An active noise reduction seismic data acquisition method

Abstract

The application discloses a kind of active noise reduction seismic data acquisition methods, comprising the following steps: complex topography is laid several single points, and an active noise reduction seismic node is placed on each single point;Before seismic acquisition excitation, according to actual situation, environmental noise is measured multiple times, and noise characteristic analysis is carried out on environmental noise, and environmental noise is identified;During the formal acquisition process of seismic data, the noise signal in the seismic data collected this time is identified according to the measured environmental noise characteristics, and a reverse noise signal is generated, and the reverse noise signal is superimposed in the collected seismic data to actively reduce noise;The seismic data after noise reduction is output through a wireless communication module, as the seismic data collected by the point seismic station, and is remotely sent to the console for quality monitoring.The application can solve the low signal-to-noise ratio problem caused by the inability of existing single-point seismic nodes to be combined in the field when collecting in complex areas.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of earthquake prediction, in particular to an active noise reduction seismic data acquisition method. BACKGROUND

[0002] The seismic exploration industry has a long history of using shotpoint and receiver combination to eliminate coherent noise in the acquisition process. In theory, the combination receiving technology suppresses the regular noise wave between each receiver in the single channel during signal acquisition to improve the signal-to-noise ratio of seismic data. In order to suppress random interference, the designed combination point distance is greater than or equal to the coherence radius. The receiver combination strengthens the effective wave energy, suppresses the interference wave, and improves the signal-to-noise ratio of the data. However, because the frequencies of the interference wave and the effective wave are cross-interlaced, the combination receiving suppresses the interference wave while also reducing the effective wave energy, especially the high-frequency part which is severely attenuated by the combination receiving.

[0003] In addition, in actual operation, the theoretical combination is difficult to adapt to the changes of field noise and terrain, and errors are generated for each receiver in the combination. The inconsistency of ground coupling and the error of in-group static correction pollute the seismic data. In summary, the more complex the combination form is, the more difficult it is to carry out in complex terrain, and the larger the sampling error of the wave field is. At the same time, using multiple receiver combinations increases the field cost.

[0004] Currently, the industry begins to try to use a single receiver to acquire seismic data. This method has certain applicability in complex terrain where it is difficult to deploy a receiver combination, but this method reduces the difficulty of deployment and acquisition cost while significantly reducing the suppression ability of seismic interference waves. SUMMARY

[0005] The purpose of the present application is to provide an active noise reduction seismic data acquisition method to solve the technical problems in the prior art.

[0006] To solve the above technical problems, the present application specifically provides the following technical solutions:

[0007] An active noise reduction seismic data acquisition method, comprising the following steps:

[0008] Step 100, deploying a plurality of single points on complex terrain, and placing a single active noise reduction seismic node device on each single point;

[0009] Step 200, before seismic acquisition and excitation, the seismic node device measures the environmental noise multiple times according to the actual situation, and analyzes the noise characteristics of the measured environmental noise to obtain the characteristic parameters of the environmental noise;

[0010] Step 300, acquiring seismic data by using the seismic node device, identifying noise signals in the acquired seismic data according to the measured environmental noise characteristic parameters, and generating reverse noise signals of the noise signals, and superimposing the reverse noise signals and the acquired seismic data to perform active noise reduction;

[0011] Step 400, outputting the noise-reduced seismic data through the wireless communication module as the seismic data collected by the point seismic station, and remotely sending to the control console for quality monitoring.

[0012] As a preferred scheme of the present application, in step 200, the measured environmental noise is subjected to noise characteristic analysis to determine random noise and regular noise in the environmental noise.

[0013] The regular noise is an interference wave with a certain dominant frequency and apparent velocity, and the dominant frequency and propagation direction of the random noise are not fixed.

[0014] As a preferred scheme of the present application, in step 200, on the environmental noise shot gather, the random noise and the regular noise are automatically distinguished by using a computer through image segmentation and edge detection, and the distinguished random noise and regular noise are subjected to noise characteristic analysis respectively.

[0015] As a preferred scheme of the present application, in step 200, the characteristic parameters of the environmental noise include the following methods:

[0016] Step 201, generating a frequency spectrum graph of the multiple-acquired environmental noise, and analyzing the frequency and amplitude changes in the frequency spectrum graph of the environmental noise to identify the regular noise and the random noise in the environmental noise.

[0017] Step 202, calculating the mean and variance of the random noise by using a Bayesian statistical system, and taking the mean and variance of the random noise as prior information of the random noise.

[0018] Step 203, recording the occurrence position of the regular noise in the frequency spectrum graph, and counting the dominant frequency and apparent velocity of the regular noise.

[0019] As a preferred scheme of the present application, for the regular noise with a certain dominant frequency, the dominant frequency is automatically scanned, and the dominant frequency and position information are recorded.

[0020] For the regular noise with a certain apparent velocity, the noise phase axis is automatically tracked on the frequency spectrum graph profile of the environmental noise, the apparent velocity is scanned, and the apparent velocity and position information are recorded.

[0021] As a preferred scheme of the present application, in the step 300, generating the reverse noise signal of the noise signal comprises: generating a reverse regular noise signal of the regular noise, and generating a reverse random noise signal of the random noise.

[0022] The reverse regular noise signal and the reverse random noise signal are superimposed with the current seismic data to perform active noise reduction, so as to obtain the seismic signal in which the regular noise and the random noise are eliminated.

[0023] As a preferred scheme of the present application, the specific implementation steps of generating the reverse random noise signal of the random noise are as follows:

[0024] The mean and the variance of the random noise are used as the prior information.

[0025] The current seismic data are used as the observation data, the seismic data are superposition data of the effective signal and the environmental noise, and a likelihood function of the seismic data is constructed, so that the seismic data are simplified as the sum of the effective signal and the random signal.

[0026] The prior information and the likelihood function are multiplied to construct a posterior probability distribution function, the posterior probability distribution function is used as a target function to predict the random noise in the collected seismic data, and a reverse random noise signal of the current seismic data is generated.

[0027] As a preferred scheme of the present application, the specific implementation steps of generating the reverse regular noise signal of the regular noise are as follows:

[0028] According to the occurrence position of the regular noise in the frequency spectrum diagram, the dominant frequency and the apparent velocity of the regular noise are counted as retrieval parameters, the regular noise in the current seismic data is searched, and the regular noise is tracked.

[0029] A reverse regular noise signal of the identified regular noise is generated.

[0030] As a preferred scheme of the present application, the regular noise is searched in the current seismic data by using the position information as an index, and then the regular noise is adaptively identified by using the dominant frequency and the apparent velocity information.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] The present application provides a seismic single-point acquisition node applied to a complex terrain, which can actively reduce the environmental noise in the field, can better control the quality of the acquisition result, can reduce the acquisition construction cost caused by combination, and can solve the problems of low signal-to-noise ratio and ineffective quality control caused by the fact that the existing seismic single-point acquisition node cannot be combined in the field in a complex area. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required to be used in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by the provided drawings without creative labor for those skilled in the art.

[0034] Figure 1 The flowchart of the seismic data acquisition method provided for the embodiment of the present application is shown.

[0035] Figure 2 The structural block diagram of the seismic node device provided for the embodiment of the present application is shown.

[0036] Figure 3 The flowchart of the ambient noise suppression process provided for the embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0038] Embodiment 1

[0039] As shown in the following, Figure 1 The present application provides an active noise reduction seismic data acquisition method. Although the use of a single geophone to acquire seismic data reduces the difficulty of layout and acquisition cost, it significantly reduces the suppression ability of seismic interference waves.

[0040] The present embodiment uses the principle of "reverse noise reduction". Before seismic data acquisition, the ambient noise of the layout environment is first acquired, and the regular noise and random noise in the ambient noise are identified. The characteristic parameters of the regular noise and random noise in the ambient noise are marked using statistical concepts. Therefore, in actual seismic data acquisition, the characteristic parameters are compared in advance to identify the noise signals in the seismic data, and the corresponding reverse noise signals are generated. After superimposing the seismic data, the noise after the seismic data is eliminated, so that the present embodiment can actively reduce the field environmental noise and better control the quality of the acquisition results.

[0041] The seismic data acquisition method of the present embodiment includes the following steps:

[0042] Step 100, several single points are laid out on complex terrain, and a single seismic node device for active noise reduction is placed on each single point.

[0043] Step 200, before seismic acquisition shooting, the seismic node device measures the environmental noise multiple times according to the actual situation, and analyzes the noise characteristics of the measured environmental noise to count the characteristic parameters of the environmental noise.

[0044] In step 200, the noise characteristics of the measured environmental noise are analyzed to determine the random noise and regular noise in the environmental noise; the regular noise is an interference wave with a certain dominant frequency and apparent velocity, and the dominant frequency and propagation direction of the random noise are not fixed.

[0045] In step 200, the characteristic parameters of the environmental noise specifically include the following methods:

[0046] Step 201, generating a frequency spectrum diagram of the environmental noise collected multiple times, and analyzing the frequency and amplitude changes in the frequency spectrum diagram of the environmental noise to identify the regular noise and random noise in the environmental noise;

[0047] Step 202, calculating the mean and variance of the random noise using a Bayesian statistical system, and taking the mean and variance of the random noise as prior information of the random noise;

[0048] Step 203, recording the occurrence position of the regular noise in the frequency spectrum diagram, and counting the dominant frequency and apparent velocity of the regular noise.

[0049] Among them, for the regular noise with a certain dominant frequency, the dominant frequency is automatically scanned, and the dominant frequency and position information are recorded; for the regular noise with a certain apparent velocity, the noise phase axis is automatically tracked on the frequency spectrum diagram profile of the environmental noise, the apparent velocity is scanned, and the apparent velocity and position information are recorded.

[0050] That is, when the environmental noise is collected in advance, the random noise and regular noise in the environmental noise are determined, and the characteristic parameters of the environmental noise are counted, such as the mean and variance of the random noise, and the occurrence position of the regular noise in the frequency spectrum diagram, and the dominant frequency and apparent velocity of the regular noise.

[0051] It should be noted that seismic data acquisition generates a shot gather for each shot, and each shot gather is composed of data collected by several single points. In the denoising work of the seismic data processing link, a shot gather is operated, and noise is eliminated on the shot gather.

[0052] In addition, the noise recognition and elimination in the embodiment is performed on the shot gather, and the collection of several environmental noises forms a shot gather of the environmental noises, and the feature analysis of the environmental noises is also completed on the shot gather.

[0053] In step 300, the seismic node device is used to collect seismic data, noise signals in the collected seismic data are recognized according to the measured environmental noise feature parameters, and a reverse noise signal of the noise signal is generated.

[0054] In step 300, the reverse noise signal of the noise signal includes a reverse regular noise signal of the regular noise and a reverse random noise signal of the random noise.

[0055] The reverse regular noise signal and the reverse random noise signal are superimposed on the collected seismic data to perform active noise reduction, and seismic signals eliminating the regular noise and the random noise are obtained.

[0056] The specific implementation steps of generating the reverse random noise signal of the random noise are as follows:

[0057] The mean value and the variance of the random noise are used as the prior information.

[0058] The collected seismic data are used as observation data, the seismic data are superposition data of effective signals and environmental noises, a likelihood function of the seismic data is constructed, the seismic data are simplified as a sum of effective signals and random signals, and the seismic data are simplified as a sum of effective signals and random signals.

[0059] The prior information and the likelihood function are multiplied to construct a posterior probability distribution function, the posterior probability distribution function is used as an objective function to predict the random noise in the collected seismic data, and a reverse random noise signal for the collected seismic data is generated.

[0060] Therefore, the embodiment is more suitable for processing random noises generally existing in nature within a certain length of time, and the random noise can be predicted according to statistical results.

[0061] It should be noted that the noise reduction earphone in the prior art also uses active noise control, and the principle is as follows: 1. a signal microphone arranged in the earphone detects low-frequency noise (100-1000 Hz) (currently up to 3000 Hz) in the environment that can be heard by the ear; 2. the noise signal is transmitted to a control circuit, the control circuit performs real-time operation; 3. a sound wave with the same amplitude and opposite phase of the noise is emitted through a Hi-Fi loudspeaker to cancel the noise; 4. therefore, the noise disappears and cannot be heard, and the noise reduction earphone has an instant processing characteristic, and therefore it does not distinguish between regular noise and random noise.

[0062] The embodiment collects continuity of the noise, divides the noise into regular noise and random noise, and generates corresponding reverse regular noise and reverse random noise for noise reduction processing, so that the noise reduction effect is better and the noise reduction is more thorough.

[0063] Further, the specific implementation steps of generating the reverse regular noise signal of the regular noise are:

[0064] According to the occurrence position of the regular noise in the frequency spectrum, the main frequency and the apparent velocity of the regular noise are counted as retrieval parameters, the regular noise in the collected seismic data is searched, and the regular noise is tracked.

[0065] A reverse regular noise signal of the identified regular noise is generated.

[0066] Specifically, in the collected seismic data, the position information is used as an index to search for the regular noise, and then the main frequency and the apparent velocity information are used to adaptively identify the regular noise, so as to improve the accuracy of retrieving the regular noise.

[0067] This step is suitable for processing regular noise in nature for a certain length of time, and can adaptively identify the regular noise according to the parameters. The active noise reduction device in the prior art, such as the active noise reduction earphone, collects noise at a certain moment, has no certain continuity, and has no regular concept.

[0068] Step 400, output the noise-reduced seismic data through the wireless communication module as the seismic data collected by the seismic station at the point, and remotely send it to the control console for quality monitoring.

[0069] It should be particularly noted that the entire acquisition process of the seismic data includes three links of acquisition, processing and interpretation. The work involved in the embodiment is performed in the acquisition link, which is equivalent to moving part of the work that should be performed in the processing link to the acquisition link, thereby improving the signal-to-noise ratio of the acquired seismic data, improving the intelligence of the acquisition link, and better controlling the quality of single-point acquisition.

[0070] Embodiment 2

[0071] The seismic node device mentioned in embodiment 1, as shown in Figure 2 and Figure 3 , specifically includes a seismic detector 1, a noise detection module 2, a central processor 3, a communication module 4 and a power module 5. The power module 5 is connected with the central processor 3 and provides power for the central processor 3.

[0072] The seismic detector 1 is used to collect seismic data of the working environment and environmental noise in the working environment.

[0073] The noise detection module 2 is connected with the output end of the geophone 1, and is used for receiving the environmental noise collected by the geophone 1 multiple times and performing noise feature statistical analysis on the collected environmental noise.

[0074] The noise detection module 2 generates a frequency spectrum diagram of the environmental noise collected multiple times, and analyzes the frequency and amplitude changes in the frequency spectrum diagram of the environmental noise to identify the regular noise and the random noise in the environmental noise.

[0075] The noise detection module 2 further comprises a random noise marking unit, which calculates the mean and variance of the random noise by using a Bayesian statistical system, and takes the mean and variance of the random noise as prior information of the random noise.

[0076] For field seismic signal collection, the noise is generally divided into regular noise and random noise. The regular noise is usually an interference wave with a certain dominant frequency and apparent velocity, such as surface wave, sound wave, etc. The random noise has no fixed dominant frequency and propagation direction, such as wind blowing, grass movement, machine operation, etc.

[0077] The effective frequency band of the seismic signal is generally between 0 Hz and 200 Hz, and the signals exceeding the range can be directly removed by a filtering system, so the noise removal process in the embodiment is specifically to eliminate the noise in the seismic signal of 0 Hz-200 Hz.

[0078] The central processor 3 is connected with the geophone 1 and the noise detection module 2, and generates an inverse noise signal of the environmental noise statistically analyzed by the noise detection module 2, and uses the inverse noise signal to perform active noise reduction on the seismic data.

[0079] It should be noted that the real-time collected seismic data is superimposed data of effective signals and environmental noise, the seismic data is simplified as a sum of effective signals and random signals, and the central processor 3 is provided with a function model construction module for constructing a likelihood function of the seismic data, so as to perform digital processing on the seismic data.

[0080] The central processor 3 comprises a regular noise inverse signal generation unit and a regular noise suppression unit, and the regular noise inverse signal generation unit generates a corresponding inverse signal based on the frequency spectrum diagram of the regular noise.

[0081] The regular noise suppression unit superimposes the inverse signal of the regular noise with the seismic data collected by the geophone 1 to generate seismic data after suppressing the regular noise.

[0082] The central processor 3 further comprises a random noise reverse signal generating unit and a random noise suppression unit, the random noise reverse signal generating unit multiplies the prior information with the likelihood function, constructs a posterior probability distribution function and directly calculates the random noise carried in the seismic data to generate the reverse signal corresponding to the random noise.

[0083] The random noise suppression unit superimposes the reverse signal of the random noise with the seismic data collected by the seismic detector 1 to generate the seismic data after suppressing the random noise.

[0084] The communication module 4 is connected with the central processor 3, and the communication module 4 is used to receive the seismic data after active noise reduction and output the seismic data after active noise reduction through wireless communication.

[0085] It should be particularly pointed out that in seismic signal processing, the regular noise is usually partially suppressed by the detector combination in the acquisition stage, and the remaining regular noise is further suppressed by subsequent data processing after being transmitted to the console. However, the detector combination will cause problems such as frequency reduction, mixed wave, inconsistency of corresponding characteristics between each detector in the string, and reduction of the fidelity of field seismic acquisition signals. At the same time, the workload in the field will also be increased compared with single detector acquisition. Single detector acquisition has the above advantages, but the signal-to-noise ratio of the existing detector acquisition is low and the quality is poor compared with detector combination acquisition.

[0086] However, the embodiment of the present application is different from the prior art in that:

[0087] The noise detection module 2 and the central processor 3 are integrated in the existing seismic detector, that is, active noise reduction is performed at the acquisition end of the seismic node, so as to improve the signal-to-noise ratio of the acquisition signal. In addition, although the active noise reduction earphone in the prior art also utilizes the principle of generating a reverse signal for active noise reduction, that is, the active noise reduction principle of the seismic detector is the same as that of the active noise reduction earphone in that both generate a reverse signal to suppress environmental noise.

[0088] However, the difference between the two is that the active noise reduction earphone collects the current noise, generates a reverse signal, considers that the noise at this moment is the same as that at the next moment, and superimposes the reverse signal with the noise at the next moment for noise reduction.

[0089] However, the active noise reduction of the seismic acquisition needs to continue for a certain period of time, and it is achieved by collecting environmental noise multiple times, analyzing the frequency, amplitude and other characteristics of the environmental noise, identifying the environmental noise at the acquisition time, generating a reverse signal at the acquisition time, and suppressing the environmental noise.

[0090] Since the active noise reduction earphone is characterized by instantaneity, a reverse signal is generated immediately after the ambient noise is collected, and a signal at a certain moment is processed. Due to the instantaneity, the noise cannot be classified as regular and random, and more is to distinguish the sudden noise. If there is no sudden noise, the error of the noise between two short moments can be ignored. However, the seismic data acquisition cannot achieve such instant reverse, because the effective signal needs time to propagate underground, and the seismic data needs a long time to record. According to the characteristics, the noise on the shot gather can be classified into regular and random noise, and therefore the embodiment generates the corresponding reverse signals of the regular noise and the random noise, so that the noise reduction effect is better.

[0091] The above examples are only exemplary embodiments of the present application and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements to the present application within the spirit and protection scope of the present application, and such modifications or equivalent replacements shall also be considered to fall within the protection scope of the present application.

Claims

1. An active noise reduction seismic data acquisition method characterized by, The method comprises the following steps: Step 100, laying a plurality of single points on complex terrain, and placing a single seismic node device for active noise reduction at each single point, wherein the seismic node device comprises a geophone (1), a noise detection module (2), a central processor (3), a communication module (4) and a power module (5); The geophone (1) is used to collect seismic data of the working environment and environmental noise in the working environment; The noise detection module (2) is connected with the output end of the geophone (1), and the noise detection module (2) is used to receive the environmental noise collected by the geophone (1) multiple times and perform noise feature statistical analysis on the collected environmental noise; The noise detection module (2) generates a frequency spectrum diagram of the environmental noise collected multiple times, and analyzes the frequency and amplitude changes in the frequency spectrum diagram of the environmental noise to identify regular noise and random noise in the environmental noise; Step 200, before seismic acquisition and excitation, the seismic node device measures the environmental noise multiple times according to the actual situation, and performs noise feature analysis on the measured environmental noise to count the characteristic parameters of the environmental noise; In step 200, the noise feature analysis is performed on the measured environmental noise to determine the random noise and the regular noise in the environmental noise; Wherein, the regular noise is an interference wave with a certain dominant frequency and apparent velocity, and the dominant frequency and propagation direction of the random noise are not fixed; In step 200, the characteristic parameters of the environmental noise specifically include the following methods: Step 201, generating a frequency spectrum diagram of the environmental noise collected multiple times, and analyzing the frequency and amplitude changes in the frequency spectrum diagram of the environmental noise to identify regular noise and random noise in the environmental noise; Step 202, calculating the mean and variance of the random noise by using a Bayesian statistical system, and taking the mean and variance of the random noise as prior information of the random noise; Step 203, recording the appearance position of the regular noise in the frequency spectrum diagram, and counting the dominant frequency and apparent velocity of the regular noise; Step 300, collecting seismic data by using the seismic node device, identifying noise signals in the collected seismic data according to the measured environmental noise characteristic parameters, and generating reverse noise signals of the noise signals, and superimposing the reverse noise signals and the collected seismic data for active noise reduction; Step 400, outputting the seismic data after noise reduction through the wireless communication module as the seismic data collected by the seismic station at this point, and remotely sending to the control console for quality monitoring.

2. The active noise reduction seismic data acquisition method according to claim 1, wherein in step 200, on the environmental noise shot gather, the random noise and the regular noise are automatically distinguished by using a computer through image segmentation and edge detection, and the distinguished random noise and regular noise are subjected to noise feature analysis respectively.

3. The active noise reduction seismic data acquisition method according to claim 1, wherein for the regular noise with a certain dominant frequency, the dominant frequency is automatically scanned, and the dominant frequency and position information are recorded. ​ ​ The method comprises the following steps: automatically tracking a noise phase axis on a frequency spectrum profile of the ambient noise, scanning a visual velocity, and recording visual velocity and position information of the noise with a certain visual velocity.

4. The method of claim 1, wherein, In the step 300, generating the reverse noise signal of the noise signal comprises: generating a reverse regular noise signal of the regular noise, and generating a reverse random noise signal of the random noise; The reverse regular noise signal and the reverse random noise signal are superimposed on the seismic data collected this time to perform active noise reduction, so as to obtain the seismic signal in which the regular noise and the random noise are eliminated.

5. The method of claim 4, wherein, The specific implementation steps for generating the reverse random noise signal of the random noise are as follows: The mean and variance of the random noise are used as prior information; The seismic data collected this time are used as observation data, the seismic data are superimposed data of effective signals and ambient noise, and a likelihood function of the seismic data is constructed, so that the seismic data are simplified as a sum of effective signals and random signals; The prior information and the likelihood function are multiplied to construct a posterior probability distribution function, the posterior probability distribution function is used as an objective function to predict the random noise in the collected seismic data, and a reverse random noise signal for the seismic data collected this time is generated.

6. The method of claim 4, wherein, The specific implementation steps for generating the reverse regular noise signal of the regular noise are as follows: The main frequency and the visual velocity of the regular noise are used as retrieval parameters to search for the regular noise in the seismic data collected this time according to the appearance position of the regular noise on the frequency spectrum, and the regular noise is tracked. The reverse regular noise signal of the identified regular noise is generated.

7. The method of claim 6, wherein, The regular noise is searched in the seismic data collected this time by using the position information as an index, and then the regular noise is adaptively identified by using the main frequency and the visual velocity information.

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