An earthquake detection system based on a subsea crawler

Abstract

The present invention provides a seismic detection system based on a subsea crawler, belonging to the field of geophysical technology, including: an investigation ship, a subsea crawler, a seismic source, a receiver, and a control chip. Among them, the investigation ship is connected to the subsea crawler by a towing rope, a drag cable is arranged at the tail of the subsea crawler, the seismic source and the receiver are fixed on the drag cable, an electronic cabin is arranged inside the subsea crawler, the data control chip is arranged in the electronic cabin, and the control chip is connected to the drive unit, the seismic source, and the receiver of the subsea crawler and conducts data interaction; a seismic detection control module is arranged in the control chip, which is used to set the vibration parameters of the seismic source and preprocess the data collected by the receiver, and finally obtain seismic detection data; a data transmission device is also arranged 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 investigation ship. It solves the problem of low comprehensiveness and accuracy existing in the existing methods.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical technologies, and more particularly, relates to a seismic detection system based on a subsea crawler. Background Art

[0002] Subsea seismic exploration, as an important means in fields such as oil and gas exploration and subsea tectonic research, 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, subsea seismic exploration faces more complex working environments and technical challenges. First, the subsea terrain is complex and variable, and the seabed texture varies greatly, which leads to significant differences in the propagation characteristics of seismic waves in different media, causing difficulties in data processing and interpretation. Second, the underwater environment poses higher requirements for the working stability of seismic instruments, and at the same time, factors such as water pressure and temperature also affect the instrument performance and data quality. Moreover, due to the limited placement of subsea receiving equipment, uniform coverage cannot be achieved as on land, which further reduces the reliability of the data. For the commonly used subsea seismic OBS / OBC detection technology, although the OBS / OBC is located on the seabed, since the seismic source is located on the sea surface, the seismic waves excited are attenuated and diffused by the deep seawater, making it difficult to achieve comprehensive and accurate seismic detection. Summary of the Invention

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

[0004] The present invention is implemented as follows:

[0005] The present invention provides a seismic detection system based on a subsea crawler, which includes: a survey ship, a subsea crawler, a seismic source, a receiver, and a control chip. The survey ship is connected to the subsea crawler by a towing rope. A drag cable is arranged at the tail of the subsea crawler. The seismic source and the receiver are fixed on the drag cable. An electronic cabin is arranged inside the subsea crawler, and the data control chip is arranged in the electronic cabin. The control chip is connected to the drive unit, the seismic source, and the receiver of the subsea crawler and conducts data interaction. A seismic detection control module is arranged in the control chip, which is used to set the vibration parameters of the seismic source and preprocess the data collected by the receiver to finally obtain seismic detection data. A data transmission device is also arranged 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] Based on the above technical solution, the seismic detection system based on a subsea 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 seabed crawler adopts a crawler-type seabed crawler or a multi-legged seabed 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 array contacts the seabed sediment, which can not only excite longitudinal waves (P-waves) but also excite transverse waves (S-waves), and can improve the output efficiency of the seismic source.

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

[0012] Furthermore, the nodal receiver array includes a plurality of three-component geophones and a plurality of hydrophones. Among them, 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.

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

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

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

[0016] S10. Control the seabed crawler to crawl in a predetermined detection area, obtain the pose information of the seabed crawler, and at the same time control the seismic source to intermittently emit seismic waves and receive the reflected waves through the receiver;

[0017] S20. Adopt a sliding window and use the Bayesian anomaly detection method to calculate the set of anomaly points in the first reflected wave, including amplitude anomaly points, frequency anomaly points, and phase anomaly points;

[0018] S30. Align the set of anomaly points with the seismic waves according to the order of the intermittently emitted seismic waves; extract the characteristics of the seismic waves using a sliding window, denoted as seismic wave characteristics, and calculate the correlation degree between the seismic wave characteristics and the set of anomaly points;

[0019] S40. In the seismic wave characteristics, delete the seismic wave characteristics with a correlation degree greater than a preset correlation degree threshold to obtain the remaining characteristics, and use a preset characteristic-parameter equation set to calculate the seismic wave parameters corresponding to the remaining characteristics as optimization parameters;

[0020] S50. Control the seismic source to emit new seismic waves according to the optimization parameters, and receive new reflected waves through a receiver; and preprocess the new reflected waves to obtain preprocessed new reflected waves;

[0021] S60. Align the waveforms of the new seismic waves and the preprocessed new reflected waves and perform travel-time analysis, and calculate the propagation time and velocity of the new seismic waves in different media;

[0022] S70. Use the pre-established velocity correction equation set to perform dynamic correction processing on the preprocessed new reflected waves to eliminate the time difference caused by different propagation paths of seismic waves;

[0023] S80. Perform spectral analysis and amplitude recovery on the corrected preprocessed new reflected waves to improve the signal-to-noise ratio and resolution of the signal, and use the obtained results as detection data.

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

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

[0026] The steps for setting the correlation degree threshold specifically include:

[0027] 1. Collect historical detection data, including seismic wave characteristics and corresponding abnormal point sets in 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 the input and the presence or absence of abnormal point sets as the output;

[0029] 3. Use the cross-validation method to evaluate the performance of the model under different correlation degree thresholds, including accuracy, recall rate, and F1 score;

[0030] 4. Select the correlation degree threshold that can achieve the best balance between accuracy and recall rate.

[0031] The velocity correction equation set 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 formula expressions and explanations of each equation:

[0033] 1. Frequency equation:

[0034] f = f0 + α·log(d) + β·T + γ·ρ + ε f ;

[0035] Where f is the optimized seismic source frequency; f0 is the initial frequency; d is the detection depth; T is the seabed temperature; ρ is the seabed sediment density; α, β, γ are undetermined coefficients; ε f is the error term.

[0036] Parameter acquisition method:

[0037] d is directly measured by a depth gauge; T is measured by a temperature sensor; ρ is estimated by sampling analysis or acoustic logging.

[0038] 2. Amplitude equation:

[0039] A = A0·e -λd ·(1 + μ·v + ν·a) + ε A ;

[0040] Where A is the optimized seismic source amplitude; A0 is the initial amplitude; d is the detection depth; v is the speed of the seabed crawler; a is the acceleration of the seabed crawler; λ, μ, ν are undetermined coefficients; ε A is the error term.

[0041] Parameter acquisition method:

[0042] v and a are measured by the speedometer and accelerometer on the seabed crawler.

[0043] 3. Duration equation:

[0044] t = t0 + ω·sin(θ) + φ·cos(ψ) + χ·H + ε t ;

[0045] Where t is the optimized seismic source duration; t0 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 undetermined coefficients; ε t is the error term.

[0046] Parameter acquisition method:

[0047] θ is obtained by seabed terrain measurement; ψ is measured by a gyroscope; H is measured by a depth gauge.

[0048] 4. P-wave velocity correction equation:

[0049]

[0050] Where V p is the corrected P-wave velocity; 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 , np is a coefficient to be determined; ε p is an error term.

[0051] Parameter acquisition method:

[0052] P is obtained by measurement with a pressure sensor; S is estimated by resistivity logging; is calculated from the variation of pressure with depth.

[0053] 5. S-wave velocity correction equation:

[0054]

[0055] In the formula, 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 are coefficients to be determined; ε s is an error term.

[0056] Parameter acquisition method:

[0057] σ is obtained by measurement with a stress sensor; φ is estimated by density logging or neutron logging; f is directly obtained from the source parameters.

[0058] 6. Multiple wave correction equation:

[0059]

[0060] In the formula, 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 is the sound velocity in water; V p is the formation P-wave velocity; θ i is the incident angle; ε m is an error term.

[0061] Parameter acquisition method:

[0062] h is obtained by measurement with a depth gauge; V w is obtained by measurement with a sound velocity meter; θ i is calculated by the ray tracing method.

[0063] 7. NMO correction equation:

[0064]

[0065] In the formula, T NMO is the NMO correction time; T0 is the zero-offset travel time; x is the offset; V RMSis the root mean square velocity; ε N is the error term.

[0066] Parameter acquisition method:

[0067] T0 is obtained from the initially processed seismic records; x is calculated from the position information of the subsea crawler; V RMS is estimated by the velocity analysis method.

[0068] 8. DMO correction equation:

[0069]

[0070] In the formula, T DMO is the DMO correction time; T NMO is the NMO correction time; x is the offset; θ is the dip angle of the reflection point; V int is the interval velocity; T0 is the zero-offset travel time; ε D is the error term.

[0071] Parameter acquisition method:

[0072] θ is obtained from the interpretation of the seismic profile; V int is estimated by the interval velocity analysis method.

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

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

[0075]

[0076] In the formula, P(A|D) is the posterior probability of the anomaly occurrence given the observed data D; P(D|A) is the likelihood probability of the observed data in the case of the anomaly; P(A) is the prior probability of the anomaly occurrence; P(D) is the marginal probability of the observed data.

[0077] The determination criteria for the anomaly points are as follows:

[0078] 1. Amplitude anomaly points:

[0079] |A i -μ A |>k A ·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 amplitudes within the sliding window; σ Ais the standard deviation of the amplitude within the sliding window; k A is the amplitude anomaly determination coefficient, usually taking a value of 2 - 3.

[0081] 2. Frequency anomaly 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 value of the main frequencies within the sliding window; σ f is the standard deviation of the main frequencies within the sliding window; k f is the frequency anomaly determination coefficient, usually taking a value of 2 - 3.

[0084] 3. Phase anomaly points:

[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 value of the instantaneous phases within the sliding window; σ φ is the standard deviation of the instantaneous phases within the sliding window; k φ is the phase anomaly determination coefficient, usually taking a value of 2 - 3.

[0087] Parameter acquisition method:

[0088] 1. Amplitude A i is directly obtained from the original data collected by the receiver.

[0089] 2. Main frequency f i is obtained by performing a Fourier transform on each time window and then finding the frequency point with the maximum energy:

[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 is calculated through Hilbert transform:

[0093]

[0094] where x i is the original signal, and H[x i is the Hilbert transform of x i .

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

[0096]

[0097] where N is the length of the sliding window, and x i represents amplitude, frequency or phase data.

[0098] 5. The optimal value of the anomaly determination coefficient k can be determined by the cross - validation method, and 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].[[]]END]]

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

[0101] c. For each k value, calculate the anomaly points using the training set, and then evaluate the detection performance (such as the F1 score) on the validation set.

[0102] d. Select the k value with the best performance on the validation set.

[0103] Through the above method, the anomaly points in the reflected wave can be comprehensively detected, including anomalies in amplitude, frequency and phase. These anomaly points may indicate special changes in the underground geological structure or problems in the data acquisition process, which are of great significance for subsequent data processing and geological interpretation.

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

[0105] Among them, the specific steps of step S20 include: using a sliding window to statistically analyze the amplitude, frequency, and phase characteristics of the received seismic reflection wave signals; using the Bayesian anomaly detection method to calculate the probability of anomaly occurrence for each characteristic under the given observed data; when the amplitude, frequency, or phase exceeds the mean within the sliding window plus or minus 2 - 3 times the standard deviation, it is determined as the corresponding anomaly 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] Among them, the specific steps of step S30 include: aligning and pairing each anomaly point with the corresponding seismic wave reflection signal according to the emission timing of the seismic source; using the method of sliding window to extract the amplitude, frequency, phase and other characteristics of each seismic wave reflection signal, and calculating the correlation degree between these characteristics and the anomaly point set. Through this step, the internal connection between the seismic wave characteristics and the anomaly points can be deeply explored, laying a foundation for the subsequent calculation of optimization parameters.

[0107] Among them, the specific steps of step S40 include: setting a reasonable correlation degree threshold, such as 0.7, removing the seismic wave characteristics with a correlation degree greater than this threshold to obtain the remaining characteristics; then, using the pre - established characteristic - parameter equation sets such as frequency equation, amplitude equation, and duration equation to calculate the seismic source parameters corresponding to the remaining characteristics as the optimization parameters. Through this step, the characteristics most relevant to the anomaly points can be screened out from a large number of seismic wave characteristics, and the optimal seismic source parameters can be calculated based on them, providing a basis for subsequent seismic source optimization.

[0108] Among them, the specific steps of step S50 include: using the optimization parameters calculated in step S40 to control the seismic 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 reflections, including steps such as denoising, band - pass filtering, and waveform correction, to obtain the pre - processed new reflection wave data. Through this step, new seismic reflection data with better quality can be obtained by using the optimized seismic source parameters, providing a basis for subsequent velocity correction and amplitude recovery.

[0109] Among them, the specific steps of step S60 include: using methods such as correlation analysis or least - squares method to align the waveforms of the newly emitted seismic waves and the received new reflection waves to eliminate the time offset caused by the movement of the sub - sea crawler; according to the waveform alignment result, calculating the propagation time and velocity of the new seismic waves in the water layer, sediment layer, and other media. Through this step, accurate velocity information of the formation can be obtained, providing necessary parameters for subsequent dynamic correction processing.

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

[0111] Among them, the step S80 specifically includes: performing Fourier transform on the corrected reflected wave signal to obtain spectral information; adopting amplitude recovery methods such as geometric divergence correction and absorption compensation to eliminate the amplitude attenuation caused by factors such as formation absorption and divergence, and improving the signal-to-noise ratio of the reflected wave; comprehensively using the results of spectral 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 a seismic detection system based on a seabed crawler provided by the present invention are as follows:

[0113] First of all, the present invention adopts a controllable seismic source, such as a transducer seismic source or an air gun seismic source, etc., which can emit seismic waves with adjustable frequency, amplitude, and duration, and cooperate with a control chip to realize the dynamic optimization of the seismic source parameters. Compared with the traditional fixed seismic source, this controllable seismic source can intelligently adjust the emission parameters according to the real-time obtained seabed environment information, such as formation depth, temperature, density, etc., making the characteristics of the seismic waves more in line with the local geological conditions, thereby greatly improving the 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 set modeling, velocity correction equation sets, etc. Through these algorithms, the characteristics such as the amplitude, frequency, and phase of the seismic reflected wave can be comprehensively analyzed, and the feature points that may reflect geological anomalies can be intelligently identified; based on these key features, a mathematical model describing the propagation law of seismic waves is established to realize the fine optimization of key factors such as seismic source parameters and propagation velocity; finally, for the complex seabed environment, a variety of velocity correction methods are adopted to effectively eliminate the time shift caused by formation anisotropy, multiple reflections, etc., greatly improving the reliability of the data.

[0115] Compared with the existing submarine seismic exploration technology, the method of the present invention has the following remarkable advantages: 1) It has the ability of intelligent perception and adaptive optimization, and can dynamically adjust the seismic source parameters and data processing strategies according to the real-time obtained submarine environment information, greatly improving the detection accuracy; 2) It adopts advanced data processing algorithms, can comprehensively analyze the characteristics of seismic reflection waves, and excavate hidden geological information, providing more reliable basic data for interpretation; 3) By using hardware devices such as a controllable seismic source and a crawler-type underwater crawler or a multi-legged underwater crawler, more comprehensive and accurate seismic data can be obtained in a complex submarine environment.

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

[0117] Figure 1 It is a schematic diagram of the composition of the seismic detection system based on an underwater crawler provided by the present invention;

[0118] Figure 2 It is a flowchart of the steps executed by the seismic detection control module;

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

[0120] Figure 4 It is a schematic diagram of the wireless system of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0121] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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] As Figure 1 shown, it is a schematic diagram of the composition of a seismic detection system based on an underwater crawler provided by the present invention. The method includes the following steps: Among them, it includes: a survey ship, an underwater crawler, a seismic source, a receiver, and a control chip. Among them, the survey ship is connected to the underwater crawler through a towing rope. A drag cable is arranged at the tail of the underwater crawler. The seismic source and the receiver are fixed on the drag cable. An electronic cabin is arranged inside the underwater crawler. The data control chip is arranged in the electronic cabin. The control chip is connected to the drive unit, the seismic source, and the receiver of the underwater crawler and conducts data interaction; a seismic detection control module is arranged in the control chip, which is used to set the vibration parameters of the seismic source and preprocess the data collected by the receiver to finally obtain seismic detection data; a data transmission device is also arranged in the electronic cabin. The control chip is electrically connected to the data transmission device. 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. Control the seabed crawler to crawl in a predetermined detection area, obtain the pose information of the seabed crawler, and at the same time control the seismic source to intermittently emit seismic waves and receive the reflected waves through the receiver;

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

[0126] S30. Align the anomaly point set with the seismic waves according to the order of the intermittently emitted seismic waves; extract the characteristics of the seismic waves using a sliding window, denoted as seismic wave characteristics, and calculate the correlation degree between the seismic wave characteristics and the anomaly point set;

[0127] S40. In the seismic wave characteristics, delete the seismic wave characteristics with a correlation degree greater than a preset correlation degree threshold to obtain the remaining characteristics, and use a preset characteristic-parameter equation set to calculate the seismic wave parameters corresponding to the remaining characteristics as optimization parameters;

[0128] S50. Control the seismic source to emit new seismic waves according to the optimization parameters, and receive the new reflected waves through the receiver; and preprocess the new reflected waves to obtain preprocessed new reflected waves;

[0129] S60. Perform waveform alignment and travel time analysis on the new seismic waves and the preprocessed new reflected waves, and calculate the propagation time and speed of the new seismic waves in different media;

[0130] S70. Use a pre-established velocity correction equation set to perform dynamic correction processing on the preprocessed new reflected waves to eliminate the time difference caused by different propagation paths of seismic waves;

[0131] S80. Perform spectrum analysis and amplitude recovery on the corrected preprocessed new reflected waves to improve the signal-to-noise ratio and resolution of the signal, and the obtained result is used as detection data.

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

[0133] Step S10: Control the subsea crawler to crawl in a predetermined detection area, obtain the pose information of the subsea crawler, and at the same time control the seismic source to intermittently emit seismic waves and receive the reflected waves through the receiver. First, through sensor modules such as depth gauges, altimeters, gyroscopes, and accelerometers on the subsea crawler, obtain the real-time position information of the subsea crawler in the detection area, including depth, altitude, heading angle, pitch angle, etc. At the same time, use the control chip to control the seismic source to emit intermittent seismic waves. After each emission, record the seismic wave signals reflected back through the receiver array. The purpose of this step is to obtain subsea terrain information and original seismic reflection wave data, providing basic data for subsequent signal processing and geological interpretation.

[0134] Step S20: Use a sliding window and the Bayesian anomaly detection method to calculate the set of anomaly points in the first reflected wave, including amplitude anomaly points, frequency anomaly points, and phase anomaly points. First, divide the received seismic reflection wave signals into multiple short-time windows, and perform statistical analysis on the amplitude, frequency, and phase characteristics within each time window. Then, use the Bayesian anomaly detection method to calculate the probability of anomaly occurrence for each characteristic under the given observed data. Specifically, for the amplitude characteristic, when the amplitude exceeds the mean within the sliding window plus or minus 2 - 3 times the standard deviation, it is determined as an amplitude anomaly point; for the frequency characteristic, when the frequency exceeds the mean within the sliding window plus or minus 2 - 3 times the standard deviation, it is determined as a frequency anomaly point; for the phase characteristic, when the phase exceeds the mean within the sliding window plus or minus 2 - 3 times the standard deviation, it is determined as a phase anomaly point. The purpose of this step is to automatically identify the characteristic points in the original reflected wave that may reflect geological anomalies, providing clues for subsequent processing and analysis.

[0135] Step S30: Align the set of anomaly points with the seismic waves according to the order of the intermittently emitted seismic waves; use a sliding window to extract the characteristics of the seismic waves, denoted as seismic wave characteristics, and calculate the correlation between the seismic wave characteristics and the set of anomaly points. First, according to the emission timing of the seismic source, align and pair each anomaly point with the corresponding seismic wave reflection signal. Then, for the characteristics of each seismic wave reflection signal, such as amplitude, frequency, phase, etc., use the sliding window method for extraction and statistical analysis, denoted as seismic wave characteristics. Finally, calculate the correlation between the seismic wave characteristics and the set of anomaly points, that is, the correlation degree. The higher the correlation degree, the stronger the correlation between the seismic wave characteristics and the anomaly points. The purpose of this step is to deeply explore the internal relationship between the seismic wave characteristics and the anomaly points, laying a foundation for 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: Perform spectral analysis and amplitude recovery on the preprocessed new reflected wave after correction to improve the signal-to-noise ratio and resolution of the signal, and use the obtained result as detection data. First, perform Fourier transform on the corrected reflected wave signal to obtain spectral information. Then, adopt appropriate amplitude recovery methods, such as geometric divergence correction, absorption compensation, etc., to eliminate the amplitude attenuation caused by factors such as formation absorption and divergence, and improve the signal-to-noise ratio of the reflected wave. Finally, comprehensively utilize the results of spectral analysis and amplitude recovery to obtain high-quality seismic detection data, providing support for subsequent geological interpretation.

[0141] Specifically, the principle of the present invention is: construct an intelligent seismic exploration system based on a subsea crawler. This system mainly includes components such as a survey ship, a subsea crawler, a seismic source, a receiver, and a control chip. Among them, the survey ship is connected to the subsea crawler through a towing rope, providing energy and data transmission support for it; the subsea crawler is responsible for maneuvering flexibly in the complex seabed terrain, carrying detection devices such as a seismic source and a receiver, and obtaining comprehensive seismic data; the control chip coordinates the work of each component to realize functions such as environmental perception, parameter optimization, and data processing.

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

[0143] Next, analyze and process the received reflected wave signals. First, use the Bayesian anomaly detection method to automatically identify amplitude anomaly points, frequency anomaly points, and phase anomaly points in the reflected wave. These anomaly points may reflect changes in the underground geological structure. Then, according to the timing of the seismic source emission, pair the anomaly points with the corresponding seismic reflection signals, and statistically analyze the characteristics such as amplitude, frequency, and phase of each reflection signal. By calculating the correlation degree between the characteristics and the anomaly points, the key characteristics most relevant to the anomaly can be screened out.

[0144] Based on these key characteristics, the present invention establishes a set of characteristic-parameter equations, including a frequency equation, an amplitude equation, and a duration equation, to describe the variation law of various parameters during the propagation of seismic waves. These equations consider factors such as formation depth, temperature, density, etc., and can more accurately predict the seismic wave characteristics corresponding to the seismic source parameters. The control chip uses these equations to optimize the parameters such as the frequency, amplitude, and duration of the seismic source, making the seismic wave characteristics more consistent with the current seabed geological environment.

[0145] Meanwhile, 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, DMO correction, etc., which are used to eliminate the time shift caused by formation anisotropy, multiple reflections, etc. The control chip dynamically corrects the reflected wave signal according to the velocity information of each medium obtained above by using these equations, so as to keep the time axis consistent and greatly improve the reliability of the data.

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

[0147] To better understand and implement the present invention, a specific Embodiment 1 of the seismic detection control module in the present invention is provided below, and the steps of this Embodiment 1 are described in detail as follows:

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

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

[0150] In step S20, use a sliding window and the Bayesian anomaly detection method to calculate the set of anomaly points in the first reflected wave, including amplitude anomaly points, frequency anomaly points, and phase anomaly points. First, divide the received seismic reflection wave signal into multiple short-time windows, and perform statistical analysis on the amplitude, frequency, and phase characteristics within each time window. Denote the amplitude within the i-th time window as A i , the frequency as f i , and the phase as φ i .

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

[0152]

[0153] Wherein, P(A|D) is the posterior probability of the occurrence of an anomaly under the given observed data D; P(D|A) is the likelihood probability of the observed data in the case of an anomaly; P(A) is the prior probability of the occurrence of an anomaly; P(D) is the marginal probability of the observed data.

[0154] For the amplitude feature, when |A i - μ A | > k A · σ A ), it is determined as an amplitude anomaly point; wherein, μ A and σ A are respectively the mean and standard deviation of the amplitudes within the sliding window, and k A is the amplitude anomaly determination coefficient, usually taking a value of 2 - 3.

[0155] For the frequency feature, when |f i - μ f | > k f · σ f ), it is determined as a frequency anomaly point; wherein, μ f and σ f are respectively the mean and standard deviation of the main frequencies within the sliding window, and k f is the frequency anomaly determination coefficient, usually taking a value of 2 - 3.

[0156] For the phase feature, when |φ i - μ φ | > k φ · σ φ ), it is determined as a phase anomaly point; wherein, μ φ and σ φ are respectively the mean and standard deviation of the instantaneous phases within the sliding window, and k φ is the phase anomaly determination coefficient, usually taking a value of 2 - 3.

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

[0158] In step S30, according to the order of the intermittent emitted seismic waves, align the set of anomaly points with the seismic waves; extract the features of the seismic waves using a sliding window, denoted as seismic wave features, and calculate the correlation degree between the seismic wave features and the set of anomaly points.

[0159] First, according to the emission time sequence of the seismic source, that is, the seismic wave emitted at the t - th time corresponds to the t - th reflected wave, pair up each anomaly point with the corresponding seismic wave reflection signal for alignment.

[0160] Then, extract and statistically analyze the features of each seismic wave reflection signal. Denote the amplitude feature of the t - th seismic wave reflection signal as A t , and the frequency feature as ft , the phase feature is φ t . By using the sliding window method, the mean and standard deviation of these features can be calculated, denoted as

[0161] Finally, calculate the correlation degree between the seismic wave features and the set of abnormal points. The correlation degree r can be defined as:

[0162]

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

[0164] The purpose of this step is to deeply explore the internal connection between the seismic wave features and the abnormal points, and lay a foundation for the subsequent calculation of optimized parameters.

[0165] In step S40, among the seismic wave features, delete the seismic wave features with a correlation degree greater than the preset correlation degree threshold to obtain the remaining features, and use the preset feature-parameter equation set to calculate the seismic wave parameters corresponding to the remaining features as the optimized parameters.

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

[0167] Then, use the pre-established feature-parameter equation set to model the remaining features to obtain the optimized values of the source parameters. These feature-parameter equation sets include:

[0168] Frequency equation:

[0169] f = f0 + α·log(d) + β·T + γ·ρ + ε f

[0170] In the formula, f0 is the initial frequency, d is the detection depth, T is the seabed temperature, ρ is the seabed sediment density, α, β, γ are undetermined coefficients, and εf is the error term.

[0171] Amplitude equation:

[0172] A = A0·e -λd ·(1 + μ·v + ν·a) + ε A

[0173] where \(A_0\) is the initial amplitude, \(\lambda\), \(\mu\), \(\nu\) are undetermined coefficients, \(v\) is the speed of the subsea crawler, \(a\) is the acceleration of the subsea crawler, and \(\varepsilon\) A is the error term.

[0174] Duration equation:

[0175] \(t = t_0+\omega\cdot\sin(\theta)+\varphi\cdot\cos(\psi)+\chi\cdot H+\varepsilon\) t

[0176] where \(t_0\) is the initial duration, \(\theta\) is the seabed slope, \(\psi\) is the pitch angle of the subsea crawler, \(H\) is the seawater depth, \(\omega\), \(\varphi\), \(\chi\) are undetermined coefficients, and \(\varepsilon\) t is the error term.

[0177] 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, calculate the optimal source parameters based on them, and provide a basis for subsequent source optimization.

[0178] In step S50, according to the optimization parameters, control the source to emit new seismic waves, and receive new reflected waves through a receiver; and preprocess the new reflected waves to obtain preprocessed new reflected waves.

[0179] First, using the optimization parameters \(f\), \(A\), \(t\) calculated in step S40, control the source to emit new seismic waves through a control chip. The receiver records the new seismic wave reflection signals, and then preprocess these new reflected waves, including steps such as denoising, band filtering, and waveform correction, to obtain the preprocessed new reflected wave data.

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

[0181] In step S60, perform waveform alignment and travel time analysis on the new seismic waves and the preprocessed new reflected waves, and calculate the propagation time and velocity of the new seismic waves in different media.

[0182] First, use methods such as correlation analysis or the least squares method to align the waveforms of the newly emitted seismic wave \(x(t)\) and the received new reflected wave \(y(t)\) to eliminate the time shift caused by the movement of the subsea crawler. The objective function for waveform alignment is:

[0183] \(J=\int[x(t)-\alpha y(t - \tau)]\) 2 \(dt\);

[0184] where \(\alpha\) is the amplitude adjustment coefficient and \(\tau\) is the time shift. Solving for \(\alpha\) and \(\tau\) that minimize \(J\) can complete the waveform alignment.

[0185] Then, according to the waveform alignment result, calculate the propagation time and velocity of the new seismic wave in the water layer, sedimentary layer, and other media. For the i-th layer of the medium, its propagation time T i and velocity V i satisfy:

[0186]

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

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

[0189] In step S70, use the pre-established velocity correction equations to perform NMO processing on the preprocessed new reflection wave to eliminate the time difference caused by different propagation paths of the seismic wave.

[0190] The pre-established velocity correction equations include:

[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 are undetermined coefficients, and 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 are undetermined coefficients, and ε s is the error term.

[0197] Multiple-wave correction equation:

[0198]

[0199] 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 is the sound velocity in water, V pis 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, T0 is the zero-offset travel time, x is the offset, 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 dip angle of the reflection point, V int is the interval 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 processing on the new reflected wave to eliminate the time shift 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 and provide good basic data for subsequent spectral analysis and amplitude recovery.

[0208] In step S80, perform spectral analysis and amplitude recovery on the preprocessed new reflected wave after correction to improve the signal-to-noise ratio and resolution of the signal, and use the obtained result as the detection data.

[0209] First, perform Fourier transform on the corrected reflected wave signal x(t) to obtain the spectrum X(f):

[0210] X(f) = ∫x(t)e -j2πft dt;

[0211] Then, adopt appropriate amplitude recovery methods, such as geometric divergence correction and absorption compensation, etc., to eliminate the amplitude attenuation caused by factors such as formation absorption and divergence. Geometric divergence correction can use the following formula:

[0212]

[0213] where, A cis the corrected amplitude, A is the original amplitude, d0 is the reference distance, and d is the actual distance. Absorption compensation can be achieved by using:

[0214] A a = A·e αd ;

[0215] where α is the attenuation coefficient, which can be determined through experiments or estimated using empirical formulas.

[0216] Finally, by comprehensively applying the results of spectral analysis and amplitude recovery, high-quality seismic exploration data are obtained, providing support for subsequent geological interpretation.

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

[0218] As Figures 3 - 4 shown, Embodiment 2 of the present invention proposes a seismic exploration system based on a subsea crawler, which mainly consists of a survey ship, a subsea crawler, a seismic source, receivers, and a control chip. The overall structure and working principle of the system are as follows:

[0219] 1. Overall structure of the system

[0220] 1.1 Survey ship

[0221] The survey ship is the command center and data processing center of the entire exploration system. The following equipment is configured on the survey ship:

[0222] a) Satellite navigation system: used to real-time locate the position of the survey ship, with an accuracy of up to centimeter level.

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

[0224] c) Power supply unit: provides stable power supply for the entire system, including the electrical energy required by the subsea crawler.

[0225] d) Data processing center: equipped with high-performance computers, used to receive, store, and process the seismic exploration data transmitted back by the subsea crawler.

[0226] 1.2 Subsea crawler

[0227] The subsea crawler is the core component of this system. Adopting a tracked subsea crawler, it can walk stably on complex seabed terrains. The subsea crawler mainly includes the following parts:

[0228] a) Driving unit: composed of a motor and a crawler, 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 devices installed inside.

[0230] c) Sensor module: At least includes a depth gauge, an altimeter, a gyroscope, and an accelerometer, used to obtain the pose information and environmental parameters of the crawler.

[0231] 1.3 Seismic source

[0232] The seismic source uses a controllable seismic source, which can be either 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 subsea crawler, and its vibration 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 nodal receiver array or a multi-channel seismic cable, fixed on the towing cable at the tail of the subsea crawler. Preferably, a nodal 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 cabin of the subsea crawler and is the "brain" of the entire system. The control chip is mainly responsible for the following functions:

[0237] a) Conduct data interaction with the drive unit, seismic source, and receiver of the subsea crawler.

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

[0239] c) Preprocess the data collected by the receiver to obtain seismic exploration data.

[0240] d) Send the seismic exploration data to the survey ship through the data transmission device.

[0241] 2. System working principle

[0242] 2.1 System deployment

[0243] After the survey ship arrives at the predetermined detection area, the subsea crawler is put into the sea. The subsea crawler is connected to the survey ship through a towing rope, using a cabled connection method. The electrical energy required by the crawler is transmitted by the survey ship through the towing rope, and real-time data transmission is also achieved.

[0244] 2.2 Seismic exploration process

[0245] After the subsea crawler reaches the seabed, it starts to crawl along the preset path. During the crawling process, the seismic exploration control module in the control chip performs the following steps:

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

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

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

[0249]

[0250] In the formula, P(A|D) is the posterior probability of the occurrence of an anomaly under the given observed data D; P(D|A) is the likelihood probability of the observed data in the case of an anomaly; P(A) is the prior probability of the occurrence of an anomaly; P(D) is the marginal probability of the observed data.

[0251] The determination criteria for anomaly points are as follows:

[0252] 1) Amplitude anomaly points:

[0253] |A i -μ A |>k A ·σ A ;

[0254] 2) Frequency anomaly points:

[0255] |f i -μ f |>k f ·σ f ;

[0256] 3) Phase anomaly points:

[0257] |φ i -μ φ |>k φ ·σ φ ;

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

[0259] S30: Align the set of anomaly points with the seismic waves according to the order of intermittently emitting seismic waves. Use a sliding window to extract the features of the seismic waves, denoted as seismic wave features, and calculate the correlation degree between the seismic wave features and the set of anomaly points.

[0260] S40: In the seismic wave characteristics, delete the seismic wave characteristics with a correlation degree greater than the preset correlation degree threshold to obtain the remaining characteristics, and use the preset characteristic-parameter equation set to calculate the seismic wave parameters corresponding to the remaining characteristics as the optimization parameters.

[0261] The preset characteristic-parameter equation set includes:

[0262] 1) Frequency equation:

[0263] f = f0 + α·log(d) + β·T + γ·ρ + ε f ;

[0264] 2) Amplitude equation:

[0265] A = A0·e -λd ·(1 + μ·v + ν·a) + ε A ;

[0266] 3) Duration equation:

[0267] t = t0 + ω·sin(θ) + φ·cos(ψ) + χ·H + ε t ;

[0268] S50: According to the optimization parameters, control the seismic source to emit new seismic waves, and receive the new reflected waves through the receiver. Preprocess the new reflected waves to obtain the preprocessed new reflected waves.

[0269] S60: Perform waveform alignment and travel time analysis on the new seismic waves and the preprocessed new reflected waves, and calculate the propagation time and velocity of the new seismic waves in different media.

[0270] S70: Use the pre-established velocity correction equation set to perform dynamic correction on the preprocessed new reflected waves to eliminate the time difference caused by different propagation paths of the seismic waves.

[0271] The velocity correction equation set includes:

[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 spectral analysis and amplitude recovery on the preprocessed new reflected waves after correction to improve the signal-to-noise ratio and resolution of the signal, and use the obtained results 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. The data transmission can be carried out through data cables or wireless channels. In this embodiment, a wired connection method is adopted, and the data is transmitted at high speed through the optical fiber in the towing rope.

[0285] 2.4 Data processing and interpretation

[0286] After the data processing center on the survey ship receives the seismic detection data, it performs further processing and interpretation, including but not limited to:

[0287] a) Data quality control: Remove noise, correct static errors, etc.

[0288] b) Velocity analysis: Conduct detailed velocity analysis to establish an accurate velocity model.

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

[0290] d) Post-stack processing: Include removing multiple waves, frequency filtering, amplitude balancing, etc.

[0291] e) Geological interpretation: According to the processed seismic profiles, conduct geological interpretation work such as formation division, fault identification, reservoir prediction, etc.

[0292] 3. System advantages

[0293] 3.1 High-precision seismic detection

[0294] By towing the seismic source and receiver with a subsea crawler, close contact between the seismic source and receiver and the seabed can be achieved, greatly improving the propagation efficiency of the seismic source energy and the sensitivity of the receiver. At the same time, the stable performance of the subsea crawler ensures the continuity and consistency of data acquisition.

[0295] 3.2 Adaptive parameter optimization

[0296] The system realizes the adaptive optimization of seismic detection parameters by analyzing the abnormal points in the reflected waves in real time and dynamically adjusting the seismic source parameters. This method can automatically adjust the detection strategy according to different geological environments, improving the detection efficiency and accuracy.

[0297] 3.3 Real-time data processing and transmission

[0298] The control chip performs real-time preprocessing on the received seismic data and transmits it to the survey ship through a high-speed data link, enabling geological experts to 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 adopts multiple velocity correction equations, including P-wave, S-wave, multiple wave, NMO, and DMO corrections, comprehensively considering the propagation characteristics of seismic waves in complex geological environments, and greatly improving the accuracy and resolution of seismic data.

[0301] 4. Key technical parameters

[0302] 4.1 Submarine crawler

[0303] - Maximum working depth: 6000 meters;

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

[0305] - Endurance time: ≥24 hours;

[0306] - Positioning accuracy: ±0.5 meters;

[0307] 4.2 Seismic source

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

[0309] - Maximum output power: 5000 J;

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

[0311] 4.3 Receiver

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

[0313] - Dynamic range: 140 dB;

[0314] - Trace interval: 1 m - 25 m.

[0315] To further better understand and implement the present invention, the following provides Example 3 of a specific application scenario of the present invention: A certain marine oil and gas unit plans to carry out exploration activities in a certain sea area of the South China Sea and needs to adopt high-precision submarine 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 a submarine crawler proposed by the present invention and carry out a three-month practical application.

[0316] Phase 1: System deployment

[0317] First, the unit allocated a professional survey ship with a displacement of 3,000 tons 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 crawler-type subsea crawler was developed, which is 4 meters long, 2 meters wide, and 1.5 meters high, and the total weight of the whole machine is about 5 tons. An electronic cabin is provided inside the crawler for installing core components such as control chips and data transmission equipment. A drag cable, 30 meters long, is installed at the tail of the crawler, and both the seismic source and the receiver array are fixed on the cable.

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

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

[0320] After the above-mentioned hardware equipment was installed and debugged, the survey ship sailed to the predetermined exploration area, slowly lowered the mooring anchor, and maintained a relatively stable working state. The subsea crawler is connected to the survey ship through a towing rope and then slowly sinks to the seabed and starts autonomous cruising in the designated area.

[0321] Phase II: Intelligent Detection

[0322] After entering the exploration area, the subsea crawler first uses sensors such as depth gauges, altimeters, and gyroscopes carried on it to collect its own position information in real time. The control chip transmits this pose data to the survey ship to establish an accurate three-dimensional coordinate system of the crawler on the seabed.

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

[0324] For each group 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 the standard deviation σ A , σ f , σφ Then, using the Bayesian anomaly detection method, evaluate the probability of anomaly occurrence of these features under the current observed data:

[0325]

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

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

[0328]

[0329] where x i is the reflected wave feature, y i is the abnormal point set, and are their means respectively. The higher the correlation degree r, the closer the relationship between the feature and the abnormal point.

[0330] Through the above analysis, the control chip screens out the key features highly correlated with the abnormal points, and uses the pre-established feature-parameter equation set to calculate the corresponding seismic 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] where d is the detection depth, T is the seabed temperature, ρ is the seabed sediment density, v is the crawler speed, a is the crawler acceleration, θ is the seabed 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 seismic source emission command, and the seismic source then adjusts its frequency to 35 Hz, amplitude to 7 MPa, and duration to 0.15 s, and emits seismic waves towards the seabed again. The receiver records the new reflected signal and transmits it back to the control chip for subsequent processing.

[0336] Meanwhile, 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 speeds of seismic waves in different media such as the water layer and the sediment layer. Taking the water layer as an example, it is measured that V w = 1500 m / s; for the sediment layer, V p = 2800 m / s, V s = 1200 m / s.

[0337] With this velocity information, the control chip then calls the preset velocity correction equation set to perform dynamic correction processing on the new reflected wave signal. Taking the P-wave velocity correction as an example:

[0338]

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

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

[0341] The third stage: data transmission and analysis

[0342] After completing a set of data acquisition and preprocessing, the control chip transmits the results to the survey ship through a data cable or a 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 is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A seismic detection system based on a subsea crawler, characterized in that, Including: A survey ship, a subsea crawler, a seismic source, a receiver, and a control chip. Among them, the survey ship is connected to the subsea crawler by a towing rope. A drag cable is arranged at the tail of the subsea crawler. The seismic source and the receiver are fixed on the drag cable. An electronic cabin is arranged inside the subsea crawler, and the control chip is arranged in the electronic cabin. The control chip is connected to the drive unit, the seismic source, and the receiver of the subsea crawler and conducts data interaction. A seismic detection control module is arranged inside the control chip, which is used to set the vibration parameters of the seismic source and preprocess the data collected by the receiver to finally obtain seismic detection data. A data transmission device is also arranged 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. The seismic detection control module is used to perform the following steps: S10. Control the subsea crawler to crawl in a predetermined detection area, obtain the pose information of the subsea crawler, and at the same time control the seismic source to intermittently emit seismic waves and receive the reflected waves through the receiver. S20. Adopt a sliding window and use the Bayesian anomaly detection method to calculate the anomaly point set in the reflected waves, including amplitude anomaly points, frequency anomaly points, and phase anomaly points. S30. Align the anomaly point set with the seismic waves according to the order of the intermittently emitted seismic waves. Extract the characteristics of the seismic waves using a sliding window, denoted as seismic wave characteristics, and calculate the correlation degree between the seismic wave characteristics and the anomaly point set. S40. In the seismic wave characteristics, delete the seismic wave characteristics with a correlation degree greater than a preset correlation degree threshold to obtain the remaining characteristics, and use a preset characteristic-parameter equation set to calculate the seismic wave parameters corresponding to the remaining characteristics as optimization parameters. S50. Control the seismic source to emit new seismic waves according to the optimization parameters and receive the new reflected waves through the receiver. And preprocess the new reflected waves to obtain preprocessed new reflected waves. S60. Perform waveform alignment and travel time analysis on the new seismic waves and the preprocessed new reflected waves, and calculate the propagation time and speed of the new seismic waves in different media. S70. Use a pre-established velocity correction equation set to perform dynamic correction processing on the preprocessed new reflected waves to eliminate the time difference caused by different propagation paths of the seismic waves. S80. Perform spectral analysis and amplitude recovery on the corrected preprocessed new reflected waves to improve the signal-to-noise ratio and resolution of the signal, and use the obtained result as detection data.

2. The seismic detection system based on a subsea crawler according to claim 1, wherein, The survey ship is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

3. The seismic detection system based on a subsea crawler according to claim 2, characterized in that, The subsea crawler adopts a tracked subsea crawler or a multi-legged subsea crawler.

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

5. The seismic detection system based on a subsea crawler according to claim 4, characterized in that, The vibration surface of the seismic source emission array contacts the seabed sediment.

6. The seismic detection system based on a subsea crawler according to claim 5, characterized in that, The receiver is a nodal receiver array or a multi-channel seismic cable.

7. The seismic detection system based on a subsea crawler according to claim 6, wherein The node receiver array includes a plurality of three-component geophones and a plurality of hydrophones. Among them, 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 subsea crawler according to claim 7, characterized in that, The data transmission device sends the seismic detection data to the survey ship through a data cable or a wireless channel.

9. A seismic detection system based on a subsea crawler according to claim 8, wherein, A sensor module is further provided on the seabed crawler, at least including a depth gauge, an altimeter, a gyroscope, and an accelerometer.

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