Marine seismic data acquisition method and system

Abstract

The invention discloses a marine seismic data acquisition method and system, and belongs to the field of marine seismic exploration, and the method comprises the steps: a sensor monitors reflected seismic waves in real time, and transmits data to a sea surface receiving station through underwater radio; in a real-time data acquisition process, data is transmitted to a sea surface platform through an underwater acoustic communication system, and transmission frequency and data density are optimized according to a working mode of a sensor; noise signals are removed through a digital signal processing algorithm, and gain processing is carried out on the received signals by using an FIR (Finite Impulse Response) filter; carrying out three-dimensional modeling according to the propagation time, reflection intensity and frequency characteristics of seismic waves by utilizing a Kirchhoff inversion method, and constructing a seabed structure diagram; and performing multiple identification and analysis on the acquired data by adopting a multiple removal algorithm, and analyzing and optimizing a reflected wave model through phase difference. Interference waves generated by multiple reflections of the seabed medium are removed, the quality and precision of the seismic data are remarkably improved, and the transmission efficiency is improved by combining dynamic adjustment of the transmission frequency and the data density.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine seismic exploration, and more specifically, particularly relates to a marine seismic data acquisition method and system. Background Art

[0002] With the increasing importance of resources such as natural gas hydrates and marine oil and gas, and the gradual implementation of the strategic deployment to explore deeper into the earth, marine seismic exploration plays an important role in marine research and development. Among them, the acquisition method of marine deep seismic information is the technical foundation.

[0003] Currently, the main methods of marine seismic exploration mainly include two types, namely marine streamer seismic and ocean bottom seismic. Both use a geophysical exploration ship to generate a seismic source in water. The difference is that the geophones of the streamer are sealed in a streamer floating in water, while the ocean bottom seismographs arrange the geophones on the seabed in a certain way. In addition, in terms of marine seismic monitoring, domestic and foreign institutions have developed floating seismographs, which are suspended at a certain depth in seawater and can record natural earthquake signals for a long time and over a large range as they move with ocean currents. Marine streamer seismic acquisition has the characteristics of low cost, high efficiency, and short cycle, and has a relatively high imaging resolution for the middle and shallow strata. However, in areas with complex geological structures, due to the limited length of the streamer, it is difficult to receive the reflection wave signals of deep and steep structures, and the imaging effect is not good. Ocean bottom seismographs can reveal the velocity information of deeper strata, but the instrument cost and construction cost are huge, and it is often impossible to achieve a large number of deployments, and the imaging resolution for shallow strata is not high. How to combine the advantages of streamer seismic and seismographs is an important challenge in marine seismic information acquisition.

[0004] Currently, most marine seismic data acquisition methods rely on underwater sensors to transmit data to a sea surface receiving station for processing through acoustic or electromagnetic waves. However, the transmission speed of underwater signals is slow and may be affected by the marine environment, which will cause delays in data transmission. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above or existing problems of marine seismic data acquisition methods and systems, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a method for collecting marine seismic data, including: sensors continuously monitor the reflected seismic waves and transmit the data to the sea surface receiving station through underwater radio; during real-time data collection, the data is transmitted to the sea surface platform through an underwater acoustic communication system, and the transmission frequency and data density are optimized according to the working mode of the sensors; through digital signal processing algorithms, noise signals are removed, and a FIR filter is used to perform gain processing on the received signals; the Kirchhoff inversion method is used to perform three-dimensional modeling based on the propagation time, reflection intensity, and frequency characteristics of the seismic waves to construct a seabed structure diagram; a multiple wave removal algorithm is used to identify and analyze multiple waves in the collected data, and the reflection wave model is optimized through phase difference analysis.

[0009] As a preferred solution of the marine seismic data collection method described in the present invention, wherein: the sensors continuously monitor the reflected seismic waves and transmit the data to the sea surface receiving station, including:

[0010] A dynamic path selection mechanism is adopted. Through the feedback mechanism between the sensors and the sea surface receiving station, the communication link quality is evaluated in real time, and the data transmission path is dynamically adjusted.

[0011] As a preferred solution of the marine seismic data collection method described in the present invention, wherein: the data is transmitted to the sea surface platform through an underwater acoustic communication system, and the transmission frequency and data density are optimized according to the working mode of the sensors, including:

[0012] In the case of strong seismic activity or near the seismic source, the sensors will switch to the high-density working mode, increasing the sampling frequency and data accuracy to capture more detailed seismic waveforms; when the seismic activity is relatively stable or there is no significant vibration, the sensors will enter the low-density mode.

[0013] As a preferred solution of the marine seismic data collection method described in the present invention, wherein: through digital signal processing algorithms, noise signals are removed, and a FIR filter is used to perform gain processing on the received signals, including:

[0014] The signal is transformed into the frequency domain through fast Fourier transform, the noise frequency components are suppressed, and finally it is restored to the time domain through inverse Fourier transform;

[0015] The FIR filter has a finite impulse response and is realized through convolution operation. The specific implementation formula is as follows:

[0016]

[0017] wherein, y[n] is the filter output, x[n] is the input signal, h[k] is the coefficient of the FIR filter, and M is the order of the filter;

[0018] According to the frequency characteristics of the noise, the spectrum of the input signal is analyzed by Fourier transform to determine the frequency components of the noise. According to the frequency band characteristics of the signal, a low-pass FIR filter is selected, and the received signal is filtered through convolution operation to remove the unnecessary noise components;

[0019] For the gain processing of the FIR filter, it is achieved by adjusting the magnitude of the filter coefficients. The relationship between the gain G and the filter coefficients h[k] is:

[0020] h[k] ← G·h[k]

[0021] where G is the gain factor and h[k] are the original coefficients of the filter.

[0022] As a preferred embodiment of the marine seismic data acquisition method described in the present invention, wherein: the three-dimensional modeling is performed using the Kirchhoff inversion method based on the propagation time, reflection intensity, and frequency characteristics of seismic waves to construct a seabed structure diagram, including:

[0023] For the reflecting medium, the reflection of seismic waves is expressed as:

[0024]

[0025] where R(t) is the received reflection signal, A i is the reflection intensity of the i-th reflection layer, T i is the propagation time of the i-th reflection layer, δ is the Dirac pulse, and N is the number of reflection layers;

[0026] The data of the reflected waves are collected by marine seismic exploration instruments, including the arrival time, amplitude, and frequency characteristics of each reflected wave; according to the propagation time and reflection intensity of the seismic waves, preliminary inversion calculations are performed to obtain the preliminary velocity model and density distribution of the underground medium, and the velocity distribution of each point is deduced according to the different propagation times; the model is optimized through multiple iterations to obtain the velocity model, and the density difference and physical properties of each layer are deduced through the relationship between the reflection intensity and the reflection coefficient.

[0027] As a preferred embodiment of the marine seismic data acquisition method described in the present invention, wherein: the multiple wave removal algorithm is used to identify and analyze the multiple waves in the collected data, including:

[0028] If the received signal s(t) is composed of the primary wave s 1 (t) and the multiple wave s m (t):

[0029] s(t) = s 1 (t) + s m (t)

[0030] where s1 (t) is the primary reflection wave, s m (t) is the multiple reflection wave;

[0031] In the frequency domain, the Fourier transform S(f) of the signal s(t) is expressed as:

[0032] S(f) = S 1 (f) + S m (f)

[0033] where S 1 (f) and S m (f) are the frequency domain representations of the primary wave and the multiple wave respectively;

[0034] The frequency domain representations of the primary wave and the multiple wave are S 1 (f) and S m (f) respectively, and their phases are φ 1 (f) and φ m (f) respectively. Then the phase difference Δφ(f) is:

[0035] Δφ(f) = φ m (f) - φ 1 (f)

[0036] In the multiple wave, due to its different propagation paths, the multiple wave is identified and removed by different frequencies.

[0037] As a preferred solution of the marine seismic data acquisition method described in the present invention, wherein: the model of optimizing the reflection wave by phase difference analysis includes:

[0038] By calculating the phase difference, the primary wave and the multiple wave are identified, the phase difference is used as a feature for signal separation, by removing the multiple wave, the model of the reflection wave is optimized. After removing the multiple wave, the accuracy of the inversion model will be improved, and through the optimized reflection wave data, the structural model of the underground medium can be accurately restored;

[0039] According to the phase difference characteristics of the multiple wave, phase difference correction is used to remove the multiple wave. In the frequency domain, after removing the multiple wave, the frequency domain expression of the signal becomes:

[0040]

[0041] The corrected frequency domain signal is subjected to inverse Fourier transform to obtain the time domain signal after removing the multiple wave.

[0042] An ocean seismic data acquisition system, comprising: a data monitoring module for the real-time monitoring of reflected seismic waves by sensors and transmitting the data to a sea surface receiving station via underwater radio; a data transmission module for, during the real-time data acquisition process, transmitting the data to a sea surface platform via an underwater acoustic communication system and optimizing the transmission frequency and data density according to the working mode of the sensors; a data processing module for removing noise signals through digital signal processing algorithms and performing gain processing on the received signals using a FIR filter; a model construction module for performing three-dimensional modeling based on the propagation time, reflection intensity, and frequency characteristics of seismic waves using the Kirchhoff inversion method to construct a seabed structure diagram; and a data analysis module for identifying and analyzing multiple waves in the acquired data using a multiple wave removal algorithm and optimizing the model of reflected waves through phase difference analysis.

[0043] A computing device, the computing device comprising:

[0044] At least one processor, a memory, and an input / output unit;

[0045] Wherein, the memory is used for storing a computer program, and the processor is used for calling the computer program stored in the memory to execute the steps of the ocean seismic data acquisition method.

[0046] A computer-readable storage medium, which includes instructions that, when running on a computer, cause the computer to execute the steps of the ocean seismic data acquisition method.

[0047] The beneficial effects of the present invention are as follows: By adopting a multiple wave removal algorithm and phase difference analysis, the present invention can effectively remove interference waves generated by multiple reflections of seabed media, significantly improving the quality and accuracy of seismic data. By optimizing the underwater wireless communication system and combining the dynamic adjustment of data transmission frequency and data density, the data transmission efficiency is greatly improved, and the transmission delay is reduced. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0049] Figure 1 It is a flowchart of an ocean seismic data acquisition method provided by an embodiment of the present invention.

[0050] Figure 2 It is a schematic structural diagram of an ocean seismic data acquisition system provided by an embodiment of the present invention.

[0051] Figure 3 A schematic structural diagram of a medium according to an embodiment of the present invention is shown.

[0052] Figure 4 A schematic structural diagram of a computing device according to an embodiment of the present invention is shown.

[0053] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed implementation manners

[0054] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0055] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0056] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0057] Embodiment

[0058] The following refers to Figure 1 , Figure 1 which is a flowchart of a marine seismic data acquisition method provided for an embodiment of the present invention. It should be noted that the implementation manner of the present invention can be applied to any applicable scenario.

[0059] Figure 1 The process of the marine seismic data acquisition method provided for an embodiment of the present invention shown includes:

[0060] S1: Sensors monitor the reflected seismic waves in real time and transmit the data to the sea surface receiving station through underwater radio.

[0061] Preferably, a dynamic path selection mechanism is adopted. Through the feedback mechanism between the sensors and the sea surface receiving station, the communication link quality is evaluated in real time, and the data transmission path is dynamically adjusted.

[0062] Furthermore, the sea surface receiving station sends a link quality report to the underwater sensor nodes through a feedback mechanism, indicating changes in the transmission path. This feedback information can include signal quality, delay duration, bit error rate, etc. The underwater sensors dynamically adjust the path selection based on the feedback information. The feedback mechanism ensures that the transmission path can be adjusted according to changes in the actual environment.

[0063] To prevent overloading of a single path or excessive signal attenuation, the system can dynamically switch the transmission path according to the real-time link quality. For example, when the signal attenuation of the current path is severe, the sensor will automatically select a path with better signal quality for data transmission. Multiple underwater sensors can adopt a load balancing mechanism to distribute the data across multiple paths for transmission, thereby avoiding overloading of a certain path and improving the transmission efficiency and robustness of the entire network.

[0064] Use a delay perception algorithm to calculate the delay of each possible communication path and select the path with the minimum delay. Evaluate the bit error rate based on the actual transmitted data. If the bit error rate is too high, it indicates poor signal quality, and the system should select a more reliable path.

[0065] S2: During real-time data acquisition, the data is transmitted to the sea surface platform through the underwater acoustic communication system, and the transmission frequency and data density are optimized according to the working mode of the sensor.

[0066] Preferably, near a strong earthquake activity or the earthquake source, the sensor will switch to the high-density working mode, increasing the sampling frequency and data accuracy to capture more detailed seismic waveforms; when the earthquake activity is relatively stable or there is no significant vibration, the sensor will enter the low-density mode.

[0067] Furthermore, different vibration intensity thresholds are set. When significant changes occur in the amplitude, frequency, or waveform of the seismic wave, the system determines that the earthquake activity is relatively strong and needs to switch to the high-density mode.

[0068] For example, a threshold is set. When the vibration frequency detected by the accelerometer exceeds 100 Hz and the amplitude exceeds a certain set value, the system determines that the earthquake activity is strong and enters the high-density mode.

[0069] Once the system detects strong earthquake activity, the sensor control module will immediately increase the sampling frequency and accuracy to ensure high-precision seismic wave data is collected. At this time, the sensor enhances the detail of the data by adjusting the sampling rate (such as from 50 times per second to 1000 times per second) and increasing the number of data storage bits (such as from 8 bits to 16 bits); when the earthquake activity becomes stable or there is no significant vibration, the sensor control module will reduce the sampling frequency and accuracy to save power and storage resources. For example, the sampling frequency can be reduced from 1000 times per second to 50 times per second, and the data accuracy can be reduced to 8 bits.

[0070] Furthermore, assume that the vibration signal measured by the sensor is S(t), where t is time, and S(t) includes information such as amplitude and frequency. The standard for judging the vibration intensity can be based on amplitude and frequency characteristics. For example:

[0071]

[0072] where T is the length of a time window. If the vibration intensity is greater than the set threshold T gihh , it enters the high-density mode; if it is lower than the threshold T low , it enters the low-density mode.

[0073] Assume that the sampling frequency is f in the high-density mode high , and the sampling frequency is f in the low-density mode low , and the number of bits of each sampled data is N high and N low , then the data transmission rates are respectively:

[0074] R high = f high ×N high

[0075] R low = f low ×N low

[0076] By dynamically adjusting the sampling frequency and data precision, the data transmission rate is adjusted according to the seismic activity intensity to optimize the transmission bandwidth.

[0077] S3: Through digital signal processing algorithms, remove the noise signal and use a FIR filter to perform gain processing on the received signal.

[0078] Preferably, the signal is transformed into the frequency domain through a fast Fourier transform, the noise frequency components are suppressed, and finally it is restored to the time domain through an inverse Fourier transform;

[0079] The FIR filter has a finite impulse response and is implemented through a convolution operation. The specific implementation formula is as follows:

[0080]

[0081] where y[n] is the filter output, x[n] is the input signal, h[k] is the coefficient of the FIR filter, and M is the order of the filter;

[0082] According to the frequency characteristics of the noise, analyze the spectrum of the input signal through Fourier transform to determine the frequency components of the noise. According to the frequency band characteristics of the signal, select a low-pass FIR filter and filter the received signal through convolution operation to remove the unwanted noise components;

[0083] For the gain processing of the FIR filter, it is achieved by adjusting the magnitude of the filter coefficients. The relationship between the gain G and the filter coefficients h[k] is as follows:

[0084] h[k] ← G·h[k]

[0085] Where G is the gain factor and h[k] are the original coefficients of the filter.

[0086] Furthermore, assuming the noise frequency range is f noise , the spectrum of the signal is modified in the frequency domain to reduce or completely eliminate the noise frequency components. Set the magnitude of the noise frequency range to zero:

[0087]

[0088] Through frequency domain processing, the noise frequency components are removed to obtain the processed frequency domain signal.

[0089] S4: Use the Kirchhoff inversion method to perform 3D modeling based on the propagation time, reflection intensity, and frequency characteristics of seismic waves to construct a seabed structure diagram.

[0090] Preferably, for the reflection medium, the reflection of seismic waves is expressed as:

[0091]

[0092] Where R(t) is the received reflection signal, A i is the reflection intensity of the i-th reflection layer, T i is the propagation time of the i-th reflection layer, δ is the Dirac pulse, and N is the number of reflection layers;

[0093] Collect the data of reflected waves through marine seismic exploration instruments, including the arrival time, amplitude, and frequency characteristics of each reflected wave; perform preliminary inversion calculations based on the propagation time and reflection intensity of seismic waves to obtain the preliminary velocity model and density distribution of the underground medium, and infer the velocity distribution of each point according to the different propagation times; optimize the model through multiple iterations to obtain the velocity model, and infer the density difference and physical properties of each layer based on the relationship between the reflection intensity and the reflection coefficient.

[0094] Furthermore, use marine seismic exploration instruments to collect seismic wave reflection data, collect the arrival time, amplitude, and frequency characteristics of each reflected wave, and the time interval and sampling rate of signal acquisition need to be optimized according to factors such as exploration depth, wave velocity, and noise environment.

[0095] According to the arrival time t i and amplitude A i, initially infer the structure of the underground medium. The arrival time of the reflected wave for each layer corresponds to the reflection intensity; the propagation speed of seismic waves in different media (i.e., formation velocity) affects the arrival time of the reflected wave.

[0096] Based on the arrival time t of the reflected wave i , use ray tracing method or wave equation to calculate the propagation path of seismic waves.

[0097] Calculate the reflection coefficient R based on the reflection intensity and amplitude i , the magnitude of the reflection coefficient is related to the density difference and wave velocity difference of the underground medium. The calculation formula of the reflection coefficient is:

[0098]

[0099] where, Z 上 and Z 下 are the impedances of the upper and lower media respectively, defined as Z = ρ·v (the product of density and wave velocity). Based on the reflection coefficient, the impedance difference of each underground layer can be inferred.

[0100] Based on the propagation time t i and the known reflection coefficient R i , the velocity model v of the underground medium can be initially deduced through the following formula i :

[0101]

[0102] where, d is the distance from the seismic source to the receiving point, and t i is the propagation time of the i-th reflected wave.

[0103] The initial velocity model can be further optimized through the data of multiple reflected waves, precisely invert the arrival time of each reflected wave, and deduce the velocity and density of the medium at different depths underground through the time difference.

[0104] Use the gradient inversion method to optimize the initial velocity model. The time and amplitude differences provided by each reflected wave can be used to adjust the velocity distribution of each layer of the initial model through gradient optimization. By minimizing the inversion error function, the error between the calculated arrival time of the reflected wave and the actually measured arrival time of the reflected wave is minimized, thereby optimizing the velocity model. By gradually adjusting the velocity distribution v of the underground medium i , the velocity v layer and density ρ layer of each layer are gradually optimized. Use nonlinear optimization methods, such as the least squares method, to perform multiple iterative adjustments on the model. Each round of iteration will update the parameters of each layer in the model.

[0105] S5: Use the multiple wave removal algorithm to identify and analyze the collected data, and optimize the reflection wave model through phase difference analysis.

[0106] Preferably, if the received signal s(t) is composed of the primary wave s 1 (t) and the multiple wave s m (t):

[0107] s(t) = s 1 (t) + s m (t)

[0108] where s 1 (t) is the primary reflection wave and s m (t) is the multiple reflection wave;

[0109] In the frequency domain, the Fourier transform S(f) of the signal s(t) is expressed as:

[0110] S(f) = S 1 (f) + S m (f)

[0111] where S 1 (f) and S m (f) are the frequency domain representations of the primary wave and the multiple wave respectively;

[0112] The frequency domain representations of the primary wave and the multiple wave are S 1 (f) and S m (f) respectively, and their phases are φ 1 (f) and φ m (f), then the phase difference Δφ(f) is:

[0113] Δφ(f) = φ m (f) - φ 1 (f)

[0114] In the multiple wave, its propagation path is different, and the multiple wave is identified and removed by different frequencies.

[0115] Preferably, by calculating the phase difference, the primary wave and the multiple wave are identified, the phase difference is used as a feature for signal separation, and by removing the multiple wave, the reflection wave model is optimized. After removing the multiple wave, the accuracy of the inversion model will be improved, and the structural model of the underground medium can be accurately restored through the optimized reflection wave data;

[0116] According to the phase difference characteristics of the multiple wave, phase difference correction is used to remove the multiple wave. In the frequency domain, after removing the multiple wave, the frequency domain expression of the signal becomes:

[0117]

[0118] The corrected frequency-domain signal is subjected to inverse Fourier transform to obtain the time-domain signal after multiple wave removal.

[0119] Further, assume that the original signal s(t) collected by the seabed sensor is a superposition of primary reflections and multiple reflections, with a sampling frequency of 2000 Hz and a sampling duration of 10 seconds. The signal contains strong noise and multiple reflections. The collected signal is subjected to Fourier transform to obtain the frequency-domain representation S(f). At the same time, the primary reflection and multiple reflections are also subjected to Fourier transform, respectively obtaining S 1 (f) and S m (f).

[0120] Calculate the phase difference Δφ(f) between the primary wave and the multiple waves. For example, at the 50 Hz frequency point, assume that the phase of the primary wave is φ1(50) = 1.2 rad, while the phase of the multiple wave is φm(50) = 3.4 rad, then Δφ(50) = 3.4 - 1.2 = 2.2 rad; through frequency-selective filtering, remove the multiple reflection components in S m (f). Obtain the frequency-domain signal S 1 (f) that only contains the primary wave. Perform inverse Fourier transform on S1(f) to obtain the time-domain signal s 1 (t), which is the clear primary reflection wave after multiple wave removal.

[0121] After introducing the method of the exemplary embodiment of the present invention, next, refer to Figure 2 An ocean seismic data acquisition system of the exemplary embodiment of the present invention will be described. The system includes:

[0122] A data monitoring module for real-time monitoring of the reflected seismic waves by the sensor and transmitting the data to the sea surface receiving station through underwater radio;

[0123] A data transmission module for transmitting the data to the sea surface platform through an underwater acoustic communication system during real-time data acquisition and optimizing the transmission frequency and data density according to the working mode of the sensor;

[0124] A data processing module for removing noise signals through digital signal processing algorithms and performing gain processing on the received signals using a FIR filter;

[0125] A model construction module for performing three-dimensional modeling based on the propagation time, reflection intensity, and frequency characteristics of seismic waves using the Kirchhoff inversion method to construct a seabed structure diagram;

[0126] A data analysis module for identifying and analyzing multiple waves in the collected data using a multiple wave removal algorithm and optimizing the reflection wave model through phase difference analysis.

[0127] After introducing the methods and apparatuses of the exemplary embodiments of the present invention, next, reference is made to Figure 3 to describe the computer-readable storage medium of the exemplary embodiments of the present invention. Please refer to Figure 3 , which shows that the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will implement the steps recorded in the above method embodiments. For example, sensors continuously monitor the reflected seismic waves and transmit the data to the sea surface receiving station through underwater radio; during the real-time data acquisition process, the data is transmitted to the sea surface platform through an underwater acoustic communication system, and the transmission frequency and data density are optimized according to the working mode of the sensors; through a digital signal processing algorithm, noise signals are removed, and a FIR filter is used to perform gain processing on the received signals; the Kirchhoff inversion method is used to perform three-dimensional modeling based on the propagation time, reflection intensity, and frequency characteristics of the seismic waves to construct a seabed structure diagram; a multiple wave removal algorithm is used to identify and analyze multiple waves in the collected data, and the reflection wave model is optimized through phase difference analysis; the specific implementation manners of each step will not be repeated here.

[0128] It should be noted that examples of the computer-readable storage medium may also 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 optical and magnetic storage media, which will not be elaborated one by one here.

[0129] After introducing the methods, apparatuses, and media of the exemplary embodiments of the present invention, next, reference is made to Figure 4 to the computing device for marine seismic data acquisition of the exemplary embodiments of the present invention.

[0130] Figure 4 The block diagram of an exemplary computing device 40 suitable for implementing the embodiments of the present invention is shown. The computing device 40 may be a computer system or a server. Figure 4 The shown computing device 40 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0131] As Figure 4 shown, the components of the computing device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 connecting different system components (including the system memory 402 and the processing unit 401).

[0132] Computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computing device 40, including volatile and non-volatile media, removable and non-removable media.

[0133] System memory 402 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 can be used to read and write on non-removable, non-volatile magnetic media ( Figure 4 not shown, commonly referred to as a "hard disk drive"). Although not shown in Figure 4 a disk drive can be provided for reading and writing on removable non-volatile disks (such as "floppy disks"), and an optical disk drive for reading and writing on removable non-volatile optical disks (such as CD-ROM, DVD-ROM or other optical media). In these cases, each drive can be connected to bus 403 through one or more data media interfaces. System memory 402 can include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0134] A program / utility 4025 having a set (at least one) of program modules 4024 can be stored, for example, in system memory 402, and such program modules 4024 include but are not limited to: an operating system, one or more application programs, other program modules, and program data, and the implementation of a network environment may be included in each or some combination of these examples. Program modules 4024 generally perform the functions and / or methods in the embodiments described in the present invention.

[0135] Computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, a pointing device, a display, etc.). Such communication can be carried out through an input / output (I / O) interface 405. And, computing device 40 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 406. As Figure 4 shown, network adapter 406 communicates with other modules (such as processing unit 401, etc.) of computing device 40 through bus 403. It should be understood that although Figure 4 not shown in, other hardware and / or software modules can be used in conjunction with computing device 40.

[0136] The processing unit 401 executes various functional applications and data processing by running the programs stored in the system memory 402. For example, the sensor monitors the reflected seismic waves in real time and transmits the data to the sea surface receiving station through underwater radio. During the real-time data acquisition process, the data is transmitted to the sea surface platform through the underwater acoustic communication system, and the transmission frequency and data density are optimized according to the working mode of the sensor. Through digital signal processing algorithms, noise signals are removed, and the received signals are gain-processed using FIR filters. The Kirchhoff inversion method is used to perform three-dimensional modeling based on the propagation time, reflection intensity, and frequency characteristics of the seismic waves to construct a seabed structure diagram. The multiple wave removal algorithm is used to identify and analyze the multiple waves in the collected data, and the reflection wave model is optimized through phase difference analysis. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the synchronous escape routing device based on multi-commodity flow are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0137] In the description of the present invention, it should be noted that the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0138] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0139] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0140] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist physically separately for each unit, or two or more units may be integrated into one unit.

[0142] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0143] Finally, it should be noted that: the above-described embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than to limit it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0144] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the shown operations must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

Claims

1. A method for collecting marine seismic data, characterized in that: include: The sensors monitor the reflected seismic waves in real time and transmit the data to a receiving station on the surface via underwater radio; During real-time data collection, data is transmitted to the surface platform via an underwater acoustic communication system, with the transmission frequency and data density optimized according to the sensor’s operating mode; Through digital signal processing algorithms, noise signals are removed and FIR filters are used to perform gain processing on the received signals; The Kirchhoff inversion method is used to perform three-dimensional modeling based on the propagation time, reflection intensity and frequency characteristics of seismic waves to construct a seafloor structure map; The multiple wave removal algorithm is used to identify and analyze multiple waves in the collected data, and the reflection wave model is optimized through phase difference analysis.

2. The marine seismic data acquisition method according to claim 1, characterized in that: The sensor monitors the reflected seismic waves in real time and transmits the data to a receiving station on the sea surface via underwater radio, including: A dynamic path selection mechanism is adopted to evaluate the quality of the communication link in real time and dynamically adjust the data transmission path through the feedback mechanism between the sensor and the sea surface receiving station.

3. The marine seismic data acquisition method according to claim 1, characterized in that: The data is transmitted to the surface platform via an underwater acoustic communication system, and the transmission frequency and data density are optimized according to the working mode of the sensor, including: When seismic activity is strong or near the epicenter, the sensor will switch to high-density working mode, increase the sampling frequency and data accuracy to capture more detailed seismic waveforms; when the seismic activity is relatively stable or there is no significant shaking, the sensor will enter low-density mode.

4. The marine seismic data acquisition method according to claim 1, characterized in that: The method of removing noise signals by a digital signal processing algorithm and performing gain processing on the received signal by using an FIR filter includes: The signal is converted to the frequency domain through fast Fourier transform, the noise frequency components are suppressed, and finally it is restored to the time domain through inverse Fourier transform; The FIR filter has a finite impulse response and is implemented through a convolution operation. The specific implementation formula is as follows: Where y[n] is the filter output, x[n] is the input signal, h[k] is the coefficient of the FIR filter, and M is the order of the filter; According to the frequency characteristics of the noise, the spectrum of the input signal is analyzed by Fourier transform to determine the frequency components of the noise. According to the frequency band characteristics of the signal, a low-pass FIR filter is selected to filter the received signal through convolution operation to remove unnecessary noise components. The gain processing of the FIR filter is achieved by adjusting the size of the filter coefficient. The relationship between the gain G and the filter coefficient h[k] is: h[k]←G·h[k] Where G is the gain factor and h[k] is the original coefficient of the filter.

5. The marine seismic data acquisition method according to claim 1, characterized in that: The Kirchhoff inversion method is used to perform three-dimensional modeling according to the propagation time, reflection intensity and frequency characteristics of seismic waves to construct a seabed structure map, including: For a reflecting medium, the reflection of a seismic wave is expressed as: Where R(t) is the received reflected signal, A i is the reflection intensity of the i-th reflection layer, T i is the propagation time of the i-th reflector, δ is the Dirac pulse, and N is the number of reflectors; The reflected wave data are collected by marine seismic exploration instruments, including the arrival time, amplitude and frequency characteristics of each reflected wave. A preliminary inversion calculation is performed based on the propagation time and reflection intensity of the seismic waves to obtain a preliminary velocity model and density distribution of the underground medium, and the velocity distribution of each point is deduced based on the difference in propagation time. The model is optimized through multiple iterations to obtain a velocity model, and the density differences and physical properties of each layer are deduced through the relationship between reflection intensity and reflection coefficient.

6. The marine seismic data acquisition method according to claim 1, characterized in that: The multiple wave removal algorithm is used to identify and analyze multiple waves of the collected data, including: If the received signal s(t) consists of a primary wave s1(t) and multiple waves s m (t) Composition: s(t)=s1(t)+s m (t) Among them, s1(t) is the first reflected wave, s m (t) is the multiple reflection wave; In the frequency domain, the Fourier transform S(f) of the signal s(t) is expressed as: S(f)=S1(f)+S m (f) Among them, S1(f) and S m (f) Frequency domain representation of primary wave and multiple waves respectively; The frequency domain representations of the primary wave and the multiple waves are S1(f) and S m (f), their phases are φ1(f) and φ m (f), then the phase difference Δφ(f) is: Δφ(f)=φm(f)+φ1(f) Multiple waves have different propagation paths, and the multiple waves are identified and removed by the difference in frequency.

7. The marine seismic data acquisition method according to claim 1, characterized in that: The model for optimizing reflected waves by phase difference analysis includes: By calculating the phase difference, the primary wave and multiple waves are identified, and the phase difference is used as a feature for signal separation. By removing the multiple waves, the reflection wave model is optimized. After removing the multiple waves, the accuracy of the inversion model will be improved. Through the optimized reflection wave data, the structural model of the underground medium can be accurately restored. According to the phase difference characteristics of multiple waves, phase difference correction is used to remove multiple waves. In the frequency domain, after removing multiple waves, the frequency domain expression of the signal becomes: The corrected frequency domain signal is subjected to inverse Fourier transform to obtain the time domain signal after removing multiple waves.

8. A marine seismic data acquisition system, characterized in that: include: Data monitoring module, which is used by sensors to monitor reflected seismic waves in real time and transmit the data to a surface receiving station via underwater radio; The data transmission module is used to transmit data to the surface platform through the underwater acoustic communication system during real-time data collection, and optimize the transmission frequency and data density according to the working mode of the sensor; The data processing module is used to remove noise signals through digital signal processing algorithms and perform gain processing on the received signals using FIR filters; The model building module is used to construct a seafloor structure map by using the Kirchhoff inversion method to perform three-dimensional modeling based on the propagation time, reflection intensity and frequency characteristics of seismic waves; The data analysis module is used to identify and analyze multiple waves of the collected data using a multiple wave removal algorithm, and to optimize the reflection wave model through phase difference analysis.

9. A computing device, comprising: at least one processor, memory, and input-output unit; Wherein, the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the method as claimed in claim 1 ～ 7. The steps of the marine seismic data acquisition method described in any one of claims 7.

10. A computer-readable storage medium comprising instructions, which, when executed on a computer, enables the computer to execute the steps of the marine seismic data acquisition method according to any one of claims 1 to 7.

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