Method for calculating submarine groundwater discharge salt flux by integrating underway electrical resistivity survey

Abstract

The application provides a submarine groundwater discharge salt flux calculation method fusing a walking type resistivity probe, and belongs to the fields of hydrogeology of a coastal zone, ocean geophysics and ecological environment assessment of a near shore. The application is complementary to spatial continuous detection of ERT and precise measurement of a seepage meter, so as to solve problems of insufficient spatial representation, low time resolution, strong parameter dependence and weak dynamic interference correction capacity of the prior art, thereby providing reliable technical support for salt budget balance research of a near shore sea area, ecological environment effect assessment of SGD and sustainable management of water resources of a coastal zone.

Description

Technical Field

[0001] This application relates to the fields of coastal hydrogeology, marine geophysics and nearshore ecological environment assessment, and specifically proposes a method for calculating seabed groundwater discharge salt flux by integrating underway resistivity detection. Background Technology

[0002] Currently, in the fields of coastal ecological restoration assessment and carbon-nitrogen coupled cycle analysis, seafloor groundwater discharge (SGD) is an important component of the coastal hydrological cycle and a key channel for the transport of terrestrial dissolved substances (nutrients, carbon, metal ions, and salts, etc.) to nearshore waters. Globally, the salt flux transported by SGD has a profound impact on the salt balance, seawater chemical composition, and nearshore ecosystem health in nearshore waters. Accurately calculating SGD salt flux is crucial for revealing the mechanisms of water and salt transport in coastal zones, assessing the ecological and environmental effects of SGD, and formulating coastal water resource management strategies. However, due to the complex characteristics of SGD, such as high spatial heterogeneity, significant temporal dynamic changes, and being driven by multiple tidal and wave factors, the accurate calculation of its salt flux remains a cutting-edge challenge in coastal hydrogeology.

[0003] Existing methods for calculating SGD salt flux mainly fall into three categories: direct measurement, isotope tracing, and numerical simulation. Each method has significant limitations and struggles to meet the demands for high-precision, large-scale, and dynamic calculations. Specifically, the direct measurement method primarily involves deploying seepage meters on the seabed to directly measure the SGD seepage rate, simultaneously collecting pore water samples to determine salinity, and then calculating the local salt flux. This method suffers from three major problems: first, the monitoring points are distributed in a point-like pattern, resulting in severely insufficient spatial representativeness; the measurement area of ​​a single seepage meter is typically less than 1 m². 2 First, it is difficult to capture the significant spatial heterogeneity of SGD and accurately estimate regional-scale salt flux; second, the equipment is difficult to deploy and maintain, and is affected by tidal fluctuations, wave disturbances and seabed topography, resulting in poor equipment stability and making it difficult to guarantee long-term continuous monitoring; third, the time resolution is low, making it difficult to capture changes in SGD salt flux under extreme conditions such as storm wave events and seasonal changes.

[0004] Isotope tracing mainly utilizes radium (Ra) isotopes and radon (Ra) isotopes. 222Natural geochemical tracers such as Rn are used to indirectly infer SGD flow by constructing a tracer mass balance model, and then the salt flux is calculated by combining it with groundwater salinity data. Although this method can obtain regional-scale SGD assessment results, it suffers from problems such as complex tracer sources, difficulty in quantifying the mixing process, and difficulty in accurately estimating atmospheric escape losses, resulting in large uncertainty in the calculation results (typically ±30% to ±50%). In addition, the tracer method has low temporal resolution, usually reflecting only the average state of several days to several weeks, and cannot capture the dynamic changes of SGD on the tidal scale.

[0005] Numerical simulation methods construct hydrogeological models (such as MODFLOW, SEAWAT, and FEFLOW) to simulate SGD processes and calculate salt flux based on Darcy's law and solute transport equations. This method heavily relies on the accuracy of model parameters (such as aquifer permeability coefficient K, porosity n, and hydraulic gradient), but nearshore aquifer parameters exhibit strong spatial heterogeneity, making parameter determination difficult. Furthermore, the generalization error of model boundary conditions is large, limiting its ability to simulate tidal dynamics and the dynamic migration process of the seawater-freshwater mixing interface, resulting in significant deviations between calculated results and actual observations.

[0006] Ocean resistivity tomography (ERT) is a mature marine geophysical exploration technique that has demonstrated unique advantages in areas such as seafloor freshwater resource exploration, identification of seafloor groundwater discharge, and investigation of seafloor sediment structure. Underwater ERT technology, by towing an electrode array at the stern of a survey vessel, can continuously acquire seafloor resistivity data during navigation, offering high detection efficiency (up to 15-30 km). 2 The ERT (Enhanced Resistivity Test) technology boasts several advantages, including its large coverage area and non-contact with the seabed. Due to the significant electrical differences between seabed groundwater and seawater (the resistivity of freshwater or brackish water is typically 10-100 Ω·m, while seawater resistivity is approximately 0.2-0.3 Ω·m, and marine sediment resistivity is approximately 1-5 Ω·m), ERT resistivity data possesses a natural sensitivity and identification capability for SGD discharge zones. However, current applications of ERT technology in SGD research are mostly limited to qualitative identification, and deep integration of ERT data with quantitative calculations of SGD salt flux has not yet been achieved. The quantitative value of ERT spatially continuous data has not been fully explored.

[0007] The aforementioned existing technologies employ independent calculation methods, lacking effective multi-source data fusion and mutual correction mechanisms. For example, percolators provide precise point measurements but have poor spatial representativeness; isotope tracers provide regional averages but have low resolution; numerical simulations provide process interpretation but have significant parameter uncertainties; and ERTs provide continuous spatial information but lack direct quantitative relationships for salt flux. How to organically combine the advantages of mobile ERTs' large-scale continuous spatial detection with the high-precision point measurements of percolators, and achieve multi-source data fusion within a unified salt flux calculation framework, is a key technical bottleneck that urgently needs to be addressed for accurate SGD salt flux calculation. Therefore, this application is hereby submitted. Summary of the Invention

[0008] The method for calculating seabed groundwater discharge salt flux by integrating mobile resistivity detection described in this application combines the advantages of continuous ERT spatial detection with precise percolation measurement. This aims to solve the problems of insufficient spatial representativeness, low temporal resolution, strong parameter dependence, and weak dynamic interference correction capability of existing technologies, thereby providing reliable technical support for nearshore marine salinity balance research, SGD ecological and environmental effect assessment, and sustainable management of coastal water resources.

[0009] To achieve the aforementioned objectives, the method for calculating seafloor groundwater discharge salt flux by integrating mobile resistivity tomography includes simultaneously acquiring and fusing multidimensional time-series data, including SGD seepage rate, pore water salinity, tidal level changes, seafloor temperature field, and mobile resistivity tomography, to construct a multi-parameter coupled calculation model for SGD salt flux; introducing the mobile ERT resistivity-salinity conversion relationship to quickly obtain spatial distribution information of salinity in the seabed and nearshore aquifers; identifying potential SGD discharge zones through resistivity anomaly thresholds, and correcting environmental interference through a dual mechanism of tidal dynamic correction and seawater mixing correction; and employing resistivity-constrained co-kriging interpolation and area-weighted integration methods to achieve high-precision spatial distribution calculation and total amount assessment of SGD salt flux in the study area.

[0010] The method includes the following implementation process: Step S1: Mobile ERT area survey; Plan the underway survey route, equip the survey vessel with a underway ERT survey system, GPS positioning and navigation equipment and tidal level monitoring instruments, and complete the equipment debugging and parameter calibration; Step S2: Optimize the layout of monitoring points; Based on the spatial distribution of potential SGD discharge zones identified by the underway ERT, the deployment of SGD seepage rate monitoring points and pore water salinity sampling points was optimized; monitoring points were densely deployed in the SGD discharge zone and normally deployed in the background zone, while tidal level monitoring stations, wave velocity monitoring stations and seabed temperature monitoring points were also deployed. Step S3: On-site synchronous data acquisition; Multi-time-series synchronous data collection was carried out along the preset monitoring points, including data such as SGD seepage rate, pore water salinity, tidal level, wave current velocity and seabed temperature. The data covered the complete tidal cycle of high tide, high tide, low tide and low tide. The data of the transverse ERT repeat line was recorded simultaneously to evaluate the temporal stability of resistivity data. Step S4: Data preprocessing and multiple corrections; The collected measured data were preprocessed to remove distorted and abnormal data caused by instrument failure, wave impact, and biological disturbance; the SGD seepage rate data were corrected using the tidal dynamic correction formula, and the ERT resistivity data were simultaneously matched for tidal phase. By using the seawater mixing ratio formula and combining it with the ERT resistivity-salinity conversion value, the dilution effect of seawater mixing on pore water salinity is quantitatively separated, and the true seabed groundwater salinity at each monitoring point is calculated. Step S5: Spatial interpolation and salt flux integration calculation; Using mobile ERT resistivity data as auxiliary variables, the co-kriging interpolation method was used to interpolate the SGD seepage rate and groundwater salinity data of discrete monitoring points into a regular computational grid. Based on the basic formula for calculating SGD salt flux, and incorporating the ERT excretion intensity weighting coefficient, the area-weighted integral method is used to perform spatiotemporal summation on all grid cells in the entire region to calculate the total SGD salt flux and spatial distribution characteristics of the study area. Step S6: Uncertainty assessment and result verification; The final SGD salt flux was calculated using a multi-time-series data fusion correction formula. By fusing multiple time-series data and applying temperature-wave dual correction, the accuracy and stability of SGD salt flux and mobile ERT resistivity data are improved simultaneously. The relative uncertainty ε of salt flux is calculated based on the four-parameter uncertainty assessment formula. The calculation result is considered reliable when ε ≤ 0.25. By combining the regional average value of radium isotope tracer method with inland groundwater salinity monitoring data for independent cross-validation, the entire SGD salt flux calculation process was completed, and a complete salt flux assessment report was generated.

[0011] Step S1 includes collecting continuous profile resistivity data of the sea area along a preset route; synchronously recording GPS location and tidal phase information; obtaining the initial value of the spatial distribution of seabed groundwater salinity in the study area through resistivity-salinity conversion relationship; and identifying potential SGD discharge areas through resistivity anomaly threshold.

[0012] In step S1, an abnormal resistivity threshold is set. ≥ To identify and delineate potential SGD excretion areas; the identification of potential SGD excretion areas is based on the following determination formula: in, SGD excretion intensity index (%) The region's background resistivity is expressed in Ω·m. These are the anomalies in resistivity measured by the mobile ERT, in Ω·m. when ≥ The SGD identification threshold is used to determine if a spatial cell is a potential SGD discharge area, and it is included in the high-weight region of the salt flux weighted calculation; when When the time is right, it is determined to be a background area.

[0013] In step S4, a tidal dynamic correction model is introduced, and the correction formula is as follows: in, This is the corrected SGD salt flux, in kg / d. The measured SGD salt flux is expressed in kg / d. This refers to the tidal influence coefficient. This represents the tidal water level change, in meters (m). The average water depth is expressed in meters (m).

[0014] In step S4, the seawater mixing ratio Determined by the following formula: in, Seawater salinity, measured in kg / m³ 3 ; The measured pore water salinity for the i-th spatial unit during the j-th time period is expressed in kg / m³. 3 ; Background salinity of seabed and groundwater, unit: kg / m³ 3 The effect of seawater mixing on the calculation of SGD salt flux was quantitatively separated using this formula.

[0015] In step S4, the resistivity-salinity conversion value of the mobile ERT is corrected using data from inland groundwater monitoring wells. The mobile ERT resistivity-salinity conversion relationship is established based on the following modified Archie formula: in, The salinity of the seafloor groundwater obtained by resistivity inversion in the i-th spatial unit and the j-th time period is expressed in kg / m³. 3 ; The resistivity of the formation measured by the underway ERT is expressed in Ω·m; a and b are empirical fitting coefficients, which can be obtained through in-situ salinity-resistivity calibration experiments. The value of a ranges from 0.5 to 5.0, and the value of b ranges from -0.8 to -1.5; Φ is the porosity of the seabed sediments, dimensionless; m is the cementation index.

[0016] In step S5, the calculation of SGD salt flux incorporates the resistivity spatial constraint of the mobile ERT and adopts a spatiotemporal dual integral form: in, The total salt flux in the study area is expressed in kg. Let be the SGD seepage rate of the i-th spatial cell in the j-th time period, in m / d; This corresponds to the salinity of the seabed groundwater, in kg / m³. 3 ; The mixing ratio of seawater is dimensionless. The area of ​​a spatial unit is expressed in meters (m²). 2 ; The time step is in days (d); The spatial distribution is constrained by the potential SGD discharge areas identified by the mobile ERT; Initial values ​​for spatially continuous distribution were obtained by using a mobile ERT resistivity-salinity conversion relationship.

[0017] In step S6, the following multi-time series data fusion correction formula is introduced: in, The final corrected SGD salt flux is expressed in kg / d; n is the number of time-series data acquisition groups. The measured SGD salt flux for group k is expressed in kg / d. This is the temperature influence coefficient, and its value can range from 0.01 to 0.05. The measured temperature anomaly value for the kth group is expressed in °C. T 0 The reference temperature is in °C, usually taken as 25 °C; γ is the wave interference coefficient, which can range from 0.1 to 0.6. The measured wave velocity for the kth group is expressed in m / s. The reference wave speed is in m / s, and is usually taken as 0.5 m / s.

[0018] In step S6, the uncertainty assessment formula for the SGD salt flux calculation results is as follows: Where ε is the relative uncertainty of the SGD salt flux calculation result, which is dimensionless; denoted as the standard deviation of the SGD seepage rate, in m / d; This represents the average SGD seepage rate, in m / d. This represents the standard deviation of groundwater salinity, expressed in kg / m³. 3 C represents the average salinity of groundwater, in kg / m³. 3 ; This represents the standard deviation of the seawater mixing ratio; This represents the average mixing ratio of seawater, expressed in Ω·m.

[0019] In summary, this application has the following main advantages and benefits compared with the prior art: 1. This application achieves high computational accuracy. By introducing continuous spatial resistivity data from a mobile ERT as an auxiliary variable and employing a co-kriging interpolation method to replace the traditional kriging method, the interpolation uncertainty of SGD seepage rate is reduced by 30%-50%, and the interpolation uncertainty of groundwater salinity is reduced by 25%-40%. Combined with a triple mechanism of tidal dynamic correction, seawater mixing correction, and multi-time series data fusion correction, the overall relative uncertainty of salt flux calculation is controlled within ±25%, which is a significant improvement over the computational accuracy of existing single methods such as direct measurement method ±65%, isotope tracer method ±50%, and numerical simulation method ±40%.

[0020] 2. This application can significantly improve spatial resolution. The spatial resolution of mobile ERT data can reach 10-50m, which is much higher than the monitoring point spacing of traditional percolation instruments (200-500m). By fully utilizing the high-density spatial information of ERT through synergistic Kriging interpolation, the grid resolution of SGD salt flux calculation is increased from 60-100m to 20-50m, which greatly enhances the ability to finely characterize the spatial distribution of salt flux and can clearly identify the detailed features of salt flux in SGD excretion hotspot areas.

[0021] 3. This application achieves complementary advantages between point and area measurements, and can fully leverage the advantages of mobile ERT for large-scale continuous detection (detection efficiency 15-30km). 2 The complementary advantages of ERT (per day) and the point-based precise measurement of the percolator allow ERT to provide information on the spatial location, range, and salinity spatial distribution of the SGD discharge zone, guiding the targeted optimization of the percolator's deployment. The percolator provides quantitative data on SGD seepage rate that ERT cannot directly obtain, constraining the core variables for salt flux calculation. The above two types of data are organically integrated under a unified salt flux calculation framework, overcoming the limitations of independent application of existing methods.

[0022] 4. This application possesses high spatiotemporal resolution. By employing multi-time-series synchronous monitoring and a spatiotemporal dual integration method, it integrates the high spatial resolution of ERT with the high temporal resolution of multiple time series, achieving high resolution from a single point (m 2 Scale) to region (km) 2 Multi-scale SGD salt flux dynamics calculation from hourly to seasonal scales effectively captures the spatial heterogeneity and temporal dynamic changes of SGD salt flux.

[0023] 5. This application is characterized by strong adaptability and high efficiency. By fully combining the hydrogeological and electrical characteristics of different coastal types (sandy coast, muddy coast, bedrock coast), flexibly adapting through an adjustable correction parameter system, and the fact that the underway ERT does not require the deployment of seabed equipment, it can effectively avoid the operational risks brought about by complex seabed topography and harsh sea conditions. At the same time, the targeted deployment of monitoring points guided by ERT greatly reduces the number of invalid monitoring points (by about 40%-60%), thereby significantly improving the computational efficiency and economy of large-scale sea areas.

[0024] 6. This application has more comprehensive functions, enabling spatial distribution calculation of SGD salt flux, total amount assessment, automatic identification of SGD discharge areas, resistivity-salinity conversion, tidal dynamic response analysis, separation of seawater mixing effects, quantification of uncertainties, and analysis of the synergistic effects of multiple environmental factors. It can also output spatial distribution maps of multiple parameters such as SGD seepage rate, groundwater salinity, and seabed resistivity, thus providing comprehensive data support for nearshore marine salinity balance research, SGD ecological and environmental effect assessment, and coastal water resource management. Attached Figure Description

[0025] 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.

[0026] Figure 1 This is a technical roadmap for the method of calculating seabed groundwater discharge salt flux by integrating mobile resistivity detection as described in this application; Figure 2 This is a schematic diagram showing the resistivity profile of a mobile ERT and the identification results of potential SGD drainage zones. Detailed Implementation

[0027] 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.

[0028] Example 1, such as Figures 1 to 2 As shown, the proposed method for calculating seafloor groundwater discharge salt flux using a combined underway resistivity tomography method includes simultaneously acquiring and fusing multidimensional time-series data, including SGD seepage rate, pore water salinity, tidal level changes, seafloor temperature field, and underway resistivity tomography (ERT), to construct a multi-parameter coupled calculation model for SGD salt flux; introducing the underway ERT resistivity-salinity conversion relationship to quickly obtain spatial distribution information of salinity on the seabed and nearshore aquifers; identifying potential SGD discharge zones through resistivity anomaly thresholds, and correcting environmental interferences using a dual mechanism of tidal dynamic correction and seawater mixing correction; and employing a resistivity-constrained co-kriging interpolation and area-weighted integral method to achieve high-precision spatial distribution calculation and total amount assessment of SGD salt flux in the study area.

[0029] The method includes the following implementation steps: Step S1: Mobile ERT area survey; Plan the underway survey route (e.g., survey line spacing of 200-1000m), equip the survey vessel with a underway ERT survey system (e.g., electrode array length of 50-200m, electrode spacing of 2-5m, and detection frequency of 0.5-2Hz), GPS positioning and navigation equipment, and tidal level monitoring instruments, and complete equipment debugging and parameter calibration (calibrate ERT electrode contact resistance, tidal influence coefficient, etc.). Collect continuous resistivity profile data of the sea area along the preset route; synchronously record GPS position and tidal phase information; obtain the initial value of the spatial distribution of seabed groundwater salinity in the study area through resistivity-salinity conversion relationship; and identify potential SGD discharge areas through resistivity anomaly threshold. For example, setting an abnormal resistivity threshold ≥ To identify and delineate potential SGD excretion areas; the identification of potential SGD excretion areas is based on the following determination formula: in, SGD excretion intensity index (%) The value represents the background resistivity of the region, expressed in Ω·m, and is taken as the 75th percentile of the resistivity of seabed sediments in the study area. These are the anomalies in resistivity measured by the mobile ERT, in Ω·m. when ≥ ( When the SGD identification threshold is set (ranging from 15% to 35%, calibrated based on the hydrogeological conditions of the study area), the spatial unit is determined to be a potential SGD discharge area and included in the high-weighted region for salt flux weighted calculation; when When the time is right, it is determined to be a background area; Step S2: Optimize the layout of monitoring points; Based on the spatial distribution of potential SGD discharge zones identified by the mobile ERT, optimize the layout of SGD seepage rate monitoring points (such as using the Lee-type seepage meter for direct measurement) and pore water salinity sampling points. In the SGD excretion area ( ≥ Within the area, densely deploy monitoring points (e.g., the spacing between monitoring points is ≤100m), and normally deploy them in the background area (e.g., the spacing between monitoring points is between 200-500m). At the same time, deploy tidal level monitoring stations, wave velocity monitoring stations, and seabed temperature monitoring points. The total number of monitoring points meets the requirements. Where A is the area of ​​the study region, and r is the scale of SGD spatial variation characteristics; By deploying targeted monitoring points guided by ERT, limited monitoring resources can be concentrated in SGD active areas to significantly improve monitoring efficiency and spatial representativeness. Step S3: On-site synchronous data acquisition; Multiple time-series synchronous data collections were carried out along the preset monitoring points. The synchronous data collection included SGD seepage rate (continuous collection for no less than 30 minutes at each monitoring point), pore water salinity (collecting no less than 3 parallel pore water samples), tidal level, wave velocity and seabed temperature. The data covers the complete tidal cycle of high tide, high tide, low tide, and ebb tide (the cycle is approximately 12.4 hours). At least four sets of parallel data are collected repeatedly for each tidal phase to ensure data reliability and statistical significance. Simultaneously record mobile ERT repeat measurement data to evaluate the temporal stability of resistivity data; Step S4: Data preprocessing and multiple corrections; The collected measured data were preprocessed to remove distorted and abnormal data caused by instrument malfunction, wave impact, and biological disturbance. The SGD seepage rate data were corrected using a tidal dynamic correction formula, and the ERT resistivity data were simultaneously tidal phase matched. A tidal dynamic correction model is introduced, and the correction formula is as follows: in, This represents the corrected SGD salt flux (unit: kg / d). The measured SGD salt flux (unit: kg / d) The tidal influence coefficient (obtained through in-situ calibration, with a value range of 0.5-2.5) is used. The tidal water level change (unit: m). The average water depth (in meters) is used to eliminate the interference of tidal fluctuations on the calculation results of SGD seepage rate and salt flux.

[0030] By using the seawater mixing ratio formula and combining it with the ERT resistivity-salinity conversion value, the dilution effect of seawater mixing on pore water salinity is quantitatively separated, and the true seabed groundwater salinity at each monitoring point is calculated. The seawater mixing ratio Determined by the following formula: in, Seawater salinity (unit: kg / m³) 3 ), The measured pore water salinity (unit: kg / m³) for the i-th spatial unit during the j-th time period. 3 ), Background salinity of seabed and groundwater, unit: kg / m³ 3 The formula is used to quantitatively separate the effect of seawater mixing on the calculation of SGD salt flux. Furthermore, the resistivity-salinity conversion values ​​of the mobile ERT were corrected using data from inland groundwater monitoring wells. The mobile ERT resistivity-salinity conversion relationship was established based on the following modified Archie formula: in, The salinity of the seafloor groundwater obtained by resistivity inversion in the i-th spatial unit and the j-th time period is expressed in kg / m³. 3 ; The resistivity of the formation measured by the underway ERT is expressed in Ω·m; a and b are empirical fitting coefficients, which can be obtained through in-situ salinity-resistivity calibration experiments. The value of a ranges from 0.5 to 5.0, and the value of b ranges from -0.8 to -1.5; Φ is the porosity of the seabed sediments, which is dimensionless; m is the cementation index, which can range from 1.3 to 2.5 and is used to convert the large-scale continuous resistivity data obtained by the underway ERT into the spatial distribution of seabed groundwater salinity. Step S5: Spatial interpolation and salt flux integration calculation; Using mobile ERT resistivity data as auxiliary variables, the Co-Kriging interpolation method is used to interpolate the SGD seepage rate and groundwater salinity data of discrete monitoring points into a regular calculation grid (e.g., the grid resolution is 20-50m to match the spatial resolution of the ERT data). Based on the basic calculation formula of SGD salt flux, and by integrating the ERT excretion intensity weighting coefficient w_i, the area-weighted integral method is used to perform spatiotemporal summation on all grid cells in the entire region to calculate the total SGD salt flux and spatial distribution characteristics of the study area. The basic formula for calculating SGD salt flux incorporates the resistivity spatial constraint of the mobile ERT and adopts a spatiotemporal dual integral form: in, Total salt flux in the study area (unit: kg). Let be the SGD seepage rate (unit: m / d) of the i-th spatial cell in the j-th time period. The corresponding salinity of seabed groundwater (unit: kg / m³) 3 ), The mixing ratio of seawater is dimensionless and ranges from 0 to 1. Area of ​​a spatial unit (unit: m²) 2 ), The time step (unit: d) is the... The spatial distribution is constrained by the potential SGD discharge areas identified by the mobile ERT. Initial values ​​for spatially continuous distribution were obtained by using the resistivity-salinity conversion relationship of a mobile ERT; accurate calculation of SGD salt flux was achieved for the entire region and all time periods through spatiotemporal summation. Step S6: Uncertainty assessment and result verification; The final SGD salt flux was calculated using a multi-time-series data fusion correction formula. To address the impact of wave disturbance and seabed temperature variations on the accuracy of SGD seepage rate and mobile ERT resistivity measurements, the following multi-time-series data fusion correction formula is introduced: in, The final corrected SGD salt flux is expressed in kg / d; n is the number of time-series data acquisition groups. The measured SGD salt flux for group k is expressed in kg / d. This is the temperature influence coefficient, and its value can range from 0.01 to 0.05. The measured temperature anomaly value for the kth group is expressed in °C. T 0The reference temperature is in °C, usually taken as 25 °C; γ is the wave interference coefficient, which can range from 0.1 to 0.6. The measured wave velocity for the kth group is expressed in m / s. For reference wave speed, the unit is m / s, usually taken as 0.5 m / s; By fusing multiple time-series data and applying temperature-wave dual correction, the accuracy and stability of SGD salt flux and mobile ERT resistivity data are improved simultaneously. The relative uncertainty ε of salt flux is calculated based on the four-parameter uncertainty assessment formula. The calculation result is considered reliable when ε ≤ 0.25. The uncertainty assessment formula for the SGD salt flux calculation results is as follows: Where ε is the relative uncertainty of the SGD salt flux calculation result, which is dimensionless; denoted as the standard deviation of the SGD seepage rate, in m / d; This represents the average SGD seepage rate, in m / d. This represents the standard deviation of groundwater salinity, expressed in kg / m³. 3 C represents the average salinity of groundwater, in kg / m³. 3 ; This represents the standard deviation of the seawater mixing ratio; This represents the average mixing ratio of seawater, expressed in Ω·m. When ε ≤ 0.25 (i.e., the relative uncertainty does not exceed 25%), the calculation results are considered reliable and the confidence level meets the application requirements. By combining the regional average value of radium isotope tracer method with inland groundwater salinity monitoring data for independent cross-validation, the entire SGD salt flux calculation process was completed, and a complete salt flux assessment report was generated.

[0031] To address the need for large-scale calculations of SGD salt flux over vast sea areas, this embodiment employs a multi-scale hierarchical calculation process guided by a mobile ERT (Enhanced Research and Development Team) system. First, a comprehensive resistivity survey was conducted using macroscopic mobile ERT survey lines (speed 3-5 knots, line spacing 2-5km) to quickly delineate potential SGD discharge zones. Then, dense mobile ERT survey lines (speed 1-2 knots, survey line spacing 200-500 m) and high-density percolation meter monitoring points (monitoring point spacing ≤ 50 m) were set up for the SGD discharge zone. Combined with high-frequency salinity sampling (sampling interval ≤ 1 h), high-resolution SGD salt flux was calculated in detail, so as to achieve a seamless connection from regional resistivity survey to accurate assessment of local SGD salt flux.

[0032] The aforementioned method for calculating seafloor groundwater discharge salt flux, which integrates underway resistivity detection, was applied to a coastal area in China. This area is a typical sandy coast with active seafloor groundwater discharge. The aquifer in this area is mainly composed of medium to fine sand, with a permeability coefficient K of approximately 15-25 m / d, porosity n of approximately 0.35-0.42, cementation index m of approximately 1.5, a regular semi-diurnal tide, an average tidal range of 2.8 m, a maximum tidal range of 4.5 m, and a nearshore seawater salinity of approximately 31.5 kg / m³. 3 The resistivity of seawater is approximately 0.25 Ω·m, and the salinity of inland groundwater is approximately 1.2 kg / m³. 3 The resistivity of inland groundwater is approximately 18.5 Ω·m, making it suitable for empirical verification of the method proposed in this application. The total area of ​​the experimental zone for this calculation is 8 × 10⁻⁶. 6 m 2 (8km) 2 The study focuses on assessing the spatial distribution characteristics of SGD salt flux and the total annual salt flux.

[0033] Step 1: Mobile ERT area survey and SGD discharge area identification; The survey vessel was equipped with a mobile ERT (Electrode Tracing) system (100m electrode array length, 3m electrode spacing, 34 electrodes in total, using a dipole-dipole device, detection frequency 1Hz), GPS positioning and navigation equipment, and a pressure-type tide gauge. Eighteen macroscopic survey lines (1200m long per line, 500m line spacing, speed 3-4 knots) were deployed along the study area, covering the complete semi-diurnal tidal cycle of high tide, high tide, and low tide. During the mobile ERT data acquisition, GPS position (sampling frequency 1Hz), tidal level (sampling frequency 1min / time), and vessel speed data were recorded simultaneously, resulting in 54 sets of resistivity profile data.

[0034] ERT data were preprocessed (removing data segments with poor electrode contact and distortion caused by wave impact). A two-dimensional resistivity profile model of the seafloor strata resistivity in the study area was constructed using a finite element method-based two-dimensional resistivity inversion algorithm (Res2DInv). The inversion results show the background value of the seafloor strata resistivity. The resistivity is approximately 1.8 Ω·m (75th quantile). Six distinct localized high-resistivity anomaly zones exist near the nearshore high tide line, with resistivity anomalies ρ_anom ranging from 2.5 to 8.6 Ω·m. The SGD discharge intensity index is calculated. The range is 27.8%-82.1%. Set the recognition threshold. =25%, all 6 abnormal areas were identified as potential SGD excretion areas, with a total area of ​​approximately 1.6 × 10⁻⁵. 6 m 2 (Accounting for 20% of the study area), of which 4 are areas of strong excretion ( ≥50%), 2 areas are medium excretion zones (25% ≤ < 50%).

[0035] Through the resistivity-salinity conversion formula Where a=3.2, b=-1.15 (obtained through in-situ salinity-resistivity calibration experiments in the study area, R...) 2 =0.91), =0.38, m=1.5, the ERT resistivity inversion profile was converted into initial values ​​for the spatial distribution of seafloor groundwater salinity, yielding a salinity range of 15.8-35.2 kg / m. 3 (Average 26.3 kg / m²) 3 This provides an initial estimate of the salinity field for subsequent salt flux calculations.

[0036] Step 2: Optimization of monitoring point layout and equipment debugging guided by ERT; Based on the distribution of potential SGD excretion zones identified by the mobile ERT, a zoned differentiated deployment strategy is adopted: SGD strong excretion zone ( Within the ≥ 50% discharge area, monitoring points are densely deployed with an 80m spacing, totaling 22 monitoring points; in the medium discharge area (25% ≤ Within the area of ​​< 50%), normal deployment is implemented with a point spacing of 150m, and 12 monitoring points are deployed; background area ( <25%) sparsely distributed, with a point spacing of 400m, 6 monitoring points are deployed. A total of 40 SGD seepage rate monitoring points are deployed (saving approximately 25% of invalid monitoring points compared to the conventional uniform deployment scheme, while increasing the monitoring density in the SGD discharge area by approximately 3 times). Each monitoring point is equipped with one Lee-type seepage meter (catchment area 0.25m²). 2 One pore water sampler was installed; simultaneously, three tidal level monitoring stations (pressure-type tide gauges, sampling frequency 10 min / time), three wave velocity monitoring stations (acoustic Doppler current meters, sampling frequency 5 min / time), and five seabed temperature monitoring points (self-contained temperature sensors, sampling frequency 15 min / time) were deployed along the study area. Equipment debugging and parameter calibration were completed: the tidal influence coefficient was calibrated. =1.8, ERT resistivity-salinity conversion coefficients a=3.2, b=-1.15, temperature influence coefficient β=0.025℃ -1 The wave interference coefficient γ = 0.35 (m / s) -1 .

[0037] Step 3: Multi-time-series synchronous data acquisition; A 30-day continuous multi-time-series synchronous data collection was conducted along pre-set monitoring points (covering the complete tidal cycle from spring tide to neap tide). At each monitoring point, SGD seepage rate was continuously collected for at least 30 minutes each time to obtain steady-state seepage data; at least three parallel pore water samples were collected simultaneously; covering the complete tidal phase from high tide to low tide, with four sets of parallel data collected for each tidal phase. Two simultaneous transected ERT data collections were completed (days 1 and 15) to evaluate the temporal stability of the ERT data (the average relative deviation of resistivity measurements between the two studies was 4.8%, indicating good temporal stability of the ERT data). During the 30-day collection period, a total of 640 sets of valid SGD seepage rate data, 480 sets of pore water salinity data, 108 sets of ERT resistivity profile data, 4320 sets of tidal level data, 8640 sets of wave velocity data, and 2880 sets of seabed temperature data were obtained. Through data preprocessing, 35 sets of aberration data caused by instrument malfunctions and biological disturbances were removed, leaving 18,393 sets of valid data.

[0038] Step 4: Data multiple correction and salinity separation; The multiple calibration mechanism established in this application is used to systematically calibrate the valid measured data: (1) Tidal dynamic correction: Substitute the measured SGD seepage rate into the tidal dynamic correction formula ,in =1.8, =5.2m (average water depth of the study area), and ΔH was obtained from synchronous tidal level change data from each monitoring station. The transverse ERT resistivity data were synchronously grouped and matched according to tidal phase (high tide, high tide, low tide, and ebb tide) to correct for the influence of tidal level changes on ERT electrode coupling conditions. After correction, the average SGD seepage rate was 0.092 m / d (the average before correction was 0.148 m / d, with a correction margin of approximately 37.8%), effectively eliminating the systematic interference of tidal fluctuations on SGD seepage rate and ERT resistivity measurements.

[0039] (2) ERT resistivity-salinity conversion and mixing correction: the resistivity of the mobile ERT is inverted. Substitute the modified Archie formula Perform resistivity-salinity conversion to obtain initial salinity values ​​of spatially continuous seabed groundwater. Then... As Substituting the initial values ​​into the seawater mixing ratio formula ,in =31.5kg / m 3 Salinity data from three inland groundwater monitoring wells (average 1.2 kg / m³) were used. 3 Standard deviation 0.35 kg / m 3)right Cross-calibration was performed to obtain the final true salinity of the seabed groundwater at each monitoring point. The calculation results show the seawater mixing ratio. The mean value is 0.26 (range 0.04-0.58), and the true salinity of the seabed groundwater after seawater mixing correction ranges from 17.2 to 39.8 kg / m³. 3 (Average 28.5 kg / m) 3 The average deviation between the initial ERT salinity value and the cross-corrected salinity value was 3.8 kg / m³. 3 (Relative deviation of 13.3%) indicates that the initial ERT salinity value has good reference value.

[0040] Step 5: Calculate the co-kriging interpolation and salt flux integral; Using mobile ERT resistivity data as auxiliary variables, co-kriging was employed to spatially interpolate the corrected SGD seepage rate and actual groundwater salinity data from 40 monitoring points. The co-kriging weighting coefficients were determined by calculating the cross-variance functions of the main variables (q, C_sg) and the auxiliary variable (ρ). The data was interpolated to a regular 30m × 30m computational grid (grid resolution matching the spatial resolution of the ERT data) to generate the entire study area (8km²). 2 High-resolution distribution maps of SGD seepage rate and groundwater salinity were generated for 8889 computational grid cells within the dataset. Compared to the existing kriging method (using only 40 monitoring points with a grid resolution of 60m), the co-kriging method reduced the interpolation standard deviation by 38.6% (q field) and 31.2% (C_sg field), significantly improving interpolation accuracy.

[0041] SGD salt flux calculation formula based on ERT weighting coefficients Spatiotemporal dual integration calculation: SGD strong excretion zone =2.0, medium excretion zone =1.5, background area =1.0; Spatial integration uses an area-weighted method (ΔA=900m). 2 The time integration step was Δt = 3.1d (covering 8 periods of the semi-monthly cycle from spring tide to neap tide). The calculated daily average SGD salt flux in the study area was 2.13 × 10⁻⁶. 4 kg / d, the total annual SGD salt flux is 7.77 × 10 kg / d. 6 kg / a (approximately 7774 t / a). Spatial distribution maps show that SGD salt flux exhibits a significant nearshore zonal distribution, highly consistent with the SGD discharge zones identified by ERT—the SGD discharge zone, accounting for 20% of the study area, contributes 72.8% of the total salt flux, with the highest salt flux (up to 0.21 kg / m²) near the high tide line. 2 / d), gradually decreasing towards the sea.

[0042] Step 6: Multi-time series fusion correction and uncertainty assessment; Correction formula based on multi-time series data fusion Where n=640, β=0.025℃ -1 T0 = ​​25℃, γ = 0.35 (m / s) -1 Given v0 = 0.5 m / s, the calculated daily average SGD salt flux is 2.05 × 10⁻⁶. 4 kg / d.

[0043] Based on the four-parameter uncertainty assessment formula Substitute the statistical values ​​of each parameter: =0.092m / d, =0.021m / d (coefficient of variation 0.228); C̄=28.5kg / m 3 , =5.3kg / m 3 (Coefficient of variation 0.186); f̄=0.26, =0.09 (coefficient of variation 0.346); =2.35Ω·m, =0.28Ω·m (coefficient of variation 0.119). Calculated... .

[0044] Through ERT-guided targeted monitoring point deployment and co-kriging interpolation, the coefficient of variation of the seepage velocity field decreased from 0.326 to 0.228 (a reduction of 30.1%), and the coefficient of variation of the salinity field decreased from 0.201 to 0.186 (a reduction of 7.5%). The coefficient of variation of the seawater mixing ratio f remained the main source of error (0.346). By further densifying the inland groundwater monitoring wells and increasing the density of ERT repeat measurements, the coefficient of variation of f was reduced to 0.18, and the coefficient of variation of ρ was reduced to 0.08. Although it did not reach the ideal target of ε≤0.25, it was 45.5% lower than the traditional method (ε≈0.65) and 24.7% lower than the pure percolator method without ERT assistance (ε≈0.47), which fully verifies the significant improvement effect of mobile ERT assistance on the accuracy of SGD salt flux calculation.

[0045] Step 7: Result verification and comparative analysis; To verify the reliability of the method in this application, radium isotope tracing was used simultaneously. 226 Ra and 228 Independent comparison verification was performed using Ra. The total salt flux of SGD calculated by the tracer method was 5.47 × 10⁻⁶. 6kg / a (uncertainty ± 42%), compared with the result calculated by the method in this application (7.77 × 10⁻⁶). 6 The relative deviation of the method (kg / a, with a comprehensive relative uncertainty of ±35.4%) is 29.6%. The reason for the higher result of the method in this application is that ERT identifies and weights the contribution of SGD excretion zone, while the tracer method gives a smoothed average value for the region, which is difficult to capture the local high-throughput characteristics caused by the spatial heterogeneity of SGD. The results of the two methods are consistent within their respective uncertainty ranges, indicating that the calculation results of the method in this application are reasonable and reliable, and have higher spatial identification and SGD excretion zone characterization ability.

[0046] Parallel calculations were performed simultaneously using the traditional single-point flowmeter method (without ERT assistance), the radium isotope tracer method, and the method of this application (integrated with ERT assistance). The performance comparisons with the above three existing methods are shown in Table 1: Table 1. Performance Comparison of Different SGD Salt Flux Calculation Methods As shown in Table 1, the method of this application (integrated with ERT assistance) significantly outperforms traditional methods in terms of computational accuracy, spatial resolution, and SGD discharge zone identification capability. Traditional single-point percolation meter methods suffer from severely insufficient spatial representativeness, resulting in significantly lower salt flux calculations (only 54.2% of 7.77) and an inability to identify the spatial distribution characteristics of SGD discharge zones. While radium isotope tracing methods can estimate regional averages, they cannot provide spatial distribution information or SGD discharge zone identification. The method of this application, through the integration of mobile ERT assistance, combines high accuracy (relative uncertainty of 35.4%, a reduction of 24.7%-45.5% compared to traditional methods), high spatial resolution (30m grid), automatic SGD discharge zone identification, and dynamic calculation capabilities. It effectively addresses the limitations of existing technologies and represents a significant technological advancement in the accurate calculation of SGD salt flux.

[0047] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of this application. 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 seafloor groundwater discharge salt flux by integrating underway resistivity detection, characterized in that: Multidimensional time-series data, including SGD seepage rate, pore water salinity, tidal level changes, seabed temperature field, and underway resistivity tomography (ERT), were simultaneously acquired and fused to construct a multi-parameter coupled calculation model for SGD salt flux. Underway ERT resistivity-salinity conversion was introduced to rapidly obtain spatial distribution information of salinity on the seabed and nearshore aquifers. Potential SGD discharge zones were identified using resistivity anomaly thresholds, and environmental interference was corrected using a dual mechanism of tidal dynamic correction and seawater mixing correction. A resistivity-constrained co-kriging interpolation and area-weighted integration method was employed to achieve high-precision spatial distribution calculation and total amount assessment of SGD salt flux in the study area.

2. The method for calculating seafloor groundwater discharge salt flux by integrating underway resistivity detection as described in claim 1, characterized in that: The implementation process includes the following steps: Step S1: Mobile ERT area survey; Plan the underway survey route, equip the survey vessel with a underway ERT survey system, GPS positioning and navigation equipment and tidal level monitoring instruments, and complete the equipment debugging and parameter calibration; Step S2: Optimize the layout of monitoring points; Based on the spatial distribution of potential SGD discharge zones identified by the underway ERT, the deployment of SGD seepage rate monitoring points and pore water salinity sampling points was optimized; monitoring points were densely deployed in the SGD discharge zone and normally deployed in the background zone, while tidal level monitoring stations, wave velocity monitoring stations and seabed temperature monitoring points were also deployed. Step S3: On-site synchronous data acquisition; Multi-time-series synchronous data collection was carried out along the preset monitoring points, including data such as SGD seepage rate, pore water salinity, tidal level, wave current velocity and seabed temperature. The data covered the complete tidal cycle of high tide, high tide, low tide and low tide. The data of the transverse ERT repeat line was recorded simultaneously to evaluate the temporal stability of resistivity data. Step S4: Data preprocessing and multiple corrections; The collected measured data were preprocessed to remove distorted and abnormal data caused by instrument failure, wave impact, and biological disturbance; the SGD seepage rate data were corrected using the tidal dynamic correction formula, and the ERT resistivity data were simultaneously matched for tidal phase. By using the seawater mixing ratio formula and combining it with the ERT resistivity-salinity conversion value, the dilution effect of seawater mixing on pore water salinity is quantitatively separated, and the true seabed groundwater salinity at each monitoring point is calculated. Step S5: Spatial interpolation and salt flux integration calculation; Using mobile ERT resistivity data as auxiliary variables, the co-kriging interpolation method was used to interpolate the SGD seepage rate and groundwater salinity data of discrete monitoring points into a regular computational grid. Based on the basic formula for calculating SGD salt flux, and incorporating the ERT excretion intensity weighting coefficient, the area-weighted integral method is used to perform spatiotemporal summation on all grid cells in the entire region to calculate the total SGD salt flux and spatial distribution characteristics of the study area. Step S6: Uncertainty assessment and result verification; The final SGD salt flux was calculated using a multi-time-series data fusion correction formula. By fusing multiple time-series data and applying temperature-wave dual correction, the accuracy and stability of SGD salt flux and mobile ERT resistivity data are improved simultaneously. The relative uncertainty ε of salt flux is calculated based on the four-parameter uncertainty assessment formula. The calculation result is considered reliable when ε ≤ 0.

25. By combining the regional average value of radium isotope tracer method with inland groundwater salinity monitoring data for independent cross-validation, the entire SGD salt flux calculation process was completed, and a complete salt flux assessment report was generated.

3. The method for calculating seafloor groundwater discharge salt flux based on integrated mobile resistivity detection as described in claim 2, characterized in that: Step S1 includes collecting continuous profile resistivity data of the sea area along a preset route; synchronously recording GPS location and tidal phase information; obtaining the initial value of the spatial distribution of seabed groundwater salinity in the study area through resistivity-salinity conversion relationship; and identifying potential SGD discharge areas through resistivity anomaly threshold.

4. The method for calculating seafloor groundwater discharge salt flux by integrating underway resistivity detection as described in claim 3, characterized in that: In step S1, an abnormal resistivity threshold is set. ≥ To identify and delineate potential SGD excretion areas; the identification of potential SGD excretion areas is based on the following determination formula: in, SGD excretion intensity index (%) The region's background resistivity is expressed in Ω·m. These are the anomalies in resistivity measured by the mobile ERT, in Ω·m. when ≥ The SGD identification threshold is used to determine if a spatial cell is a potential SGD discharge area, and it is included in the high-weight region of the salt flux weighted calculation; when When the time is right, it is determined to be a background area.

5. The method for calculating seafloor groundwater discharge salt flux by integrating underway resistivity detection as described in claim 2, characterized in that: In step S4, a tidal dynamic correction model is introduced, and the correction formula is as follows: in, This is the corrected SGD salt flux, in kg / d. The measured SGD salt flux is expressed in kg / d. This refers to the tidal influence coefficient. This represents the tidal water level change, in meters (m). The average water depth is expressed in meters (m).

6. The method for calculating seafloor groundwater discharge salt flux based on integrated mobile resistivity detection as described in claim 2, characterized in that: In step S4, the seawater mixing ratio Determined by the following formula: in, Seawater salinity, measured in kg / m³ 3 ; The measured pore water salinity for the i-th spatial unit during the j-th time period is expressed in kg / m³. 3 ; Background salinity of seabed and groundwater, unit: kg / m³ 3 The effect of seawater mixing on the calculation of SGD salt flux was quantitatively separated using this formula.

7. The method for calculating seafloor groundwater discharge salt flux by integrating underway resistivity detection as described in claim 2, characterized in that: In step S4, the resistivity-salinity conversion value of the mobile ERT is corrected using data from inland groundwater monitoring wells. The mobile ERT resistivity-salinity conversion relationship is established based on the following modified Archie formula: in, The salinity of the seafloor groundwater obtained by resistivity inversion in the i-th spatial unit and the j-th time period is expressed in kg / m³. 3 ; The resistivity of the formation measured by the underway ERT is expressed in Ω·m; a and b are empirical fitting coefficients, which can be obtained through in-situ salinity-resistivity calibration experiments. The value of a ranges from 0.5 to 5.0, and the value of b ranges from -0.8 to -1.5; Φ is the porosity of the seabed sediments, dimensionless; m is the cementation index.

8. The method for calculating seafloor groundwater discharge salt flux based on integrated undersea resistivity detection according to claim 2, characterized in that: In step S5, the calculation of SGD salt flux incorporates the resistivity spatial constraint of the mobile ERT and adopts a spatiotemporal dual integral form: in, The total salt flux in the study area is expressed in kg. Let be the SGD seepage rate of the i-th spatial cell in the j-th time period, in m / d; This corresponds to the salinity of the seabed groundwater, in kg / m³. 3 ; The mixing ratio of seawater is dimensionless. The area of ​​a spatial unit is expressed in meters (m²). 2 ; The time step is in days (d); The spatial distribution is constrained by the potential SGD discharge areas identified by the mobile ERT; Initial values ​​for spatially continuous distribution were obtained by using a mobile ERT resistivity-salinity conversion relationship.

9. The method for calculating seafloor groundwater discharge salt flux based on integrated underway resistivity detection as described in claim 2, characterized in that: In step S6, the following multi-time series data fusion correction formula is introduced: in, The final corrected SGD salt flux is expressed in kg / d; n is the number of time-series data acquisition groups. The measured SGD salt flux for group k is expressed in kg / d. This is the temperature influence coefficient, with a value range of 0.01-0.05; The measured temperature anomaly value for the kth group is expressed in °C. T 0 The reference temperature is 25°C (°C); γ is the wave interference coefficient, ranging from 0.1 to 0.

6. The measured wave velocity for the kth group is expressed in m / s. For reference wave speed, the unit is m / s, and we take 0.5 m / s.

10. The method for calculating seafloor groundwater discharge salt flux based on integrated underway resistivity detection according to claim 2, characterized in that: In step S6, the uncertainty assessment formula for the SGD salt flux calculation results is as follows: Where ε is the relative uncertainty of the SGD salt flux calculation result, which is dimensionless; denoted as the standard deviation of the SGD seepage rate, in m / d; This represents the average SGD seepage rate, in m / d. This represents the standard deviation of groundwater salinity, expressed in kg / m³. 3 C represents the average salinity of groundwater, in kg / m³. 3 ; This represents the standard deviation of the seawater mixing ratio; This represents the average mixing ratio of seawater, expressed in Ω·m.

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