merged data of the water-land detection merged common shot gather according to an embodiment of the present application;

FIG. 6(a) is a schematic diagram of water and land inspection and common midpoint gather superposition water inspection data according to an embodiment of the present application;

FIG. 6(b) is a schematic diagram of land detection data superimposed by a water and land detection merged common midpoint gather according to an embodiment of the present application;

FIG. 6(c) is a schematic diagram of existing surface inspection and common midpoint gather superimposed surface inspection and data;

FIG. 6(d) is a schematic diagram of land and water inspection merged data by merging common midpoint gathers and overlapping land and water inspection according to an embodiment of the present application;

FIG. 7(a) is a schematic diagram of a spectrum of water surface detection data overlapped by a common midpoint gather according to an embodiment of the present application;

FIG. 7(b) is a schematic diagram of land detection data spectrum superimposed by a water and land detection merged common midpoint gather according to an embodiment of the present application;

fig. 7(c) is a schematic diagram of the spectra of the existing water and land test merged common midpoint gather superimposed water and land test merged data;

FIG. 7(d) is a schematic diagram of a merged data spectrum of a water and land test merged common midpoint gather superposition according to an embodiment of the present application;

figure 8 is a block diagram of the components of an embodiment of the apparatus for combined processing of geophone seismic data according to the present application.

Detailed Description

The embodiment of the application provides a method and a device for merging and processing seismic data of an amphibious detector.

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The embodiment of the application provides a method for merging and processing seismic data of an amphibious detector. The method provides hydrophone seismic data and land-geophone seismic data for a target area.

FIG. 1 is a flow chart of an embodiment of a method of the present application for hydrophone seismic data consolidation processing. As shown in fig. 1, the method for merging and processing the seismic data of the land geophone comprises the following steps.

S101: and carrying out phase processing on the land geophone seismic data to obtain the land geophone seismic data after the phase processing.

Because the hydrophone and the land detector have different receiving mechanisms, so that the phases of the data received by the two detectors are different, before the two data are combined, the land detector seismic data needs to be subjected to phase processing in order to effectively balance the land and water phases. And carrying out phase analysis on the actual hydrophone seismic data and the land geophone seismic data to obtain a phase correction factor.

In this embodiment, the phase processing may specifically include:

calculating a Kazeir window Hilbert factor sequence h according to the following formula:

wherein, I₀(x) Is a first class of zero order modified bessel function, β denotes the kezier window function parameter, M denotes the kezier window function length, α ═ M/2. the sequence of kezier window hilbert factors is described by two parameters β and M.

Computing the Hilbert transform G of the land geophone seismic data according to the following formula^H：

G^H＝G⁰H (formula 2)

Wherein, the symbol "+" represents convolution operation, and h is a Kaze-Window Hilbert factor sequence.

Phase processing of the geophone seismic data is performed according to the following equation:

G＝G⁰cosΦ-G^Hsin phi (equation 3)

Wherein G is land geophone seismic data after phase processing, G0 is land geophone seismic data, GH is Hilbert transform of land geophone seismic data, and phi is a phase correction factor.

S102: and performing first amplitude processing on the hydrophone seismic data to obtain hydrophone seismic data after amplitude processing, and performing second amplitude processing on the land geophone seismic data after phase processing to obtain land geophone seismic data after amplitude processing.

In the process of combining and processing the seismic data of the hydrophone and the seismic data of the land geophone, the energy and the effective width between the two geophone receiving data are different due to different receiving mechanisms of the hydrophone and the land geophone. Therefore, amplitude processing of the geophone seismic data is required to facilitate subsequent geophone seismic data consolidation.

In this embodiment, the hydrophone seismic data may be subjected to a first amplitude processing using the following equation:

In this embodiment, the phase processed geophone seismic data may be subjected to a second amplitude processing using the following equation:

wherein,representing said amplitude-processed geophone seismic data, G_i,jRepresenting said phase-processed geophone seismic data, A_GRepresenting mean absolute of land detector gather dataThe value amplitude, i represents the sequence number of the time window reference track, i is 1,2,., L represents the total number of time window parameter tracks, j represents the sequence number of time sample points of the time window parameter, j is 1,2,., N represents the number of time sample points of the time window parameter, and the symbol | | · | | is an absolute value operator.

S103: and respectively carrying out Fourier transform on the hydrophone seismic data and the land detector seismic data after the amplitude processing to obtain Fourier transformed hydrophone seismic data and Fourier transformed land detector seismic data.

In the present embodiment, the hydrophone seismic data H after fourier transform is calculated according to the following formula_i[k]And Fourier transformed land geophone seismic data G_i[k]：

NL＝2^m≥N

Wherein H_i,jFor amplitude-processed hydrophone seismic data, G_i,jThe land geophone seismic data after amplitude processing is carried out; i represents the sequence number of the time window reference track, i is 1,2, and L represent the total number of time window parameter tracks, j represents the sequence number of time sample points of the time window parameter, j is 1,2, and N represents the number of time sample points of the time window parameter. l is an imaginary unit, and²NL-1, where k is 0,1,2, NL is the number of data fourier transform samples, and m is a suitable positive integer.

S104: and calculating a calibration factor according to the hydrophone seismic data after amplitude processing and the land geophone seismic data after amplitude processing, and calculating the sea depth and the sea bottom reflection coefficient of the target area according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform.

In the OBC data including the seawater seismic multiples, the energy of the data is composed of the energy of the underground reflected waves and the energy of the seawater seismic multiples. The autocorrelation of the data includes the autocorrelation of the subsurface reflection and the autocorrelation of the seawater ringing multiples. Since the autocorrelation function energy of the underground reflected waves is concentrated near the autocorrelation function zero point, the autocorrelation energy of the seawater ringing multiples is located far away from the autocorrelation function zero point. After the multiple times of seawater ringing are eliminated, because the multiple times of wave energy is eliminated in the seismic data, the energy of underground reflected waves is left, namely the autocorrelation function value near the zero point is increased, and the autocorrelation function value far away from the zero point is reduced. The autocorrelation function variance-norm minimization can reflect this relationship, and therefore, for efficient estimation of the calibration factor, is typically used as a criterion for determining the calibration factor. In addition, after the multiple wave waves of the seawater ringing are eliminated, the multiple wave energy is already eliminated in the seismic data, and the energy of underground reflected waves is left. Therefore, for efficient estimation of the sea-bottom reflection coefficient, energy minimization is generally used as a criterion for determining the sea-bottom reflection coefficient.

In this embodiment, calculating a calibration factor according to the amplitude-processed hydrophone seismic data and the amplitude-processed land geophone seismic data may specifically include:

(1) calculating an autocorrelation function of the amplitude processed hydrophone seismic data and an autocorrelation function of the amplitude processed land geophone seismic data, respectively, and a cross-correlation function associated with the amplitude processed hydrophone seismic data and the amplitude processed land geophone seismic data;

specifically, the combination of the land and water inspection is carried out according to the following formula:

wherein S is_i,jRepresenting the combined data of the surface geophones,representing the amplitude processed hydrophone seismic data,i represents the sequence number of a time window reference channel, i is 1,2, the temperature, L, L represents the total channel number of time window parameters, j represents the sequence number of time sampling points of the time window parameters, and j is 1,2, the temperature, N, N represents the time sampling points of the time window parameters;

calculating the combined autocorrelation function A of each land and water detector according to the following formula_i,k：

Wherein k represents the delay sequence number of the correlation function, and M represents the half-branch length of the correlation function;

substituting equation 7 into equation 8 and expanding, then there are:

summing the lane sequence number i across equation 9, then there is:

order:

wherein, a_kRepresenting the mean autocorrelation function of the hydrophone seismic data, b_kRepresenting the mean cross-correlation function of seismic data of land and water receivers, c_kRepresenting the average autocorrelation function of the seismic data of the land geophone;

(2) calculating a maximum variance modulus equation coefficient according to the autocorrelation function and the cross-correlation function of the hydrophone seismic data and the land geophone seismic data after amplitude processing;

specifically, substituting equation 11 into equation 10, there are:

A_k＝a_k+b_kα+c_kα²(formula 12)

By squaring both sides of equation 12, then there is:

squaring both sides of equation 13, then there is:

the sum of the autocorrelation function sequence numbers k on both sides of equation 14 results in:

in the formula,

the autocorrelation function has a formula of maximum square difference mode calculation

Wherein, P_mRepresents the maximum variance mode equation coefficient, m is 0,1, 2.

(3) Calculating a calibration factor characteristic equation coefficient according to the maximum variance module equation coefficient;

specifically, the derivation of the scaling factor α on both sides of equation 17 is:

and the derivative is 0 to obtain a 7 th order characteristic equation of the calibration factor α

T₀+T₁α+T₂α²+…+T₇α⁷Either 0 (formula 19)

Wherein, the characteristic equation coefficient T of the calibration factor_kIs composed of

T_k＝(k+1)P_k+1K is 0,1,2, 7 (formula 20)

(4) Constructing a calibration factor characteristic equation according to the calibration factor characteristic equation coefficient, and solving the calibration factor characteristic equation to obtain a calibration factor characteristic equation root;

specifically, the calibration factor is constructed according to equation 19 based on the characteristic equation coefficients of the calibration factorThe sub-characteristic equation is solved, and then the calibration factor characteristic equation is solved to obtain 7 characteristic roots α corresponding to the calibration factor characteristic equation_n，n＝1,2,3,...,7；

(5) And calculating a maximum variance model according to the maximum variance model equation coefficient and the calibration factor characteristic equation root, and determining an optimal calibration factor according to the maximum variance model.

Specifically, the following formula can be used to calculate the maximum square difference mode:

In this embodiment, the calculating the depth of the sea water in the target area according to the hydrophone seismic data after fourier transform and the land geophone seismic data after fourier transform may specifically include:

(1) calculating an average cross-correlation spectrum of the geophone seismic data according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform;

specifically, the average cross-correlation spectrum of the geophone seismic data may be calculated using the following formula:

wherein, R < k >]Representing the average cross-correlation spectrum, H, of the geophone seismic data_i[k]Representing said Fourier transformed hydrophone seismic data, G_i[k]Representing said Fourier transformed geophone seismic data,andare each independently of H_i[k]And G_i[k]Is in complex conjugate relation;

(2) calculating an average cross-correlation function of the seismic data of the land and water detectors according to the average cross-correlation spectrum of the seismic data of the land and water detectors;

specifically, the average cross-correlation function r [ n ] of the geophone seismic data is calculated according to the following formula:

wherein n is 1,2, 3.., NL;

(3) and calculating the sea water depth of the target area according to the average cross-correlation function of the seismic data of the land and water detectors.

Specifically, the position corresponding to the maximum value of the average cross-correlation function of the seismic data of the land and water geophones is the value m of the sample point of the double-travel time of the seawater:

calculating the seawater depth D according to the seawater two-way travel time sample point value m:

where Δ t represents the seismic data time sampling interval in seconds(s) and V represents the seawater velocity in meters per second (m/s).

In this embodiment, the calculating the submarine reflection coefficient of the target area according to the hydrophone seismic data after fourier transform and the land seismic data after fourier transform may specifically include:

(1) calculating first wave field combination data, second wave field combination data and third wave field combination data according to the hydrophone seismic data after Fourier transform and the land geophone seismic data after Fourier transform;

specifically, according to the hydrophone seismic data after Fourier transform and the land seismic data after Fourier transform, uplink wave field data, first-order delay downlink wave field data, second-order delay uplink wave field data, second-order delay downlink wave field data and third-order delay downlink wave field data are calculated;

the operator expression of the backsus sea water ringing multiple inverse filter is as follows:

wherein R is the sea bottom reflection coefficient, R_sFor sea surface reflection coefficient, Z is a delay operator, and the expression is

Z＝e^lωτ(formula 28)

Wherein l is an imaginary unit, and l is²Omega is angular frequency and is radian/second (omega/s), tau is water layer two-way travel time and is second(s), and the relation between the depth of seawater and the frequency is-1

Wherein V is the speed of seawater in meters per second (m/s), D is the depth of seawater in meters (m);

according to the seismic source ringing and micro-bending reflection ringing multi-wave model, in order to effectively eliminate seismic source ringing and micro-bending reflection ringing multi-wave, a Backus seawater ringing multi-wave inverse filter operator is used for multiplying the combined data of an uplink wave field and a downlink wave field. Namely, it is

S_i(ω)＝[(1-Z)H_i(ω)+(1+Z)G_i(ω)]B (omega) (equation 30)

In the formula, S_i(omega) Fourier transform data for eliminating interference wave field of sea water ringing multiples, H_i(omega) is hydrophone seismic data after Fourier transform, G_i(omega) is land detector seismic data after Fourier transform, and i represents the sequence number of a common detector point gather data channel; 1,2,3, L represents the total number of channels of the common probe gather data; b (omega) is a backsus seawater ringing multiple inverse filter operator;

substituting equation 27 into equation 30, there is:

its time domain expression is

Wherein ". X" represents a convolution operation, S_i,jEliminating the data of sea water ringing multiple interfering wave field,is amplitude processed hydrophone seismic data,is land geophone seismic data after amplitude processing; o is₁[j]A first-order backsus sea water ringing multiple inverse filter operator is shown and is also called as a first-order delay operator; o is₂[j]The second-order backsus sea water ringing multiple inverse filter operator is shown and is also called as a second-order delay operator; o is₃[j]The third-order backsus sea water ringing multiple inverse filter operator is shown and is also called as a third-order delay operator; and is

O₁[j]＝δ[j-τ/Δt]

O₂[j]＝δ[j-2τ/Δt]

O₃[j]＝δ[j-3τ/Δt](formula 33)

Wherein δ [ j ] represents a unit pulse operator, and

computing the first wavefield combination data based on the up-going wavefield data and the first-order delayed down-going wavefield data; computing the second wavefield combination data based on the first order delayed up-going wavefield data and the second order delayed down-going wavefield data; calculating the third wavefield combination data based on the second order delayed upgoing wavefield data and the third order delayed downgoing wavefield data;

order to

u_i,j＝(H_i,j+G_i,j)-(H_i,j-G_i,j)*O₁[j]

v_i,j＝-2R_s(H_i,j+G_i,j)*O₁[j]+2R_s(H_i,j-G_i,j)*O₂[j]

In the formula u_i,jRepresenting combined data of the first wave field, v_i,jRepresenting combined data of the second wave field, w_i,jRepresenting third wave field combination data;

(2) calculating a sea-bottom reflection coefficient characteristic equation coefficient according to the first wave field combination data, the second wave field combination data and the third wave field combination data;

specifically, substituting equation 35 into equation 32, there are:

S_i,j＝u_i,j+v_i,jR+w_i,jR²(equation 36)

Equation 36 squares, then there is:

summing i on both sides of equation 37, then there is:

order to

Wherein E represents the data energy, d₀、d₁、d₂、d₃And d₄Representing the characteristic equation coefficient of the sea bottom reflection coefficient;

(3) constructing a submarine reflection coefficient characteristic equation according to the submarine reflection coefficient characteristic equation coefficients, and solving the submarine reflection coefficient characteristic equation to obtain a plurality of submarine reflection coefficient values;

specifically, substituting equation 39 into equation 38, there is:

E＝d₀+d₁R+d₂R²+d₃R³+d₄R⁴(formula 40)

The derivation of the sea-bottom reflection coefficient R on both sides of equation 40 is:

let the energy derivative be 0, obtain the characteristic equation of the sea bottom reflection coefficient R:

d₁+2d₂R+3d₃R²+4d₄R³either 0 (formula 42)

Solving the characteristic formula 42 can obtain three values R of the sea bottom reflection coefficient₁、R₂And R₃；

(4) Wave field data energy respectively corresponding to the seabed reflection coefficient values is respectively calculated according to the seabed reflection coefficient values, and the optimal seabed reflection coefficient in the plurality of seabed reflection coefficient values is determined according to the wave field data energy.

Specifically, the three values of the sea bottom reflection coefficient are substituted into the formula 40 to obtain three energy values E₁、E₂And E₃. Namely, it is

Determining an optimal sea bottom reflection coefficient R of the plurality of sea bottom reflection coefficient values according to the following formula_best：

S105: based on the sea depth, a mirror image combination factor is determined.

In this embodiment, the mirror combination factor may be determined using the following formula:

wherein Z represents a delay operator, (1-Z) and (1+ Z) represent the mirror combining factor, l is an imaginary unit, and l is²Where ω denotes angular frequency, D denotes sea depth, and V denotes sea velocity.

S106: and carrying out mirror image combination processing on the hydrophone seismic data of the target area and the land geophone seismic data of the target area based on the calibration factor, the seawater depth, the seabed reflection coefficient and the mirror image combination factor to obtain the land geophone merged data of the target area.

In the present embodiment, the mirror image combining process may be performed using the following formula:

S＝O_H[n]*H+O_G[n]*G

wherein S represents the hydrophone merged data, H represents hydrophone seismic data for the target region, and G represents land geophone seismic data for the target region; represents a convolution operator; o is_H[n]Representing water detection data combination operators, O_G[n]Representing a land survey data combination operator, n representing a mirror image combination operator sequence number, n-NOp, -NOp +1, -NOp +2,. multidot., -1,0, 1.. multidot.,. NOp-2, NOp-1, NOp, (2NOp +1) representing a mirror image combination operator length,. DELTA.t representing a seismic data time sampling interval, α representing an optimal calibration factor, A representing an expected absolute value amplitude of the merged data, A_HRepresenting the mean absolute amplitude, A, of the hydrophone gather data_GRepresents the mean absolute amplitude of the land detector gather data; r_sRepresenting the water surface reflection coefficient; r represents the optimal sea bottom reflection coefficient; z represents a delay operator, (1-Z) and (1+ Z) represent the mirror combining factor; l is an imaginary unit, and²where ω denotes an angular frequency, D denotes a sea depth, V denotes a sea velocity, and Φ denotes a phase correction factor obtained when the land geophone seismic data is subjected to phase processing.

Before step S101, the embodiment of the present application may further include preprocessing hydrophone seismic data and land geophone seismic data of the target area; correspondingly, in step S102, the hydrophone seismic data of the target area is preprocessed hydrophone seismic data, and the land geophone seismic data of the target area is preprocessed land geophone seismic data. The pretreatment in the embodiment of the present application specifically includes: the method includes labeling the hydrophone seismic data and the land geophone seismic data, defining an observation system, and performing operations such as separation, denoising, filtering, velocity analysis, stacking and the like on the hydrophone seismic data and the land geophone seismic data, but it should be noted that the above-mentioned preprocessing operations are only for better illustrating the present invention, and other preprocessing methods can be adopted for processing, and the method is not limited by itself.

After step S106, embodiments of the present application may further include profiling the combined hydrophone data and storing the combined hydrophone data.

According to the embodiment of the method for merging and processing the seismic data of the amphibious detector, the corresponding parameter range value and the scanning step length do not need to be preset, the optimal calibration factor and the optimal seabed reflection coefficient are directly calculated by adopting a correlation function method, the calculation amount is small, and the calculation speed is high; meanwhile, through the phase processing of land geophone seismic data and the amplitude processing of land geophone seismic data in advance, low-frequency noise is reduced, and amplitude energy interference is reduced, so that the calculation accuracy of the sea water depth is improved. According to the embodiment of the application, the mirror image combination factor is introduced according to the calculated calibration factor, the sea water depth and the sea bottom reflection coefficient, and the combination processing of the seismic data of the hydrophone and the seismic data of the land geophone is realized quickly and accurately, so that the influence of the interference of the multiple waves of the sea water singing in the seismic data is effectively eliminated, and the signal-to-noise ratio and the resolution ratio of the seismic data are improved.

For clarity of explanation, the following description is made with reference to the accompanying drawings:

as can be seen from the comparison of the land and water inspection merged common-probe gather data in fig. 2(a) to 2(d), the comparison of the land and water inspection merged common-shot gather data in fig. 4(a) to 4(d), and the comparison of the land and water inspection merged common-center gather data in fig. 6(a) to 6(d), the multiple-wave interference generated by the water layer is effectively eliminated by using the parameters such as the average root mean square amplitude of the water inspection data, the average root mean square amplitude of the land inspection data, the calibration factor, the sea bottom reflection coefficient, the sea water two-way travel time, the sea water depth and the like calculated by the embodiment of the present application, the effective frequency band of the sea bottom cable data is effectively widened, and the signal-to-noise ratio of the OBC data.

The abscissa and ordinate in fig. 3(a) to 3(d), fig. 5(a) to 5(d), and fig. 7(a) to 7(d) are frequency and amplitude, respectively. As can be seen from the land and water detection merged common-probe gather data spectrum comparison of fig. 3(a) to 3(d), the land and water detection merged common-shot gather data spectrum comparison of fig. 5(a) to 5(d), and the land and water detection merged common-center gather data spectrum comparison of fig. 7(a) to 7(d), in particular, fig. 3(a) to 3(d), fig. 3(a) land and water detection merged common-probe gather data of an embodiment of the present application, an effective frequency band low frequency Fw1 is 22.5 hertz (Hz), a high frequency Fw2 is 44.0Hz, a center frequency (depending on a dominant frequency) Fpw is 33.25Hz, an absolute bandwidth Bw is 21.5Hz, and a time resolution Trw is 13.1 ms; fig. 3(b) land and water detection and co-detection point gather land detection data according to an embodiment of the present application, where the effective frequency band Fl1 is 18.0Hz, the high frequency Fl2 is 32.0Hz, the center frequency (apparent dominant frequency) Fpl is 25.0Hz, the absolute bandwidth Bl is 14.0Hz, and the time resolution Trl is 17.4 ms; fig. 3(c) merged data of the existing land and water detection merged common detection wave point gather land and water detection, effective frequency band Fc1 is 18.0Hz, high frequency Fc2 is 32.0Hz, center frequency (apparent dominant frequency) Fpc is 25.0Hz, absolute bandwidth Bc is 14.0Hz, and time resolution Trw is 17.4 ms; fig. 3(d) land and water detection merged data of a merged common detector gather for land and water detection according to an embodiment of the present application, where the effective frequency band Fn1 is 6.5Hz, the high frequency Fn2 is 62.5Hz, the center frequency (apparent dominant frequency) Fpn is 34.5Hz, the absolute bandwidth Bn is 56.0Hz, and the time resolution Trn is 12.6 ms; the three parameters of center frequency (dominant frequency) Fp, absolute bandwidth B and time resolution Tr are key parameters for determining seismic data resolution. Compared with the existing land and water detection combined common-detection-point gather land and water detection combined data, the land and water detection combined common-detection-point gather land and water detection combined data has the advantages that the center frequency (apparent dominant frequency) is larger (larger 9.5Hz), the absolute bandwidth is wider (wider 42.0Hz), the low frequency is lower (lower 11.5Hz), the high frequency is higher (higher 30.5Hz), and the time resolution is higher (higher 4.8 ms). Therefore, by using the parameters of the average root mean square amplitude of the water detection data, the average root mean square amplitude of the land detection data, the calibration factor, the submarine reflection coefficient, the double-journey traveling time of the seawater, the seawater depth and the like calculated by the embodiment of the application, the effective frequency band of the submarine cable data is effectively widened, and the OBC data resolution is improved.

Figure 8 is a block diagram of the components of an embodiment of the apparatus for combined processing of geophone seismic data according to the present application. The apparatus may provide hydrophone seismic data and land-geophone seismic data for the target area. As shown in fig. 8, the apparatus may include: the system comprises a phase processing module 100, an amplitude processing module 200, a Fourier transform module 300, a parameter calculation module 400, a mirror combination factor determination module 500 and a water and land inspection data merging module 600.

The phase processing module 100 may be configured to perform phase processing on the geophone seismic data to obtain phase-processed geophone seismic data.

The amplitude processing module 200 may be configured to perform first amplitude processing on the hydrophone seismic data to obtain amplitude-processed hydrophone seismic data, and perform second amplitude processing on the phase-processed land geophone seismic data to obtain amplitude-processed land geophone seismic data;

the fourier transform module 300 may be configured to perform fourier transform on the amplitude-processed hydrophone seismic data and land geophone seismic data, respectively, to obtain fourier-transformed hydrophone seismic data and fourier-transformed land geophone seismic data.

The parameter calculation module 400 may be configured to calculate a calibration factor according to the amplitude-processed hydrophone seismic data and the amplitude-processed land geophone seismic data, and calculate the sea depth and the sea bottom reflection coefficient of the target area according to the fourier-transformed hydrophone seismic data and the fourier-transformed land geophone seismic data.

The mirror combination factor determination module 500 may be configured to determine a mirror combination factor based on the seawater depth.

The land and water detection data merging module 600 may be configured to perform mirror combination processing on the hydrophone seismic data of the target area and the land geophone seismic data of the target area based on the calibration factor, the sea depth, the sea bottom reflection coefficient, and the mirror combination factor to obtain land and water geophone merged data of the target area.

The device embodiment of the combination processing of the seismic data of the land and water detectors corresponds to the method embodiment of the combination processing of the seismic data of the land and water detectors, the method embodiment of the combination processing of the seismic data of the land and water detectors can be realized, and the technical effects of the method embodiment can be obtained.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most popular applications. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The apparatuses and modules illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations as the present application.

From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. With this understanding in mind, the present solution, or portions thereof that contribute to the prior art, may be embodied in the form of a software product, which in a typical configuration includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The computer software product may include instructions for causing a computing device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in the various embodiments or portions of embodiments of the present application. The computer software product may be stored in a memory, which may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transient media), such as modulated data signals and carrier waves.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

While the present application has been described with examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the spirit of the application.

Claims

1. A method of combined processing of hydrophone seismic data and geophone seismic data for a target area, the method comprising:

determining a mirror image combination factor based on the sea water depth;

performing mirror image combination processing on the hydrophone seismic data of the target area and the land geophone seismic data of the target area based on the calibration factor, the seawater depth, the seabed reflection coefficient and the mirror image combination factor to obtain land and water geophone combination data of the target area; wherein the mirror image combination processing is performed using the following formula:

S＝O_H[n]*H+O_G[n]*G

wherein S represents the hydrophone merged data, H represents hydrophone seismic data for the target region, and G represents land geophone seismic data for the target region; represents a convolution operator;O_H[n]representing water detection data combination operators, O_G[n]Representing a land survey data combination operator, n representing a mirror image combination operator sequence number, n-NOp, -NOp +1, -NOp +2,. multidot., -1,0, 1.. multidot.,. NOp-2, NOp-1, NOp, (2NOp +1) representing a mirror image combination operator length,. DELTA.t representing a seismic data time sampling interval, α representing an optimal calibration factor, A representing an expected absolute value amplitude of the merged data, A_HRepresenting the mean absolute amplitude, A, of the hydrophone gather data_GRepresents the mean absolute amplitude of the land detector gather data; r_sRepresenting the water surface reflection coefficient; r represents the optimal sea bottom reflection coefficient; z represents a delay operator, (1-Z) and (1+ Z) represent the mirror combining factor; l is an imaginary unit, and²where ω denotes an angular frequency, D denotes a sea depth, V denotes a sea velocity, and Φ denotes a phase correction factor obtained when the land geophone seismic data is subjected to phase processing.

2. A method of hydrophone seismic data fusion processing as described in claim 1, wherein said hydrophone seismic data is subjected to first amplitude processing using the following equation:

wherein,representing said amplitude-processed hydrophone seismic data, H_i,jRepresenting hydrophone seismic data, A_HRepresenting the average absolute amplitude of the hydrophone gather data, i representing the sequence number of the time window reference trace, i being 1,2,the point number and the symbol | | · | | are operators taking absolute values.

3. A method of hydrophone seismic data fusion processing as described in claim 1 wherein said phase processed geophone seismic data is subjected to a second amplitude processing using the following equation:

4. A method of hydrophone seismic data fusion processing as described in claim 1, wherein said calculating calibration factors from said amplitude processed hydrophone seismic data and said amplitude processed geophone seismic data comprises:

5. The method for merging and processing seismic data of land and water detectors as claimed in claim 4, wherein said calculating a maximum variance module from said maximum variance module equation coefficients and said scaling factor characteristic equation root and determining an optimal scaling factor from said maximum variance module comprises:

the following formula is used to calculate the maximum square difference mode:

6. A method of hydrophone seismic data fusion processing as claimed in claim 1, wherein said calculating a depth of water in said target area from said fourier transformed hydrophone seismic data and said fourier transformed geophone seismic data comprises:

7. A method of hydrophone seismic data fusion processing as described in claim 1, wherein said calculating seafloor reflection coefficients for said target area from said fourier transformed hydrophone seismic data and said fourier transformed land seismic data comprises:

8. The method of surface geophone seismic data combination processing according to claim 7, wherein said calculating first, second and third wavefield combination data from said fourier transformed hydrophone seismic data and said fourier transformed land geophone seismic data comprises:

9. An apparatus for combined processing of hydrophone seismic data and geophone seismic data for a target area, said apparatus comprising: the device comprises a phase processing module, an amplitude processing module, a Fourier transform module, a parameter calculation module, a mirror image combination factor determination module and a water and land inspection data merging module; wherein,

the land and water detection data merging module is used for carrying out mirror image combination processing on the hydrophone seismic data of the target area and the land geophone seismic data of the target area based on the calibration factor, the sea water depth, the sea bottom reflection coefficient and the mirror image combination factor to obtain land and water geophone merged data of the target area; the water and land inspection data merging module is used for carrying out the mirror image combination processing by adopting the following formula:

S＝O_H[n]*H+O_G[n]*G