High-precision detection method for rock-filled foundation in deep sea area

Abstract

The invention relates to the technical field of geophysical exploration, in particular to a high-precision detection method for a rock-filled foundation in a deep sea area. A plurality of detectors are arranged at the target measuring point in a set mode; performing continuous data acquisition at the target measuring point by adopting a natural source frequency imaging method and an active source surface wave exploration method to obtain low-frequency surface wave data and high-frequency surface wave data of the target measuring point; respectively processing the low-frequency surface wave data and the high-frequency surface wave data by adopting a frequency division fragmentation statistical method; and according to the processed low-frequency surface wave data and the high-frequency surface wave data, carrying out fragmentation superposition to obtain a geological detection result of the target measuring point. According to the method, natural source frequency imaging is combined with a surface wave exploration technology, so that the applicability of frequency imaging in riprap layer-sludge geological scene detection with relatively strong transverse speed change can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical detection technology, and in particular to a high-precision detection method for deep sea rock-filled foundations. Background Art

[0002] With the continuous development of the national economy, "land reclamation" has gradually become an important engineering means to solve the growing "human-land contradiction". Marine silt strata are common in coastal areas, which have the characteristics of high water content, high compressibility, low permeability, low shear strength, thixotropy and significant rheology. Stone-casting (or stone-casting to squeeze silt) has become the most commonly used method for foundation treatment in such areas due to its low cost and high construction efficiency. The principle of this method is to throw stone fillers into the fluidized silt, use the deadweight of the stones to squeeze out the silt soil, change the structural properties of the silt soil, and form drainage channels through the gaps between the stones, so that the silt can be drained and consolidated smoothly, and combined with the filling stones to form an overall stable foundation with a certain strength.

[0003] However, the riprap method also has a series of disadvantages: 1. If the construction is not done properly during the riprap process, the riprap will cause the silt and silty soft soil layer on the top of the seabed to be impacted by the backfill load in a short period of time, resulting in shear failure, thereby generating a large slip surface to form a silt bag and produce an arch siltation phenomenon; 2. The mud-rock interface formed by riprap construction usually fluctuates greatly, and uneven settlement is prone to occur in the later stage; therefore, in order to achieve accurate riprap construction quality control, evaluate engineering effects, discover potential problems, and provide a scientific reference basis for guiding subsequent construction maintenance and construction, it is particularly important to detect the depth, thickness and cross-sectional morphology of the riprap layer, and determine the location and scale of the silt bag and other parameters.

[0004] Traditional detection of riprap and silt packs mainly relies on drilling. However, the result of this method is only a "one-hole view". To form a relatively continuous detection profile, a large number of drill holes need to be laid out, which faces the disadvantages of low construction efficiency and high cost. Previous generations have introduced a series of geophysical methods for detecting riprap and silt packs. According to literature reports, geophysical radar, high-density electrical method and transient surface wave method are more commonly used for geophysical exploration in marine riprap and silt geological scenes. For example, Ge Shuangcheng et al. (2008) used a 16MHz low-frequency ground penetrating radar to detect a riprap project on an island and obtained the reflection wave information of the bottom of the riprap layer of about 35m, but could not obtain the information of the mud-rock mixed layer; Wei Yongqiang et al. (2010) used ground penetrating radar to detect the structure of a reclamation dam on the southeast coast. By measuring the main interface structure of the riprap layer-mud-rock mixed layer-silt layer shallower than 24 meters, Zhu Ruihu et al. (2016) used ground penetrating radar detection to try to establish a method for calculating the volume of riprap in the seaport, and used examples to prove its effectiveness. They pointed out that the method itself is not complicated, and the accuracy of the method depends more on the professional quality of the practitioners, including the determination of the riprap range in the radar image, the selection of relative dielectric constant, and the familiarity with the engineering and geological conditions. Tan Lei et al. (2023) used ground penetrating radar to detect a breakwater project and roughly obtained the interface of the riprap layer shallower than 15 meters. In summary, although existing scholars have achieved certain detection results, they are still in the stage of individual cases and single method attempts, and have not yet formed a set of efficient and accurate method systems. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a high-precision detection method for deep sea rock-filled foundations, which method sets a plurality of detectors in a set manner at a target measuring point; uses a natural source frequency imaging method and an active source surface wave exploration method to perform continuous data collection at the target measuring point to obtain low-frequency surface wave data and high-frequency surface wave data of the target measuring point; uses a frequency division and slice statistical method to process the low-frequency surface wave data and the high-frequency surface wave data respectively; and performs slice superposition according to the processed low-frequency surface wave data and the high-frequency surface wave data to obtain the geological detection result of the target measuring point. The present invention can significantly improve the applicability of frequency imaging in the detection of riprap-silt geological scenes with strong lateral velocity changes by combining natural source frequency imaging with surface wave exploration technology.

[0006] The present invention adopts the following technical scheme, a high-precision detection method for deep sea rock-filled foundation, comprising:

[0007] Set up multiple detectors in a set manner at the target measuring point;

[0008] The natural source frequency imaging method is used to continuously collect data at the target measuring point to obtain the low-frequency surface wave data of the target measuring point;

[0009] The active source surface wave exploration method is used to continuously collect data at the target measuring point to obtain the high-frequency surface wave data of the target measuring point;

[0010] The frequency division and slice statistics method is used to process the low-frequency surface wave data and the high-frequency surface wave data respectively;

[0011] According to the processed low-frequency surface wave data and high-frequency surface wave data, slice superposition is performed to obtain the geological detection result of the target measuring point.

[0012] Furthermore, the setting method is as follows:

[0013] Eight survey lines are arranged and numbered at the target measuring point; among them, five survey lines numbered 1, 5, 6, 7, and 8 are set in the direct filling area of the target measuring point, and three survey lines numbered 2, 3, and 4 are set in the transition area of the target measuring point.

[0014] Furthermore, the geophones include: a seismic geophone with a main frequency of 0.2Hz×10Hz and a 5Hz seismic geophone equipped with an impact source;

[0015] Among them, the natural source frequency imaging method uses a seismic geophone with a main frequency of 0.2Hz×10Hz;

[0016] The active source surface wave exploration method uses a 5Hz seismic geophone equipped with an impact source.

[0017] Furthermore, using the natural source frequency imaging method to continuously collect data at the target measuring point includes:

[0018] Using a seismic acquisition node containing a seismic geophone with a main frequency of 0.2Hz×10Hz;

[0019] Data is continuously collected at the target measuring point for 20 - 30 minutes through a linear arrangement method.

[0020] Furthermore, before using the active source surface wave exploration technology to continuously collect data at the target measuring point, it also includes:

[0021] Using the circular active source method and the square array natural source method to perform consistency detection on the geophones used in the active source surface wave exploration technology.

[0022] Furthermore, using the active source surface wave exploration technology to continuously collect data at the target measuring point includes:

[0023] Using a GSDE-3 type light hammering source with a 30kg counterweight and equipped with 4 groups of acceleration springs, lifting it to a height of 80cm and then hovering and releasing it, and using the light hammering source to excite surface waves for data collection beside each geophone.

[0024] Further, the frequency division and slice statistics method is specifically as follows:

[0025] Divide the low-frequency surface wave data and the high-frequency surface wave data into multiple segments with a set time length respectively, perform S transformation on each segment with the set time length to transform it into the frequency domain, and perform frequency division cutting on each segment with the set time length after division according to the set frequency range.

[0026] Further, the method for processing the low-frequency surface wave data includes:

[0027] Suppress abnormal amplitude noise, surface wave noise, and single-frequency interference on the low-frequency surface wave data in sequence to obtain the original low-frequency surface wave data;

[0028] Extract the resonance frequency characteristics in the original low-frequency surface wave data as the objective function;

[0029] Use an improved genetic algorithm to perform inversion on the objective function in the depth domain.

[0030] Further, the method for processing the high-frequency surface wave data includes: data denoising, spectrum analysis, dispersion curve picking, and dispersion curve inversion operations.

[0031] The beneficial effects of the present invention are as follows: When the present invention performs data acquisition, a multi-band detector combination is adopted, which can realize the acquisition of data with a relatively wide frequency band. Through the combination of natural source frequency imaging and surface wave exploration technology, frequency imaging is performed through the shear wave velocity field provided by surface wave exploration, greatly improving the applicability of frequency imaging in the detection of the riprap layer - silt geological scenario with strong lateral velocity variation; in the processing of surface wave data, the present invention innovatively uses the frequency division and slice statistics method, which shortens the observation time, improves the data utilization rate, and improves the construction efficiency; in the process of surface wave slice stacking, a weight coefficient is further introduced, effectively suppressing the interference of near-field random noise, reducing the environmental sensitivity of exploration, enhancing the adaptability, and avoiding the disadvantages of traditional seismic exploration that cannot collect data in a multi-work environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is a schematic flow chart of a high-precision detection method for a deep sea rock-filled foundation in an embodiment of the present invention;

[0034] Figure 2Schematic diagram of the layout position of the survey line for an embodiment of the present invention;

[0035] Figure 3 Schematic diagram of the basic process of data processing for the natural source frequency imaging method in an embodiment of the present invention;

[0036] Figure 4 Schematic diagram of the comparison test results with a collection duration of 10 minutes in an embodiment of the present invention;

[0037] Figure 5 Schematic diagram of the comparison test results with a collection duration of 20 minutes in an embodiment of the present invention;

[0038] Figure 6 Schematic diagram of the comparison test results with a collection duration of 30 minutes in an embodiment of the present invention;

[0039] Figure 7 Schematic diagram of the comparison test results with a trace interval of 0.5 m in an embodiment of the present invention;

[0040] Figure 8 Schematic diagram of the comparison test results with a trace interval of 1 m in an embodiment of the present invention;

[0041] Figure 9 Schematic diagram of the comparison test results with a trace interval of 2 m in an embodiment of the present invention;

[0042] Figure 10 Schematic diagram of the comparison test results with a trace interval of 4 m in an embodiment of the present invention;

[0043] Figure 11 Schematic diagram of the comparison test results with a frequency band range of 1 - 20 Hz in an embodiment of the present invention;

[0044] Figure 12 Schematic diagram of the comparison test results with a frequency band range of 1 - 60 Hz in an embodiment of the present invention;

[0045] Figure 13 Schematic diagram of the comparison test results with a time window of 20 seconds and a stacking number of 90 times in an embodiment of the present invention;

[0046] Figure 14 Schematic diagram of the comparison test results with a time window of 120 seconds and a stacking number of 15 times in an embodiment of the present invention;

[0047] Figure 15 Schematic diagram of the detection results of the survey line numbered 1 in an embodiment of the present invention. Detailed implementation manners

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] A schematic flow chart of a high-precision detection method for a deep sea rock-filled foundation in an embodiment of the present invention is as Figure 1 shown and includes:

[0050] Set a plurality of geophones at the target measuring points in a set manner;

[0051] The data collected by the seismograph is the convolution result of the external vibration signal and the instrument equipment response signal. From the analysis of the principle of frequency imaging technology, it can be known that the purpose of data collection is to obtain the characteristic frequency information of the formation through statistical methods. Therefore, the more consistent the response of the instrument equipment to the vibration signals of different frequencies in the key frequency band, the more accurately the inherent characteristic frequency components of the formation can be restored; generally, the same vibration sensor (geophone) has its own natural frequency (or called the main frequency), and each sensor with a natural frequency has a good response to the vibration signals within a corresponding frequency band range; for example, a geophone with a main frequency of 10 Hz has a relatively sensitive and basically consistent response to the frequencies from 10 to 1000 Hz, but the response sensitivity to frequencies below 10 Hz is relatively poor; a geophone with a main frequency of 0.2 Hz is generally more sensitive to the frequencies from 1 Hz to 100 Hz, but there is a certain degree of instability, which may cause distortion in the statistical analysis of the formation characteristic frequencies to a certain extent; for this reason, in the embodiments of the present invention, geophones with 0.2 Hz and 10 Hz are installed on the same seismograph at the same time to form a multi-band geophone combination for data collection. In this way, on the one hand, the consistency of the frequency response from 10 Hz to 100 Hz can be improved, and on the other hand, the effective receiving frequency can be broadened to above 100 Hz, which is beneficial to improving the vertical resolution through high-frequency signals.

[0052] In a specific embodiment of the present invention, the self-check function of the original equipment is further expanded. The device status is downloaded into the mobile field book in real time by using the Bluetooth module of the device itself and transmitted to the cloud through the 4G network to ensure that the device status is known and controllable in real time. During the field detection process, the quality of the data collected by the device can be monitored in real time through the cloud platform.

[0053] The specific method for setting the geophones in a set manner in the embodiments of the present invention is as follows:

[0054] In the embodiments of the present invention, the natural source measurement points and the active source surface wave measurement points are fully reused to achieve the purpose of combining frequency imaging with active source surface waves. The active source surface wave imaging provides a shallow shear wave velocity field to adapt to the characteristics of large lateral velocity changes at the target measurement points. The active detection data can be simultaneously subjected to tomographic inversion or a basic velocity model can be obtained simultaneously. By importing the velocity field into the velocity field step of frequency imaging, frequency imaging can be achieved. In the embodiments of the present invention, 8 survey lines are designed, among which five lines, namely 1, 5, 6, 7, and 8, are located in the direct filling area, and the other three lines, 2, 3, and 4, are located in the transition area. The specific parameters are shown in Table 1:

[0055] Table 1 Schematic Diagram of the Basic Situation of Survey Line Design

[0056]

[0057]

[0058] The schematic diagram of the layout position structure of the survey lines is as Figure 2 shown. In the embodiments of the present invention, the distance between survey lines is 2 meters, and the observation duration is set to 20 minutes. At the same time, the geophone equipment is placed on the surface and leveled with fill soil until the bubble is centered, and the due north of the equipment is aligned with the geographical north.

[0059] Continuous data acquisition is carried out at the target measurement points using the natural source frequency imaging method to obtain the low-frequency surface wave data of the target measurement points;

[0060] In the embodiments of the present invention, the natural source frequency imaging method uses an ALLSEIS-4C integrated four-channel seismic acquisition node. This seismic acquisition node includes an orthogonal three-component high-sensitivity short-period 0.2Hz geophone + a 10Hz vertical geophone, and has the advantages of high precision, wide frequency band, large dynamic range, etc. Through long-term observation, the multi-modal resonance characteristic frequencies of the geological body are obtained, and thus parameters such as the longitudinal wave velocity, density, and shear wave velocity of the geological body are obtained.

[0061] During on-site detection, a linear arrangement is adopted, and the continuous acquisition duration is 20 minutes to ensure obtaining the basic geological structure within 0 - 50m in depth. The acquisition nodes are placed stably and vertically along the survey line at a channel spacing, with an inclination angle of ±5°, and N generally points north.

[0062] Continuous data acquisition is carried out at the target measurement points using the active source surface wave exploration method to obtain the high-frequency surface wave data of the target measurement points;

[0063] In the embodiment of the present invention, before the equipment for the active source surface wave exploration method arrives at the work area and starts working, equipment consistency test and inspection should be carried out. The consistency test adopts the circular active source method (5 shots excited by a sledgehammer) and the square array natural source method (observation time of 10 minutes). When observing in a circle, N points to the center of the circle; when observing in a square array, N uniformly points north. When the equipment consistency test results show that all geophones are in good working condition and the consistency of amplitude and phase response is very good, subsequent formal data acquisition can be carried out.

[0064] In the embodiment of the present invention, a GSDE-3 type light hammering vibration source with a 30 kg counterweight is used. It is lifted to a height of 80 cm and then hovers. Through the action of 4 groups of acceleration springs on the counterweight, after release, it impacts the ground surface to excite surface waves. The distance between node geophones is 2 m, and they are placed stably and vertically. 24 geophones are arranged in a straight line in sequence. A light hammering vibration source is used to excite surface waves beside each geophone one by one, so as to collect surface wave data through the geophones.

[0065] The frequency division and slice statistics method is used to process the low-frequency surface wave data and the high-frequency surface wave data respectively;

[0066] In the embodiment of the present invention, the low-frequency surface wave data and the high-frequency surface wave data are respectively divided into multiple segments with a set duration, and each segment with a set duration is subjected to S transform to be converted into the frequency domain. Frequency division cutting is performed on each segment with a set duration after division according to a set frequency range. Since the S transform combines the short-time Fourier transform and the wavelet transform, it captures the amplitude and phase information of the noise cross-correlation at different time points and frequencies through the sliding window Fourier transform, that is, frequency division and slice.

[0067] Time slicing and frequency slicing both belong to data quality control, which can increase the signal-to-noise ratio. For example, when there is a certain amount of valid information in the data, even if the energy of the valid signal is weak, it can be effectively highlighted in the frequency domain attributes and approach a stable value with accumulation; however, if there is only random noise data, accumulation makes this attribute approach zero. There may be an environmental noise source at a certain specific frequency or a certain specific frequency band in the short-time data acquisition, resulting in a low signal-to-noise ratio of the relevant data trace information. In order to more effectively improve work efficiency and screen out valid data information, it is necessary to further perform frequency division analysis and processing on the data to avoid waste of data resources.

[0068] The active source surface wave imaging data processing also includes steps such as data denoising, spectrum analysis, dispersion curve picking, and dispersion curve inversion, etc.; the natural source surface wave data processing has one more step of cross-correlation processing. It is necessary to first extract the Green's function through cross-correlation before the dispersion curve can be extracted. The Fresnel body tomo imaging data processing mainly includes steps such as initial value picking, coordinate transformation, initial velocity model establishment, and inversion iteration. Specifically:

[0069] The basic process of data processing for the natural source frequency imaging method is as follows: After importing the original data into the processing software, first, the observation system should be accurately defined according to the field measurement results and the field construction design; then, fine preprocessing of the original data is carried out, mainly including: suppressing abnormal amplitude noise (outliers), surface wave noise, single-frequency interference, etc.; on the basis of obtaining the original low-frequency surface wave data with a relatively high signal-to-noise ratio, the resonance frequency characteristics are extracted and used as the objective function, and an improved genetic algorithm is used to directly perform inversion in the depth domain to obtain the thickness of the geological body and its elastic parameters. Finally, decorative processing such as elevation correction is carried out to form the result data for the final interpretation. The specific frequency imaging processing flow chart is shown in Figure 3.

[0070] Performing piecewise superposition according to the processed low-frequency surface wave data and the high-frequency surface wave data to obtain the geological detection result of the target measuring point.

[0071] In another embodiment of the present invention:

[0072] The embodiment of the present invention gives an example of the accuracy comparison experiment of the detection method of the present invention, including tests with different trace spacings, comparative tests with different time lengths, comparative tests with different window lengths, and comparative tests with different frequency band ranges.

[0073] The schematic diagram of the comparative test results with different time lengths is as Figure 4 , Figure 5 and Figure 6 shown, which is the natural source frequency imaging result diagram of the survey line numbered 1 in the embodiment of the present invention. The trace spacing is 1m, and the acquisition durations are 10min, 20min, and 30min respectively. By comparison, it can be seen that the signal-to-noise ratio on the section with an acquisition duration of 10 minutes is relatively low; the signal-to-noise ratio on the section with an acquisition duration of 20 minutes is significantly improved; the section with an acquisition duration of 30 minutes is not much different from the 20-minute section. Considering the construction efficiency, 20 minutes is selected as the observation duration for the formal acquisition.

[0074] The schematic diagram of the test results with different trace spacings is as Figure 7 , Figure 8 , Figure 9 and Figure 10 shown, which is the result diagram of different trace spacings of the survey line numbered 1 in the embodiment of the present invention when the natural source frequency imaging acquisition duration is 20min. Among them, different trace spacings include 0.5m, 1m, 2m, and 4m. By comparison, it can be seen that when the acquisition duration is 20min, the 2m trace spacing has a higher horizontal characterization accuracy of the formation compared to the 4m trace spacing; as the trace spacing is further reduced, the local details in the formation are highlighted, but it is not helpful for the determination of the interface. Considering the comprehensive field detection efficiency, selecting a trace spacing of 2m as the detection parameter has the best effect.

[0075] Schematic diagram of the comparison test results for different frequency band ranges is as follows Figure 11 and Figure 12 shown. The data collected by the seismic nodes includes data from 0.2 Hz to greater than 100 Hz. For ordinary targets, a frequency of 1 - 30 Hz is usually selected. The overall depth of the detection of the riprap layer - silt geology is relatively shallow, and a higher vertical resolution is required. From theoretical analysis, increasing the frequency is beneficial to improving the resolution. However, the high - frequency signals mainly correspond to the shallow strata. Therefore, theoretically, increasing the frequency preferentially improves the resolution of the shallow strata. In the embodiment of the present invention, the first group of test frequency bands ( Figure 11 ) is selected as 1 - 20 Hz, and the second group of test frequency bands ( Figure 12 ) is selected as 1 - 60 Hz, and other parameters are kept the same; by comparison, it can be found that at positions shallower than - 10 meters, Figure 12 obviously contains more information than Figure 11 (especially near 0 meters). The difference between the two groups is very small at depths deeper than - 10 meters; increasing the frequency band has not a very obvious effect on the resolution of the shallow strata for identifying the riprap layer - silt interface. The main reason is that the layer represented by the high - frequency signal is shallower than the riprap layer - silt interface. Considering that there may be external interference signals of 50 Hz mains electricity in some detection sites, it is a better choice to use 1 - 45 Hz as the main frequency band.

[0076] Schematic diagram of the comparison test results for different time window lengths is as follows Figure 13 and Figure 14 shown. The most critical step in frequency imaging data processing is to statistically analyze the resonance frequency of the strata under external excitation conditions. During the statistical process, the entire data usually needs to be cut into segments of time, and the statistics are carried out within a single time segment, and then the results in each time segment are superimposed, that is, the piece - by - piece superposition method. The significance of segmentation is to avoid the shielding of weak signals by the global dominant signal through normalization within the segment. The significance of superposition is to enhance the time - invariant signal and weaken the time - varying signal. It should be noted that the product of the length of the time window and the number of superposition times is equal to the total observation time. Therefore, there is a relationship of mutual compensation between the two. When the total time is 30 minutes, Figure 13 the time window in Figure 14 is selected as 20 seconds, and the number of superposition times is 90 times; Figure 13 the time window in Figure 14 is selected as 120 seconds, and the number of superposition times is 15 times. Through comparison, it can be clearly found that:

[0077] In summary, in the embodiment of the present invention, it is determined to use a 2 - meter trace interval, a frequency band of 1 - 45 Hz, and a 20 - s time window as the main processing parameters for detection.

[0078] In the embodiments of the present invention, for the 8 detection lines detected, after data processing and imaging, the interface between the riprap layer and the silt can be relatively clearly identified on the section, which usually shows a strong difference in apparent wave impedance (the dotted line in the figure). Among them, the continuity of the riprap layer in Lines 1, 3, and 4 is relatively good, and there are obvious discontinuities in the riprap layer in Lines 2, 6, 7, and 8, suspected to be "silt bags" caused by silt arching. Effective imaging up to a depth of 40 meters has been achieved for all sections. Taking the schematic diagram of the detection result of Line No. 1 as an example, as Figure 15 shown, it can be seen that the undulation of the interface of the riprap layer in the section from 40 meters to 120 meters of the detection line is relatively small, and the thickness of the riprap layer is about 5 - 6 meters. However, there are relatively obvious changes in the thickness of the riprap layer in the section from 0 to 40 meters. The thickness in the range of 0 - 10 meters may be greater than 20 meters, and it quickly changes to a thickness within 10 meters between 20 - 40 meters. The riprap layer on the interface shows a relatively strong amplitude, while the layer below the interface is a silt layer with a weaker amplitude.

[0079] In summary, through the improvement of the natural source frequency imaging technology of the preferred method from the aspects of hardware, data processing algorithms, on-site test experiments, etc., the present invention has formed a set of "frequency imaging + active source surface wave" combined technology to achieve high-precision detection of silt bags in deep sea reclamation foundations. At the same time, the modification of the geophone hardware has further broadened the frequency acquisition width and improved the exploration resolution. The technical process of simultaneous detection of frequency imaging and active source surface wave has improved the accuracy and stability of the detection results. Through on-site actual verification, the detection accuracy of the detection method proposed by the present invention for detecting silt bags in deep sea reclamation foundations can reach within 1.5 meters, and the detection depth for distinguishing the interface between the riprap layer and the silt layer is greater than 40 meters.

[0080] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-precision detection method for deep sea rock-filled foundation, characterized in that: include: Set up multiple detectors in a set manner at the target measuring point; The natural source frequency imaging method is used to continuously collect data at the target measuring point to obtain the low-frequency surface wave data of the target measuring point; Active source surface wave exploration method is used to continuously collect data at the target measuring point to obtain high-frequency surface wave data of the target measuring point; The low-frequency surface roll data and the high-frequency surface roll data are processed respectively by using a frequency-division and slice-division statistical method; The processed low-frequency surface roll data and the processed high-frequency surface roll data are superimposed in slices to obtain the geological detection result of the target measuring point.

2. A high-precision detection method for deep sea rock-fill foundation according to claim 1, characterized in that: The setting method is: Eight survey lines are arranged at the target survey point and are numbered; among them, five survey lines numbered 1, 5, 6, 7, and 8 are set in the direct filling area of ​​the target survey point, and three survey lines numbered 2, 3, and 4 are set in the transition area of ​​the target survey point.

3. The high-precision detection method for deep sea rock-fill foundation according to claim 1 is characterized by: The geophones include: a geophone with a main frequency of 0.2 Hz×10 Hz and a 5 Hz geophone equipped with an impact source; Wherein, the natural source frequency imaging method uses a 0.2 Hz × 10 Hz seismic detector; The active surface wave exploration method uses a 5 Hz seismic detector equipped with an impact source.

4. The high-precision detection method for deep sea rock-fill foundation according to claim 1 is characterized by: The natural source frequency imaging method is used to continuously collect data at the target measurement point, including: A seismic acquisition node containing a seismic detector with a main frequency of 0.2Hz×10Hz is used; Data collection is performed continuously for 20-30 minutes at the target measuring point using a linear arrangement.

5. The high-precision detection method for deep sea rock-filled foundation according to claim 1 is characterized by: Before continuous data collection at the target measuring point using active surface wave exploration technology, the following steps are also required: The circular active source method and the square array natural source method are used to detect the consistency of the detectors used in the active source surface wave exploration technology.

6. The high-precision detection method for deep sea rock-fill foundation according to claim 1 is characterized by: Active surface wave exploration technology is used to continuously collect data at the target measuring point, including: A GSDE-3 light hammer source with a 30kg counterweight and 4 sets of acceleration springs was used. After being lifted to a height of 80cm, it was suspended and released. The light hammer source was used to excite surface waves next to each geophone for data collection.

7. The high-precision detection method for deep sea rock-fill foundation according to claim 1 is characterized by: The frequency division and fragmentation statistical method is specifically as follows: The low-frequency surface roll data and the high-frequency surface roll data are respectively divided into a plurality of segments of set time lengths, and each segment of the set time length is converted into a frequency domain by performing an S transform, and each segment of the set time length after division is frequency-divided and cut according to a set frequency range.

8. The high-precision detection method for deep sea rock-fill foundation according to claim 1 is characterized by: The method for processing the low-frequency surface roll data comprises: performing abnormal amplitude noise suppression, surface roll noise suppression and single frequency interference suppression operations on the low-frequency surface roll data in sequence to obtain original low-frequency surface roll data; extracting the resonant frequency characteristics in the original low-frequency surface wave data as the target function; The objective function is inverted in the depth domain using an improved genetic algorithm.

9. The high-precision detection method for deep sea rock-fill foundation according to claim 1 is characterized by: The method for processing the high-frequency surface wave data includes: data denoising, spectrum analysis, dispersion curve picking and dispersion curve inversion operations.

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