Seismic detection system based on seabed crawler

Abstract

The invention provides a seismic detection system based on a seabed crawl device, and belongs to the technical field of geophysics, the seismic detection system comprises a survey ship, the seabed crawl device, a seismic source, a receiver and a control chip, the survey ship is connected with the seabed crawl device through a traction rope, a dragging cable is arranged at the tail of the seabed crawl device, the seismic source and the receiver are fixed on the dragging cable, and the control chip is connected with the control chip. An electronic cabin is arranged in the seabed crawl device, the data control chip is arranged in the electronic cabin, and the control chip performs data interaction with a driving unit, a seismic source and a receiver of the seabed crawl device; a seismic detection control module is arranged in the control chip and is used for setting vibration parameters of a seismic source and preprocessing data acquired by the receiver to finally obtain seismic detection data; a data transmission device is further arranged in the electronic cabin, the control chip is electrically connected with the data transmission device, and the data transmission device is used for sending seismic detection data to an investigation ship. The problem that an existing method is not high in comprehensiveness and accuracy is solved.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical technology, and in particular, relates to a seismic detection system based on a seafloor crawler. Background Art

[0002] As an important means in the fields of oil and gas exploration and submarine structural research, submarine seismic exploration has received extensive attention in recent years. By detecting the characteristics of reflected waves in the strata, the underground structure can be analyzed, providing an important basis for resource development and geological research. Compared with land seismic exploration, submarine seismic exploration faces more complex working environment and technical challenges. First, the submarine topography is complex and changeable, and the seabed texture varies greatly, which will lead to significant differences in the propagation characteristics of seismic waves in different media, bringing difficulties to data processing and interpretation. Secondly, the underwater environment places higher requirements on the working stability of seismic instruments, and factors such as water pressure and temperature will also affect instrument performance and data quality. Furthermore, due to the limited deployment of submarine receiving equipment, it is impossible to achieve uniform coverage like on land, which further reduces the reliability of the data. The commonly used submarine seismic OBS / OBC detection technology, although OBS / OBC is located on the seabed, it is difficult to achieve comprehensive and accurate seismic detection because the source is located on the sea surface and the excited seismic waves are attenuated and diffused by deep seawater. Summary of the invention

[0003] In view of this, the present invention provides a seismic detection system based on a seafloor crawler, which can solve the technical problems of low comprehensiveness and accuracy in existing seafloor seismic detection methods.

[0004] The present invention is achieved in that:

[0005] The present invention provides a seismic detection system based on a seabed crawler, which includes: a survey ship, a seabed crawler, a seismic source, a receiver and a control chip, wherein the survey ship is connected to the seabed crawler through a traction rope, a towing cable is provided at the tail of the seabed crawler, the seismic source and the receiver are fixed on the towing cable, an electronic cabin is provided in the seabed crawler, and a data control chip is provided in the electronic cabin, and the control chip exchanges data with a driving unit of the seabed crawler, the seismic source and the receiver; a seismic detection control module is provided in the control chip, which is used to set the vibration parameters of the seismic source and pre-process the data collected by the receiver to finally obtain seismic detection data; a data transmission device is also provided in the electronic cabin, the control chip is electrically connected to the data transmission device, and the data transmission device is used to send the seismic detection data to the survey ship.

[0006] On the basis of the above technical solution, the seismic detection system based on the seafloor crawler of the present invention can also be improved as follows:

[0007] Among them, the survey ship is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

[0008] Furthermore, the seafloor crawler is a tracked seafloor crawler or a multi-legged seafloor crawler.

[0009] Furthermore, the seismic source is a controllable seismic source, including any one of a controllable transducer seismic source and a controllable air gun seismic source.

[0010] Furthermore, the vibration surface of the seismic source transmitting array contacts the seabed sediments, which can stimulate both longitudinal waves (P waves) and transverse waves (S waves), and can improve the seismic source output efficiency.

[0011] Furthermore, the receiver is a node-type receiver array or a multi-channel seismic cable.

[0012] Furthermore, the node-type receiver array includes a plurality of three-component detectors and a plurality of hydrophones, wherein the three-component detectors are used to receive vector seismic signals in the X, Y, and Z directions, and the hydrophones are used to receive scalar seismic signals.

[0013] Furthermore, the data transmission device sends the seismic detection data to the survey ship via a data cable or a wireless channel.

[0014] Furthermore, the seabed crawler is also provided with a sensor module, which at least includes a depth gauge, an altimeter, a gyroscope and an accelerometer.

[0015] Furthermore, the earthquake detection control module is used to perform the following steps:

[0016] S10, controlling the seafloor crawler to crawl in a predetermined detection area, obtaining position information of the seafloor crawler, and controlling the seismic source to intermittently transmit seismic waves and receive reflected waves through the receiver;

[0017] S20, using a sliding window and a Bayesian anomaly detection method to calculate an abnormal point set in the first reflected wave, including amplitude abnormal points, frequency abnormal points, and phase abnormal points;

[0018] S30, aligning the abnormal point set with the seismic wave according to the order of the intermittently emitted seismic waves; extracting the features of the seismic wave using a sliding window, recording them as seismic wave features, and calculating the correlation between the seismic wave features and the abnormal point set;

[0019] S40, deleting the seismic wave features whose correlation is greater than a preset correlation threshold value from the seismic wave features to obtain the remaining features, and using a preset feature-parameter equation group to calculate the seismic wave parameters corresponding to the remaining features as optimization parameters;

[0020] S50, according to the optimization parameters, controlling the seismic source to emit new seismic waves, and receiving new reflected waves through the receiver; and preprocessing the new reflected waves to obtain preprocessed new reflected waves;

[0021] S60, performing waveform alignment and travel time analysis on the new seismic wave and the pre-processed new reflection wave, and calculating the propagation time and speed of the new seismic wave in different media;

[0022] S70, using a pre-established velocity correction equation group, performing dynamic correction processing on the pre-processed new reflection wave to eliminate the time difference caused by different seismic wave propagation paths;

[0023] S80, performing spectrum analysis and amplitude recovery on the corrected pre-processed new reflected wave to improve the signal-to-noise ratio and resolution of the signal, and obtaining the result as detection data.

[0024] The vibration parameters of the earthquake source include frequency, amplitude and duration.

[0025] The preset characteristic-parameter equation group includes a frequency equation, an amplitude equation and a duration equation.

[0026] The step of setting the correlation threshold specifically includes:

[0027] 1. Collect historical detection data, including seismic wave characteristics and corresponding abnormal point sets under different geological environments;

[0028] 2. Use a machine learning algorithm (such as support vector machine or random forest) to train a classification model with seismic wave characteristics as input and the presence or absence of anomaly points as output;

[0029] 3. Use cross-validation method to evaluate the performance of the model at different relevance thresholds, including precision, recall and F1 score;

[0030] 4. Choose a relevance threshold that strikes the best balance between precision and recall.

[0031] The velocity correction equation group includes a P-wave velocity correction equation, an S-wave velocity correction equation, a multiple wave correction equation, an NMO correction equation and a DMO correction equation.

[0032] The following are the formulas and explanations of the equations:

[0033] 1. Frequency equation:

[0034] f=f 0 +α·log(d)+β·T+γ·ρ+ε f ;

[0035] Where, f is the optimized source frequency; f 0 is the initial frequency; d is the detection depth; T is the seabed temperature; ρ is the seabed sediment density; α, β, γ are unknown coefficients; ε f is the error term.

[0036] Parameter acquisition method:

[0037] d is obtained by direct measurement through a depth gauge; T is obtained by measurement through a temperature sensor; and ρ is estimated through sampling analysis or sonic logging.

[0038] 2. Amplitude equation:

[0039] A=A 0 ·e -λd ·(1+μ·v+ν·a)+ε A ;

[0040] Where A is the optimized source amplitude; A 0 is the initial amplitude; d is the detection depth; v is the speed of the seafloor crawler; a is the acceleration of the seafloor crawler; λ, μ, ν are unknown coefficients; ε A is the error term.

[0041] Parameter acquisition method:

[0042] v and a are measured by the velocimeter and accelerometer on the seafloor crawler.

[0043] 3. Duration equation:

[0044] t=t 0 +ω·sin(θ)+φ·cos(ψ)+χ·H+ε t ;

[0045] Where, t is the optimized earthquake source duration; t 0 is the initial duration; θ is the seabed slope; ψ is the pitch angle of the seabed crawler; H is the seawater depth; ω, φ, χ are unknown coefficients; ε t is the error term.

[0046] Parameter acquisition method:

[0047] θ is obtained by measuring the seabed topography; ψ is obtained by measuring the gyroscope; and H is obtained by measuring the depth gauge.

[0048] 4. P wave velocity correction equation:

[0049]

[0050] Where V p is the corrected P wave velocity; V p0is the initial P wave velocity; P is the pore pressure; S is the rock saturation; is the pressure gradient; k p ,m p ,n p is the unknown coefficient; ε p is the error term.

[0051] Parameter acquisition method:

[0052] P is obtained by measuring with a pressure sensor; S is estimated by resistivity logging; Calculated from the change in pressure with depth.

[0053] 5. S wave velocity correction equation:

[0054]

[0055] Where V s is the corrected S-wave velocity; V s0 is the initial S-wave velocity; σ is the effective stress; φ is the porosity; f is the main frequency; k s ,m s ,n s is the unknown coefficient; ε s is the error term.

[0056] Parameter acquisition method:

[0057] σ is obtained by measuring with stress sensors; φ is estimated by density logging or neutron logging; and f is directly obtained by the source parameters.

[0058] 6. Multiple wave correction equation:

[0059]

[0060] Where, T m is the travel time after multiple wave correction; T p is the original P wave travel time; h is the water depth; V w V is the speed of sound in water; p is the formation P wave velocity; θ i is the incident angle; ε m is the error term.

[0061] Parameter acquisition method:

[0062] h is obtained by measuring with a depth gauge; V w Obtained by measuring with a sound velocity meter; θ i Calculated by ray tracing method.

[0063] 7.NMO correction equation:

[0064]

[0065] Where, T NMO T is the NMO correction time; 0 is the zero offset travel time; x is the offset distance; V RMS is the root mean square velocity; ε N is the error term.

[0066] Parameter acquisition method:

[0067] T 0 Obtained from the initial processed seismic records; x is calculated from the position information of the seafloor crawler; V RMS Estimated by velocity analysis method.

[0068] 8.DMO correction equation:

[0069]

[0070] Where, T DMO T is the DMO correction time; NMO is the NMO correction time; x is the offset; θ is the inclination angle of the reflection point; V int is the interlayer velocity; T 0 is the zero offset travel time; ε D is the error term.

[0071] Parameter acquisition method:

[0072] θ is obtained through seismic section interpretation; V int It is estimated by interlayer velocity analysis method.

[0073] These equations cover several key links in the earthquake detection process, including source parameter optimization, velocity correction and multiple wave suppression, etc. Through the application of these equations, the accuracy and resolution of seafloor earthquake detection can be significantly improved.

[0074] The Bayesian anomaly detection method is specifically expressed as follows:

[0075]

[0076] Where P(A|D) is the posterior probability of anomaly occurrence given observed data D; P(D|A) is the likelihood probability of observed data under abnormal conditions; P(A) is the prior probability of anomaly occurrence; and P(D) is the marginal probability of observed data.

[0077] The criteria for determining abnormal points are as follows:

[0078] 1. Amplitude abnormal point:

[0079] |A i -μ A |>kA ·σ A ;

[0080] In the formula, A i is the amplitude of the i-th sampling point; μ A is the mean value of the amplitude in the sliding window; σ A is the standard deviation of the amplitude in the sliding window; k A is the amplitude anomaly determination coefficient, usually 2 to 3.

[0081] 2. Frequency abnormal points:

[0082] |f i -μ f |>k f ·σ f ;

[0083] In the formula, f i is the main frequency of the i-th time window; μ f is the mean of the main frequency in the sliding window; σ f is the standard deviation of the main frequency in the sliding window; k f It is the frequency anomaly determination coefficient, usually 2 to 3.

[0084] 3. Phase abnormality point:

[0085] |φ i -μ φ |>k φ ·σ φ ;

[0086] In the formula, φ i is the instantaneous phase of the i-th sampling point; μ φ is the mean of the instantaneous phase in the sliding window; σ φ is the standard deviation of the instantaneous phase in the sliding window; k φ is the phase anomaly determination coefficient, usually 2 to 3.

[0087] Parameter acquisition method:

[0088] 1. Amplitude A i Obtained directly from the raw data collected by the receiver.

[0089] 2. Main frequency f i By performing Fourier transform on each time window and then finding the frequency point with the maximum energy, we can obtain:

[0090] f i = argmax f |F i (f)| 2 ;

[0091] In the formula, F i(f) is the Fourier transform of the i-th time window.

[0092] 3. Instantaneous phase φ i Calculated by Hilbert transform:

[0093]

[0094] In the formula, x i is the original signal, H[x i ] is x i The Hilbert transform of .

[0095] 4. The mean μ and standard deviation σ are calculated from the data in the sliding window:

[0096]

[0097] Where N is the length of the sliding window, x i Represents amplitude, frequency, or phase data.

[0098] 5. The optimal value of the abnormality determination coefficient k can be determined by cross-validation method. The specific steps are as follows:

[0099] a. Select a set of candidate k values, such as [1.5, 2.0, 2.5, 3.0, 3.5].

[0100] b. Divide the dataset into training set and validation set.

[0101] c. For each k value, the outliers are calculated using the training set, and then the detection performance (such as F1 score) is evaluated on the validation set.

[0102] d. Select the k value that performs best on the validation set.

[0103] Through the above methods, we can comprehensively detect abnormal points in the reflected waves, including abnormalities in amplitude, frequency and phase. These abnormal points may indicate special changes in the underground geological structure or problems in the data collection process, which is of great significance for subsequent data processing and geological interpretation.

[0104] Wherein, the step S10 specifically includes: controlling the seafloor crawler to crawl in the predetermined detection area, obtaining the real-time position information of the seafloor crawler, including depth, height, heading angle, and pitch angle, etc.; at the same time, using the control chip to control the source to intermittently emit seismic waves, and recording the reflected seismic wave signals through the receiver array. Through this step, the seafloor topography information and the original seismic reflection wave data can be obtained, providing a basic basis for subsequent signal processing and geological interpretation.

[0105] Wherein, the step S20 specifically includes: using a sliding window to statistically analyze the amplitude, frequency and phase characteristics of the received seismic reflection wave signal; using the Bayesian anomaly detection method to calculate the probability of anomaly occurrence of each feature under given observation data; when the amplitude, frequency or phase exceeds the mean value within the sliding window plus or minus 2-3 times the standard deviation, it is determined as the corresponding abnormal point. Through this step, the characteristic points in the original reflection wave that may reflect geological anomalies can be automatically identified, providing clues for subsequent processing and analysis.

[0106] The step S30 specifically includes: aligning and pairing each abnormal point with the corresponding seismic wave reflection signal according to the emission timing of the earthquake source; extracting the amplitude, frequency, phase and other characteristics of each seismic wave reflection signal by the sliding window method, and calculating the correlation between these characteristics and the abnormal point set. Through this step, the intrinsic connection between the seismic wave characteristics and the abnormal points can be deeply explored, laying the foundation for the subsequent optimization parameter calculation.

[0107] Wherein, the step S40 specifically includes: setting a reasonable correlation threshold, such as 0.7, eliminating the seismic wave features with a correlation greater than the threshold to obtain the remaining features; then, using the pre-established frequency equation, amplitude equation, duration equation and other feature-parameter equations, calculating the source parameters corresponding to the remaining features as optimization parameters. Through this step, the features most relevant to the abnormal points can be screened out from a large number of seismic wave features, and the optimal source parameters can be calculated based on them, providing a basis for subsequent source optimization.

[0108] Wherein, the step S50 specifically includes: using the optimization parameters calculated in step S40, controlling the source to emit new seismic waves through the control chip; the receiver records the new seismic wave reflection signal, and then preprocesses these new reflection waves, including denoising, frequency band filtering, waveform correction and other steps, to obtain preprocessed new reflection wave data. Through this step, the optimized source parameters can be used to obtain new seismic reflection data with better quality, providing a basis for subsequent velocity correction and amplitude recovery.

[0109] Wherein, the step S60 specifically includes: using correlation analysis or least square method to align the waveforms of the newly transmitted seismic waves with the newly received reflected waves to eliminate the time offset caused by the movement of the seafloor crawler; and calculating the propagation time and speed of the new seismic waves in the water layer, sediment layer and other media according to the waveform alignment results. Through this step, accurate velocity information of the formation can be obtained, providing necessary parameters for subsequent dynamic correction processing.

[0110] Wherein, the step S70 specifically includes: using the pre-established velocity correction equations such as the P wave velocity correction equation, the S wave velocity correction equation, the multiple wave correction equation, the NMO correction equation and the DMO correction equation, and performing dynamic correction processing on the new reflection wave according to the velocity information in each medium calculated in step S60, to eliminate the time offset caused by factors such as formation anisotropy and multiple reflections. Through this step, the time axis of the reflection wave signal can be kept consistent in space, providing good basic data for subsequent spectrum analysis and amplitude recovery.

[0111] Wherein, the step S80 specifically includes: performing Fourier transform on the corrected reflected wave signal to obtain spectrum information; using amplitude recovery methods such as geometric divergence correction and absorption compensation to eliminate amplitude attenuation caused by factors such as formation absorption and divergence, and improve the signal-to-noise ratio of the reflected wave; and comprehensively using the results of spectrum analysis and amplitude recovery to obtain high-quality seismic detection data to provide support for subsequent geological interpretation. Through this step, the resolution and reliability of the signal can be improved, providing valuable detection data for the final geological interpretation.

[0112] Compared with the prior art, the beneficial effects of the seismic detection system based on the seafloor crawler provided by the present invention are:

[0113] First, the present invention uses a controllable source, such as a transducer source or an airgun source, which can emit seismic waves with adjustable frequency, amplitude and duration, and cooperates with a control chip to achieve dynamic optimization of source parameters. Compared with traditional fixed sources, this controllable source can intelligently adjust the emission parameters based on real-time seabed environmental information, such as stratum depth, temperature, density, etc., so that the characteristics of seismic waves are more in line with local geological conditions, thereby greatly improving detection accuracy.

[0114] Secondly, the present invention integrates a variety of advanced algorithms in the data processing link, including Bayesian anomaly detection, feature-parameter equation group modeling, velocity correction equation group, etc. Through these algorithms, the amplitude, frequency, phase and other characteristics of seismic reflection waves can be comprehensively analyzed, and characteristic points that may reflect geological anomalies can be intelligently identified; based on these key characteristics, a mathematical model describing the propagation law of seismic waves is established to achieve fine optimization of key factors such as source parameters and propagation velocity; finally, in view of the complex seabed environment, a variety of velocity correction methods are used to effectively eliminate the time offset caused by formation anisotropy, multiple reflections, etc., greatly improving the reliability of the data.

[0115] Compared with the existing seabed seismic exploration technology, the method of the present invention has the following significant advantages: 1) It has intelligent perception and adaptive optimization capabilities, and can dynamically adjust the source parameters and data processing strategies according to the seabed environmental information obtained in real time, thereby greatly improving the detection accuracy; 2) It adopts advanced data processing algorithms, which can comprehensively analyze the characteristics of seismic reflection waves, mine hidden geological information, and provide more reliable basic data for interpretation; 3) It uses hardware equipment such as controllable seismic sources and tracked crawlers or multi-legged seabed crawlers to obtain more comprehensive and accurate seismic data in complex seabed environments.

[0116] In summary, the present invention solves the technical problems of low comprehensiveness and accuracy in existing seabed earthquake detection methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 A schematic diagram of the composition of a seismic detection system based on a seafloor crawler provided by the present invention;

[0118] Figure 2 A flow chart of steps executed by a seismic detection control module;

[0119] Figure 3 is a schematic diagram of a wired system according to Embodiment 2 of the present invention;

[0120] Figure 4 This is a system diagram of a wireless method according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0121] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0122] like Figure 1 As shown is a schematic diagram of the composition of a seismic detection system based on a seabed crawler provided by the present invention. The method comprises the following steps: wherein, it comprises: a survey ship, a seabed crawler, a seismic source, a receiver and a control chip, wherein the survey ship is connected to the seabed crawler by a traction rope, a towing cable is provided at the tail of the seabed crawler, the seismic source and the receiver are fixed on the towing cable, an electronic cabin is provided in the seabed crawler, a data control chip is provided in the electronic cabin, the control chip interacts with the driving unit, the seismic source and the receiver of the seabed crawler for data; a seismic detection control module is provided in the control chip for setting the vibration parameters of the seismic source and preprocessing the data collected by the receiver to finally obtain seismic detection data; a data transmission device is also provided in the electronic cabin, the control chip is electrically connected to the data transmission device, and the data transmission device is used to send the seismic detection data to the survey ship.

[0123] The earthquake detection control module is used to perform the following steps:

[0124] S10, controlling the seafloor crawler to crawl in a predetermined detection area, obtaining position information of the seafloor crawler, and controlling the seismic source to intermittently transmit seismic waves and receive reflected waves through the receiver;

[0125] S20, using a sliding window and a Bayesian anomaly detection method to calculate an abnormal point set in the first reflected wave, including amplitude abnormal points, frequency abnormal points, and phase abnormal points;

[0126] S30, aligning the abnormal point set with the seismic wave according to the order of the intermittently emitted seismic waves; extracting the features of the seismic wave using a sliding window, recording them as seismic wave features, and calculating the correlation between the seismic wave features and the abnormal point set;

[0127] S40, deleting the seismic wave features whose correlation is greater than a preset correlation threshold value from the seismic wave features to obtain the remaining features, and using a preset feature-parameter equation group to calculate the seismic wave parameters corresponding to the remaining features as optimization parameters;

[0128] S50, according to the optimization parameters, controlling the seismic source to emit new seismic waves, and receiving new reflected waves through the receiver; and preprocessing the new reflected waves to obtain preprocessed new reflected waves;

[0129] S60, performing waveform alignment and travel time analysis on the new seismic wave and the pre-processed new reflection wave, and calculating the propagation time and speed of the new seismic wave in different media;

[0130] S70, using a pre-established velocity correction equation group, performing dynamic correction processing on the pre-processed new reflection wave to eliminate the time difference caused by different seismic wave propagation paths;

[0131] S80, performing spectrum analysis and amplitude recovery on the corrected pre-processed new reflected wave to improve the signal-to-noise ratio and resolution of the signal, and obtaining the result as detection data.

[0132] The specific implementation methods of the above steps are described in detail below:

[0133] Step S10, control the seabed crawler to crawl in the predetermined detection area, obtain the position information of the seabed crawler, and control the source to intermittently transmit seismic waves and receive reflected waves through the receiver. First, through the sensor modules such as the depth meter, altimeter, gyroscope and accelerometer on the seabed crawler, obtain the real-time position information of the seabed crawler in the detection area, including depth, height, heading angle, pitch angle, etc. At the same time, use the control chip to control the source to transmit intermittent seismic waves, and after each transmission, record the reflected seismic wave signal through the receiver array. The purpose of this step is to obtain seabed topography information and original seismic reflection wave data, and provide basic data for subsequent signal processing and geological interpretation.

[0134] Step S20, using a sliding window and a Bayesian anomaly detection method, calculates the set of abnormal points in the first reflection wave, including amplitude abnormal points, frequency abnormal points, and phase abnormal points. First, the received seismic reflection wave signal is divided into multiple short time windows, and the amplitude, frequency, and phase characteristics in each time window are statistically analyzed. Then, the Bayesian anomaly detection method is used to calculate the probability of abnormal occurrence of each feature under given observation data. Specifically, for the amplitude feature, when the amplitude exceeds the mean value in the sliding window plus or minus 2-3 times the standard deviation, it is determined to be an amplitude abnormal point; for the frequency feature, when the frequency exceeds the mean value in the sliding window plus or minus 2-3 times the standard deviation, it is determined to be a frequency abnormal point; for the phase feature, when the phase exceeds the mean value in the sliding window plus or minus 2-3 times the standard deviation, it is determined to be a phase abnormal point. The purpose of this step is to automatically identify feature points that may reflect geological anomalies in the original reflection wave, and provide clues for subsequent processing and analysis.

[0135] Step S30, align the abnormal point set with the seismic wave according to the order of the intermittently emitted seismic waves; extract the characteristics of the seismic wave using a sliding window, record it as a seismic wave characteristic, and calculate the correlation between the seismic wave characteristic and the abnormal point set. First, align and pair each abnormal point with the corresponding seismic wave reflection signal according to the emission timing of the source. Then, extract and statistically analyze the characteristics of each seismic wave reflection signal, such as amplitude, frequency, phase, etc., using a sliding window method, and record it as a seismic wave characteristic. Finally, calculate the correlation between the seismic wave characteristic and the abnormal point set, that is, the correlation. The higher the correlation, the stronger the correlation between the seismic wave characteristic and the abnormal point. The purpose of this step is to deeply explore the intrinsic connection between the seismic wave characteristic and the abnormal point, and lay the foundation for the subsequent optimization parameter calculation.

[0136] Step S40, in the seismic wave features, the seismic wave features with a correlation greater than a preset correlation threshold are deleted to obtain the remaining features, and the seismic wave parameters corresponding to the remaining features are calculated as optimization parameters using the preset feature-parameter equation group. First, a reasonable correlation threshold is set, such as 0.7. For seismic wave features with a correlation greater than the threshold, they are removed from the feature set to obtain the remaining features. Then, the pre-established feature-parameter equation group, including the frequency equation, the amplitude equation and the duration equation, is used to calculate the source parameters corresponding to the remaining features, such as frequency, amplitude and duration, as optimization parameters. The purpose of this step is to screen out the features most relevant to the abnormal points from a large number of seismic wave features, and calculate the optimal source parameters based on them, so as to provide a basis for subsequent source optimization.

[0137] Step S50, according to the optimization parameters, control the source to emit new seismic waves, and receive new reflected waves through the receiver; and pre-process the new reflected waves to obtain pre-processed new reflected waves. First, the optimization parameters calculated in step S40 are used to control the source to emit new seismic waves through the control chip. The receiver records the new seismic wave reflection signals, and then pre-processes these new reflected waves, including steps such as denoising, frequency band filtering, and waveform correction, to obtain pre-processed new reflected wave data. The purpose of this step is to use the optimized source parameters to obtain new seismic reflection data with better quality, providing a basis for subsequent velocity correction and amplitude recovery.

[0138] Step S60, waveform alignment and travel time analysis are performed on the new seismic wave and the pre-processed new reflection wave, and the propagation time and speed of the new seismic wave in different media are calculated. First, the newly transmitted seismic wave and the received new reflection wave are waveform aligned by correlation analysis or least square method to eliminate the time offset caused by the movement of the seabed crawler. Then, based on the waveform alignment results, the propagation time and speed of the new seismic wave in the water layer, sediment layer and other media are calculated. The purpose of this step is to obtain accurate velocity information of the stratum and provide necessary parameters for subsequent dynamic correction processing.

[0139] Step S70, using the pre-established velocity correction equation group, the pre-processed new reflection wave is subjected to dynamic correction processing to eliminate the time difference caused by the different propagation paths of the seismic wave. The pre-established velocity correction equation group includes a P-wave velocity correction equation, an S-wave velocity correction equation, a multiple wave correction equation, an NMO correction equation, and a DMO correction equation. According to the velocity information in each medium calculated in step S60, these equations are used to perform dynamic correction processing on the new reflection wave to eliminate the time offset caused by factors such as formation anisotropy and multiple reflections. The purpose of this step is to make the time axis of the reflection wave signal consistent in space, so as to provide good basic data for subsequent spectrum analysis and amplitude recovery.

[0140] Step S80, spectrum analysis and amplitude recovery are performed on the corrected pre-processed new reflection wave to improve the signal-to-noise ratio and resolution of the signal, and the obtained result is used as the detection data. First, the corrected reflection wave signal is Fourier transformed to obtain spectrum information. Then, a suitable amplitude recovery method, such as geometric divergence correction and absorption compensation, is used to eliminate the amplitude attenuation caused by factors such as formation absorption and divergence, and improve the signal-to-noise ratio of the reflection wave. Finally, the results of spectrum analysis and amplitude recovery are comprehensively used to obtain high-quality seismic detection data to provide support for subsequent geological interpretation.

[0141] Specifically, the principle of the present invention is to build an intelligent seismic exploration system based on a seafloor crawler. The system mainly includes components such as a survey ship, a seafloor crawler, a seismic source, a receiver, and a control chip. Among them, the survey ship is connected to the seafloor crawler through a towing rope to provide it with energy and data transmission support; the seafloor crawler is responsible for flexible maneuvering in complex seafloor terrain, carrying detection equipment such as seismic sources and receivers, and obtaining comprehensive seismic data; the control chip coordinates the work of various components to realize functions such as environmental perception, parameter optimization, and data processing.

[0142] The entire exploration process can be summarized as follows: First, the seafloor crawler moves autonomously in the predetermined area, using sensors such as depth gauges, altimeters, and gyroscopes to obtain its own real-time position information, providing a basis for subsequent data positioning and correction. At the same time, the control chip controls the source to emit intermittent seismic waves, and the receiver records the reflected signals.

[0143] Next, the received reflected wave signals are analyzed and processed. First, the Bayesian anomaly detection method is used to automatically identify the amplitude anomalies, frequency anomalies, and phase anomalies in the reflected waves. These anomalies may reflect changes in the underground geological structure. Then, according to the timing of the source emission, the anomalies are paired with the corresponding seismic reflection signals, and the amplitude, frequency, phase and other characteristics of each reflection signal are counted. By calculating the correlation between the features and the anomalies, the key features most related to the anomalies can be screened out.

[0144] Based on these key features, the present invention establishes a set of characteristic-parameter equations, including frequency equations, amplitude equations, and duration equations, which are used to describe the changing laws of various parameters during the propagation of seismic waves. These equations take into account factors such as stratum depth, temperature, and density, and can more accurately predict the seismic wave characteristics corresponding to the source parameters. The control chip uses these equations to optimize the parameters such as the frequency, amplitude, and duration of the source, so that the seismic wave characteristics are more in line with the current seabed geological environment.

[0145] At the same time, the present invention also establishes a set of velocity correction equations, including P-wave velocity correction, S-wave velocity correction, multiple wave correction, NMO correction and DMO correction, etc., to eliminate the time offset caused by formation anisotropy, multiple reflections, etc. The control chip uses these equations to dynamically correct the reflected wave signal based on the velocity information of each medium obtained above, so that the time axis remains consistent, greatly improving the reliability of the data.

[0146] Finally, the corrected reflected wave signal is subjected to spectrum analysis and amplitude recovery processing to further improve the signal-to-noise ratio and resolution of the signal and obtain the final high-quality detection data.

[0147] In order to better understand and implement the present invention, a specific embodiment 1 of the earthquake detection control module in the present invention is provided below. The steps of this embodiment 1 are described in detail as follows:

[0148] In step S10, the seafloor crawler is controlled to crawl in the predetermined detection area, the position information of the seafloor crawler is obtained, and at the same time, the source is controlled to intermittently transmit seismic waves and receive reflected waves through the receiver. First, the real-time position information of the seafloor crawler in the detection area can be obtained through the sensor modules such as the depth meter, altimeter, gyroscope and accelerometer on the seafloor crawler. Among them, the depth information can be directly measured by the depth meter, recorded as d; the height information can be measured by the altimeter, recorded as h; the heading angle θ and the pitch angle ψ can be obtained by measuring the gyroscope; and the accelerometer can provide the acceleration information a of the crawler.

[0149] At the same time, the control chip is used to control the source to emit intermittent seismic waves. After each emission, the reflected seismic wave signal is recorded by the receiver array. The purpose of this step is to obtain seabed topography information and original seismic reflection wave data, providing a basic basis for subsequent signal processing and geological interpretation.

[0150] In step S20, a sliding window is used to calculate the abnormal point set in the first reflection wave using the Bayesian anomaly detection method, including amplitude abnormal points, frequency abnormal points and phase abnormal points. First, the received seismic reflection wave signal is divided into multiple short time windows, and the amplitude, frequency and phase characteristics in each time window are statistically analyzed. The amplitude in the i-th time window is A i , frequency is f i , the phase is φ i .

[0151] Then, the Bayesian anomaly detection method is used to calculate the probability of anomaly occurrence of each feature under given observation data. The principle of Bayesian anomaly detection is as follows:

[0152]

[0153] Where P(A|D) is the posterior probability of anomaly occurrence given observed data D; P(D|A) is the likelihood probability of observed data under abnormal conditions; P(A) is the prior probability of anomaly occurrence; and P(D) is the marginal probability of observed data.

[0154] For the amplitude characteristics, when |A i -μ A |>k A ·σ A When , it is determined as an amplitude abnormal point; where μ A and σ A are the mean and standard deviation of the amplitude in the sliding window, respectively, A is the amplitude anomaly determination coefficient, usually 2 to 3.

[0155] For frequency characteristics, when |f i -μ f |>k f ·σ f When , it is determined as a frequency abnormal point; where μ f and σ f are the mean and standard deviation of the main frequency in the sliding window, k f is the frequency anomaly determination coefficient, usually 2 to 3.

[0156] For the phase characteristics, when |φ i -μ φ |>k φ ·σ φ When , it is determined as a phase abnormal point; where μ φ and σ φ are the mean and standard deviation of the instantaneous phase in the sliding window, k φ is the phase anomaly determination coefficient, usually 2 to 3.

[0157] The purpose of this step is to automatically identify characteristic points in the original reflection waves that may reflect geological anomalies, providing clues for subsequent processing and analysis.

[0158] In step S30, the abnormal point set is aligned with the seismic wave according to the order of the intermittently emitted seismic waves; a sliding window is used to extract the characteristics of the seismic wave, recorded as seismic wave characteristics, and the correlation between the seismic wave characteristics and the abnormal point set is calculated.

[0159] First, according to the emission sequence of the earthquake source, that is, the tth emitted seismic wave corresponds to the tth reflected wave, each abnormal point is aligned and paired with the corresponding seismic wave reflection signal.

[0160] Then, the characteristics of each seismic wave reflection signal are extracted and statistically analyzed. The amplitude characteristic of the tth seismic wave reflection signal is denoted as A t , the frequency characteristic is ft , the phase characteristic is φ t Using the sliding window method, the mean and standard deviation of these features can be calculated, denoted as

[0161] Finally, the correlation between the seismic wave characteristics and the abnormal point set is calculated. The correlation r can be defined as:

[0162]

[0163] In the formula, x i is the seismic wave characteristic, y i is the set of outliers, and are their means respectively. The higher the correlation, the stronger the correlation between the seismic wave characteristics and the abnormal point.

[0164] The purpose of this step is to deeply explore the intrinsic connection between seismic wave characteristics and abnormal points, laying the foundation for subsequent optimization parameter calculations.

[0165] In step S40, the seismic wave features whose correlation is greater than a preset correlation threshold are deleted from the seismic wave features to obtain the remaining features, and the seismic wave parameters corresponding to the remaining features are calculated as optimization parameters using a preset feature-parameter equation group.

[0166] First, set a reasonable correlation threshold r t , for example, 0.7. For seismic wave features with a correlation greater than the threshold, they are removed from the feature set to obtain the remaining feature set {A i ,f i ,φ i}.

[0167] Then, the remaining features are modeled using the pre-established characteristic-parameter equations to obtain the optimized values ​​of the source parameters. These characteristic-parameter equations include:

[0168] Frequency equation:

[0169] f=f 0 +α·log(d)+β·T+γ·ρ+ε f

[0170] In the formula, f 0 is the initial frequency, d is the detection depth, T is the seabed temperature, ρ is the seabed sediment density, α, β, γ are unknown coefficients, and εf is the error term.

[0171] Amplitude equation:

[0172] A=A 0 ·e -λd·(1+μ·v+ν·a)+ε A

[0173] In the formula, A 0 is the initial amplitude, λ, μ, ν are unknown coefficients, v is the speed of the seafloor crawler, a is the acceleration of the seafloor crawler, ε A is the error term.

[0174] Duration equation:

[0175] t=t 0 +ω·sin(θ)+φ·cos(ψ)+χ·H+ε t

[0176] Where, t 0 is the initial duration, θ is the seafloor slope, ψ is the pitch angle of the seafloor crawler, H is the seawater depth, ω, φ, χ are unknown coefficients, ε t is the error term.

[0177] The purpose of this step is to screen out the features most relevant to the anomaly from a large number of seismic wave features, and calculate the optimal source parameters based on them, providing a basis for subsequent source optimization.

[0178] In step S50, according to the optimization parameters, the seismic source is controlled to emit new seismic waves, and new reflected waves are received by the receiver; and the new reflected waves are preprocessed to obtain preprocessed new reflected waves.

[0179] First, the optimized parameters f, A, t calculated in step S40 are used to control the source to emit new seismic waves through the control chip. The receiver records the new seismic wave reflection signal, and then preprocesses these new reflection waves, including denoising, frequency band filtering, waveform correction and other steps, to obtain the preprocessed new reflection wave data.

[0180] The purpose of this step is to use the optimized source parameters to obtain new seismic reflection data with better quality, providing a basis for subsequent velocity correction and amplitude recovery.

[0181] In step S60, waveform alignment and travel time analysis are performed on the new seismic wave and the pre-processed new reflection wave to calculate the propagation time and speed of the new seismic wave in different media.

[0182] First, the newly transmitted seismic wave x(t) and the received new reflection wave y(t) are aligned by correlation analysis or least squares method to eliminate the time offset caused by the movement of the seafloor crawler. The objective function of waveform alignment is:

[0183] J = ∫[x(t)-αy(t-τ)] 2 dt;

[0184] In the formula, α is the amplitude adjustment coefficient, and τ is the time offset. Solving α and τ that minimize J can complete the waveform alignment.

[0185] Then, based on the waveform alignment results, the propagation time and velocity of the new seismic wave in the water layer, sediment layer and other media are calculated. For the i-th layer of media, its propagation time T i and speed V i satisfy:

[0186]

[0187] Where, d i is the thickness of the i-th layer of medium.

[0188] The purpose of this step is to obtain accurate velocity information of the formation and provide necessary parameters for subsequent dynamic correction processing.

[0189] In step S70, the pre-established velocity correction equation group is used to perform dynamic correction processing on the pre-processed new reflection wave to eliminate the time difference caused by the different propagation paths of the seismic wave.

[0190] The pre-built set of velocity correction equations includes:

[0191] P wave velocity correction equation:

[0192]

[0193] Where, V p0 is the initial P wave velocity, P is the pore pressure, S is the rock saturation, is the pressure gradient, k p ,m p ,n p is the unknown coefficient, ε p is the error term.

[0194] S wave velocity correction equation:

[0195]

[0196] Where, V s0 is the initial S-wave velocity, σ is the effective stress, φ is the porosity, f is the main frequency, k s ,m s ,n s is the unknown coefficient, ε s is the error term.

[0197] Multiple correction equation:

[0198]

[0199] Where, T mis the travel time after multiple wave correction, T p is the original P wave travel time, h is the water depth, V w is the speed of sound in water, V p is the formation P wave velocity, θ i is the incident angle, ε m is the error term.

[0200] NMO correction equation:

[0201]

[0202] Where, T NMO is the NMO correction time, T 0 is the zero offset travel time, x is the offset distance, V RMS is the root mean square velocity, ε N is the error term.

[0203] DMO correction equation:

[0204]

[0205] Where, T DMO is the DMO correction time, θ is the reflection point inclination, V int is the interlayer velocity, ε D is the error term.

[0206] According to the velocity information in each medium calculated in step S60, V p ,V s ,V w Etc., use these equations to perform dynamic correction on the new reflection wave to eliminate the time offset caused by factors such as formation anisotropy and multiple reflections.

[0207] The purpose of this step is to make the time axis of the reflected wave signal consistent in space, providing good basic data for subsequent spectrum analysis and amplitude recovery.

[0208] In step S80, spectrum analysis and amplitude recovery are performed on the corrected preprocessed new reflected wave to improve the signal-to-noise ratio and resolution of the signal, and the obtained result is used as detection data.

[0209] First, the corrected reflected wave signal x(t) is transformed by Fourier to obtain the spectrum X(f):

[0210] X(f)=∫x(t)e -j2πft dt;

[0211] Then, appropriate amplitude recovery methods, such as geometric divergence correction and absorption compensation, are used to eliminate the amplitude attenuation caused by formation absorption, divergence and other factors. Geometric divergence correction can be used using the following formula:

[0212]

[0213] In the formula, A c is the corrected amplitude, A is the original amplitude, d 0 is the reference distance, d is the actual distance. Absorption compensation can be achieved by:

[0214] A a =A·e αd ;

[0215] Where α is the attenuation coefficient, which can be determined experimentally or estimated by empirical formula.

[0216] Finally, by comprehensively using the results of spectrum analysis and amplitude recovery, high-quality seismic detection data are obtained to provide support for subsequent geological interpretation.

[0217] In order to further better understand and implement the present invention, a specific embodiment 2 of the present invention is provided below:

[0218] like Figure 3-4 As shown, this embodiment 2 proposes a seismic detection system based on a seafloor crawler, which is mainly composed of a survey ship, a seafloor crawler, a seismic source, a receiver, and a control chip. The overall structure and working principle of the system are as follows:

[0219] 1. Overall system structure

[0220] 1.1 Survey vessel

[0221] The survey ship is the command center and data processing center of the entire detection system. The survey ship is equipped with the following equipment:

[0222] a) Satellite navigation system: used to locate the position of the survey vessel in real time with an accuracy of up to centimeters.

[0223] b) Underwater positioning system: used to locate the position of the seabed crawler, usually using an ultra-short baseline (USBL) positioning system.

[0224] c) Power supply unit: Provides a stable power supply for the entire system, including the power required by the seabed crawler.

[0225] d) Data Processing Center: Equipped with high-performance computers, it is used to receive, store and process seismic detection data transmitted by the seabed crawler.

[0226] 1.2 Seafloor Crawler

[0227] The seabed crawler is the core component of this system. It adopts a crawler-type seabed crawler, which can walk stably in complex seabed terrain. The seabed crawler mainly includes the following parts:

[0228] a) Driving unit: It consists of a motor and tracks, and is used to drive the crawler to move on the seabed.

[0229] b) Electronic cabin: A sealed cabin with electronic components such as control chips and data transmission equipment installed inside.

[0230] c) Sensor module: includes at least a depth meter, an altimeter, a gyroscope and an accelerometer, which are used to obtain the position information and environmental parameters of the crawler.

[0231] 1.3 Source

[0232] The seismic source is a controllable seismic source, which can be any of a controllable transducer seismic source or a controllable air gun seismic source. The seismic source is fixed on the towing cable at the tail of the seabed crawler, and its vibrating surface maintains good contact with the seabed sediment to improve the seismic source output efficiency.

[0233] 1.4 Receiver

[0234] The receiver can be a node-type receiver array or a multi-channel seismic cable, fixed on the towing cable at the tail of the seafloor crawler. Preferably, a node-type receiver array is used, including multiple three-component geophones and multiple hydrophones. The three-component geophones are used to receive vector seismic signals in the X, Y, and Z directions, and the hydrophones are used to receive scalar seismic signals.

[0235] 1.5 Control chip

[0236] The control chip is installed in the electronic compartment of the submarine crawler and is the "brain" of the entire system. The control chip is mainly responsible for the following functions:

[0237] a) Exchange data with the driving unit, seismic source and receiver of the seafloor crawler.

[0238] b) Set the vibration parameters of the source.

[0239] c) Preprocessing the data collected by the receiver to obtain seismic detection data.

[0240] d) Send the seismic detection data to the survey ship through data transmission equipment.

[0241] 2. System working principle

[0242] 2.1 System Deployment

[0243] After the survey ship reaches the predetermined detection area, it will drop the seafloor crawler into the sea. The seafloor crawler is connected to the survey ship through a towing rope, using a cable connection method. The power required by the crawler is transmitted by the survey ship through the towing rope, and real-time data transmission is achieved at the same time.

[0244] 2.2 Earthquake detection process

[0245] After the seafloor crawler reaches the seafloor, it starts crawling along the preset path. During the crawling process, the seismic detection control module in the control chip performs the following steps:

[0246] S10: Control the seafloor crawler to crawl in the predetermined detection area, obtain the position information of the seafloor crawler, and at the same time control the source to intermittently emit seismic waves and receive reflected waves through the receiver.

[0247] S20: Using a sliding window and a Bayesian anomaly detection method, calculate the abnormal point set in the first reflected wave, including amplitude abnormal points, frequency abnormal points, and phase abnormal points.

[0248] The specific representation of the Bayesian anomaly detection method is as follows:

[0249]

[0250] Where P(A|D) is the posterior probability of anomaly occurrence given observed data D; P(D|A) is the likelihood probability of observed data under abnormal conditions; P(A) is the prior probability of anomaly occurrence; and P(D) is the marginal probability of observed data.

[0251] The criteria for determining abnormal points are as follows:

[0252] 1) Amplitude abnormal point:

[0253] |A i -μ A |>k A ·σ A ;

[0254] 2) Frequency abnormal points:

[0255] |f i -μ f |>k f ·σ f ;

[0256] 3) Phase abnormal point:

[0257] |φ i -μ φ |>k φ ·σ φ ;

[0258] Among them, A i ,f i ,φ i are the amplitude, main frequency and instantaneous phase of the i-th sampling point respectively; μ and σ are the mean and standard deviation of the corresponding parameters respectively; k is the abnormality determination coefficient.

[0259] S30: Align the abnormal point set with the seismic wave according to the order of discontinuously transmitting the seismic wave, extract the features of the seismic wave using a sliding window, record them as seismic wave features, and calculate the correlation between the seismic wave features and the abnormal point set.

[0260] S40: Among the seismic wave features, delete the seismic wave features whose correlation is greater than a preset correlation threshold to obtain the remaining features, and use the preset feature-parameter equation group to calculate the seismic wave parameters corresponding to the remaining features as optimization parameters.

[0261] The preset characteristic-parameter equation sets include:

[0262] 1) Frequency equation:

[0263] f=f 0 +α·log(d)+β·T+γ·ρ+ε f ;

[0264] 2) Amplitude equation:

[0265] A=A 0 ·e -λd ·(1+μ·v+ν·a)+ε A ;

[0266] 3) Duration equation:

[0267] t=t 0 +ω·sin(θ)+φ·cos(ψ)+χ·H+ε t ;

[0268] S50: According to the optimized parameters, the seismic source is controlled to emit new seismic waves, and the new reflected waves are received by the receiver, and the new reflected waves are pre-processed to obtain pre-processed new reflected waves.

[0269] S60: Perform waveform alignment and travel time analysis on the new seismic wave and the pre-processed new reflection wave, and calculate the propagation time and speed of the new seismic wave in different media.

[0270] S70: Using the pre-established velocity correction equation group, perform dynamic correction processing on the pre-processed new reflection wave to eliminate the time difference caused by the different propagation paths of the seismic wave.

[0271] The velocity correction equations include:

[0272] 1) P wave velocity correction equation:

[0273]

[0274] 2) S wave velocity correction equation:

[0275]

[0276] 3) Multiple wave correction equation:

[0277]

[0278] 4) NMO correction equation:

[0279]

[0280] 5) DMO correction equation:

[0281]

[0282] S80: Perform spectrum analysis and amplitude recovery on the corrected pre-processed new reflected wave to improve the signal-to-noise ratio and resolution of the signal, and the obtained result is used as detection data.

[0283] 2.3 Data Transmission

[0284] The control chip sends the processed seismic detection data to the survey ship through the data transmission device. Data transmission can be carried out through a data cable or a wireless channel. In this embodiment, a cable connection method is adopted, and the data is transmitted at high speed through the optical fiber in the traction rope.

[0285] 2.4 Data processing and interpretation

[0286] After receiving the seismic detection data, the data processing center on the survey ship will further process and interpret it, including but not limited to:

[0287] a) Data quality control: removing noise, correcting static errors, etc.

[0288] b) Speed ​​analysis: Conduct detailed speed analysis and establish an accurate speed model.

[0289] c) Prestack time migration: Use the velocity model to perform time domain migration on the data.

[0290] d) Post-stack processing: including removal of multiple waves, frequency filtering, amplitude balancing, etc.

[0291] e) Geological interpretation: Based on the processed seismic profiles, geological interpretation work such as stratigraphic division, fault identification, and reservoir prediction is carried out.

[0292] 3. System advantages

[0293] 3.1 High-precision earthquake detection

[0294] By dragging the source and receiver with the seafloor crawler, the source and receiver can be in close contact with the seafloor, greatly improving the transmission efficiency of the source energy and the sensitivity of the receiver. At the same time, the stability of the seafloor crawler ensures the continuity and consistency of data acquisition.

[0295] 3.2 Adaptive parameter optimization

[0296] The system analyzes the abnormal points in the reflected waves in real time and dynamically adjusts the source parameters to achieve adaptive optimization of earthquake detection parameters. This method can automatically adjust the detection strategy according to different geological environments and improve detection efficiency and accuracy.

[0297] 3.3 Real-time data processing and transmission

[0298] The control chip pre-processes the received seismic data in real time and transmits it to the survey ship through a high-speed data link, so that geological experts can obtain detection results in a timely manner and adjust the detection plan as needed.

[0299] 3.4 Comprehensive application of multiple correction methods

[0300] The system uses a variety of velocity correction equations, including P-wave, S-wave, multiple wave, NMO and DMO corrections, which fully consider the propagation characteristics of seismic waves in complex geological environments and greatly improve the accuracy and resolution of seismic data.

[0301] 4. Key technical parameters

[0302] 4.1 Seafloor Crawler

[0303] -Maximum operating depth: 6000 meters;

[0304] -Crawling speed: 0-2 m / s;

[0305] -Battery life: ≥24 hours;

[0306] - Positioning accuracy: ±0.5 meters;

[0307] 4.2 Source

[0308] -Frequency range: 10-2000Hz;

[0309] -Maximum output power: 5000J;

[0310] - Emission interval: adjustable, minimum 0.1 second;

[0311] 4.3 Receiver

[0312] - Sampling rate: 1-16kHz, adjustable;

[0313] - Dynamic range: 140dB;

[0314] - Track spacing: 1m-25m.

[0315] In order to further better understand and implement the present invention, the following provides Example 3 of a specific application scenario of the present invention: a marine oil and gas unit plans to conduct exploration activities in a certain sea area in the South China Sea, and needs to adopt high-precision seabed seismic exploration technology to obtain accurate geological information to guide subsequent mining work. The unit decides to adopt the intelligent seismic detection system based on seabed crawlers proposed in the present invention and carry out a three-month practical application.

[0316] Phase 1: System deployment

[0317] First, the unit deployed a 3,000-ton professional survey ship and installed supporting equipment such as a satellite navigation system, an underwater positioning system, and a power supply unit on the ship. At the same time, a tracked seabed crawler was developed, which is 4 meters long, 2 meters wide, 1.5 meters high, and weighs about 5 tons. The crawler is equipped with an electronic cabin for installing core components such as control chips and data transmission equipment. A towing cable is installed at the tail of the crawler, which is 30 meters long, and the source and receiver array are fixed on the cable.

[0318] The unit selected a controllable airgun source as the seismic wave source, and the vibration surface of the source array is in direct contact with the seabed sediment to improve the energy coupling efficiency. The receiver uses a node receiver array, including a 3-component seismic detector and a hydrophone, with a total of 24 detection units, an array length of 30 meters, and a spacing of 1.5 meters.

[0319] The control chip uses a high-performance ARM processor, with built-in functional units such as seismic detection control module, velocity correction module and data transmission module. The chip can communicate bidirectionally with the seismic source, receiver and various sensors on the seafloor crawler to achieve intelligent control of the entire exploration operation.

[0320] After the hardware equipment is installed and debugged, the survey ship sails to the designated exploration area, slowly lowers the mooring anchor, and maintains a relatively stable working state. The seafloor crawler is connected to the survey ship through a towing rope, then slowly sinks to the seabed and begins to cruise autonomously in the designated area.

[0321] Phase 2: Intelligent Detection

[0322] After entering the exploration area, the seafloor crawler first uses the onboard depth meter, altimeter, gyroscope and other sensors to collect its own position information in real time. The control chip transmits these position data to the survey ship to establish the crawler's precise three-dimensional coordinate system on the seafloor.

[0323] Subsequently, the control chip sends out a seismic wave transmission command every 10 seconds through the communication interface with the seismic source. According to the command, the seismic source transmits seismic waves to the seabed with the initial parameters of f = 30Hz, A = 5MPa, and t = 0.1s. The receiver array records the reflected signals of these seismic waves in real time and transmits the digital data to the control chip for preprocessing.

[0324] For each set of reflected wave signals, the control chip first uses the sliding window method to calculate the statistical characteristics of its amplitude, frequency and phase, including the mean μ A ,μ f ,μ φ and standard deviation σ A ,σ f ,σ φ Then, the Bayesian anomaly detection method is used to evaluate the probability of anomalies of these features under the current observed data:

[0325]

[0326] Among them, A i represents the amplitude, frequency or phase characteristics, and D is the current observation data. According to experience, when P(A i |D)>0.9, the feature point is judged as an abnormal point.

[0327] Next, the control chip pairs the abnormal points with the corresponding reflected wave signals according to the source emission order. Then, the sliding window method is used to extract the amplitude A, frequency f and phase φ characteristics of each set of reflected waves, and calculate their correlation coefficient r with the abnormal point set:

[0328]

[0329] Among them, x i is the reflection wave characteristic, y i is the set of outliers, and are their means respectively. The higher the correlation r is, the closer the feature is to the outlier.

[0330] Through the above analysis, the control chip screens out the key features that are highly correlated with the abnormal points, and uses the pre-established feature-parameter equation group to calculate the corresponding source optimization parameters:

[0331] Frequency equation: f = 30 + 2.5·log(d) + 1.8·T + 0.6·ρ + ε f ;

[0332] Amplitude equation: A=5·e -0.12d ·(1+0.35·v+0.2·a)+ε A ;

[0333] Duration equation: t = 0.1 + 0.08·sin(θ) + 0.05·cos(ψ) + 0.02·H + ε t ;

[0334] Among them, d is the detection depth, T is the seafloor temperature, ρ is the seafloor sediment density, v is the crawler speed, a is the crawler acceleration, θ is the seafloor slope, ψ is the crawler pitch angle, H is the seawater depth, ε f ,ε A ,ε t is the error term.

[0335] According to these optimized parameters, the control chip issues a new source launch command, and the source adjusts the frequency to 35Hz, the amplitude to 7MPa, and the duration to 0.15s, and launches seismic waves to the seabed again. The receiver records the new reflection signal and transmits it back to the control chip for subsequent processing.

[0336] At the same time, the control chip uses the method in step S60 to perform waveform alignment and travel time analysis on the new reflected wave signal, and calculates the propagation speed of seismic waves in different media such as water layers and sediment layers. Taking the water layer as an example, V w =1500m / s; for sedimentary layer, V p =2800m / s,V s =1200m / s.

[0337] With this speed information, the control chip immediately calls the preset speed correction equation group to dynamically correct the new reflected wave signal. Take P wave speed correction as an example:

[0338]

[0339] Among them, P = 15MPa is the pore pressure, S = 0.7 is the rock saturation, is the pressure gradient. Through this correction, the time offset caused by factors such as formation anisotropy can be effectively eliminated.

[0340] Similarly, the control chip also applies multiple wave correction, NMO correction and DMO correction methods to further optimize the time axis of the reflected wave signal. Finally, the corrected signal is subjected to spectrum analysis and amplitude recovery processing to improve the signal-to-noise ratio and resolution, and obtain the final high-quality detection data.

[0341] Phase 3: Data transmission and analysis

[0342] After completing a set of data collection and preprocessing, the control chip transmits the results to the survey ship through a data cable or wireless channel. The data transmission equipment on the survey ship aggregates and stores these data to form a complete seismic detection data set.

[0343] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A seismic detection system based on a seafloor crawler, characterized in that: include: A survey vessel, a seabed crawler, a seismic source, a receiver and a control chip, wherein the survey vessel is connected to the seabed crawler by a towing rope, a towing cable is provided at the tail of the seabed crawler, the seismic source and the receiver are fixed on the towing cable, an electronic cabin is provided in the seabed crawler, the data control chip is provided in the electronic cabin, the control chip exchanges data with the driving unit of the seabed crawler, the seismic source and the receiver; a seismic detection control module is provided in the control chip, which is used to set the vibration parameters of the seismic source and pre-process the data collected by the receiver to finally obtain seismic detection data; a data transmission device is also provided in the electronic cabin, the control chip is electrically connected to the data transmission device, and the data transmission device is used to send the seismic detection data to the survey vessel.

2. A seismic detection system based on a seafloor crawler according to claim 1, characterized in that: The survey vessel is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

3. A seismic detection system based on a seafloor crawler according to claim 2, characterized in that: The seabed crawler is a tracked seabed crawler or a multi-legged seabed crawler.

4. A seismic detection system based on a seafloor crawler according to claim 3, characterized in that: The seismic source is a controllable seismic source, including any one or more combinations of a controllable transducer seismic source, a controllable electromagnetic seismic source, a controllable hydraulic seismic source, and a controllable air gun seismic source.

5. A seismic detection system based on a seafloor crawler according to claim 4, characterized in that: The vibration surface of the seismic source transmitting array is in contact with the seabed sediments.

6. A seismic detection system based on a seafloor crawler according to claim 5, characterized in that: The receiver is a node-type receiver array or a multi-channel seismic cable.

7. A seismic detection system based on a seafloor crawler according to claim 6, characterized in that: The node-type receiver array includes a plurality of three-component geophones and a plurality of hydrophones, wherein the three-component geophones are used to receive vector seismic signals in the X, Y, and Z directions, and the hydrophones are used to receive scalar seismic signals.

8. A seismic detection system based on a seafloor crawler according to claim 7, characterized in that: The data transmission device sends the seismic detection data to the survey ship via a data cable or a wireless channel.

9. A seismic detection system based on a seafloor crawler according to claim 8, characterized in that: The seabed crawler is also provided with a sensor module, which at least includes a depth gauge, an altimeter, a gyroscope and an accelerometer.

10. A seismic detection system based on a seafloor crawler according to claim 9, characterized in that: The earthquake detection control module is used to perform the following steps: S10, controlling the seafloor crawler to crawl in a predetermined detection area, obtaining position information of the seafloor crawler, and controlling the seismic source to intermittently transmit seismic waves and receive reflected waves through the receiver; S20, using a sliding window and a Bayesian anomaly detection method to calculate an abnormal point set in the first reflected wave, including amplitude abnormal points, frequency abnormal points, and phase abnormal points; S30, aligning the abnormal point set with the seismic wave according to the order of the intermittently emitted seismic wave; Using a sliding window to extract the characteristics of the seismic wave, recorded as seismic wave characteristics, and calculating the correlation between the seismic wave characteristics and the abnormal point set; S40, deleting the seismic wave features whose correlation is greater than a preset correlation threshold value from the seismic wave features to obtain the remaining features, and using a preset feature-parameter equation group to calculate the seismic wave parameters corresponding to the remaining features as optimization parameters; S50, according to the optimization parameters, controlling the seismic source to emit new seismic waves, and receiving new reflected waves through the receiver; and preprocessing the new reflected waves to obtain preprocessed new reflected waves; S60, performing waveform alignment and travel time analysis on the new seismic wave and the pre-processed new reflection wave, and calculating the propagation time and speed of the new seismic wave in different media; S70, using a pre-established velocity correction equation group, performing dynamic correction processing on the pre-processed new reflection wave to eliminate the time difference caused by different seismic wave propagation paths; S80, performing spectrum analysis and amplitude recovery on the corrected pre-processed new reflected wave to improve the signal-to-noise ratio and resolution of the signal, and obtaining the result as detection data.

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