A deep-sea environment identification and evaluation method based on an improved ground sound model

Abstract

This invention discloses a method for deep-sea environment identification and assessment based on an improved geosonic model, belonging to the field of marine geological exploration and deep-sea mineral resource development technology. The method involves collecting geosonic and topographic data via a survey vessel equipped with advanced equipment, extracting and correcting geosonic parameters such as reflection coefficient, sound velocity, and attenuation coefficient. Based on the classic Biot-Stoll model, a geosonic model adapted to deep-sea sediments is constructed through skeleton parameter optimization, permeability correction, and high-frequency attenuation mechanism improvement, combined with a laboratory measured database. Physical parameters are inverted and converted into soil mechanics parameters, and the mining environment is assessed in three dimensions, including foundation bearing capacity, slope stability, and excavability. A comprehensive report with four suitability levels and confidence scores is output. This invention eliminates the need for expensive in-situ testing, achieving large-scale and accurate assessment, and providing a scientific basis for mining planning and risk control.

Description

Technical Field

[0001] This invention relates to the field of marine geological exploration and deep-sea mineral resource development technology, and in particular to a method for deep-sea environment identification and assessment based on an improved ground acoustic model. Background Technology

[0002] The deep seabed contains abundant polymetallic nodules, cobalt-rich crusts, and sulfide mineral resources. Safe and efficient extraction of these resources urgently requires precise assessment of the engineering geological properties of seabed sediments. Assessment parameters include, but are not limited to, sediment porosity, density, grain size distribution, shear strength, bearing capacity, and penetration resistance. These parameters directly determine the mobility of mining equipment, the stability of the seabed foundation, and the risk of geological hazards induced by mining operations. Current methods for obtaining engineering parameters of seabed sediments have the following shortcomings:

[0003] In-situ testing methods, such as seabed static cone penetration testing, can obtain high-precision in-situ mechanical parameters, but they are extremely costly and inefficient, and can only provide discrete point data, making it difficult to meet the needs of large-scale mining area assessment. Drilling sampling methods obtain rock core samples through drilling for laboratory geotechnical testing, but they have problems such as high cost, long cycle, easy sample disturbance, and limited spatial coverage.

[0004] Existing geophysical exploration methods, such as multibeam bathymetry and shallow seismic profiling, can rapidly and extensively acquire information on seabed topography and shallow stratigraphic structure. Current technologies also include methods for simple seabed classification using acoustic data. However, most of these methods remain at the level of qualitative or semi-quantitative identification of stratigraphic interfaces, unable to directly quantify detailed soil mechanical parameters from acoustic responses, and thus failing to meet the precise requirements of deep-sea mining engineering design.

[0005] The Biot-Stoll (BS) model is a classic theoretical model that connects the acoustic properties of sediments with their physical properties (such as porosity and particle density). However, the standard BS model has limitations in its applicability when applied to special deep-sea sediments (such as high-porosity, fine-grained deep-sea clay). Furthermore, existing technologies lack a complete framework to further transform the basic physical parameters obtained from the BS model into mechanical parameters directly needed for mining engineering, and to provide a comprehensive engineering suitability assessment.

[0006] Therefore, there is an urgent need in this field for a technical solution that can correlate large-scale geophysical exploration data with key soil mechanics parameters and can rapidly and comprehensively assess the deep-sea mining environment. Summary of the Invention

[0007] The present invention aims to overcome at least one of the defects of the prior art and provide a deep-sea mining environment identification and assessment method based on an improved geoacoustic model. By using conventional marine geophysical exploration data and a geoacoustic inversion model optimized for the deep-sea environment, the key physical and mechanical parameters of seabed sediments are quantitatively obtained, and finally an intuitive mining environment suitability level is output, providing a scientific basis for mining operation planning and equipment design.

[0008] This invention protects a method for identifying and assessing deep-sea mining environments based on an improved geosonic model, comprising the following steps:

[0009] S1 Data Acquisition: Using the shallow seismic profiler and multibeam echo sounder mounted on the survey vessel, a seaborne survey is conducted on the deep-sea area to be assessed, and ground acoustic data, including acoustic wave reflection signals and seabed topography data, are collected simultaneously.

[0010] S2 Geoacoustic Parameter Extraction and Preprocessing: The collected geoacoustic data is processed to extract key geoacoustic parameters from the sound wave reflection signal, including but not limited to the seabed reflection coefficient, the sound wave propagation velocity in the sediment layer, and the sound wave attenuation coefficient, and deep-sea environment correction is performed.

[0011] S3 geosonic model and soil mechanics parameter prediction: including:

[0012] S31 Establishment and Improvement of the Geoacoustic Model: Based on the classic Biot-Stoll (BS) theoretical model, laboratory measured data (including acoustic data and corresponding porosity, density, grain size, shear strength, etc.) of typical sediments (such as silty clay, clay, etc.) in the frequency range of 20kHz-200kHz are introduced as a training and validation database. By optimizing the model parameters and boundary conditions, the model is made more suitable for the characteristics of high porosity and fine grains of deep-sea sediments, thus constructing an improved "deep-sea mining geoacoustic model".

[0013] S32 Physical parameter inversion: The geosonic parameters extracted in step S2 are input into the deep-sea mining geosonic model. Through inversion calculation, the basic physical parameters of the seabed sediments are obtained, mainly including porosity, wet density, and average grain size.

[0014] S33 Substrate Classification and Soil Mechanics Parameter Prediction: Based on the porosity, density, and particle size parameters obtained from the inversion, a preliminary classification of the substrate type is first performed (e.g., clay, silty clay, etc.). Subsequently, based on the statistical relationships or empirical equations established from the laboratory measured database, the basic physical parameters obtained from the inversion are converted into the range of soil mechanics parameters required for the project, including bearing capacity, undrained shear strength, and equivalent penetration resistance.

[0015] S4 Engineering Environmental Assessment: Based on the soil mechanics parameters predicted in step S33, combined with the seabed slope (obtained from multibeam topography data), the key indicators of mining operations are assessed, including: foundation bearing capacity, seabed slope stability, and excavability / mineability of sediments.

[0016] S5 Integrated Output and Visualization: Integrating all results from S2-S4, a final assessment report is generated. The report includes: spatially distributed deep-sea environmental parameter maps (such as porosity distribution maps and shear strength distribution maps); a mining environment suitability grading map based on preset thresholds, dividing the assessment area into four levels: "Excellent," "Good," "Medium," and "Poor"; each assessment unit's results are accompanied by a "confidence level" index, which is calculated based on factors such as ground acoustic data quality and model inversion residuals, and is used to characterize the reliability of the assessment results.

[0017] Compared with existing technologies, the beneficial effects of this invention are:

[0018] 1. Greater efficiency and economy: Using conventional geophysical exploration data (shallow stratigraphic profiler, multibeam echo sounder data) can achieve large-scale and continuous prediction of seabed soil mechanical parameters, without relying on expensive and inefficient in-situ tests (such as seabed static cone penetration test) and drilling sampling, which greatly reduces the time and economic costs of data acquisition. At the same time, it overcomes the shortcomings of traditional methods that can only obtain discrete point data and have limited spatial coverage, and meets the needs of assessment of large-scale deep-sea mining areas.

[0019] 2. Improved Model Adaptability and Parameter Conversion Accuracy: An improved deep-sea mining acoustic model optimizes the empirical formulas for skeleton parameters, corrects the permeability calculation equation, and introduces a high-frequency viscoelastic attenuation mechanism, addressing the characteristics of high porosity, fine grains, and weak consolidation in deep-sea sediments. This solves the problem of insufficient applicability of the standard Biot-Stoll model in deep-sea environments. Based on statistical relationships established using a laboratory measured database, quantitative conversion from acoustic parameters to physical parameters (porosity, wet density, average particle size) and then to soil mechanics parameters (bearing capacity, undrained shear strength, equivalent penetration resistance) is achieved. This avoids the shortcomings of existing geophysical exploration methods that remain at a qualitative or semi-quantitative level, resulting in assessment results that more closely match the actual design requirements of deep-sea mining engineering.

[0020] 3. Enhanced Technical Process Completeness and Engineering Practicality: A complete technical process is provided, encompassing data acquisition, parameter extraction, model inversion, engineering evaluation, and comprehensive output, forming a closed-loop support system of "data-parameter-evaluation-decision-making." The engineering evaluation stage constructs a three-dimensional evaluation system for foundation bearing capacity, seabed slope stability, and sediment excavability, comprehensively considering actual operational influencing factors such as equipment load, terrain slope, and seismic load, making it more comprehensive and scientific compared to single-dimensional evaluations. The output results not only include spatially distributed environmental parameter maps and a four-level suitability grading map (Excellent, Good, Medium, Poor), but also include confidence indices (quantitatively calculated based on data quality and inversion residuals), intuitively presenting the reliability of the evaluation results and greatly facilitating mining engineers and decision-makers in operational planning, equipment selection, and risk control.

[0021] 4. Superior Environmental Adaptability and Anti-interference Capability: Deep-sea environmental corrections (pressure, temperature, and geometric corrections) are introduced during data processing, effectively offsetting the impact of deep-sea high pressure, low temperature, and topographic undulations on data accuracy. Physical parameter inversion employs a multi-frequency joint inversion algorithm and Bayesian regularization constraints, reducing inversion ambiguity and improving the stability and accuracy of parameter inversion. Simultaneously, residual analysis and confidence interval estimation quantify the uncertainty of parameter predictions, providing a scientific reference for the rational use of the results. Compared to existing technologies lacking systematic error correction and uncertainty assessment, the technical solution of this invention is more reliable and robust in complex deep-sea environments.

[0022] 5. Enhanced large-scale assessment and spatial representation capabilities: By leveraging mobile surveying and spatial interpolation techniques, continuous parameter distribution representation within the assessment area is achieved. Combined with visualization products generated by geographic information system technology (parameter distribution map, suitability classification map), the spatial heterogeneity of the seabed environment can be clearly presented. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the overall technical process of the deep-sea environment identification and assessment method of the present invention.

[0024] Figure 2 This is a schematic diagram of deep-sea acoustic data acquisition according to the present invention.

[0025] Figure 3 This is a flowchart of the ground acoustic parameter extraction process of the present invention.

[0026] Figure 4 This is a framework diagram of the improved deep-sea mining acoustic model of the present invention.

[0027] Figure 5 This is a flowchart of the physical parameter inversion calculation process of the present invention.

[0028] Figure 6This is a diagram showing the relationship between the substrate classification and soil mechanical parameters in this invention.

[0029] Figure 7 This is a flowchart illustrating the calculation process of the environmental assessment indicators for the present invention. Detailed Implementation

[0030] The accompanying drawings illustrate the technical solutions of the embodiments of the present invention in more detail. Throughout the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. The described embodiments are some, but not all, embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0031] Example

[0032] This embodiment provides a method for deep-sea environment identification and assessment based on an improved ground acoustic model. The specific method is as follows:

[0033] S1: Data Acquisition

[0034] Figure 1 This demonstrates the complete process from data acquisition to final evaluation output. Figure 2 This demonstrates the working principles of the research vessel, shallow seismic profiler, and multibeam echo sounder, as well as the propagation paths of sound waves in seawater and seabed sediments. This step utilizes geophysical exploration equipment mounted on the research vessel to conduct underway measurements of the deep-sea target area, simultaneously acquiring multi-source ground acoustic data. Specifically, it includes the following steps:

[0035] S11: Measurement Equipment Configuration

[0036] The research vessel is equipped with a shallow seismic profiler and a multibeam echo sounder. The shallow seismic profiler operates at frequencies from 3.5 kHz to 200 kHz and is primarily used to acquire the acoustic characteristics of shallow seabed sediments. The multibeam echo sounder typically operates at frequencies from 12 kHz to 400 kHz and is used to acquire high-precision seabed topographic data. Both systems utilize a unified positioning and navigation system to ensure accurate registration of spatial data.

[0037] S12: Acoustic signal acquisition

[0038] like Figure 2As shown, the shallow seismic profilometer emits acoustic pulses towards the seabed. The sound waves propagate through the seawater and are reflected and transmitted upon reaching the seabed interface. Part of the acoustic energy is reflected back into the water and received by the transducer, forming a seabed reflection signal; the remaining energy is transmitted into the sedimentary layer, propagating within it and undergoing multiple reflections at different stratigraphic interfaces, forming a subbottom reflection signal. The received raw acoustic signal contains rich information about the seabed sediments, and the recorded parameters include:

[0039] (1) Amplitude of the reflected signal This characterizes the intensity of the reflected echo at different times;

[0040] (2) Two-way travel time That is, the round-trip time of a sound wave from transmission to reception;

[0041] (3) Frequency components of the signal This was obtained through spectral analysis.

[0042] S13: Terrain Data Acquisition

[0043] Multibeam echo sounders transmit a fan-shaped array of acoustic beams towards the seabed and receive reflected signals from multiple points on the seabed to obtain strip-shaped, high-density water depth measurement data. The calculation formula is:

[0044]

[0045] in, The speed of sound in seawater can be calculated from temperature, salinity, and pressure parameters measured by XCTD. This refers to the two-way travel time of acoustic waves. By measuring multiple beams, a three-dimensional seabed topography model can be constructed, and the seabed slope at various locations can be calculated. .

[0046] S14: Data Quality Control

[0047] During data acquisition, real-time quality monitoring is required to remove abnormal data affected by ship noise, bubbles, multipath effects, etc. For valid data, its spatial coordinates should be recorded. , water depth Collect data on time and relevant environmental parameters (such as water temperature, salinity, and sea state) to establish a spatial database.

[0048] S2: Ground acoustic parameter extraction and preprocessing

[0049] like Figure 3This paper illustrates the process of extracting the seabed reflection coefficient, sound wave velocity, and attenuation coefficient from the raw acoustic wave reflection signal. This step processes the raw geoacoustic data acquired in step S1 to extract key geoacoustic parameters. These parameters are the inputs for subsequent geoacoustic model inversion. Specifically, it includes the following sub-steps:

[0050] S21: Extraction of seabed reflectance coefficient

[0051] Seabed reflectivity This is an important parameter characterizing the acoustic properties of the seabed interface, defined as the ratio of the reflected wave sound pressure to the incident wave sound pressure. Its extraction process is as follows:

[0052] First, the first reflection signal from the seabed is separated from the original acoustic signal. By selecting a time window, a segment of the signal corresponding to the seabed interface position during the two-way travel time is extracted. This signal segment is then subjected to amplitude normalization to eliminate the effects of transmitted energy fluctuations and geometric spread losses.

[0053] Amplitude of seabed reflection coefficient The calculation formula is:

[0054]

[0055] in, The amplitude of the received seabed reflected signal; The amplitude of the incident wave; The sound absorption coefficient in seawater; The water is deep. This is used to compensate for the absorption loss of sound waves as they propagate back and forth in seawater.

[0056] Phase of reflection coefficient It can be obtained through phase spectrum analysis or inverted using plane wave reflection theory through multi-angle measurements. The complete reflection coefficient is in complex form:

[0057]

[0058] This formula fully describes two key characteristics of sound waves reflecting off the seabed: amplitude. The ratio of the reflected wave amplitude to the incident wave amplitude indicates how much energy is reflected back; phase. The reflection coefficient represents the phase change that occurs during reflection, describing the time delay or lead of the wave. Seabed reflection not only weakens the amplitude of sound waves but also alters their phase. A single real number cannot simultaneously describe both effects, so a complex number must be used to fully represent the reflection coefficient. This is crucial for underwater acoustic modeling and seabed property analysis.

[0059] S22: Acoustic velocity extraction from sedimentary layers

[0060] Sound wave propagation speed within the sediment layer These are important parameters describing the mechanical properties of sediments. Their extraction method is based on the identification of reflective layers within the sedimentary layer.

[0061] In shallow seismic profile images, identify the subbottom reflective layer below the seafloor interface. Let the two-way travel time corresponding to seafloor reflection be... The two-way travel time corresponding to a certain bottom reflector is The depth of the reflective layer is The formula for calculating the sound velocity in the sedimentary layer is:

[0062]

[0063] For homogeneous sediments lacking a clearly defined reflective layer, sound velocity can be retrieved using multi-band measurement data and dispersion analysis. Dispersion analysis is based on the differences in the propagation speed of sound waves of different frequencies within the sediment, and is calculated using the phase difference method.

[0064]

[0065] in, The frequency of the sound wave; The phase difference between the two receiving points at frequency f; The vertical distance between the two receiving points is denoted as .

[0066] S23: Extraction of sound wave attenuation coefficient

[0067] Sound wave attenuation coefficient The rate of energy attenuation during sound wave propagation in sediments is closely related to the porosity and particle characteristics of the sediments. The extraction method is as follows:

[0068] By analyzing the amplitude attenuation pattern of reflected signals at different depths within the sedimentary layer, and using amplitude data from multiple reflective layers, the relationship between amplitude attenuation and propagation distance is established. Let the first... The depth of each reflective layer is The corresponding reflection amplitude is The sound wave attenuation coefficient can then be obtained through logarithmic linear fitting:

[0069]

[0070] in, The initial amplitude is given; factor 2 represents the round-trip propagation of the sound wave. The solution is obtained by performing least-squares fitting on data from multiple reflection layers. .

[0071] Another method is to use the spectral ratio method. Spectral analysis is performed on the seabed reflection signal and the subsurface reflection signal separately to obtain their spectra. and The logarithm of the spectral ratio has a linear relationship with frequency.

[0072]

[0073] in, This represents the distance between the two reflective layers. The frequency-dependent attenuation coefficient can be obtained through linear fitting. .

[0074] S24: Deep-sea environment correction

[0075] In deep-sea environments, the extracted ground acoustic parameters require special correction, mainly including:

[0076] (1) Pressure correction: The high pressure environment in the deep sea will affect the porosity and acoustic properties of sediments. The parameters need to be corrected according to the hydrostatic pressure corresponding to the measured water depth.

[0077] (2) Temperature correction: The temperature of the bottom water in the deep sea is close to 0℃, so the temperature parameter in the formula for calculating the speed of sound in the sea needs to be corrected to ensure the speed of sound. The accuracy of this information is unknown. The empirical formula for the speed of sound in seawater is:

[0078]

[0079] Where T is temperature (°C); S is salinity (‰); and z is depth (m).

[0080] (3) Geometric Correction: Due to the undulating seabed topography, the incident angle of the sound wave may deviate from the vertical direction. The reflection coefficient needs to be corrected for the incident angle based on the measured topographic slope. When the incident angle is... In such cases, a theoretical model of the reflection coefficient related to the incident angle needs to be introduced for correction.

[0081] After the above processing, a spatially distributed set of ground acoustic parameters is obtained: This serves as the input for the next step of ground acoustic model inversion.

[0082] S3 Ground Sound Model and Soil Mechanics Parameter Prediction

[0083] This step is the core of the invention. Figure 4 The improved scheme, input parameters, output parameters, and model optimization process of the Biot-Stoll theoretical model are shown. Figure 5 The process of solving porosity, wet density, and average particle size using geosonic parameters through an iterative inversion algorithm is illustrated. Figure 6 This illustrates the mapping relationship between physical parameter space and soil type, as well as the conversion relationship from physical parameters to soil mechanics parameters. For example... Figure 4 , Figure 5 and Figure 6As shown, an improved deep-sea mining geoacoustic model is established to transform geoacoustic parameters into sediment physical and soil mechanical parameters. This involves the following three sub-steps:

[0084] S31: Establishing and improving the ground acoustic model

[0085] like Figure 4 As shown, this step is based on the classic Biot-Stoll theoretical model and is improved and optimized to suit the special properties of deep-sea sediments.

[0086] (1) Biot-Stoll Theoretical Basis

[0087] The Biot-Stoll model treats seafloor sediments as a two-phase medium composed of a solid granular framework and porous fluids, describing the propagation characteristics of sound waves in this medium. The core of the model is establishing the acoustic properties of sediments (sound velocity, attenuation, reflection coefficient) in relation to their physical properties (porosity). Particle density Pore ​​fluid density The Biot-Stoll model has 13 input parameters, including three main categories: pore water parameters, sediment solid particle properties, and sediment skeleton properties.

[0088] bulk density of sediments for:

[0089]

[0090] in, For pore fluid density, Where n is the particle density and n is the porosity.

[0091] Fast P-wave velocity in sediments and slow longitudinal wave velocity The prediction involves solving the characteristic equation of Biot theory. The wave equation of Biot theory is as follows:

[0092]

[0093]

[0094] The above Biot theory wave equation can be applied to non-uniform media, but it is usually used specifically for homogeneous media, where all parameters are constants independent of position.

[0095] In the formula, This is called "relative displacement," used to represent the displacement of the fluid relative to a solid framework. The displacement field of the solid framework is represented by... This represents the displacement field of the pore fluid. Relative displacement is represented by . The relative displacement is defined as the integral of the displacement over its normal component on a given cross section. The above definition method results in the fluid volume transported across the cross section being negative relative to the skeleton. The second derivative of time represents inertia, and the first derivative of time... Represents viscous loss. Real parameter. It is called "torsionalism" and is used to characterize the increase in inertia caused by complex pore structures. This refers to dynamic viscosity. For high-frequency cases, Biot theory uses a frequency-dependent complex parameter to replace dynamic viscosity, and its expression is as follows: F is the complex correction factor.

[0096] The formula also includes four elastic coefficients, plane wave modulus Solid-fluid coupling coefficient Biot modulus Effective bulk modulus It is possible to establish a relationship with the medium parameters of each component of the porous elastic medium. The relationship is as follows:

[0097]

[0098]

[0099]

[0100]

[0101] In the above formulas, The bulk modulus of a single sediment particle; The bulk modulus of pore water; denoted as the bulk modulus of the solid skeleton. and Typically, it is a real number independent of frequency, while the volume modulus of a solid skeleton is... and solid skeleton shear modulus Similarly, these moduli are usually assumed to be complex numbers. The skeleton moduli of these two complex numbers can partially explain the energy loss in porous media. These moduli are usually assumed to be independent of frequency.

[0102] The potential functions for fast and slow longitudinal waves are shown in the following equations:

[0103]

[0104]

[0105] In the formula, For the longitudinal wave number, These correspond to fast and slow P-waves, respectively. Substituting the plane wave solutions expressed in the above two equations into Biot's theoretical wave equations, we obtain two simultaneous homogeneous linear equations. Setting their determinant to zero, we obtain the following equation:

[0106]

[0107] The expression for the P-wave number is obtained by solving the problem as follows:

[0108]

[0109] When the above formula is positive, it corresponds to a fast longitudinal wave, i.e., q=1; when it is negative, it corresponds to a slow longitudinal wave, i.e., q=2.

[0110] The expressions for calculating the intermediate parameters a, b, and c are as follows:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] Among them Characteristic velocity corresponding to plane wave modulus The characteristic velocity of fast waves, The characteristic velocity of a slow wave, The characteristic velocity corresponding to the solid-fluid coupling coefficient;

[0117] The expressions for the complex velocity ratio and related parameters are as follows:

[0118]

[0119] Real parameters The ratio of the phase velocity of sediment to that of seawater; actual parameter. Since this is the energy loss parameter, the sound attenuation coefficient in dB / m can be calculated using the following formula:

[0120]

[0121] (2) Analysis of deep-sea sediment characteristics

[0122] Deep-sea sediments are characterized by high porosity, fine grain size, high water content, and weak consolidation. The standard Biot-Stoll model has the following problems when applied to these sediments:

[0123] Empirical formulas for skeleton stiffness parameters are mostly based on shallow sea or terrestrial soil samples and are not applicable to deep-sea high-porosity sediments.

[0124] The relationship between permeability and porosity needs to be recalibrated under conditions of extremely high porosity.

[0125] The dispersion and attenuation mechanisms in high-frequency bands are more complex in fine-grained sediments, leading to a decrease in the accuracy of standard model predictions.

[0126] (3) Model improvement methods

[0127] To address the above issues, the Biot-Stoll model is improved as follows:

[0128] S311: Skeleton Parameter Optimization: By collecting laboratory measurement data of deep-sea sediments, a skeleton bulk modulus for deep-sea sediments was established. and shear modulus The empirical formula is as follows. The improved empirical relation is:

[0129]

[0130]

[0131] in, Porosity , , , , , The fitting parameters were optimized using measured data. Compared to the standard model, the exponential parameters in this improved model have been recalibrated for the high porosity range.

[0132] S312: Permeability Correction: Permeability of Fine-Grained Deep-Sea Sediments The relationship between porosity and particle size is expressed using the modified Kozeny-Carman equation:

[0133]

[0134] in, The average particle size; Porosity; A correction factor to account for particle shape and pore tortuosity is used, which is determined based on the microstructural characteristics of deep-sea clay.

[0135] S313: High-frequency attenuation mechanism: In the high-frequency range, a viscoelastic effect at the particle scale is introduced. The frequency dependence of the attenuation coefficient is expressed as:

[0136]

[0137] in, For viscous decay, For scattering attenuation, the scattering attenuation term is corrected using Rayleigh scattering theory for fine-grained sediments, taking into account the influence of particle size distribution.

[0138] (4) Establishment of laboratory measurement database

[0139] To validate and optimize the improved model, a laboratory experimental database was established. The database includes:

[0140] ① Different types of deep-sea sediment samples (silty clay, clay, siliceous mud, etc.);

[0141] ②Basic physical parameter to be measured: porosity wet density Dry density Particle density Moisture content Particle size distribution, etc.;

[0142] ③ Measured acoustic parameters: sound velocity in the frequency range of 20-200kHz attenuation coefficient Reflection coefficient ;

[0143] ④ Measured mechanical parameters: Undrained shear strength Compression modulus internal friction angle Cohesion wait.

[0144] The samples in the database cover sediment types typical of deep-sea mining areas. Statistical analysis of the laboratory-measured database establishes the correlations between physical, acoustic, and mechanical parameters, providing support for subsequent inversion and prediction.

[0145] (5) Model parameter optimization

[0146] Using a laboratory measured database, the model parameters were optimized through the following steps:

[0147] T1: Input samples with known physical parameters from the database into the improved Biot-Stoll model to calculate the predicted acoustic parameters (sound velocity, attenuation, reflection coefficient).

[0148] T2: Compare the predicted values ​​with the measured acoustic parameters and calculate the root mean square error (RMSE).

[0149]

[0150] in, Number of samples; and These are the predicted and measured values ​​for the i-th sample, respectively.

[0151] T3: Employ nonlinear optimization algorithms (such as genetic algorithms, particle swarm optimization, or Levenberg-Marquardt algorithms) to adjust adjustable parameters in the model (such as the exponential parameter of the skeleton modulus, the permeability correction factor, etc.) to minimize RMSE.

[0152] T4: Divide the database into training and validation sets, optimize parameters on the training set, and evaluate the model's prediction accuracy and generalization ability on the validation set.

[0153] The optimized and improved model can provide more accurate acoustic-physical parameter correlations under deep-sea sediment conditions.

[0154] S32 physical parameter inversion

[0155] like Figure 5 As shown, this step inputs the geosonic parameters extracted in step S2 into the improved deep-sea mining geosonic model, and obtains the basic physical parameters of the seabed sediments through inversion calculation. The physical parameter inversion process adopts an iterative optimization algorithm.

[0156] (1) Mathematical description of the physical parameter inversion problem

[0157] The goal of physical parameter inversion is to find a set of physical parameter vectors. (Porosity, wet density, and average particle size, respectively), to ensure that the model-predicted ground acoustic parameters best match the measured ground acoustic parameters. The objective function (error function) is defined as:

[0158]

[0159] in, , , The model predicts the reflection coefficient, sound velocity, and attenuation coefficient based on parameter X. , , The corresponding value measured in step S2; , , The weighting coefficients are determined based on the measurement accuracy of each parameter and its sensitivity to the inversion results.

[0160] The inversion problem is to find a solution:

[0161]

[0162] That is, to solve for the error function Minimum optimal parameter combination ,in

[0163] 0.5≤ ≤0.95; 1.2≤ ≤2.0 (g / cm³); 0.1≤ ≤100 (μm).

[0164] (2) Physical parameter inversion algorithm flow

[0165] The nonlinear least squares inversion algorithm is used, and the specific process is as follows:

[0166] Step 1: Initialization. Based on experience or prior information, set initial guess values ​​for the parameters. For deep-sea sediments, typical values ​​can be selected: =0.75, =1.45g / cm³, =5μm.

[0167] Step 2: Forward modeling. Set the current parameters... Input the improved Biot-Stoll model and calculate the predicted ground acoustic parameters. , , .

[0168] Step 3: Residual Evaluation. Calculate the current objective function value. and its gradient with respect to each parameter:

[0169]

[0170] The gradient can be calculated using the finite difference method or automatic differentiation techniques.

[0171] Step 4: Parameter Update. Update the parameters using gradient descent or a quasi-Newton method:

[0172]

[0173] in, The step size factor is determined through a line search; It is a Hessian matrix or an approximation thereof.

[0174] Step 5: Convergence Check. Stop iteration if any of the following conditions are met:

[0175] ① The change in the objective function is less than the threshold: ;

[0176] ② The parameter change is less than the threshold: ;

[0177] ③ Reaching the maximum number of iterations: .

[0178] Otherwise, let Return to step 2 and continue iterating.

[0179] Step 6: Output the results. Output the optimal parameters. and the corresponding minimum value of the objective function .

[0180] (3) Assessment of uncertainty in physical parameter inversion

[0181] The results of physical parameter inversion contain uncertainties, mainly stemming from measurement errors, model simplification, and differences in parameter sensitivity. To assess these uncertainties, the following method is used:

[0182] ① Parameter covariance matrix: Near the optimal solution, a second-order Taylor expansion of the objective function yields the inverse of the Hessian matrix, which provides information about the parameter covariance.

[0183] The standard deviation of the parameter is:

[0184] ② Sensitivity analysis: Calculate the relative sensitivity of each parameter to the objective function:

[0185]

[0186] Parameters with high sensitivity have higher inversion accuracy, while parameters with low sensitivity may have greater uncertainty.

[0187] ③ Monte Carlo method: Random sampling is performed within the error range of the measured parameters, and the inversion is repeated multiple times to obtain the probability distribution and confidence interval of the parameters.

[0188] (4) Multi-frequency joint inversion

[0189] To improve the accuracy and stability of the inversion, joint inversion is performed using ground acoustic data from multiple frequencies. There are M frequencies. Based on the measurement data, the expanded objective function is:

[0190]

[0191] Multi-frequency joint inversion utilizes the differences in sensitivity of different frequencies to different sediment properties, which can more comprehensively constrain inversion parameters and reduce ambiguity.

[0192] (5) Output of inversion results

[0193] For each spatial location within the measurement area By performing the above inversion process, the spatially distributed physical parameter field is obtained:

[0194] Porosity field:

[0195] Moist density field:

[0196] Average particle size field:

[0197] Simultaneously output the inversion uncertainty at each position. , , and the minimum value of the objective function As an indicator of inversion quality.

[0198] S33 Substrate Classification and Soil Mechanical Parameter Prediction

[0199] like Figure 6 As shown, this step classifies the substrate type based on the physical parameters obtained from the inversion and predicts the soil mechanics parameters required for the project.

[0200] (1) Classification of substrate types

[0201] According to the Shepard classification system in the classification of marine sediments, sediments are divided into the following main types: clay, silty clay, silt, sandy silt, etc.

[0202] The classification results are presented in the form of thematic maps, forming a distribution map of seabed sediment types. .

[0203] (2) Methods for predicting soil mechanical parameters

[0204] Based on laboratory measured databases, statistical relationships or empirical equations are established between physical parameters and soil mechanical parameters. The main predicted soil mechanical parameters include:

[0205] ① Undrained shear strength :

[0206] Undrained shear strength is an important indicator describing the shear resistance of saturated cohesive soils under rapid loading. For deep-sea sediments, It shows a significant correlation with porosity and wet density. Through regression analysis of a laboratory measurement database, the following empirical relationships were established:

[0207]

[0208] Alternatively, a multiple linear regression approach can be used:

[0209]

[0210] in, , , , , , , The regression coefficients are obtained from the experimental data in the laboratory database. With the corresponding , , The result was obtained through fitting.

[0211] Different regression equations are used for different substrate types to improve prediction accuracy. For example, different empirical relationships are established for clay and silty clay.

[0212] ② Bearing capacity :

[0213] Ultimate bearing capacity of submarine foundation This determines whether mining equipment can operate safely. For saturated soft clay foundations, using Terzaghi's bearing capacity theory, the ultimate bearing capacity can be expressed as:

[0214]

[0215] in, The cohesive force can be approximated as the undrained shear strength. ; , , For saturated clay, the bearing capacity coefficient is the internal friction angle. hour, , , ; The effective specific gravity of the sediment. ; The foundation burial depth (0 can be taken for seabed surface operations); The base width (determined based on equipment dimensions).

[0216] After simplification, the formula for ultimate bearing capacity is:

[0217]

[0218] in , This refers to the pore fluid density, typically seawater. Foundation burial depth; The base width is determined based on the equipment dimensions.

[0219] Considering safety factor The allowable load-bearing capacity is:

[0220]

[0221] Through prediction and It can calculate the bearing capacity at each location. .

[0222] ③ Equivalent penetration resistance

[0223] Penetration resistance characterizes the resistance of sediments to the penetration of mining equipment, directly affecting excavation efficiency and energy consumption. Equivalent penetration resistance. This can be obtained through the cone penetration test (CPT). For soft clay, there is an empirical relationship:

[0224]

[0225] in, The cone coefficient is typically taken as 10-20 for soft clay. This is determined by analyzing CPT data from a laboratory measurement database and... Statistical analysis of the data was conducted to determine the applicability to deep-sea sediments. value.

[0226] Another method is to establish The direct relationship with physical parameters. This can be achieved through multiple regression:

[0227]

[0228] in, , , , These are the fitting parameters.

[0229] The predicted penetration resistance is used to assess the excavability of sediments; the lower the resistance, the easier the excavation.

[0230] (3) Parameter prediction uncertainty

[0231] Predictions of soil mechanics parameters are based on empirical relationships and inherently contain uncertainties. These uncertainties can be quantified using the following methods:

[0232] ① Regression residual analysis: Calculate the standard error SE of the empirical relationship, which characterizes the dispersion of the predicted values.

[0233]

[0234] in, The number of samples; SE represents the number of parameters in the regression equation. The smaller the SE value, the more accurate the model prediction.

[0235] ② Confidence Interval Estimation: For each predicted value, a 95% confidence interval is given:

[0236]

[0237] ③ Error propagation: Considering the uncertainty of physical parameter inversion , , The uncertainty of soil mechanical parameters is calculated using the error propagation formula:

[0238]

[0239] The partial derivatives are obtained by differentiating empirical relationships.

[0240] (4) Prediction results output

[0241] For each spatial location in the assessment area Output the following soil mechanics parameters:

[0242] Undrained shear strength:

[0243] Load-bearing capacity:

[0244] Penetration resistance:

[0245] And the corresponding range of uncertainty. These parameters provide a quantitative basis for the next step of the engineering environmental assessment.

[0246] S4 Project Environmental Assessment

[0247] Figure 7 The calculation process for assessing foundation bearing capacity, slope stability, and excavability is shown. For example... Figure 7 As shown, this step, based on predicted soil mechanics parameters and combined with seabed topographic data, quantitatively assesses key engineering indicators for mining operations. The assessment includes three aspects: foundation bearing capacity, seabed slope stability, and sediment excavability.

[0248] S41: Foundation Bearing Capacity Assessment

[0249] The foundation bearing capacity assessment determines whether the seabed can support the weight of mining equipment without excessive subsidence or damage.

[0250] ① Equipment load calculation: Based on the design parameters of the mining equipment, calculate the pressure it exerts on the seabed. Let the total weight of the equipment be... The grounding area is (This could be the contact area of ​​tracks, wheels, or skis), then:

[0251]

[0252] ② Safety factor calculation: Combine the equipment pressure with the allowable bearing capacity predicted in step S33. Compare and calculate the safety factor :

[0253]

[0254] ③ Evaluation criteria: Determine the threshold for the safety factor based on engineering specifications.

[0255] It has sufficient load-bearing capacity and is suitable for operation.

[0256] The load-bearing capacity is basically met, but monitoring is required.

[0257] Insufficient load-bearing capacity, posing a risk;

[0258] The load-bearing capacity is severely insufficient, making it unsuitable for operation.

[0259] ④ Settlement Prediction: For areas with sufficient bearing capacity, further predict the settlement. Use elasticity theory or empirical formulas to estimate the settlement during equipment operation. :

[0260]

[0261] in, Basic feature size; Poisson's ratio (0.45-0.50 for soft clay); For compressibility modulus, it can be obtained through Estimate: .

[0262] If the settlement exceeds the equipment's allowable operating range, the operation plan needs to be adjusted or the equipment design needs to be improved.

[0263] S42: Stability Assessment of Submarine Slopes

[0264] Mining operations may induce instability on seabed slopes, leading to landslides or mudflows, threatening equipment safety and impacting the environment. The limit equilibrium method is used for slope stability assessment.

[0265] ①Slope Extraction: Calculate the seabed slope at each location from the multibeam topographic data obtained in step S1. The slope is calculated using the gradient of the digital elevation model (DEM):

[0266]

[0267] in, This is the seabed elevation.

[0268] ② Infinite Slope Stability Analysis: For underwater slopes, an infinite slope model is adopted, with a stability safety factor. for:

[0269]

[0270] For saturated soft clay ( ), simplified to:

[0271]

[0272] in, Cohesion is taken as the undrained shear strength. ; Effective severe; This is the potential sliding surface depth (usually taken as the sediment thickness or an empirical value).

[0273] ③ Impact of Additional Loads from Mining Operations: Consider the additional loads applied by equipment operating on slopes. These additional loads increase the slope's sliding force; the corrected safety factor is:

[0274]

[0275] in, This represents the slope length.

[0276] ④ Evaluation criteria:

[0277] Slope stability;

[0278] The situation is basically stable, but monitoring is required.

[0279] Unstable; caution is required when operating.

[0280] Unstable, operation prohibited.

[0281] ⑤ Seismic Load Consideration: In seismically active areas, the impact of seismic loads on slope stability must be considered. A quasi-static method is introduced, where the additional inertial force generated by seismic acceleration is calculated using the horizontal acceleration coefficient. calculate:

[0282]

[0283] S43: Sediment Excavability Assessment

[0284] Excavability assessment determines the ease with which sediments can be mechanically excavated, affecting mining efficiency and energy consumption.

[0285] ① Calculation of excavation resistance: Excavation resistance mainly comes from the shear strength and penetration resistance of the sediment. An empirical formula is used to calculate the excavation force per unit area. for:

[0286]

[0287] in, This is the excavation factor, which depends on the excavation method and the shape of the cutting tools, and is usually between 5 and 15. This refers to the undrained shear strength. This is the resistance to penetration.

[0288] ② Energy consumption estimation: Energy consumption per unit volume of sediment excavation for:

[0289]

[0290] in, To increase excavation speed; Excavation efficiency (excavation volume per unit time).

[0291] ③ Excavability classification: based on excavation resistance The excavability of sediments is classified into the following levels:

[0292] Easy to mine;

[0293] Medium-sized mineable area;

[0294] Difficult to excavate;

[0295] Difficult to excavate.

[0296] ④ Rheological properties considerations: For soft clay with high water content, it may exhibit fluid properties after excavation. Evaluate the rheological parameters of the sediment, such as yield stress. and plastic viscosity :

[0297]

[0298] in, Shear stress; This represents the shear rate. Rheological properties influence the transport and processing of sediments after excavation.

[0299] S4: Comprehensive Engineering Evaluation Indicators

[0300] The comprehensive engineering suitability index for mining operations is formed by combining the evaluation results from the above three aspects. :

[0301]

[0302] in, , , The scores are normalized (0-100 points) for bearing capacity, slope stability, and excavability, respectively. , , The weighting coefficients are determined based on the project priorities and must satisfy the following conditions: .

[0303] The scoring criteria for each sub-indicator are as follows:

[0304] Foundation bearing capacity rating:

[0305]

[0306]

[0307]

[0308]

[0309] Seabed slope stability rating:

[0310]

[0311]

[0312]

[0313]

[0314] Sediment excavability rating:

[0315]

[0316]

[0317]

[0318]

[0319] S5 Integrated Output and Visualization

[0320] This step integrates all the aforementioned analysis results to generate the final assessment report and visualization product. The report, combining graphics and data, provides intuitive and comprehensive support for mining engineering decisions.

[0321] (1) Generation of spatial parameter distribution map

[0322] Using geographic information system (GIS) technology, various parameter fields are displayed in the form of raster or contour lines:

[0323] ① Physical parameter diagram: including porosity distribution diagram Moist density distribution map Average particle size distribution map Different colors or grayscale values ​​are used to represent changes in parameter values, and seabed topography is overlaid as a base map to facilitate spatial positioning.

[0324] ② Substrate Type Map: Classification results of substrate The classification is displayed using a color chart, with different colors representing different substrate types (e.g., blue represents clay, green represents silty clay, yellow represents silt, etc.).

[0325] ③ Soil mechanics parameter diagram: including undrained shear strength Bearing capacity Penetration resistance Distribution maps. These maps directly reflect the mechanical properties of the seabed and are the core basis for engineering assessment.

[0326] ④ Engineering evaluation index diagram: including bearing capacity safety factor Slope stability safety factor Excavation resistance Distribution map.

[0327] All distribution maps use a unified spatial coordinate system and projection method to ensure accurate overlay of different layers.

[0328] (2) Mining environment suitability classification

[0329] Based on the comprehensive engineering suitability index The assessment area is divided into four suitability levels, forming a mining environment suitability classification map.

[0330] ① Grading standards:

[0331] Grade A: This indicates that the engineering conditions are excellent, suitable for large-scale mining operations, and the risk is low.

[0332] Grade B: This indicates that the engineering conditions are good and suitable for mining operations, but routine monitoring is required.

[0333] Grade C: This indicates that the engineering conditions are generally acceptable and mining operations can be carried out, but enhanced monitoring and engineering measures are required.

[0334] Grade D: This indicates poor engineering conditions, and mining operations are not recommended, or major engineering improvements are required.

[0335] ② Creating a hierarchical diagram: Divide each spatial unit according to... Values ​​are assigned corresponding grades, represented by different colors (e.g., green for excellent, yellow for good, orange for average, and red for poor). The grading map is overlaid with seabed topography and the distribution of proven mineral resources to help identify the best mining areas.

[0336] ③ Area statistics: Statistics on the area and proportion of each level to provide quantitative basis for mining planning:

[0337]

[0338]

[0339]

[0340] in, The total assessed area; , , , These are the areas for each level; , , , This represents the percentage of each level.

[0341] (3) Confidence assessment

[0342] To characterize the reliability of the evaluation results, a confidence index is calculated for each evaluation unit. The confidence level takes into account the following factors:

[0343] ① Ground acoustic data quality A score of 0-1 is assigned based on metrics such as signal-to-noise ratio, coverage density, and measurement consistency. High data quality indicates good performance. Close to 1.

[0344] ②Inversion residuals Find the minimum value of the inverse objective function. The degree of fit. Define the normalized residual:

[0345] , This is a preset threshold. When the residual is small... Close to 1.

[0346] ③ Parameter uncertainty Based on the parameter standard deviations calculated in steps S32 and S33, an uncertainty index is defined:

[0347] , This is the combined value of the uncertainties of all parameters (a weighted average can be used). The maximum uncertainty. (Uncertainty hours) Close to 1.

[0348] ④ Calculation of overall confidence level:

[0349]

[0350] in, , , The weighting coefficient is usually taken as... .

[0351] ⑤ Confidence level classification: The confidence level is divided into three levels:

[0352] High confidence level:

[0353] Medium confidence level:

[0354] Low confidence level:

[0355] On the suitability rating map, different symbols or transparency can be used to represent the confidence level, reminding users that supplementary investigations are needed for areas with low confidence.

[0356] (4) Preparation of the assessment report

[0357] Based on all the above information, an evaluation report containing the following contents has been prepared:

[0358] ① Project Overview: Describe the geographical location, area, survey period, methods and technical approaches used in the assessment area.

[0359] ② Data Acquisition and Processing: Summarize the acquisition parameters, data quality evaluation, and processing procedures for ground acoustic data.

[0360] ③ Ground acoustic model inversion results: Display the distribution map of physical parameters obtained from the inversion, and analyze the spatial variation characteristics and statistical laws of the parameters.

[0361] ④ Sediment classification results: Display the distribution map of sediment types and describe the characteristics and distribution range of each type of sediment.

[0362] ⑤ Soil mechanical parameter prediction: Show the distribution map of soil mechanical parameters, analyze their relationship with physical parameters, and discuss the prediction accuracy and uncertainty.

[0363] ⑥Engineering Environmental Assessment: Describe in detail the assessment results of foundation bearing capacity, slope stability, and excavability, and provide the safety factor and risk level.

[0364] ⑦ Mining Environment Suitability Classification: Displays a suitability classification map, statistics on the area and distribution of each level, and recommends preferred mining areas.

[0365] ⑧ Confidence assessment: Show the confidence distribution map and point out the low-confidence areas that need to be investigated further.

[0366] ⑨ Conclusions and Recommendations: Summarize the main conclusions of the assessment and propose recommended solutions for mining operations, including recommendations for optimal work areas, equipment selection, and engineering risks to be aware of.

[0367] Through the complete process of steps S1 to S5 above, this invention realizes the entire process from ground acoustic data acquisition to comprehensive assessment of the mining environment, providing a scientific, efficient, and economical environmental identification and assessment solution for deep-sea mining engineering.

[0368] The above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments above, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention should not depart from the spirit and scope of the present invention. Those skilled in the art can also make other changes within the spirit of the present invention and use them in the design of the present invention, as long as they do not deviate from the technical effects of the present invention. These changes made according to the spirit of the present invention should all be included within the scope of protection claimed by the present invention.

Claims

1. A method for deep-sea environment identification based on an improved ground acoustic model, characterized in that, Includes the following steps: S1: Data Acquisition: Using the shallow seismic profiler and multibeam echo sounder mounted on the survey vessel, a sea survey is conducted on the deep-sea area to be assessed, and ground acoustic data is collected simultaneously. S2: Ground acoustic parameter extraction and preprocessing: Process the raw ground acoustic data collected in step S1 to extract the target ground acoustic parameters; S3: Establishment of a deep-sea mining geoacoustic model and prediction of soil mechanics parameter range: S31: Establishing and improving the deep-sea mining acoustic model: Based on the classic Biot-Stoll theoretical model, improvements and optimizations are made to address the special properties of deep-sea sediments in order to establish a deep-sea mining acoustic model; S32: Physical parameter inversion: The acoustic parameters extracted in step S2 are input into the deep-sea mining acoustic model, and the basic physical parameters of the seabed sediments are obtained through physical parameter inversion; S33: Sediment classification and soil mechanics parameter prediction: Based on the basic physical parameters, a preliminary classification of sediment types is first performed, and then the basic physical parameters obtained by inversion calculation are converted into the range of soil mechanics parameters required for the project based on statistical relationships or empirical equations established from laboratory measured databases; The laboratory measurement database is a database obtained by introducing laboratory measurement data of typical sediments in the frequency range of 20kHz-200kHz as a training and verification database; the laboratory measurement data includes acoustic data and corresponding porosity, density, grain size and shear strength. In step S31, the improved optimization method for the classic Biot-Stoll model is as follows: S311: Skeleton Parameter Optimization: By collecting laboratory measurement data of deep-sea sediments, a skeleton bulk modulus for deep-sea sediments was established. and shear modulus The empirical formula; the improved empirical relation is: ； ； in, Porosity , , , , , The fitting parameters were obtained through optimization using measured data. S312: Permeability Correction: Permeability of Fine-Grained Deep-Sea Sediments With porosity The relationship between the particle size D and the particle size is expressed by the modified Kozeny-Carman equation: ； in, The average particle size; Porosity; To account for the correction factor of particle shape and pore tortuosity, the correction factor is determined based on the microstructural characteristics of deep-sea clay; S313: High-frequency attenuation mechanism: In the high-frequency band, a viscoelastic effect at the particle scale is introduced; the frequency dependence of the attenuation coefficient is expressed as: in, For viscous decay, For scattering attenuation, the scattering attenuation term is corrected using Rayleigh scattering theory for fine-grained sediments, taking into account the influence of particle size distribution. Step S31 further includes the step of establishing a laboratory measurement database, which includes: Different types of deep-sea sediment samples, including silty clay, clay, and siliceous mud; The basic physical parameters to be measured include porosity. wet density Dry density Particle density Moisture content Particle size distribution; Measured acoustic parameters: sound velocity in the frequency range of 20-200kHz attenuation coefficient Reflection coefficient ; Measured mechanical parameters: undrained shear strength Compression modulus internal friction angle Cohesion ; Step S31 further includes optimizing the parameters of the deep-sea mining acoustic model using the laboratory measured database, as follows: T1: Input samples with known physical parameters from the laboratory measured database into the improved Biot-Stoll model to calculate the predicted acoustic parameters; T2: Compare the predicted values ​​with the measured acoustic parameters and calculate the root mean square error (RMSE). in, Number of samples; and These are the predicted and measured values ​​for the i-th sample, respectively. T3: Employ nonlinear optimization algorithms, including genetic algorithms, particle swarm optimization, or Levenberg-Marquardt algorithms, to adjust adjustable parameters in the model, including the exponential parameter of the skeleton modulus and the permeability correction factor, to minimize RMSE. T4: Divide the laboratory test database into a training set and a validation set. Optimize parameters on the training set and evaluate the model's prediction accuracy and generalization ability on the validation set.

2. The deep-sea environment identification method based on an improved ground acoustic model according to claim 1, characterized in that, The ground acoustic parameter extraction and preprocessing in step S2 includes the following steps: S21: Extraction of seabed reflectance coefficient R: Amplitude of seabed reflection coefficient The calculation formula is: in, The amplitude of the received seabed reflected signal; The amplitude of the incident wave; The sound absorption coefficient in seawater; For water depth; This is used to compensate for the absorption loss of sound waves as they propagate back and forth in seawater; Phase of seabed reflectivity It can be obtained through phase spectrum analysis, or inverted using plane wave reflection theory through multi-angle measurements; the complete seabed reflection coefficient is in complex form as follows: Amplitude The ratio of the reflected wave amplitude to the incident wave amplitude indicates how much energy is reflected back; phase. It represents the phase change that occurs during reflection, describing the time delay or lead of the wave; S22: Acoustic velocity in the sedimentary layer extract: Let the two-way travel time corresponding to seabed reflection be . The two-way travel time corresponding to a certain seabed reflector is The seabed reflective layer is buried at a depth of The sound velocity of the sedimentary layer The calculation formula is: ； For homogeneous sediments lacking a clearly defined reflective layer, sound velocity can be retrieved using multi-band measurement data and dispersion analysis. Dispersion analysis is based on the difference in propagation speed of sound waves of different frequencies in sediments, and is calculated using the phase difference method. in, The frequency of the sound wave; The phase difference between the two receiving points at frequency f; The vertical distance between the two receiving points; S23: Sound wave attenuation coefficient extract: By analyzing the amplitude attenuation patterns of reflected signals at different depths within sedimentary layers, and utilizing amplitude data from multiple seabed reflector layers, the relationship between amplitude attenuation and propagation distance was established; let the first... The depth of the seabed reflector layer is The corresponding reflection amplitude is The sound wave attenuation coefficient can then be obtained through logarithmic linear fitting: ； in, The initial amplitude is given; factor 2 represents the round-trip propagation of the sound wave; the solution is obtained by least-squares fitting of multiple seabed reflector data. The frequency-dependent attenuation coefficient can then be obtained through linear fitting. ; Alternatively, the spectral ratio method can be used to perform spectral analysis on the seabed reflection signal and the subsurface reflection signal separately to obtain the spectrum. and The logarithm of the spectral ratio has a linear relationship with frequency as follows: ； in, Given the distance between the two reflective layers, solve for... The frequency-dependent attenuation coefficient can then be obtained through linear fitting. ; S24: Deep-sea environment correction: including pressure correction, temperature correction and geometric correction; S25: For obtaining the set of spatially distributed ground acoustic parameters: This serves as the input for the next step of ground acoustic model inversion; where... This is the set of seabed reflection coefficients. For the collection of sound velocities in the sedimentary layer, It is a set of sound wave attenuation coefficients.

3. The deep-sea environment identification method based on an improved ground acoustic model according to claim 1, characterized in that, In step S32, the physical parameter inversion employs an iterative optimization algorithm to find a set of physical parameter vectors. Where n is porosity, Let D be the wet density and D be the average particle size; the objective function is defined as: in, , , The model predicts the reflection coefficient, sound velocity, and attenuation coefficient based on parameter X. , , The corresponding value measured in step S2; , , These are weighting coefficients, determined based on the measurement accuracy of each parameter and its sensitivity to the inversion results; Solution: That is, to solve for the error function Minimum optimal parameter combination It needs to satisfy 0.5≤ ≤0.95; 1.2 g / cm³≤ ≤2.0g / cm³; 0.1μm≤ ≤100μm.

4. The deep-sea environment identification method based on an improved ground acoustic model according to claim 1, characterized in that, In step S33, the seabed sediment classification is performed according to the Shepard classification method in the classification of seabed sediments, dividing the sediments into the following types: clay, silty clay, silt, and sandy silt; the classification results are expressed in the form of a thematic map, forming a seabed sediment type distribution map. ; The method for predicting soil mechanical parameters in step S33 is based on a laboratory measured database, establishing statistical relationships or empirical equations between physical parameters and soil mechanical parameters; the main predicted soil mechanical parameters include: Undrained shear strength : ; Alternatively, a multiple linear regression approach can be used: in, , , , , , , The regression coefficients are obtained from the experimental data in the laboratory database. With corresponding porosity wet density Average particle size The results were obtained through fitting. Bearing capacity : in ; For pore fluid density, Foundation burial depth; The base width is determined based on the equipment dimensions. Considering safety factor Allowable load-bearing capacity for: Through prediction and It can calculate the bearing capacity at each location. ; Equivalent penetration resistance : in, The cone coefficient, ranging from 10 to 20; is determined by analyzing CPT data from the laboratory measurement database and... Statistical analysis of the data was conducted to determine the applicability to deep-sea sediments. value; Or establish The direct relationship with physical parameters is determined through multiple regression: in, , , , These are the fitting parameters.

5. The deep-sea environment identification method based on an improved ground acoustic model according to claim 4, characterized in that, It also includes steps for quantifying the uncertainty of the predicted soil mechanical parameters and for outputting the prediction results; The uncertainty in predicting soil mechanical parameters is quantified to determine the uncertainty range using the following method: Regression residual analysis: Calculate the standard error SE of the empirical relationship, which characterizes the dispersion of the predicted values. in, The number of samples; The number of parameters in the regression equation. and These are the predicted and measured values ​​for the i-th sample, respectively. Confidence interval estimation: for each predicted value The following 95% confidence intervals are given: Error propagation: Considering the uncertainty of physical parameter inversion , , The uncertainty of soil mechanical parameters is calculated using the error propagation formula: in, Let Y be the variance. , , These represent the variances of porosity, wet density, and average particle size, respectively, and the uncertainty of the input parameters ( , , This will propagate to the output Y, the sensitivity coefficient. , , The degree of influence of the error of each parameter is determined, and the partial derivatives are obtained by differentiating empirical relationships; Prediction results output: For each spatial location in the assessment area Output the following soil mechanics parameters: Undrained shear strength: ; Load-bearing capacity: ; Penetration resistance: ; And the corresponding range of uncertainty.

6. A deep-sea environment assessment method based on an improved ground acoustic model, characterized in that, The method for evaluating soil mechanical parameters obtained by the deep-sea environment identification method according to any one of claims 1 to 5 includes: S4: Engineering Environmental Assessment: Based on the soil mechanics parameters obtained in step S3, and combined with the seabed slope, the key indicators of the mining operation are assessed. S5: Comprehensive Output and Visualization: Generate an evaluation report based on the results of steps S1 to S4; The engineering environmental assessment in step S4 includes foundation bearing capacity, seabed slope stability, and sediment excavability. S41: The method for assessing the bearing capacity of the foundation is as follows: Equipment load calculation: Based on the design parameters of the mining equipment, calculate the pressure it exerts on the seabed. Assume the total weight of the equipment is The grounding area is ,but: ； Safety factor calculation: The equipment pressure is compared with the allowable bearing capacity predicted in step S33. Compare and calculate the safety factor : Evaluation criteria: Determine the threshold for the safety factor according to engineering specifications. It has sufficient load-bearing capacity and is suitable for operation. The load-bearing capacity is basically met, but monitoring is required. Insufficient load-bearing capacity poses a risk; The load-bearing capacity is severely insufficient, making it unsuitable for operation. Settlement prediction: estimating settlement during equipment operation. : in, Basic feature size; For Poisson's ratio, 0.45-0.50 is used for soft clay; For compressibility modulus, it can be obtained through Estimate: ; If the settlement exceeds the equipment's allowable working range, the operation plan needs to be adjusted or the equipment design needs to be improved. S42: The method for assessing the stability of seabed slopes is as follows: Slope extraction: Calculate the seabed slope at each location from the multibeam topography data obtained in step S1. The slope is calculated using the gradient of the digital elevation model (DEM). in, This refers to the seabed elevation. Infinite Slope Stability Analysis: For underwater slopes, an infinite slope model is adopted, with a stability safety factor... for: For saturated soft clay Simplified to: in, Cohesion is taken as the undrained shear strength. ; Effective severe; The potential sliding surface depth; Impact of additional loads from mining operations: Considering the additional loads applied by equipment operating on slopes; these additional loads increase the slope's sliding force, and the corrected safety factor is: in, The length of the slope; Evaluation criteria: Slope stability; The situation is basically stable, but monitoring is required. Unstable; caution is required when operating. Unstable, operation prohibited; Seismic load considerations: For seismically active regions, a quasi-static method is introduced, where the additional inertial force generated by seismic acceleration is calculated using the horizontal acceleration coefficient. calculate: ； S43: The method for assessing the excavability of sediments is as follows: Excavation resistance calculation: using empirical formulas, the excavation force per unit area. for: in, This is the excavation factor, which depends on the excavation method and the shape of the cutting tools, and is usually between 5 and 15. This refers to the undrained shear strength. For penetration resistance; Energy consumption estimation: Energy consumption per unit volume of sediment excavation for: in, To increase excavation speed; To improve efficiency; Excavability classification: based on excavation resistance The excavability of sediments is classified into the following levels: Easy to mine; Medium-sized mineable area; Difficult to excavate; Difficult to excavate; Rheological properties considered: For soft clay with high water content, evaluate the rheological parameters and yield stress of the sediment. and plastic viscosity : in, Shear stress; Shear rate; rheological properties affect the transport and treatment of sediments after excavation.

7. The deep-sea environmental assessment method according to claim 6, characterized in that, The evaluation results from steps S41, S42, and S43 are combined to form a comprehensive engineering suitability index for mining operations. : in, , , The scores are normalized scores for foundation bearing capacity, seabed slope stability, and sediment excavability, ranging from 0 to 100. , , The weighting coefficients are determined based on the project priorities and must satisfy the following conditions: ; The scoring criteria for each sub-indicator are as follows: Load-bearing capacity rating: Slope stability rating: Mining potential score: 。

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