Geophysical identification and quantification system for response of river sediment discharge flux to sea level fluctuation

Abstract

The application discloses a geophysical identification and quantification system for response of river sediment discharge flux to sea level fluctuation, relates to the technical field of estuary and coast geophysical detection, and comprises a pore water sediment cooperative resonance network, a multimode coupling acquisition module, a nonlinear resonance decoding module and a dynamic mode calibration module. In the application, a closed-loop system composed of the pore water sediment cooperative resonance network, the multimode coupling acquisition, the nonlinear resonance decoding and the dynamic mode calibration is used to modulate the resonance mode according to the dynamic change of the pore water, so that the problems of low measurement precision of the sea level fluctuation and the sediment discharge flux, large response time-aging quantization deviation, serious signal cross interference, high operation and maintenance cost and single detection function caused by the neglect of the intermediary role of the pore water in the prior art are solved, the measurement precision of the sea level fluctuation amplitude and the sediment discharge flux is improved, the response time-aging is accurately quantized, the signal cross interference and the operation and maintenance cost are reduced, the pore water flow rate of the sediment layer is synchronously inverted, and the detection function is expanded.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology for estuaries and coastlines, specifically to a geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations. Background Technology

[0002] Identifying and quantifying the response patterns of riverine sediment transport flux to sea-level fluctuations is a core technical requirement for estuarine and coastal engineering construction, shelf sedimentary evolution research, and global sea-level change response assessment. Current mainstream technologies rely on geophysical methods such as acoustics, electromagnetics, and gravity to directly collect physical signals from sediments or water bodies to analyze the correlation between sediment transport flux and sea-level changes. Examples include using acoustic backscattered signals to invert sediment concentration, estimating sea-level height through gravity field anomalies, and indirectly reflecting sediment transport intensity using electromagnetic conductivity data.

[0003] However, existing technologies are all based on the fundamental assumption that geophysical signals are directly related to sediment transport flux and sea-level changes, completely ignoring the crucial mediating role of sedimentary pore water in their interaction. Sea-level fluctuations first alter the pore water pressure gradient, thus affecting the effective stress of sediment particles, but current acoustic signal interpretation does not consider the modulation of particle collision acoustic amplitude by this pressure change. Sudden changes in sediment transport flux trigger fluctuations in pore water salinity and ion concentration, directly altering the electromagnetic response characteristics of sediments, but current electromagnetic conductivity data processing does not incorporate this coupling effect. Furthermore, pore water-mediated signal transmission exhibits a natural phase lag, and existing technologies, failing to recognize this characteristic, result in significant deviations in the time-dependent quantification of sediment transport's response to sea-level fluctuations. This limits the accuracy and reliability of existing geophysical identification and quantification systems, making it difficult to meet the demands of high-precision, multi-dimensional practical applications.

[0004] Therefore, a geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations is provided to overcome the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations, so as to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, the present invention provides a geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations, comprising a pore water sediment co-resonance network, a multimodal coupled acquisition module, a nonlinear resonance decoding module, and a dynamic modal calibration module.

[0007] The pore water sediment synergistic resonance network is formed by connecting 15 to 25 dynamic feedback resonance coupling units in a honeycomb topology through flexible conductive fibers. The pore water sediment synergistic resonance network is embedded in the sedimentary layer of the estuarine shelf transition zone.

[0008] The multimodal coupling acquisition module is connected to the fiber grating sensor in the pore water sediment co-resonance network via a quartz optical fiber, and to the ion-sensitive conductive coating in the pore water sediment co-resonance network via a wire.

[0009] The nonlinear resonance decoding module is connected to the multimodal coupling acquisition module via a data transmission line and is used to receive the seven-dimensional data set output by the multimodal coupling acquisition module.

[0010] The dynamic modal calibration module is connected to the pore water sediment co-resonance network and the nonlinear resonance decoding module via wires, respectively, for the transmission and feedback of calibration signals;

[0011] The system modulates the resonant modes through the dynamic changes in pore water.

[0012] Furthermore, the dynamic feedback resonance coupling unit of the pore water sediment synergistic resonance network is embedded in the sedimentary layer of the estuarine shelf transition zone at a depth of 0.3 m to 4 m; each dynamic feedback resonance coupling unit includes an elastic gel conductive nanotube composite framework with an elastic modulus of 1.2 MPa to 2.5 MPa, and has 3 to 5 uniformly distributed pore water feedback cavities inside, with a cavity diameter of 8 mm to 12 mm.

[0013] Furthermore, the pore water feedback chamber of each dynamic feedback resonant coupling unit is filled with one to two elastic porous spheres made of natural rubber, with a porosity of 35% to 45% and a particle size of 4 mm to 6 mm. The pore water feedback chamber wall is provided with two to three one-way permeable holes with a pore size of 10 μm to 15 μm. Each one-way permeable hole is embedded with a rubber valve with a thickness of 0.1 mm to 0.2 mm and an opening pressure of 0.05 MPa to 0.1 MPa.

[0014] Furthermore, each dynamic feedback resonant coupling unit has a porous resonant membrane wrapped around its elastic gel conductive nanotube composite framework. The porous resonant membrane has a pore size of 3 μm to 8 μm and is coated with a hydrophilic silane coating with a contact angle not exceeding 30°. A fiber optic grating sensor is attached to the center of the outer wall of the pore water feedback cavity of the elastic gel conductive nanotube composite framework. The fiber optic grating sensor has a grating length of 10 mm, a center wavelength of 1550 nm, and an attachment area of ​​5 mm × 10 mm. An ion-sensitive conductive coating is attached to the surface of the elastic gel conductive nanotube composite framework. The substrate of the ion-sensitive conductive coating is polyurethane, doped with polyaniline conductive particles.

[0015] Furthermore, the multimodal coupling acquisition module includes a resonant mode triggering unit, a photoelectric synchronous acquisition unit, and a multi-dimensional data integration unit; the resonant mode triggering unit adopts a triple triggering mechanism, with a preset resonant frequency offset threshold of ±150Hz, a resonant peak amplitude change threshold of ±35%, and a feedback cavity water flow passage time threshold of 0.5s to 2s.

[0016] Furthermore, the nonlinear resonance decoding module incorporates pressure resonance frequency coupling equation, salinity equivalent impedance peak amplitude coupling equation, and phase difference response time-dependent coupling equation. In the pressure resonance frequency coupling equation, the initial resonance frequency ranges from 1500Hz to 2000Hz, the linear pressure coefficient ranges from 30Hz / MPa to 50Hz / MPa, and the nonlinear pressure coefficient ranges from 5Hz / (MPa²) to 8Hz / (MPa²). In the salinity equivalent impedance peak amplitude coupling equation, the initial resonance peak amplitude ranges from 0.5V to 1.0V, and the salinity response coefficient ranges from 0.03 / ‰ to 0.05 / ‰.

[0017] Furthermore, the dynamic modal calibration module includes a resonant modal reference unit, a network consistency verification unit, and an adaptive correction unit; the structure of the resonant modal reference unit is consistent with that of the dynamic feedback resonant coupling unit.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. Improve the accuracy of sea level fluctuation amplitude measurement: Through the "pressure resonance modulation and feedback enhancement" mechanism of the pore water sediment synergistic resonance network, the amplitude of the modulation signal of pressure on the effective stress of sediment is significantly improved. No additional correction of acoustic signal is required, and the measurement error of sea level fluctuation amplitude is effectively controlled, solving the defect of unstable amplitude modulation of existing acoustic signals.

[0020] 2. Improve the accuracy of sediment transport flux measurement: Relying on the hysteresis buffering effect of the feedback cavity, combined with the local conductive circuit formed by the ion-sensitive conductive coating and the elastic porous ball, the signal-to-noise ratio of the coupling signal of salinity and electromagnetic properties is significantly improved, the gradient of the network equivalent impedance with the change of salinity increases, the coupling interference is naturally avoided and no data post-processing is required, and the accuracy of sediment transport flux measurement is greatly improved.

[0021] 3. Precise Quantification of Response Time: Using the water flow time through the rubber valve one-way valve as the phase difference calibration benchmark, a "conduction delay-phase difference dual verification mechanism" is established, transforming phase lag from an error term into a calibrable parameter. This significantly reduces the quantification error of the response time of sediment transport to sea level fluctuations, and completely solves the problem of time quantification deviation in existing technologies.

[0022] 4. Reduce signal cross-interference: The one-way valve design of the feedback chamber separates the transmission paths of the pressure signal and the salinity signal, significantly reducing the cross-interference rate between the two and achieving accurate separation of the two signals, thus providing a basis for the synchronous quantification of multiple parameters of sea level fluctuation and sediment transport flux.

[0023] 5. Reduced operation and maintenance costs and power consumption: The hydrophilic silane coating of the porous resonant membrane enhances the wettability of pore water. Combined with the unidirectional water flow design, it significantly reduces the siltation rate. The system can work continuously for a long time after a single deployment without the need for periodic cleaning. The triple triggering mechanism of the multimodal coupling acquisition module avoids invalid data acquisition, reduces standby power consumption, and solves the problem of frequent power supply replacement in existing technologies.

[0024] 6. Expanded detection capabilities: Based on the correlation between the water flow velocity in the feedback chamber and the opening time of the one-way valve, and combined with the principles of fluid mechanics to formulate formulas, the pore water flow rate in the sedimentary layer can be simultaneously inverted, filling the gap in existing technologies that require additional equipment to measure this parameter, and providing new basic data for the study of continental shelf sedimentary evolution.

[0025] 7. Ensure stable long-term detection accuracy: The dynamic modal calibration module uses a resonant modal reference unit and an adaptive correction unit to calculate the similarity of the resonant frequency and feedback parameters between the unit and the reference in real time. The calibration response is rapid and does not interrupt the system operation, ensuring stable detection accuracy throughout the process. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations, as described in this invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1 The present invention provides a technical solution:

[0029] See Figure 1 An example of a geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations is shown below:

[0030] I. System Overall Architecture:

[0031] The purpose of this invention is to provide a closed-loop technical solution for resonant network capture, multimodal coupling acquisition, nonlinear matrix decoding, and modal self-calibration by constructing a cross-medium resonant coupling + dynamic feedback enhancement core mechanism. This solution completely abandons the existing direct mapping logic and modulates the resonant modes through the dynamic changes of pore water, simultaneously solving the problems of pressure modulation, salinity coupling, and phase lag. The system is assembled from the following four core components:

[0032] Pore ​​water sediment synergistic resonance network (integrated dynamic feedback enhancement mechanism);

[0033] Multimodal coupling acquisition module;

[0034] Nonlinear resonance decoding module;

[0035] Dynamic modal calibration module.

[0036] The assembly relationship of each component is as follows: In the pore water sediment co-resonance network, each dynamic feedback resonance coupling unit is connected to adjacent units through flexible conductive fibers, forming a honeycomb topology; the multimodal coupling acquisition module is connected to the fiber grating sensor in the pore water sediment co-resonance network through ordinary quartz optical fiber, and is connected to the ion-sensitive conductive coating through wires; the nonlinear resonance decoding module is connected to the multimodal coupling acquisition module through data transmission lines to receive its output seven-dimensional data set; the dynamic modal calibration module is connected to the pore water sediment co-resonance network and the nonlinear resonance decoding module through wires respectively to realize the transmission and feedback of calibration signals.

[0037] II. Core Components:

[0038] (I) Pore water sediment co-resonance network:

[0039] 1. Structural design and assembly process:

[0040] The network consists of 15 to 25 dynamic feedback resonant coupling units deployed in a honeycomb topology, embedded in the sedimentary layer of the estuarine shelf transition zone at a depth of 0.3 m to 4 m, covering the active pore water zone and the sediment-water interface;

[0041] The specific structure and assembly steps of each dynamic feedback resonant coupling unit are as follows:

[0042] ① Elastic gel conductive nanotube composite framework: This framework simulates sediment particle frameworks with an elastic modulus of 1.2 MPa to 2.5 MPa, matching that of natural sediment. The framework is prepared using a compression molding process at a molding temperature of 80℃, a pressure of 5 MPa, and a holding time of 30 minutes. Three to five uniformly distributed pore water feedback cavities with a diameter of 8 mm to 12 mm are formed within the framework.

[0043] ② Elastic porous spheres: Made of natural rubber, with a porosity of 35% to 45% and a particle size of 4mm to 6mm, with nano-silver conductive particles attached to the surface, and a conductivity of not less than 10³S / m. The elastic porous spheres are directly filled into the porous water feedback chamber, with 1 to 2 spheres filling each chamber, ensuring close contact between the porous spheres and the chamber wall.

[0044] ③ One-way permeable holes: Two to three one-way permeable holes are made in the wall of the feedback chamber, with a hole diameter of 10μm to 15μm. Each one-way permeable hole is embedded with a rubber valve, the valve thickness is 0.1mm to 0.2mm, the opening pressure is 0.05MPa to 0.1MPa, and it is integrally molded by injection molding and embedded into the one-way permeable hole by interference fit;

[0045] ④ Porous resonant membrane: The pore size is 3μm to 8μm, allowing air to pass through but not sand. The membrane surface is coated with a hydrophilic silane coating with a contact angle not exceeding 30° to enhance pore water wettability. The porous resonant membrane is wrapped around the composite skeleton using a hot-pressing process. The hot-pressing temperature is 60℃, the pressure is 2MPa, and the holding time is 15 minutes.

[0046] ⑤ Fiber Bragg grating sensor: The grating is 10mm long and has a center wavelength of 1550nm. It is glued to the center of the outer wall of the feedback cavity with epoxy resin, and the bonding area is 5mm×10mm. This ensures that the sensor can directly sense the extrusion deformation of the porous sphere inside the cavity.

[0047] ⑥ Ion-sensitive conductive coating: The substrate is polyurethane, doped with polyaniline conductive particles, with a coating thickness of 50μm to 80μm and a salinity response sensitivity of not less than 0.5S / (m・‰). It is prepared by spraying, with a spraying pressure of 0.3MPa, a spraying distance of 20cm, a drying temperature of 60℃, and a drying time of 2 hours, and is uniformly adhered to the surface of the skeleton.

[0048] Inter-unit connection: connected via flexible conductive fibers with a diameter of 500 nm and a conductivity of not less than 10. 4 S / m. The fiber surface is coated with a hydrophilic electrolyte gel. The gel composition, by mass percentage, is 20 parts polyacrylamide, 5 parts potassium chloride, and 75 parts deionized water. The preparation steps are as follows: first, dissolve potassium chloride in deionized water, then add polyacrylamide and stir until completely dissolved. Let it stand for 1 hour to remove bubbles before use. The ion-sensitive conductive coating of each dynamic feedback resonant coupling unit is connected to the ion-sensitive conductive coating of the adjacent unit by winding flexible conductive fibers, with 3 turns. The connection is fixed with conductive adhesive to ensure that the pore water and the fiber form an ion-conductive circuit.

[0049] Key process description: All components are manufactured using processes that can be achieved in ordinary factories, without the need for high-precision processing equipment. Molding, injection molding, spraying and other processes are all conventional rubber processing and coating preparation processes.

[0050] 2. Working Mechanism:

[0051] Sea level wave pressure resonance modulation and feedback enhancement:

[0052] When the change in pore water pressure gradient caused by sea level rise and fall reaches 0.05 MPa or above, the pressure pushes the pore water to act on the rubber valve check valve, causing the valve to open and the water to flow unidirectionally into the pore water feedback chamber and directly impact the elastic porous ball.

[0053] The elastic porous sphere undergoes elastic deformation when squeezed by water pressure. This deformation further amplifies the overall compression or expansion of the elastic gel conductive nanotube composite framework, resulting in a higher deformation amplification factor compared to a design without a feedback cavity.

[0054] The amplified deformation drives the fiber Bragg grating sensor to change the grating period, and the resonance frequency modulation amplitude is increased, realizing resonant feedback dual amplification and capture of pressure changes.

[0055] When the pore water pressure gradient changes below 0.05 MPa, the rubber valve one-way valve closes, and the elastic porous ball in the pore water feedback chamber rebounds elastically, pushing the water in the chamber to slowly seep out. The seepage time is 2 to 3 seconds, forming a pressure response hysteresis buffer, avoiding signal jitter caused by high frequency interference, and solving the defect of unstable amplitude modulation of existing acoustic signals.

[0056] Sediment transport flux-salinity resonance coupling and feedback stabilization:

[0057] When a sudden change in sediment transport flux leads to changes in pore water salinity and ion concentration, the salinity signal rapidly penetrates through the porous resonant membrane to the surface of the elastic gel conductive nanotube composite framework, and the conductivity of the ion-sensitive conductive coating changes synchronously.

[0058] Due to the conductive properties of the surface nano-silver particles, the elastic porous spheres in the pore water feedback cavity form a local conductive loop with the ion-sensitive conductive coating and flexible conductive fibers. The response time of the equivalent impedance of this loop with changes in salinity is shortened, which is an improvement compared to the design without feedback.

[0059] The closing characteristic of the rubber valve one-way valve causes the salinity change in the pore water feedback chamber to lag behind the external pore water by 1 to 2 seconds, forming a salinity change buffer zone. This avoids impedance signal noise caused by short-term salinity fluctuations and improves the signal-to-noise ratio.

[0060] The network equivalent impedance increases with the change in salinity, and the amplitude modulation amplitude of the resonant peak is expanded, realizing the resonant coupling feedback stable capture of salinity and electromagnetic properties. It naturally integrates the coupling effect of salinity on electromagnetic signals without the need for additional data correction.

[0061] Phase hysteresis resonant conduction delay and feedback calibration:

[0062] When pore water-mediated pressure and salinity signals propagate in a resonant network, the viscous resistance of the pore water between units generates a natural resonant phase difference. ;

[0063] The water flow time t through the rubber valve one-way valve in the pore water feedback chamber is 0.5s to 2s. This time is linearly positively correlated with the pore water viscosity μ. Based on the principle of fluid mechanics, the relationship t=k×μ can be established, where k is a proportionality coefficient, which is calibrated by the diameter of the one-way permeable hole. When the diameter of the one-way permeable hole is 10μm, k=0.02s・m² / (Pa・s), and when the diameter is 15μm, k=0.05s・m² / (Pa・s). This time parameter is used as the phase difference calibration reference.

[0064] During the decoding process, to achieve accurate calibration of the phase difference, the correlation between resonant frequency and conduction delay is combined with the relationship between water flow time and inter-unit phase difference to derive the following... =2π×t×f r , where f r The resonant frequency is used to form a dual verification of the conduction delay phase difference, thereby reducing the phase calibration error;

[0065] This mechanism completely transforms phase lag from an error term into a calibrable resonant conduction characteristic parameter, providing dual data support for time-effect quantization and solving the problem of deviation in time-effect quantization in existing technologies.

[0066] (II) Multimodal Coupling Acquisition Module:

[0067] 1. Structural design and assembly:

[0068] The resonant mode triggering unit employs a triple mechanism of dual resonant thresholds plus feedback time triggering. The preset resonant frequency offset threshold is ±150Hz, the resonant peak amplitude change threshold is ±35%, and the feedback chamber water flow passage time threshold is 0.5s to 2s. Acquisition is initiated only when all three thresholds are simultaneously met, ensuring the signal originates from real pore water dynamics rather than environmental interference. The triggering unit uses a standard relay switch (model HH52P) as its core component, with a coil voltage of 12V, and connects to the photoelectric synchronous acquisition unit via wires.

[0069] The photoelectric synchronous acquisition unit integrates a fiber optic interferometer and a micro-current sensor. The fiber optic interferometer is a MICRONOPTICSsi155 with a acquisition frequency of 1kHz and a phase resolution of 0.01°; the micro-current sensor is an AD8421 with a measurement range of 1μA to 100μA and an accuracy of 0.01μA. Optical signals are transmitted via a connection between a standard quartz optical fiber and a fiber optic grating sensor in the pore water sediment co-resonance network; current signals are acquired via a shielded wire connected to an ion-sensitive conductive coating. Synchronization control is achieved using an STM32F103 timer, with a time synchronization error not exceeding 8μs, meeting accuracy requirements and being easier to implement.

[0070] Multi-dimensional data integration unit: A new feedback parameter extraction submodule is added, using an Arduino Uno microcontroller as its core. Through programming, it achieves real-time extraction of three-dimensional auxiliary parameters: one-way valve opening status, water flow time, and the amplitude variation of the multi-hole spherical valve. This unit connects to the photoelectric synchronous acquisition unit via a data bus, integrating the extracted three-dimensional auxiliary parameters with the original resonant frequency, peak amplitude, equivalent impedance, and phase difference into a seven-dimensional data set. The data output interface is RS485.

[0071] 2. Working Mechanism:

[0072] When the changes in pore water pressure and salinity meet the triple trigger threshold, the relay switch of the resonant mode triggering unit closes, activating the photoelectric synchronous acquisition unit.

[0073] The photoelectric synchronous acquisition unit continuously acquires seven-dimensional data at a frequency of 1kHz. Through the transmission path of the cellular network, it records the phase difference distribution and feedback parameter differences between different dynamic feedback resonant coupling units.

[0074] The data integration unit removes abnormal signals by verifying the consistency of feedback parameters. The verification standard is that the time difference between water flow in adjacent units does not exceed 0.3s, thereby reducing the abnormal data removal rate.

[0075] (III) Nonlinear Resonance Decoding Module:

[0076] Establishment of the coupling model:

[0077] Pressure Resonance Frequency Coupling Equation: To accurately quantify the relationship between pore water pressure changes and resonance frequency modulation, and to reflect the amplification effect of the dynamic feedback enhancement mechanism, the pressure resonance frequency coupling equation is established by combining fluid elasticity and nonlinear resonance theory as follows:

[0078] ;

[0079] in:

[0080] This is the measured resonant frequency;

[0081] The initial resonant frequency is calibrated by the dynamic feedback resonant coupling unit before leaving the factory, and its value ranges from 1500Hz to 2000Hz.

[0082] The linear pressure coefficient is determined by the elastic modulus of the elastic gel conductive nanotube composite framework, where the elastic modulus is 1.2 MPa. =50Hz / MPa, elastic modulus is 2.5MPa =30Hz / MPa;

[0083] This represents the change in pore water pressure.

[0084] The nonlinear pressure coefficient is calibrated by the nonlinear deformation characteristics of the elastic gel conductive nanotube composite framework, and its value ranges from 5Hz / (MPa²) to 8Hz / (MPa²).

[0085] The feedback enhancement coefficient is calibrated by the porosity of the elastic porous sphere, with a porosity of 35%. =0.02Hz / (mm / s), porosity 45% =0.03Hz / (mm / s);

[0086] To provide feedback on the water flow velocity within the cavity, it is calculated from the flow rate of the unidirectional permeable holes, using the following formula:

[0087] ,

[0088] in The flow rate of the one-way permeable pore. The cross-sectional area of ​​the unidirectional permeable hole, and the flow rate of the unidirectional permeable hole. The linear relationship between current and flow rate in an ion-conducting circuit is indirectly calculated using current signals acquired by a micro-current sensor.

[0089] ,

[0090] in The flow coefficient is 0.01 mm³ / (s·μA). The current value collected by the microcurrent sensor;

[0091] Salinity equivalent impedance peak amplitude coupling equation: To accurately describe the relationship between salinity changes and resonance peak amplitude and equivalent impedance, while incorporating the role of dynamic feedback stabilization mechanism, the salinity equivalent impedance peak amplitude coupling equation is established based on the principle of electromagnetic induction and nonlinear resonance theory as follows:

[0092] ,

[0093] in:

[0094] This represents the peak amplitude of the resonance.

[0095] The initial resonance peak amplitude is calibrated to a value ranging from 0.5V to 1.0V before leaving the factory.

[0096] The salinity response coefficient is calibrated by the salinity response sensitivity of the ion-sensitive conductive coating, with a sensitivity of 0.5 S / (m·‰). =0.03 / ‰, sensitivity is 0.8 S / (m·‰) =0.05 / ‰;

[0097] This represents the change in pore water salinity.

[0098] The feedback stability coefficient is calibrated by the hysteresis time of the rubber valve check valve; when the hysteresis time is 1 second... =0.01 / ‰, with a lag time of 2s =0.02 / ‰;

[0099] To provide feedback on cavity salinity lag time, it is extracted through a multi-dimensional data integration unit;

[0100] The network equivalent impedance is calculated from the current value collected by the micro-current sensor and the applied voltage. The calculation formula is:

[0101] ,

[0102] in The excitation voltage applied to the ion-sensitive conductive coating is 5V. The collected current value;

[0103] Phase difference response time-dependent coupling equation: To achieve accurate quantification of the time-dependent response of sediment transport to sea level fluctuations, the phase difference response time-dependent coupling equation is established by combining resonant conduction delay and feedback calibration mechanism as follows:

[0104] ,

[0105] in:

[0106] The response time of sediment transport to sea level fluctuations;

[0107] The average resonant phase difference between units is calculated using data acquired by a fiber optic interferometer.

[0108] The spacing between the dynamic feedback resonant coupling units is fixed at 30,000 mm during deployment.

[0109] The propagation velocity of the resonance signal in pore water is calibrated by the viscosity and permeability of the pore water, with a viscosity of 0.001 Pa·s and a permeability of 10. -12 m² =50mm / s;

[0110] The feedback calibration coefficient, ranging from 0.8 to 0.9, is determined by the porosity of the elastic porous sphere, with a porosity of 35%. =0.8, porosity 45% =0.9;

[0111] The time of water flow through the feedback chamber is extracted through a multi-dimensional data integration unit.

[0112] 2. Collaborative decoding process:

[0113] Step 1: Data Validation – Pressure Data Screening. The seven-dimensional data set output from the multi-dimensional data integration unit is input into the nonlinear resonance decoding module to extract the water flow velocity. and pressure change Based on the correlation between water flow velocity and pressure in fluid mechanics, this study verifies... and The linear fit, the fit R 2 If the pressure is not lower than 0.95, it is considered a valid pressure data point; otherwise, the data point is discarded.

[0114] Step 2: Data Validation – Salinity Data Screening. Extracting Salinity Lag Time. and salinity change Establish time-series matching relationships based on salinity diffusion patterns:

[0115] ,

[0116] Calculate the actual If the deviation from the theoretical value is no more than 0.2s, it is considered valid salinity data; otherwise, the data set is discarded.

[0117] Step 3: Parameter Back-Calculation and Triple Coordination Verification. Substituting the valid data into the pressure resonant frequency coupling equation and the salinity equivalent impedance peak amplitude coupling equation, the change in pore water pressure is obtained by solving these equations simultaneously. and salinity change Simultaneously verify the temporal relationship among the three: the pressure change occurs earlier than the salinity change, and the water flow through the feedback chamber occurs between the two. If this condition is met, proceed to the next step; otherwise, discard the data set.

[0118] Step 4: Quantification of sea level fluctuation amplitude. This involves reverse-engineering the calculated amplitude. Substituting the values ​​into the tidal resonance coupling model, which is based on the principle of estuarine tidal dynamics and considers the influence of sediment elastic deformation on pressure, the amplitude of sea level fluctuations is calculated. The calculation formula is:

[0119] ,

[0120] in The density of seawater, It is the acceleration due to gravity. The tidal calibration coefficient ensures that the final sea level fluctuation amplitude error does not exceed ±2cm, representing an improvement over existing technologies.

[0121] Step 5: Quantification of sediment transport flux intensity. The inversely derived... Substituting the sediment transport resonance diffusion model into the model, which combines sediment diffusion laws and resonance characteristics, the sediment transport flux intensity is calculated. The calculation formula is:

[0122] ,

[0123] in The sediment diffusion coefficient is... The sediment transport calibration coefficient is used, and the sediment transport flux measurement range is from 0.1 kg / (m²·s) to 100 kg / (m²·s), with an accuracy not exceeding ±5%.

[0124] Step 6: Quantification of response time. The average resonant phase difference between elements is... and the time it takes for the water to pass through the feedback chamber Substituting the phase difference response time-dependent equation, the response time of sediment transport to sea level fluctuations is calculated. The error is no more than ±30s, which completely solves the problem of timeliness quantification deviation in existing technologies.

[0125] (iv) Dynamic Modal Calibration Module:

[0126] 1. Structural design and assembly:

[0127] Resonance mode reference unit: The structure is consistent with the dynamic feedback resonance coupling unit, the difference being that its pore water feedback cavity is filled with high-density elastic porous spheres with a porosity of 20% to 25%, reducing pressure response sensitivity. The reference unit is embedded in the stable region of the sediment layer at a depth of not less than 5m to ensure that the resonance frequency fluctuation does not exceed ±3Hz, thus improving stability. It is connected to the surrounding dynamic feedback resonance coupling units through flexible conductive fibers to form a calibration reference network;

[0128] Network Consistency Verification Unit: Utilizing an STM32F407 microcontroller as its core, it connects to a multi-dimensional data integration unit via a data bus, receiving seven-dimensional data sets from each dynamic feedback resonant coupling unit and the reference unit in real time. It incorporates a consistency analysis program to determine network consistency by calculating resonant frequency similarity and feedback parameter similarity.

[0129] Adaptive Correction Unit: Integrates a D / A conversion module and a constant current source module. The D / A conversion module is a DAC8552, and the constant current source module is a REF200. The output current range is 0μA to 10μA, with an accuracy of 0.01μA. It is connected to the ion-sensitive conductive coating of each dynamic feedback resonant coupling unit via shielded wires to apply a calibration current; simultaneously, it is connected to a fiber optic grating sensor on the outer wall of the feedback cavity via wires to monitor the resonant mode changes after calibration in real time.

[0130] 2. Working Mechanism:

[0131] The network consistency verification unit calculates in real time the resonant frequency similarity between each dynamic feedback resonant coupling unit and the reference unit, as well as the feedback parameter similarity between adjacent units. The formula for calculating the resonant frequency similarity is:

[0132] ,

[0133] in The resonant frequency of the reference element; the formula for calculating the similarity of the feedback parameters is:

[0134] ,

[0135] in The water flow time is the reference unit.

[0136] When the similarity of the resonant frequencies of a certain dynamic feedback resonant coupling unit exceeds 8%, or the similarity of the feedback parameters is less than 80%, the adaptive correction unit is activated.

[0137] The adaptive correction unit queries the corresponding calibration current value based on the preset feedback parameter resonant mode mapping matrix. The mapping matrix is ​​established through pre-shipment testing, recording the optimal calibration current under different deviation conditions. For example, when the resonant frequency similarity is 10%, the calibration current is 5μA.

[0138] A weak calibration current, not exceeding 10 μA, is applied to the ion-sensitive conductive coating of the deviation unit through a constant current source module to adjust the coating conductivity. At the same time, the calibration current is conducted to the elastic porous sphere through flexible conductive fibers, which generates a weak electric field on the surface of the porous sphere, causing slight deformation and forming a current deformation synergistic correction. The calibration response time does not exceed 5 seconds.

[0139] During the calibration process, the fiber optic grating sensor collects the resonant frequency changes in real time. When the resonant frequency similarity drops below 8% and the feedback parameter similarity increases to above 80%, the adaptive correction unit stops applying the calibration current.

[0140] The calibration parameters are automatically generated by the dynamic changes of the resonance network. Based on the mapping relationship of the resonance modes of the feedback parameters, no complex algorithms such as neural networks are required. Ordinary microcontrollers can perform the calculations, ensuring that the calibration process is real-time and efficient.

[0141] III. System Workflow:

[0142] 1. Deployment Phase:

[0143] The dynamic feedback resonance coupling units of the pore water sediment co-resonance network are arranged in a honeycomb pattern at 30m intervals in the estuarine shelf transition zone, and the resonance mode reference unit is deployed in the sedimentary stable zone at the center of the network, at a depth of not less than 5m.

[0144] Before deployment, a simple pressure test was conducted to verify the opening status of the rubber valve check valve. The test tool was a manual pressure pump, and the pressure was gradually increased from 0MPa to 0.1MPa. The pressure value when the check valve opened was recorded to ensure that the water flow time through the feedback chamber of each unit was in the range of 0.5s to 2s.

[0145] A gravity-type embedding tool is used to press the unit into the deposition layer. The embedding tool weighs 50 kg. After being lifted manually, it is dropped freely to generate impact force, so that the unit is vertically embedded into the deposition layer to a preset depth, ensuring that the porous resonant membrane and pore water are completely wetted, and the wetting time does not exceed 5 minutes.

[0146] After the unit deployment is completed, connect the flexible conductive fiber, ordinary quartz optical fiber and various wires, and use a multimeter to test the conductivity of the ion conduction circuit. If the conductivity resistance does not exceed 100Ω, the connection is considered qualified.

[0147] 2. Resonance Capture Phase:

[0148] Sea level fluctuations and changes in sediment transport flux cause dynamic changes in pore water pressure and salinity. When the pressure change reaches 0.05 MPa or above, the rubber valve one-way valve opens, and water flows into the feedback chamber to impact the elastic porous ball, driving the composite skeleton to deform. The fiber optic grating sensor captures the amplified resonant frequency signal.

[0149] Salinity changes permeate to the surface of the skeleton through the porous resonant membrane, causing changes in the conductivity of the ion-sensitive conductive coating. This, in turn, generates impedance changes in the local conductive circuit formed by the flexible conductive fiber and the elastic porous sphere. The microcurrent sensor captures the impedance signal after salinity coupling.

[0150] The synchronous recording unit records feedback parameters such as the resonant phase difference between units, the opening status of the one-way valve, the water flow time, and the deformation amplitude of the porous sphere, forming a raw data set.

[0151] 3. Synchronous acquisition phase:

[0152] The resonant mode triggering unit monitors the raw data set in real time. When the resonant frequency shift reaches ±150Hz, the resonant peak amplitude change reaches ±35%, and the water flow passage time in the feedback chamber is in the range of 0.5s to 2s, the relay switch closes and the photoelectric synchronous acquisition unit is activated.

[0153] The photoelectric synchronous acquisition unit continuously acquires seven-dimensional data at a frequency of 1kHz, including resonant frequency, peak amplitude, equivalent impedance, phase difference, one-way valve opening status, water flow time, and multi-hole spherical deformation amplitude. The data is then transmitted to the multi-dimensional data integration unit via ordinary quartz optical fiber and shielded wire.

[0154] The data integration unit removes abnormal signals by verifying the consistency of feedback parameters. The water flow time difference between adjacent units is considered to be consistent if it does not exceed 0.3s. The abnormal data removal rate is controlled below 2%. The integrated seven-dimensional data group is output to the nonlinear resonance decoding module through the RS485 interface.

[0155] 4. Collaborative Decoding Phase:

[0156] The nonlinear resonance decoding module processes the seven-dimensional data set in the following order: pressure data screening for data validity verification, salinity data screening for data validity verification, parameter back-inference and triple collaborative verification, sea level fluctuation amplitude quantification, sediment transport flux intensity quantification, and response time quantification.

[0157] After processing, three sets of core data are output: sea level fluctuation amplitude, sediment transport flux intensity, and response time. The data update frequency is 1Hz, and the data is uploaded to the terminal display device in real time through the data interface.

[0158] 5. Dynamic calibration phase:

[0159] The dynamic modal calibration module receives seven-dimensional data sets from each unit and the reference unit in real time, and the network consistency verification unit continuously calculates the similarity of resonance frequencies and feedback parameters.

[0160] When a deviation unit is detected, the adaptive correction unit is activated, queries the mapping matrix to determine the calibration current, and applies it to the ion-sensitive conductive coating of the deviation unit through the constant current source module to achieve coordinated correction of current deformation.

[0161] The calibration process is carried out simultaneously with data acquisition and decoding, without interrupting the normal operation of the system, ensuring stable detection accuracy throughout the entire process.

[0162] Summarize:

[0163] Through the resonant feedback dual amplification mechanism, the amplitude of the modulation signal of the effective stress of the sediment is significantly improved, the signal recognition is greatly enhanced, no additional correction of the acoustic signal is required, and the measurement error of sea level fluctuation amplitude is effectively controlled.

[0164] The hysteresis buffering effect of the feedback cavity forms a salinity change buffer band, which significantly improves the signal-to-noise ratio of the coupling signal between salinity and electromagnetic properties. The gradient of the network equivalent impedance with salinity change is significantly increased, naturally avoiding coupling interference. No data post-processing is required, and the accuracy of sand transport flux measurement is greatly improved.

[0165] Using the water flow time through the rubber valve check valve as the phase difference calibration benchmark, a dual verification mechanism of conduction delay and phase difference is established, which effectively reduces phase calibration error, achieves accurate quantification of response time, and completely solves the problem of time quantification deviation in existing technologies.

[0166] The one-way valve design of the feedback chamber separates the transmission paths of the pressure signal and the salinity signal, significantly reducing their cross-interference rate and achieving accurate separation of the two signals, thus providing a basis for multi-parameter synchronous quantization.

[0167] The hydrophilic coating of the porous resonant membrane enhances the wettability of pore water. Combined with the unidirectional water flow design of the feedback cavity, it significantly reduces the siltation rate. The system can work continuously for a long time after a single deployment without the need for regular cleaning, which significantly reduces operation and maintenance costs.

[0168] The triple threshold triggering mechanism avoids invalid data collection, significantly reducing system standby power consumption. Combined with the selection of low-power components, the system can work continuously for a long time after a single deployment, solving the problem of frequent power supply replacement in existing technologies.

[0169] Based on the correlation between the water flow velocity in the feedback chamber and the opening time of the one-way valve, and combined with fluid mechanics principles, the pore water flow rate in the sedimentary layer can be simultaneously inverted through calculations using relevant formulas. Existing technologies cannot directly obtain this parameter and require measurement using additional equipment. This system expands the detection capabilities, provides new fundamental data for the study of shelf sedimentary evolution, and fills a technological gap in the field.

Claims

1. A geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations, characterized in that, It includes a pore water sediment co-resonance network, a multimodal coupling acquisition module, a nonlinear resonance decoding module, and a dynamic modal calibration module; The pore water sediment synergistic resonance network is formed by connecting 15 to 25 dynamic feedback resonance coupling units in a honeycomb topology through flexible conductive fibers. The pore water sediment synergistic resonance network is embedded in the sedimentary layer of the estuarine shelf transition zone. The multimodal coupling acquisition module is connected to the fiber grating sensor in the pore water sediment co-resonance network via a quartz optical fiber, and to the ion-sensitive conductive coating in the pore water sediment co-resonance network via a wire. The nonlinear resonance decoding module is connected to the multimodal coupling acquisition module via a data transmission line and is used to receive the seven-dimensional data set output by the multimodal coupling acquisition module. The dynamic modal calibration module is connected to the pore water sediment co-resonance network and the nonlinear resonance decoding module via wires, respectively, for the transmission and feedback of calibration signals; The system modulates the resonant modes through the dynamic changes in pore water.

2. The geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations as described in claim 1, characterized in that: The dynamic feedback resonance coupling unit of the pore water sediment synergistic resonance network is embedded in the sedimentary layer of the estuarine shelf transition zone at a depth of 0.3 m to 4 m. Each dynamic feedback resonance coupling unit includes an elastic gel conductive nanotube composite framework with an elastic modulus of 1.2 MPa to 2.5 MPa. It has 3 to 5 uniformly distributed pore water feedback cavities with a cavity diameter of 8 mm to 12 mm.

3. The geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations as described in claim 2, characterized in that: Each dynamic feedback resonant coupling unit has a pore water feedback chamber filled with one to two elastic porous spheres made of natural rubber with a porosity of 35% to 45% and a particle size of 4 mm to 6 mm. The pore water feedback chamber wall has two to three one-way permeable holes with a pore size of 10 μm to 15 μm. Each one-way permeable hole is embedded with a rubber valve with a thickness of 0.1 mm to 0.2 mm and an opening pressure of 0.05 MPa to 0.1 MPa.

4. The geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations as described in claim 3, characterized in that: Each dynamic feedback resonant coupling unit is externally wrapped with a porous resonant membrane with a pore size of 3 μm to 8 μm and a hydrophilic silane coating with a contact angle not exceeding 30°. A fiber optic grating sensor is attached to the center of the outer wall of the pore water feedback cavity of the elastic gel conductive nanotube composite framework. The fiber optic grating sensor has a grating length of 10 mm, a center wavelength of 1550 nm, and an attachment area of ​​5 mm × 10 mm. An ion-sensitive conductive coating is attached to the surface of the elastic gel conductive nanotube composite framework. The substrate of the ion-sensitive conductive coating is polyurethane, doped with polyaniline conductive particles.

5. The geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations as described in claim 1, characterized in that: The multimodal coupling acquisition module includes a resonant mode triggering unit, a photoelectric synchronous acquisition unit, and a multi-dimensional data integration unit. The resonant mode triggering unit adopts a triple triggering mechanism with a preset resonant frequency offset threshold of ±150Hz, a resonant peak amplitude change threshold of ±35%, and a feedback chamber water flow passage time threshold of 0.5s to 2s.

6. The geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations as described in claim 1, characterized in that: The nonlinear resonance decoding module incorporates the pressure resonance frequency coupling equation, the salinity equivalent impedance peak amplitude coupling equation, and the phase difference response time-dependent coupling equation. In the pressure resonant frequency coupling equation, the initial resonant frequency ranges from 1500Hz to 2000Hz, the linear pressure coefficient ranges from 30Hz / MPa to 50Hz / MPa, and the nonlinear pressure coefficient ranges from 5Hz / (MPa²) to 8Hz / (MPa²). In the salinity equivalent impedance peak amplitude coupling equation, the initial resonant peak amplitude ranges from 0.5V to 1.0V, and the salinity response coefficient ranges from 0.03 / ‰ to 0.05 / ‰.

7. The geophysical identification and quantification system for the response of river sediment transport flux to sea level fluctuations as described in claim 1, characterized in that: The dynamic modal calibration module includes a resonant modal reference unit, a network consistency verification unit, and an adaptive correction unit; the structure of the resonant modal reference unit is consistent with that of the dynamic feedback resonant coupling unit.

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