Method for calculating salt exchange amount of biological channel in muddy tidal flat

Abstract

The application provides a calculation method of a salt exchange amount of a biological channel of a muddy tidal flat, and belongs to the field of hydrogeology and geophysical monitoring and evaluation. Specifically, in combination with the sedimentary characteristics that the weak permeable layer of the muddy tidal flat is dense and the medium resistivity difference is significant, a diffusion-convection coupling model is used to quantitatively measure molecular diffusion and tidal convection, and a full-chain automatic calculation system from ERT time series monitoring to tidal period integration is constructed, so as to improve the salt flux calculation accuracy from the meter equivalent porous medium unit to the centimeter single biological channel scale.

Description

Technical Field

[0001] This application relates to the field of hydrogeological and geophysical monitoring and evaluation, and specifically proposes a method for calculating the salt exchange of muddy tidal flat biochannels based on resistivity tomography (ERT) time-series electrical resistivity monitoring and diffusion-convection coupling model. Background Technology

[0002] Currently, in the fields of water and salt cycle research in muddy tidal flats, coastal ecological restoration assessment, and carbon-nitrogen coupled cycle analysis, quantitative calculation of salt exchange through biological channels (such as crab burrows, worm holes, and shellfish habitat holes formed by benthic animal activities) remains a core challenge for academic circles both domestically and internationally. Although muddy tidal flats are home to a large number of benthic animals such as crabs (e.g., Tianjin thick crab, Japanese giant crab), polychaete worms, and shellfish, their burrowing activities create a dense network of biological channels (density reaching 10–100 channels / m²) in the surface and subsurface layers of the mudflat. 2 These channels are key pathways for salt exchange between tidal flat sediments and overlying seawater. However, current technologies still face several technical bottlenecks in the quantitative calculation of salt exchange in these channels, including their small size (mostly 1–10 cm in diameter), non-uniform distribution, and dynamic changes in filling state with tides, making it difficult to accurately assess their contribution to salt exchange.

[0003] Existing in-situ sampling and analysis methods mainly involve deploying monitoring wells and pore water samplers to collect pore water samples at different depths in tidal flats. Salinity is then measured in the laboratory, and Darcy's law or diffusion models are used to estimate salt flux. This method has significant limitations: First, the sampling points are distributed in a point-like pattern, failing to reflect the high-density, non-uniform distribution of biological channels, resulting in poor spatial representativeness. Second, the sampling frequency is low (usually once daily or every other day), failing to capture the dynamic changes in salt exchange within biological channels during the tidal cycle (approximately 12.4 hours). Third, the sampling process disturbs the original sediment structure, potentially damaging the natural morphology of biological channels and affecting calculation accuracy. Fourth, the surface silt of muddy tidal flats is soft and has low bearing capacity, making frequent sampling difficult and labor-intensive.

[0004] While existing isotope tracing methods (such as deuterium, oxygen-18, and radium isotopes) can be used to estimate seafloor groundwater discharge (SGD), they have inherent limitations in applications at the scale of biochannels in muddy tidal flats: First, the assumption of homogeneity in tracer mixing is difficult to satisfy in areas with dense biochannels, resulting in calculations that are macroscopic and cannot distinguish the contribution of individual channels; second, radioactive isotope analysis is costly and time-consuming, making it unsuitable for large-scale continuous monitoring; and third, bio-disturbance (bio-irrigation) can alter the natural distribution of tracers, introducing additional uncertainties.

[0005] In geophysical research, resistivity tomography (ERT) technology, while capable of identifying electrical anomalies in strata, is currently limited to static detection of stratigraphic structures: (1) it lacks a continuous quantitative conversion method from time-series resistivity data to pore water salinity; (2) it lacks precise electrical identification and parameter extraction techniques for cm-level biological channels; (3) it lacks a single-channel flux quantitative calculation model that couples tidal convection and molecular diffusion effects; and (4) it lacks a systematic integral calculation method from single-channel flux to the total exchange volume of tidal flat sections. Therefore, there is an urgent need to develop a method adapted to the complex environment of muddy tidal flats that can quantitatively calculate the salt exchange volume of biological channels, providing quantitative technical support for coastal eco-hydrological research. However, in terms of salt flux calculation, the following methods are currently lacking: The method, namely, using 1 m 3 The smallest calculation unit (φ is porosity), and (These are the pore water salinity values ​​at the current and previous moments, respectively), and the salt accumulation / release rate is calculated for each region.

[0006] From the perspective of salt flux calculation methods, existing technologies mainly have the following technical defects: (1) The calculation unit scale is too large: taking 1 m 3 Generalizing densely packed biological channels as equivalent porous media as the smallest unit fails to analyze the flux contribution of individual biological channels (typically 1–10 cm in diameter). Numerical simulations by Xiao et al. (2019) show that the pore water velocity around a single crab burrow is 2 × 10⁻⁶ times that of a channelless region. 4 This highly non-uniform flow field distribution is completely masked after homogenization. (2) Diffusion and convection were not quantified separately: Combining molecular diffusion and tidal convection into a single apparent salinity change makes it impossible to distinguish the relative contributions of the two mechanisms to salinity flux at different tidal stages. In low-permeability sediments of muddy tidal flats, molecular diffusion may be the dominant mechanism for long-term salinity exchange (timescale on the hourly level), while tidal convection exhibits periodic pulse-driven behavior (timescale on the minute level). The coupling relationship between the two needs to be decoupled and quantified. (3) Lack of flux calculation model driven by single-channel geometric parameters: Existing technologies assign values ​​to densely populated biological channels as a unified high-permeability zone, without addressing the quantitative impact of geometric parameters such as the equivalent radius r and effective length L of a single channel on flux. Under the framework of Poiseuille tube flow theory, channel permeability The permeability of a 2 cm radius channel is 4 times that of a 1 cm radius channel. The difference in channel shape directly determines its salt transport efficiency. (4) There is a lack of end-to-end connection between ERT inversion and flux calculation: ERT data is mainly used to provide initial conditions for numerical simulation. Numerical simulation is an independent process and lacks a systematic and automatic calculation process from ERT raw data → single channel flux → total tidal cross-section flux.

[0007] In view of the above, this application is hereby submitted. Summary of the Invention

[0008] The method for calculating salt exchange in muddy tidal flat biochannels described in this application aims to overcome the problems of the existing technology by combining the sedimentary characteristics of dense, poorly permeable muddy tidal flat layers and significant differences in medium resistivity. It quantifies molecular diffusion and tidal convection separately through a diffusion-convection coupling model, and constructs a fully automated calculation system from ERT time-series monitoring to tidal cycle integration, in order to improve the accuracy of salt flux calculation from meter-level equivalent porous medium units to centimeter-level single biochannel scale.

[0009] To achieve the aforementioned objectives, the method for calculating the salt exchange capacity of the biochannel in muddy tidal flats involves deploying a fixed ERT time-series monitoring system across the tidal flat cross-section and continuously collecting time-series resistivity data at fixed time intervals for different tidal phases to simultaneously monitor environmental parameters including tidal level and water temperature. The time-series resistivity data undergoes time-shift inversion and tidal error correction to construct a two-dimensional resistivity spatiotemporal distribution profile sequence. Based on a laboratory-calibrated resistivity-pore water salinity quantitative conversion model, the corrected time-series resistivity profile is transformed into a pore water salinity spatiotemporal distribution sequence. Based on biological... The low resistivity anomaly of the channels was used to establish a dual-criteria system to identify the spatial distribution of biological channels and extract geometric parameters such as the equivalent radius, effective length, and channel density of each channel. A diffusion-convection coupled single-channel salt flux calculation model was constructed. Based on the seepage-driven effect caused by tidal fluctuations, the instantaneous salt exchange flux of each biological channel was calculated time-by-time, and the independent contributions of molecular diffusion mechanism and tidal convection mechanism were quantified respectively. The instantaneous salt flux of all identified channels was integrated over time over the tidal cycle to obtain the total salt flux and flux per unit area of ​​biological channels at the monitoring section.

[0010] The method includes the following implementation process: Step S1: Deploy a fixed ERT high-density electrode array on the target section of the muddy tidal flat and simultaneously install tide level and water temperature monitoring sensors. Step S2: Collect time-series resistivity data covering the entire tidal cycle continuously at fixed time intervals to complete preprocessing and tidal error correction; Step S3: Construct a two-dimensional resistivity inversion model of the formation, invert the resistivity data at each time point into a two-dimensional resistivity profile, and refine the grid in the anomaly area; Step S4: Based on the resistivity-salinity conversion model, the corrected time-series resistivity profile is converted into a pore water salinity spatiotemporal distribution sequence. Step S5: Identify biological channels from the resistivity profile based on the dual criteria and extract the equivalent radius of each channel. Effective length and channel density ; Step S6: Calculate the instantaneous salinity flux of the diffusion and convection terms for each biological channel at each time step. ; Step S7: Perform trapezoidal numerical integration on the instantaneous flux of all channels over the tidal cycle, and statistically analyze the flux in segments according to the four tidal stages to obtain the total salt flux and flux per unit area.

[0011] Step S1 includes deploying a fixed high-density electrode array on the target cross section of the muddy tidal flat. Time-series resistivity data are continuously collected at fixed time intervals, and a single monitoring session covers no less than one complete tidal cycle. Fully automated tidal level monitoring sensors and water temperature probes were deployed simultaneously to continuously collect tidal level and water temperature time-series data around the clock. Before the resistivity data entered into subsequent processing, a multi-time-series tidal correction method was used for preprocessing. The correction formula is as follows: in, The corrected resistivity; This is the measured resistivity; The coefficient representing the influence of tidal water level changes; This represents the tidal level change at adjacent sampling times, during high tide. >0, the correction term is negative; during low tide <0, the correction term is positive.

[0012] Step S3 includes constructing a two-dimensional resistivity inversion model of the formation using the finite element method, with the inversion objective function being:

[0013] in, For data residuals; For model regularization terms; As a regularization factor; Non-uniform grid refinement inversion is performed on the low-resistivity anomaly region of the biological channel, and the refinement coefficient k satisfies the following formula: in, The base encryption coefficient; The resistivity of the clay matrix; Measured resistivity in the abnormal region; The reliability of the inversion results is assessed using the following DOI index; ,when DOI A value less than 0.2 indicates that the inversion results are reliable.

[0014] Step S4 includes establishing the resistivity-pore water salinity quantitative conversion model through the following indoor calibration process: Undisturbed sediment samples were collected from different depths in the target area. NaCl solutions with salinity of 0-100‰ were prepared and, after full saturation, resistivity was measured using a four-electrode AC impedance method. The following calibration relationship was established through least-squares nonlinear fitting:

[0015] in, C Pore ​​water salinity, in‰; ρ The measured resistivity of the sediment is expressed in Ω·m. k For media calibration Coefficient; n is the cementation index; C 0 This is the residual salinity constant, expressed in per mille (‰).

[0016] Step S4 includes performing the following resistivity-pore water resistivity conversion on surface sediments with high clay content using a modified form of Archie's law: in, F´ For effective layer factor; b This is a contribution to surface conductivity.

[0017] Step S4 includes converting the brine-containing sand layer using the following Archie formula: Where a is the lithology coefficient; φ is the porosity; and m is the cementation index; The conversion was completed by combining the empirical relationship between salinity and resistivity from Manheim et al.

[0018] In step S5, the identification of biological channels adopts the following dual-criteria combined approach: Criterion 1: The absolute value of resistivity is used as the criterion, i.e. < < ; in, For the corrected resistivity, This represents the upper limit of abnormally low resistance in biological channels. This represents the lower limit of resistivity for clayey matrices. Criterion two, using spatial gradient as the criterion, i.e. G th ; Where ∇ρ is the magnitude of the resistivity spatial gradient; G th The threshold for determining gradient anomalies is determined by adding three times the standard deviation to the mean of the resistivity gradient distribution in the background region of the profile. For pixels that simultaneously meet the above dual criteria, perform neighborhood connected component clustering analysis, and filter out false positive abnormal areas with an area of ​​less than 3 pixels or greater than 100 pixels. Geometric parameters are extracted for each valid anomaly region, and the formula for calculating the equivalent radius is as follows: ; Effective length Determined by the vertical extension depth of the anomaly zone; Channel density , where N is the total number of valid anomaly zones and A is the monitoring cross-sectional area.

[0019] In step S6, the single-channel salt flux calculation model of diffusion-convection coupling is as follows: For the i-th identified biological channel, its instantaneous salt flux at time t is... J i (t) as follows: in, The equivalent radius of the channel; The effective diffusion coefficient of the porous medium; Seawater salinity, expressed in g / m³ 3 ; The salinity of the pore water in the channel at time t is expressed in g / m³. 3 The values ​​were obtained from the resistivity-salinity conversion model. The effective length of the channel; Channel penetration rate; This refers to the fluid dynamic viscosity, measured in Pa·s. The tidal level difference between time t and the previous data collection time, in meters; the first term Describes the concentration gradient-driven molecular diffusion flux; the second term Describes the convective seepage flow driven by tidal water level changes; Channel penetration The estimation formula for tubular biological channels based on the Poiseuille flow model is: .

[0020] In step S7, the tidal period integration process is as follows: The instantaneous salinity flux of all identified biological channels within a complete tidal cycle is integralized over time and summed spatially using the following formula: in, The total salt flux is expressed in grams; N represents the total number of biological channels identified; and T represents the duration of the tidal cycle. The actual calculation uses the following trapezoidal numerical integration formula: Where M is the total number of data collection moments; Δt is the data collection time interval.

[0021] In summary, this application has the following main advantages and benefits compared with the prior art: 1. This application upgrades the existing qualitative detection technology to a quantitative calculation method, thereby effectively solving the problem of muddy tidal flats. The problem of difficulty in quantitatively assessing salt exchange in biological channels has been addressed by this study, which overcomes the shortcomings of existing technologies in continuously monitoring salt flux over time. This provides quantitative technical support for the study of water-salt cycle mechanisms in muddy tidal flats, the evaluation of coastal ecological restoration effects, and the analysis of carbon-nitrogen coupled cycles.

[0022] 2. This application innovatively elevates the calculation of salt flux from meter-level equivalent porous media units to centimeter-level single biological channel rulers. The spatial resolution is improved by 2 to 3 orders of magnitude, and it can accurately resolve biological channels with diameters of 1 to 10 cm through dual criterion identification and grid-encrypted inversion.

[0023] 3. This application innovatively achieves the decoupling and quantification of molecular diffusion and tidal convection by constructing a diffusion-convection coupled single-channel flux calculation model. Experimental verification shows that diffusion contributes approximately 62% and convection contributes approximately 38% in the muddy tidal flat biochannel environment.

[0024] 4. This application establishes an end-to-end automated calculation process from ERT raw data to salt flux output, which improves the calculation efficiency by more than 5 times compared with the manual sampling and analysis method adopted in the prior art, and controls the overall relative error within ±10%.

[0025] 5. By introducing a four-stage tidal segmented statistical method, this application can quantitatively reveal the changes in the direction and rate of salt transport in biological channels at different tidal stages, providing a technical means for a deeper understanding of the entire chain of salt circulation process driven by evaporation salt, seawater dissolution, channel infiltration, and tidal pumps. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. Some specific embodiments of this application will be described in detail below with reference to the accompanying drawings in an exemplary and non-limiting manner. The same reference numerals in the drawings designate the same or similar parts or components. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale.

[0027] Figure 1This is a flowchart illustrating the method for calculating the salt exchange capacity of the biochannels in muddy tidal flats described in this application. Figure 2 A schematic diagram of a biological channel diffusion-convection coupled salt flux calculation model; Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0029] Example 1, as Figure 1 and Figure 2 As shown, the method for calculating the salt exchange capacity of the muddy tidal flat biochannel proposed in this application involves deploying a fixed ERT time-series monitoring system on the tidal flat cross section and continuously collecting time-series resistivity data at different tidal stages at fixed time intervals to simultaneously monitor environmental parameters including tidal level and water temperature. Time-shift inversion and tidal error correction were performed on time-series resistivity data to construct a two-dimensional spatiotemporal resistivity distribution profile sequence. Based on a laboratory-calibrated quantitative conversion model of resistivity-pore water salinity, the corrected time-series resistivity profile was transformed into... Spatiotemporal distribution sequence of pore water salinity; Based on the low resistivity anomaly of biological channels, a dual criterion is established to identify the spatial distribution of biological channels and extract geometric parameters such as the equivalent radius, effective length, and channel density of each channel. A single-channel salt flux calculation model with diffusion-convection coupling was constructed. Based on the seepage driving effect caused by tidal fluctuations, the instantaneous salt exchange flux of each biological channel was calculated time-by-time, and the independent contributions of molecular diffusion mechanism and tidal convection mechanism were quantified respectively. The instantaneous salt flux of all identified channels is integrated over time over the tidal cycle to obtain the total salt flux and flux per unit area of ​​the biological channels at the monitoring section.

[0030] Specifically, the method includes the following calculation process: Step S1: Deploy a fixed ERT high-density electrode array on the target section of the muddy tidal flat and simultaneously install tide level and water temperature monitoring sensors. Specifically, a fixed high-density electrode array is deployed on the target section of the muddy tidal flat, with an electrode spacing of 1-3m, effectively covering the deep strata of 0-20m. Time-series resistivity data are continuously collected at fixed time intervals of 15-60 minutes. Each monitoring session covers at least one complete tidal cycle, such as at least 12.4 hours. It is preferable to cover multiple tidal cycles to reduce random errors. Fully automated tidal level monitoring sensors and water temperature probes were deployed simultaneously to continuously collect tidal level and water temperature time-series data around the clock. Before the resistivity data entered into subsequent processing, a multi-time-series tidal correction method was used for preprocessing. The correction formula is as follows: in, The corrected resistivity; This is the measured resistivity; The tidal level variation influence coefficient can be obtained through in-situ calibration, and its value ranges from 5 to 10 Ω·m / m. This represents the tidal level change at adjacent sampling times, in meters, during high tide. >0, the correction term is negative; during low tide <0, the correction term is positive; Step S2: Collect time-series resistivity data covering the entire tidal cycle continuously at fixed time intervals to complete preprocessing and tidal error correction; Step S3: Construct a two-dimensional resistivity inversion model of the formation, invert the resistivity data at each time point into a two-dimensional resistivity profile, and refine the grid in the anomaly area; Specifically, the time-shift inversion employs the finite element method to construct a two-dimensional formation resistivity inversion model, with the following inversion objective function: in, For data residuals; For model regularization terms; The regularization factor ranges from 0.03 to 0.1. Non-uniform grid refinement inversion is performed on the low-resistivity anomaly region of the biological channel, and the refinement coefficient k satisfies the following formula: in, The base encryption coefficient can range from 1.5 to 3.0. The resistivity of the clay matrix; To measure the resistivity in the abnormal region, ensure that 5-10 grid nodes are distributed on the cross-section of the biological channel at the cm level; The reliability of the inversion results is assessed using the following DOI index; ,when DOI A value less than 0.2 indicates that the inversion results are reliable. Step S4: Based on the resistivity-salinity conversion model, the corrected time-series resistivity profile is converted into a pore water salinity spatiotemporal distribution sequence. Specifically, the resistivity-pore water salinity quantitative conversion model was established through the following indoor calibration process: Undisturbed sediment samples were collected from different depths (e.g., 0-10cm, 10-30cm, 30-60cm, 60-100cm) in the target area. NaCl solutions with salinity of 0-100‰ were prepared and, after full saturation, resistivity was measured using a four-electrode AC impedance method. The following calibration relationship was established through least-squares nonlinear fitting: in, C Pore ​​water salinity (‰); ρ The measured resistivity of the sediment is expressed in Ω·m. k is the medium calibration coefficient, with a value range of 10-50; n is the cementation index, with a value range of 0.5-2.0; C 0 This is the residual salinity constant, expressed in per mille (‰). For surface sediments with high clay content, the following resistivity-pore water resistivity conversion is performed using a modified form of Archie's law: in, F´ For effective layer factor, the value can be 2.5; b This is a contribution term to surface conductivity, and its value can be 0.3; The following Archie formula is used to transform brine-bearing sand layers: Where a is the lithology coefficient, which can be 0.6; φ is the porosity, which can be 0.4; and m is the cementation index, which can be 1.4. The conversion was completed by combining the empirical relationship between salinity and resistivity of Manheim et al. Step S5: Identify biological channels from the resistivity profile based on the dual criteria and extract the equivalent radius of each channel. Effective length and channel density ; The identification of biological channels employs the following dual-criteria combined approach: Criterion 1: The absolute value of resistivity is used as the criterion, i.e. < < ; in, For the corrected resistivity, This represents the upper limit of the low-resistance abnormality of the biological channel, and its value can range from (0.5-3) Ω·m; This is the lower limit of resistivity of the clay matrix, and its value range can be (5-15) Ω·m; Criterion two, using spatial gradient as the criterion, i.e. G th ; Where ∇ρ is the magnitude of the resistivity spatial gradient; G th The threshold for determining gradient anomalies is determined by adding three times the standard deviation to the mean of the resistivity gradient distribution in the background region of the profile. For pixels that simultaneously meet the above dual criteria, perform neighborhood connected component clustering analysis, and filter out false positive abnormal areas with an area of ​​less than 3 pixels or greater than 100 pixels. Geometric parameters are extracted for each valid anomaly region, and the formula for calculating the equivalent radius is as follows: ; Effective length Determined by the vertical extension depth of the anomaly zone; Channel density , where N is the total number of valid anomaly zones and A is the monitoring section area; Step S6: Calculate the instantaneous salinity flux of the diffusion and convection terms for each biological channel at each time step. ; The single-channel salt flux calculation model for diffusion-convection coupling is as follows: For the i-th identified biological channel, its instantaneous salt flux at time t is... J i (t) (Unit: g / s; positive values ​​indicate net salinity entering the beach from seawater) as follows: in, The equivalent radius of the channel; This is the effective diffusion coefficient of the porous medium, and its value can range from 5 × 10⁻⁶. -10 -5×10 -9 m 2 / s; Seawater salinity, expressed in g / m³ 3 ; The salinity of the pore water in the channel at time t is expressed in g / m³. 3 The values ​​were obtained from the resistivity-salinity conversion model. The effective length of the channel; Channel penetration rate; This refers to the fluid dynamic viscosity, measured in Pa·s. The tidal level difference between time t and the previous data collection time, in meters; the first term Describes the concentration gradient-driven molecular diffusion flux; the second term Describes the convective seepage flow driven by tidal water level changes; Channel penetration The estimation formula for tubular biological channels based on the Poiseuille flow model is: ; Step S7: Perform trapezoidal numerical integration on the instantaneous flux of all channels over the tidal cycle, and statistically analyze the flux in segments according to the four tidal stages to obtain the total salt flux and flux per unit area. The tidal cycle integration process is as follows: The instantaneous salinity flux of all identified biological channels within a complete tidal cycle is integralized over time and summed spatially using the following formula: in, The total salt flux is expressed in g; N is the total number of biological channels identified; and T is the duration of the tidal cycle, which can be 44640 s. The actual calculation uses the following trapezoidal numerical integration formula: Where M is the total number of data collection moments; Δt is the data collection time interval; Furthermore, the salt flux per unit area is calculated by pressing the palm fiber: (g / (m 2 ·s)); The tidal cycle is divided into four stages: stage 1 is the initial stage of high tide; stage 2 is the high tide period, from when the seawater covers the beach to the high tide level; stage 3 is the low tide period, before the seawater recedes from the beach; and stage 4 is the late low tide period, after the seawater has completely receded from the beach. The accumulation and release rates of salt in different areas are statistically analyzed for each stage (in kg / h), and the diffusion contribution rate and the tidal convection contribution rate are calculated respectively. Furthermore, the following multi-scale hierarchical calculation process can be adopted: First, a large-scale resistivity survey of the tidal flats is completed using macroscopic ERT lines (e.g., electrode spacing between 5-10m), based on the salinity change rate. Spatial distribution identification of dense biological channels (such as...) δS >50% of high-permeability abnormal bands); Then, fine measurement lines (such as electrode spacing between 0.5-1.5m) are laid out in dense areas to carry out high-resolution time-series monitoring and flux calculation, so as to realize the hierarchical implementation from regional screening to local precise quantification; Finally, the overall flux across the entire region is obtained by weighting the area of ​​each densely populated area.

[0031] The above method for calculating salt exchange capacity in biological channels of muddy tidal flats was applied to a coastal area in China. The slope of the muddy tidal flats in this area is less than 3°, and the surface layer is low-permeability silty clay (in-situ permeability coefficient 1-2×10⁻⁶). -5 m / s), with a permeability coefficient of up to 10 for sediments containing biochannels. -4 The speed is m / s (approximately 5-10 times that of the matrix). Tides are irregular semi-diurnal, with an average tidal range of about 0.9 m. Evaporation is intense from April to July, with an average monthly evaporation of 191.4-371.6 mm. The salinity of the phreatic brine is approximately 120 g / L, belonging to a phreatic brine layer formed during the Late Holocene Huanghua marine transgression. The specific experimental verification process is as follows: Step 1: Deployment of the ERT monitoring system; A marine ERT monitoring line (AB) was established 50 m perpendicular to the coastline in the muddy tidal flats. An E60DN resistivity measurement system was used, with a total cable length of 247.5 m and 100 stainless steel electrodes spaced 2.5 m apart. A Wenner-Schlumberger array was selected, powered by a 24V DC battery, with a single-point power supply current of 1A and a time of 1 second. A single complete data acquisition session lasted approximately 20 minutes. At low tide, a 30 cm deep trench was excavated along the monitoring line to bury the cable and electrodes. Monitoring began after 3 days of stabilization. A Solinst LTC Levelogger Edge multi-parameter automatic monitoring instrument (probe buried 1 m deep, measurement interval 0.5 h) was deployed at 80 m (P1) and 160 m (P2) along the monitoring line for simultaneous verification. Sediment saturation was verified to be 98.1%–100.0%, ensuring the electrodes remained in a saturated environment throughout.

[0032] Step 2: Data Acquisition and Preprocessing; Monitoring was conducted during the peak evaporation period in July 2025, with data collected continuously for approximately 12.5 hours (one complete tidal cycle) at fixed 20-minute intervals, encompassing about 37 time points. Tidal correction was implemented. α is taken as 8 Ω·m / m.

[0033] Step 3: Time-shift inversion and grid refinement; Time-shift inversion was performed using RES2DINV v.4.05.30 software. Before inversion, topographic elevation, seawater resistivity, and tidal level data at each time point were input. Abs error was controlled to approximately 10%. A regularization factor of λ = 0.05 was used. All DOI indices were <0.2 (most <0.1), indicating a reliable inversion model. Grid refinement was implemented in the initially delineated low-resistivity anomaly zone, with a baseline refinement coefficient k0 = 2.0, ensuring that each channel cross-section was allocated ≥5 grid nodes.

[0034] Step 4: Salinity conversion; The surface clayey silt layer was modified using the Archie correction method. The fine sand layer of the submerged brine was prepared using the classic Archie formula. Using the salinity-resistivity relationship The conversion was completed, yielding the spatiotemporal distribution sequence of pore water salinity, C(x,z,t_k). The accuracy of the ERT-derived resistivity was verified using in-situ monitoring data from P1 and P2—the trends and magnitudes of Rp and Rinv were basically consistent.

[0035] Step 5: Biometric channel identification; The dual criterion parameters ρ_ch = 1.5 Ω·m and ρ_m = 12 Ω·m were set, and G_th was determined by the mean gradient of the background region plus 3 times the standard deviation. Eight-neighborhood connected component clustering was used to filter out false positive areas with an area <3 or >100 pixels. A total of 37 effective biological channels were identified: average equivalent radius... (0.8-4.2cm), average effective length (15-85 cm), channel density n_ch = 0.185 channels / m 2 The depth is 10-55 cm, consistent with the literature reports on the depth of crab burrowing.

[0036] Step 6: Single-channel flux calculation; Parameter values =1×10 -9 m 2 / s, =3×10 4 g / m 3 μ=9×10 -4 Pa·s. For each channel, the salinity time series at the center location is extracted and substituted into the flux formula time-by-time. A typical channel (r=2.1 cm, L=52 cm, K_ch=5.5×10⁻⁶) is used. -5 m 2 For example: during high tide The flux is approximately 0.032 g / s (diffusion term 0.020 + convection term 0.012), with a negative peak flux of approximately -0.018 g / s during low tide. The net flux over the entire cycle is positive (net salt input). The flux distribution histogram of 37 channels shows that the five large-diameter channels with r>3 cm contributed approximately 41% of the total flux, verifying the key control role of channel size on salt transport efficiency.

[0037] Step 7: Tidal cycle integration and stage statistics; Δt = 20 min = 1200 s, M ≈ 37, trapezoidal integral Monitoring section A = 247.5 × 2 = 495 m 2 . Q_total≈7.08 kg, F=3.2×10 -4 g / (m 2•s)≈1.15 g / (m 2 ·h). Three consecutive tidal cycles =1.18±0.09g / (m 2 The brine concentration (·h) was CV=7.6%, indicating good stability. Four-stage statistics: Stage 1: Brine loss of 10.59 kg / h → Stage 2: Decreased to 7.44 kg / h → Stage 3: Decreased to 0.95 kg / h → Stage 4: Recovered to 9.89 kg / h. Diffusion contributed 62%, and convection contributed 38%. Stage 2, with 73% of the total positive flux occurring through the biological channel, was identified as the main window for vertical salt transport.

[0038] Multi-scale computational extension: at 10000 m 2 Taking the entire tidal flat area as an example, a macro-survey (8m spacing, 10 × 100m survey lines) identified 3 densely populated areas with δS > 50% (total 2000 m). 2 ); Detailed calculations (1.0m spacing, 20 x 50m measuring lines each, 25-hour monitoring) yielded F1=1.28, F2=0.95, and F3=1.42 g / (m²). 2 •h); Area-weighted F_global = 1.22 g / (m 2 ·h).

[0039] Comparative Example: This application is compared in parallel with the Qcell method of Zhang et al. (2024) and traditional pore water sampling methods. The overall relative error of this application is ±8.5%, which is far superior to the Qcell method (±15.3%) and traditional sampling methods (±35%). Moreover, it provides diffusion / convection flux data at the single-channel scale for the first time, revealing the non-uniform distribution law that "a few large-diameter channels contribute most of the flux"—this discovery has direct guiding significance for the formulation of the strategy of "prioritizing the blocking of key channels" in tidal flat ecological restoration.

[0040] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for calculating the salt exchange capacity of biological channels in muddy tidal flats, characterized in that: A fixed ERT (Electronic Resistivity Tolerance) time-series monitoring system was deployed at the tidal flat section, continuously collecting time-series resistivity data at fixed time intervals for different tidal phases to simultaneously monitor environmental parameters including tidal level and water temperature. Time-shift inversion and tidal error correction were performed on the time-series resistivity data to construct a two-dimensional resistivity spatiotemporal distribution profile sequence. Based on a laboratory-calibrated resistivity-pore water salinity quantitative conversion model, the corrected time-series resistivity profile was transformed into a pore water salinity spatiotemporal distribution sequence. Based on the low resistivity anomaly characteristics of biological channels, a dual-criteria system was established to identify the spatial distribution of biological channels and extract the equivalent radius, effective length, and channel density geometric parameters of each channel. A diffusion-convection coupled single-channel salinity flux calculation model was constructed, calculating the instantaneous salinity exchange flux of each biological channel moment-by-moment, considering the seepage-driven effect caused by tidal fluctuations, and quantifying the independent contributions of molecular diffusion and tidal convection mechanisms. The instantaneous salinity flux of all identified channels was integrated over time along the tidal cycle to obtain the total salinity flux and flux per unit area of ​​the biological channels at the monitoring section. The single-channel salt flux calculation model for diffusion-convection coupling is as follows: For the i-th identified biological channel, its instantaneous salt flux J at time t is... i (t) is as follows: in, The equivalent radius of the channel; The effective diffusion coefficient of the porous medium; Seawater salinity, expressed in g / m³. The salinity of the pore water in the channel at time t is in g / m³ and is obtained from the resistivity-salinity conversion model. The effective length of the channel; Channel penetration rate; This refers to the fluid dynamic viscosity, measured in Pa·s. The tidal level difference between time t and the previous data collection time, in meters; the first term Describes the concentration gradient-driven molecular diffusion flux; the second term Describes the convective seepage flow driven by tidal water level changes; Channel penetration The estimation formula for tubular biological channels based on the Poiseuille flow model is: .

2. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 1, characterized in that: The following implementation process is included. Step S1: Deploy a fixed ERT high-density electrode array on the target section of the muddy tidal flat and simultaneously install tide level and water temperature monitoring sensors. Step S2: Collect time-series resistivity data covering the entire tidal cycle continuously at fixed time intervals to complete preprocessing and tidal error correction; Step S3: Construct a two-dimensional resistivity inversion model of the formation, invert the resistivity data at each time point into a two-dimensional resistivity profile, and refine the grid in the anomaly area; Step S4: Based on the resistivity-salinity conversion model, the corrected time-series resistivity profile is converted into a pore water salinity spatiotemporal distribution sequence. Step S5: Identify biological channels from the resistivity profile based on the dual criteria and extract the equivalent radius of each channel. Effective length and channel density ; Step S6: Calculate the instantaneous salt flux of diffusion and convection terms for each biological channel at each time step. ; Step S7: Perform trapezoidal numerical integration on the instantaneous flux of all channels over the tidal cycle, and statistically analyze the flux in segments according to the four tidal stages to obtain the total salt flux and flux per unit area.

3. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 2, characterized in that: Step S1 includes deploying a fixed high-density electrode array on the target cross section of the muddy tidal flat. Time-series resistivity data are continuously collected at fixed time intervals, with each monitoring session covering at least one complete tidal cycle. Fully automated tidal level monitoring sensors and water temperature probes were deployed simultaneously to continuously collect tidal level and water temperature time-series data around the clock. Before the resistivity data entered into subsequent processing, a multi-time-series tidal correction method was used for preprocessing. The correction formula is as follows: in, The corrected resistivity; This is the measured resistivity; The coefficient representing the influence of tidal water level changes; The tidal level change at adjacent sampling times, during high tide. >0, the correction term is negative; during low tide <0, the correction term is positive.

4. The method for calculating the salt exchange capacity of muddy tidal flat biological channels according to claim 2, characterized in that: Step S3 includes constructing a two-dimensional resistivity inversion model of the formation using the finite element method, with the inversion objective function being: in, For data residuals; For model regularization terms; As a regularization factor; Non-uniform grid refinement inversion is performed on the low-resistivity anomaly region of the biological channel, and the refinement coefficient k satisfies the following formula: in, The base encryption coefficient; The resistivity of the clay matrix; Measured resistivity in the abnormal region; The reliability of the inversion results is assessed using the following DOI index; When DOI < 0.2, it indicates that the inversion results are reliable.

5. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 2, characterized in that: Step S4 includes establishing the resistivity-pore water salinity quantitative conversion model through the following indoor calibration process: Undisturbed sediment samples were collected from different depths in the target area. NaCl solutions with salinity of 0-100‰ were prepared and, after full saturation, resistivity was measured using a four-electrode AC impedance method. The following calibration relationship was established through least-squares nonlinear fitting: Where C is the pore water salinity in‰; ρ is the measured resistivity of the sediment in Ω·m; and k is the medium calibration value. Coefficient; n is the cementation index; C0 is the residual salinity constant, in ‰.

6. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 2, characterized in that: Step S4 includes performing the following resistivity-pore water resistivity conversion on surface sediments with high clay content using a modified form of Archie's law: Where F´ is the effective layer factor; b is the surface conductivity contribution term.

7. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 2, characterized in that: Step S4 includes converting the brine-containing sand layer using the following Archie formula: Where a is the lithology coefficient; φ is the porosity; and m is the cementation index; The conversion was completed using the Manheim salinity-resistivity empirical relationship.

8. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 2, characterized in that: In step S5, the identification of biological channels adopts the following dual-criteria combined approach: Criterion 1: The absolute value of resistivity is used as the criterion, i.e. < < ; in, For the corrected resistivity, This represents the upper limit of abnormally low resistance in biological channels. This represents the lower limit of resistivity for clayey matrices. Criterion two, using spatial gradient as the criterion, i.e. G th ; Where ∇ρ is the magnitude of the resistivity spatial gradient; G th The threshold for determining gradient anomalies is determined by adding three times the standard deviation to the mean of the resistivity gradient distribution in the background region of the profile. For pixels that simultaneously meet the above dual criteria, perform 8-neighborhood connected component clustering analysis, and filter out false positive abnormal areas with an area of ​​less than 3 pixels or greater than 100 pixels. Geometric parameters are extracted for each valid anomaly region, and the formula for calculating the equivalent radius is as follows: ； Effective length Determined by the vertical extension depth of the anomaly zone; Channel density , where N is the total number of valid anomaly zones and A is the monitoring cross-sectional area.

9. The method for calculating the salt exchange capacity of biological channels in muddy tidal flats according to claim 2, characterized in that: In step S7, the tidal period integration process is as follows: The instantaneous salinity flux of all identified biological channels within a complete tidal cycle is integralized over time and summed spatially using the following formula: in, The total salt flux is expressed in grams; N represents the total number of biological channels identified; and T represents the duration of the tidal cycle. The actual calculation uses the following trapezoidal numerical integration formula: Where M is the total number of data collection times; Δt is the data collection time interval.

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