Method for detecting density in flowing process of mud flow at seabed mud bottom and related equipment

Abstract

The invention discloses a density detection method and related equipment in a flowing process of a mud flow at a seabed mud bottom. The method comprises the following steps: acquiring an environment parameter and a sample parameter corresponding to a mud flow sample; obtaining a first porosity and a water content through image inversion based on the mud flow pore structure image; determining an initial pore volume according to the product of the first porosity and the total volume of the sample, and correcting the initial pore volume based on the environmental parameters to obtain a pore compression volume; correcting the first porosity based on the environmental parameters to obtain a second porosity; further determining the effective total volume, quantifying by combining with the second porosity and the water content to obtain a corrected second solid volume and a corrected second liquid volume, and summarizing by combining with the product of the solid density and the liquid density to obtain the total mass of the sample; and obtaining the mud flow density according to the ratio of the total mass to the effective total volume of the sample. The method improves the reliability of mud flow density detection, and can be widely applied to the technical field of data processing.

Description

A method and related equipment for detecting density during the flow of seabed mudflow. Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and related equipment for detecting density during the flow of seabed mudflow. Background Technology

[0002] Submarine mud diapirs are tectonic phenomena formed by the upward intrusion of high-porosity, low-strength muddy materials under geostress, widely distributed in the northern slope of the South China Sea, the Gulf of Mexico, and the Black Sea. Their evolution is accompanied by the migration and reactivation of large amounts of mudflows, resulting in complex rheological behavior and geomorphic effects. Mudflow density is a key parameter determining mudflow fluidity, erosive capacity, depositional morphology, and geological hazard risk. Existing calculation methods are mostly based on simplified models, such as assuming homogeneous solid-liquid mixing or neglecting pore compression and thermo-baric effects, making it difficult to accurately reflect the true state of mudflows under high pressure, high temperature, and heterogeneous environments. Furthermore, during the flow of mudflows, the solid-liquid ratio, porosity, temperature, and pressure all dynamically change with time and space; traditional static density estimation methods cannot meet the accuracy requirements for simulating the dynamic processes of submarine mud. Summary of the Invention

[0003] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for detecting the density of seabed mudflows during their flow, aiming to solve at least one problem in the prior art.

[0004] To achieve the above objectives, one aspect of this invention proposes a method for detecting density during the flow of seabed mudflows. The method includes: acquiring environmental parameters and sample parameters corresponding to a mudflow sample; wherein the environmental parameters include the ambient temperature and ambient pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, a first solid volume, liquid density, and a first liquid volume; acquiring a mudflow pore structure image of the mudflow sample using an imaging device, and obtaining a first porosity and water content based on the mudflow pore structure image through image inversion; determining an initial pore volume based on the product of the first porosity and the total sample volume of the mudflow sample; and... The initial pore volume is corrected based on environmental parameters to obtain the pore compressible volume; the first porosity is corrected based on environmental parameters to obtain the second porosity; the effective total volume is determined based on the first solid volume, the first liquid volume, and the pore compressible volume; the corrected second solid volume and second liquid volume are obtained based on the effective total volume, the second porosity, and the water content; the solid mass is obtained by multiplying the second solid volume by the solid density, and the liquid mass is obtained by multiplying the second liquid volume by the liquid density, and then the total sample mass is obtained by summing them; the mud flow density is obtained by the ratio of the total sample mass to the effective total volume.

[0005] In some embodiments, obtaining environmental parameters and sample parameters corresponding to mudflow samples includes the following steps: based on the mudflow sample, using a laser particle size analyzer and a hydrometer to measure the density and volume fraction of various solid components in the mudflow sample; performing a weighted average density of the solid phase based on the density and volume fraction of each solid component to obtain the solid density; summing the volume fractions of all solid components to obtain the first solid volume; based on the centrifuged mudflow sample, using a hydrometer to measure the density and volume fraction of various liquid components in the mudflow sample; performing a weighted average density of the liquid phase based on the density and volume fraction of each liquid component to obtain the liquid density; summing the volume fractions of all liquid components to obtain the first liquid volume; acquiring environmental parameters from a deep-sea multi-parameter sensor; wherein the deep-sea multi-parameter sensor is located in the environment where the mudflow sample is located.

[0006] In some embodiments, obtaining the first porosity and water content based on the mudflow pore structure image through image inversion includes the following steps: performing grayscale processing on the mudflow pore structure image to obtain a grayscale image; performing histogram analysis on the grayscale image using an adaptive thresholding method to statistically obtain the pore pixel ratio as the first porosity, and then quantifying the water content by combining the liquid signal intensity or contrast agent distribution; or, inputting the mudflow pore structure image or grayscale image into a pre-trained machine learning model to obtain the first porosity and water content; wherein the machine learning model is trained based on an image training set labeled with porosity and water content.

[0007] In some embodiments, the initial pore volume is corrected based on environmental parameters to obtain the pore compressible volume, including the following steps: determining the temperature change based on the ambient temperature and the reference temperature, and determining the pressure change based on the ambient pressure and the reference pressure; correcting the initial pore volume by combining the temperature change and the pressure change with the coefficient of thermal expansion and the coefficient of pressure compressibility to obtain the pore compressible volume; wherein, the expression for the pore compressible volume is: Vp=V0×(αTΔT+βPΔP); where Vp represents the pore compressible volume, V0 represents the initial pore volume, αT represents the coefficient of thermal expansion, ΔT represents the temperature change, βP represents the coefficient of pressure compressibility, and ΔP represents the pressure change.

[0008] In some embodiments, a second porosity is obtained by correcting the first porosity based on environmental parameters, including the following steps: determining the temperature change based on the ambient temperature and the reference temperature, and determining the pressure change based on the ambient pressure and the reference pressure; correcting the first porosity by combining the temperature change and the pressure change with the coefficient of thermal expansion and the pore compressibility coefficient to obtain the second porosity; wherein, the expression for the second porosity is: φT=φ0(1-CpΔP+αTΔT); where φT represents the second porosity, φ0 represents the first porosity, Cp represents the pore compressibility coefficient, ΔP represents the pressure change, αT represents the coefficient of thermal expansion, and ΔT represents the temperature change.

[0009] In some embodiments, determining the effective total volume based on the first solid volume, the first liquid volume, and the pore compression volume includes the following steps: adding the first solid volume to the first liquid volume, and then subtracting the pore compression volume to obtain the effective total volume.

[0010] In some embodiments, the corrected second solid volume and second liquid volume are obtained by quantifying based on the effective total volume, second porosity, and water content, including the following steps: multiplying the complementary value of the second porosity to 1 by the effective total volume to obtain the second solid volume; and multiplying the effective total volume, second porosity, and water content to obtain the second liquid volume.

[0011] To achieve the above objectives, another aspect of this invention proposes a density detection device for seabed mudflow during bottom flow. The device includes: a first module for acquiring environmental parameters and sample parameters corresponding to a mudflow sample; wherein the environmental parameters include the ambient temperature and ambient pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, a first solid volume, liquid density, and a first liquid volume; a second module for acquiring a mudflow pore structure image of the mudflow sample using an imaging device, and obtaining a first porosity and water content based on the mudflow pore structure image through image inversion; and a third module for determining an initial pore volume based on the product of the first porosity and the total sample volume of the mudflow sample, and then, based on the environmental parameters... The initial pore volume is corrected to obtain the pore compressible volume; the fourth module is used to correct the first porosity based on environmental parameters to obtain the second porosity; the fifth module is used to determine the effective total volume based on the first solid volume, the first liquid volume, and the pore compressible volume; the sixth module is used to quantify the corrected second solid volume and second liquid volume based on the effective total volume, the second porosity, and the water content; the seventh module is used to obtain the solid mass based on the product of the second solid volume and the solid density, and the liquid mass based on the product of the second liquid volume and the liquid density, and then sum them to obtain the total sample mass; the eighth module is used to obtain the mud flow density based on the ratio of the total sample mass to the effective total volume.

[0012] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0013] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0014] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for detecting the density of a seabed mudflow during its flow. This method acquires environmental parameters and sample parameters corresponding to the mudflow sample; wherein, the environmental parameters include the ambient temperature and ambient pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, first solid volume, liquid density, and first liquid volume; an image of the mudflow pore structure of the mudflow sample is acquired using an imaging device, and the first porosity and water content are obtained through image inversion based on the mudflow pore structure image; the product of the first porosity and the total sample volume of the mudflow sample is then calculated. The initial pore volume is determined, and the initial pore volume is corrected based on environmental parameters to obtain the pore compressibility volume. The first porosity is corrected based on environmental parameters to obtain the second porosity. The effective total volume is determined based on the first solid volume, the first liquid volume, and the pore compressibility volume. The corrected second solid volume and second liquid volume are obtained based on the effective total volume, the second porosity, and the water content. The solid mass is obtained by multiplying the second solid volume by the solid density, and the liquid mass is obtained by multiplying the second liquid volume by the liquid density. These are then combined to obtain the total sample mass. The mud flow density is obtained by the ratio of the total sample mass to the effective total volume. This invention, through acquiring environmental temperature and pressure parameters of mudflow samples and dynamically correcting the initial pore volume and porosity, effectively quantifies the influence of pore compression and thermo-baric effects under high pressure and high temperature environments. This overcomes the problem of large estimation deviations in traditional models under extreme conditions, making the density results more closely reflect the actual seabed conditions. Furthermore, this invention utilizes imaging equipment to acquire images of the mudflow pore structure and obtains porosity and water content through image inversion. This intuitively reflects the microscopic non-uniformity of the mudflow, avoiding the simplistic assumption of homogeneous solid-liquid mixing in traditional methods, thus more accurately describing the actual composition and flow behavior of the mudflow. Simultaneously, this invention, through volume and porosity correction, can adapt to the spatiotemporal changes in solid-liquid ratio, porosity, temperature, and pressure during mudflow flow. Specifically, this method integrates environmental parameters, sample parameters, and image data, and through multi-step correction and quantification of solid and liquid volumes, ensures the completeness of the calculation of the total sample mass and effective total volume. The resulting mudflow density is more reliable, providing more accurate data support for geological hazard risk assessment. Attached Figure Description

[0016] Figure 1 is a schematic diagram of an implementation environment for a method for detecting density during the flow of a seabed mudflow provided in an embodiment of the present invention; Figure 2 is a flowchart of a method for detecting density during the flow of a seabed mudflow provided in an embodiment of the present invention; Figure 3 is a schematic diagram of the unfolded flow of step S100 provided in an embodiment of the present invention; Figure 4 is a schematic diagram of the unfolded flow of obtaining the first porosity and water content through image inversion provided in an embodiment of the present invention; Figure 5 is a schematic diagram of the unfolded flow of volume correction provided in an embodiment of the present invention; Figure 6 is a schematic diagram of the unfolded flow of obtaining river runoff through hydraulic geometry transformation provided in an embodiment of the present invention; Figure 7 is a schematic diagram of the unfolded flow of step S400 provided in an embodiment of the present invention; Figure 8 is a structural schematic diagram of a density detection system during the flow of a seabed mudflow provided in an embodiment of the present invention; Figure 9 is a structural schematic diagram of a density detection device during the flow of a seabed mudflow provided in an embodiment of the present invention; Figure 10 is a structural schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0019] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0021] In related technologies, existing calculation methods are mostly based on simplified models, such as assuming uniform solid-liquid mixing or ignoring pore compression and thermo-pressure effects, which makes it difficult to accurately reflect the real state of mud flow under high pressure, high temperature, and heterogeneous environments. In addition, during the flow of mud, its solid-liquid ratio, porosity, temperature, and pressure all change dynamically with time and space, and traditional static density estimation methods cannot meet the accuracy requirements for simulating the dynamic processes of seabed mud.

[0022] In view of this, this invention provides a method and related equipment for detecting the density of a seabed mudflow during its flow. This method involves acquiring environmental parameters and sample parameters corresponding to the mudflow sample. The environmental parameters include the ambient temperature and pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, first solid volume, liquid density, and first liquid volume. An image of the mudflow pore structure is acquired using an imaging device. Based on the mudflow pore structure image, the first porosity and water content are obtained through image inversion. The initial pore volume is determined by multiplying the first porosity by the total volume of the mudflow sample. Environmental parameters are used to correct the initial pore volume to obtain the pore compressible volume; the first porosity is corrected based on the environmental parameters to obtain the second porosity; the effective total volume is determined based on the first solid volume, the first liquid volume, and the pore compressible volume; the corrected second solid volume and second liquid volume are obtained based on the effective total volume, the second porosity, and the water content; the solid mass is obtained by multiplying the second solid volume by the solid density, and the liquid mass is obtained by multiplying the second liquid volume by the liquid density, and then the total sample mass is obtained by summing them; the mud flow density is obtained by the ratio of the total sample mass to the effective total volume. This invention, through acquiring environmental temperature and pressure parameters of mudflow samples and dynamically correcting the initial pore volume and porosity, effectively quantifies the influence of pore compression and thermo-baric effects under high pressure and high temperature environments. This overcomes the problem of large estimation deviations in traditional models under extreme conditions, making the density results more closely reflect the actual seabed conditions. Furthermore, this invention utilizes imaging equipment to acquire images of the mudflow pore structure and obtains porosity and water content through image inversion. This intuitively reflects the microscopic non-uniformity of the mudflow, avoiding the simplistic assumption of homogeneous solid-liquid mixing in traditional methods, thus more accurately describing the actual composition and flow behavior of the mudflow. Simultaneously, this invention, through volume and porosity correction, can adapt to the spatiotemporal changes in solid-liquid ratio, porosity, temperature, and pressure during mudflow flow. Specifically, this method integrates environmental parameters, sample parameters, and image data, and through multi-step correction and quantification of solid and liquid volumes, ensures the completeness of the calculation of the total sample mass and effective total volume. The resulting mudflow density is more reliable, providing more accurate data support for geological hazard risk assessment.

[0023] It is understood that the density detection method for seabed mudflow during the flow process provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0024] Figure 1 illustrates an implementation environment according to an embodiment of the present invention. Referring to Figure 1, this implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0025] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0026] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0027] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0028] For example, based on the implementation environment shown in FIG1, this embodiment of the invention provides a method for detecting the density during the flow of a seabed mudflow. The following description takes the application of this method for detecting the density during the flow of a seabed mudflow in server 101 as an example. It can be understood that this method for detecting the density during the flow of a seabed mudflow can also be applied to terminal 102.

[0029] Referring to Figure 2, which is an optional flowchart of a method for detecting density during the flow of a seabed mudflow according to an embodiment of the present invention, the execution subject of this method can be any of the aforementioned computer devices (including servers or terminals). The method in Figure 2 may include, but is not limited to, steps S100 to S800.

[0030] Step S100: Obtain the environmental parameters and sample parameters corresponding to the mudflow sample; wherein, the environmental parameters include the ambient temperature and ambient pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, first solid volume, liquid density, and first liquid volume; it should be noted that, in some embodiments, as shown in Figure 3, step S100 may include the following steps: S110: Based on the mudflow sample, use a laser particle size analyzer and a hydrometer to determine the solid component density and solid component volume fraction of various solid components in the mudflow sample; S120: Perform a weighted average density calculation based on the solid component density and solid component volume fraction of each solid component to obtain the solid phase weighted average density. S130: The volume fractions of all solid components are summed to obtain the first solid volume; S140: Based on the sludge sample after centrifugation, the liquid component density and volume fraction of each liquid component in the sludge sample are measured using a densitometer; S150: The liquid component density and volume fraction of each liquid component are mixed and weighted to obtain the weighted average density of the liquid phase as the liquid density; S160: The volume fractions of all liquid components are summed to obtain the first liquid volume; S170: Environmental parameters are collected from a deep-sea multi-parameter sensor; wherein, the deep-sea multi-parameter sensor is set at the environment where the sludge sample is located.

[0031] For example, in some specific implementations, mudflow samples can be collected by seabed drilling, coring or remote sensing technology, and their sampling depth, geographic coordinates and environmental parameters (temperature, pressure, sedimentary layer, etc.) can be recorded.

[0032] The measurement of solid phase volume and density can be achieved by: using a laser particle size analyzer and a hydrometer to determine the volume distribution and density of solid particles, and combining this with a mineral composition spectrum to determine the weighted average density of the solid portion. .

[0033] (The volume distribution and density of each particle size fraction of the solid particles in the mudflow were determined using a laser particle size analyzer and a hydrometer. The mineral composition spectra obtained by X-ray diffraction (XRD) or energy dispersive spectroscopy (EDS) were then used to determine the volume fraction and density of each mineral component. The weighted average density ρs of the solid portion was calculated based on the volume weighting principle of particle size distribution. The calculation formula is as follows:)

[0034]

[0035] Where ρi is the density of the i-th mineral or particle size fraction, and Vi is its corresponding volume fraction. This weighted average density ρs reflects the overall density characteristics of solid particles in the mud flow and is used for subsequent calculations of the overall mud flow density and flow characteristics.

[0036] The liquid phase volume and density measurement can be achieved by measuring the volume and density of the liquid portion, including seawater, pore water and organic liquid components, through centrifugation and a density meter, to obtain ρl and Vl.

[0037] The solid and liquid phases in the mudflow sample were separated using centrifugation, and the volume and density parameters of the liquid fraction were measured using a hydrometer. The liquid phase mainly consisted of a multi-component system including seawater, pore water, and organic liquids. First, the volume fraction V of each component was measured separately. With density Then, based on the principle of volume weighting of mixed fluids, the weighted average density of the overall liquid phase is calculated. With total volume The calculation formula is as follows:

[0038]

[0039] in, Let Vj be the density of the j-th liquid component, and Vj be its corresponding volume fraction. This mixed density formula can accurately reflect the comprehensive density characteristics of multiphase liquids in submarine mudflows, providing input parameters for subsequent calculations of the overall mudflow density.

[0040] Among them, deep-sea multi-parameter sensors can be used to monitor the ambient temperature T and pressure P of the mud flow in real time, which can be used as parameters for subsequent volume correction.

[0041] Step S200: Obtain an image of the pore structure of the mudflow sample using an imaging device. Based on the pore structure image, obtain the first porosity and water content through image inversion. It should be noted that in some embodiments, as shown in Figure 4, obtaining the first porosity and water content based on the pore structure image through image inversion may include the following steps: S210: Perform grayscale processing on the pore structure image to obtain a grayscale image; S220: Based on the grayscale image, perform histogram analysis using an adaptive threshold method to statistically obtain the pore pixel ratio as the first porosity, and then combine this with the liquid signal intensity or contrast agent distribution to quantify and obtain the water content; or, S230: Input the pore structure image or grayscale image into a pre-trained machine learning model to obtain the first porosity and water content; wherein the machine learning model is trained based on an image training set labeled with porosity and water content.

[0042] For example, in some specific implementations, images of the pore structure of mud flow are obtained by CT scanning or nuclear magnetic resonance imaging (NMR), and the porosity φ and water content ω are calculated by combining the image inversion algorithm. Specifically, it can be implemented as follows: (1) Inversion algorithm based on threshold segmentation: Histogram analysis is performed on the grayscale image of CT or NMR, and a suitable grayscale threshold (e.g., Otsu adaptive thresholding method) is selected to divide the image into solid phase and pore phase regions. The porosity φ is obtained by statistically analyzing the proportion of pore pixels to total pixels; combined with the liquid signal intensity or contrast agent distribution, the proportion of liquid in the pores, i.e., the water content ω, can be further calculated.

[0043] (2) Machine learning-based inversion algorithm: The convolutional neural network (CNN) or random forest algorithm is used to learn from the labeled CT / NMR training samples to achieve automatic identification and segmentation of solid phase, liquid phase and gas phase. The porosity φ and water content ω are obtained through voxel statistics, which has higher accuracy.

[0044] Step S300: Determine the initial pore volume based on the product of the first porosity and the total volume of the mudflow sample. Correct the initial pore volume based on environmental parameters to obtain the pore compressible volume. It should be noted that in some embodiments, as shown in Figure 5, correcting the initial pore volume based on environmental parameters to obtain the pore compressible volume may include the following steps: S310: Determine the temperature change based on the ambient temperature and the reference temperature, and determine the pressure change based on the ambient pressure and the reference pressure; S320: Correct the initial pore volume using the temperature change and pressure change combined with the coefficient of thermal expansion and the coefficient of pressure compressibility to obtain the pore compressible volume; where the expression for the pore compressible volume is: Vp = V0 × (αTΔT + βPΔP); where Vp represents the pore compressible volume, V0 represents the initial pore volume, αT represents the coefficient of thermal expansion, ΔT represents the temperature change, βP represents the coefficient of pressure compressibility, and ΔP represents the pressure change.

[0045] For example, in some specific embodiments, αT (coefficient of thermal expansion) is the coefficient of relative change in pore volume of the mud flow system under temperature changes, with units of K. - ¹. Its value can be obtained through the following methods: experimental determination: measuring the pore volume change at different temperatures under constant pressure and fitting the result to obtain αT; literature reference method: empirical parameters (1.0 × 10⁻⁶) of similar mud or sediments can be cited. -4 ~3.0×10 -4 K -1 This coefficient is specific to mudflow materials and should be corrected based on the sample's moisture content and mineral composition.

[0046] βP (pressure compressibility coefficient) is the compressibility response coefficient of mud flow pore volume as a function of pressure, with units of MPa. - ¹. Its value can be obtained through: experimental method: using isothermal loading experiments to measure the change in pore volume under different pressures; literature method: taking empirical values ​​from typical seabed mud or soft clay (approximately 1.0 × 10⁻⁶). -3 ~5.0×10 -3  MPa -1 ); βP also has material properties and should be adjusted according to the regional mudflow characteristics.

[0047] ΔT and ΔP (changes in temperature and pressure): ΔT = T - T0, which is the difference between the sample temperature and the reference temperature; ΔP = P - P0, which is the difference between the actual pore pressure of the sample and the reference pressure. Reference conditions T0 and P0 are usually taken as sea surface temperature and hydrostatic pressure, or as the average value of sampling points.

[0048] Step S400: Based on environmental parameters, the first porosity is corrected to obtain the second porosity. It should be noted that in some embodiments, step S400 may include the following steps: determining the temperature change based on the ambient temperature and the reference temperature, and determining the pressure change based on the ambient pressure and the reference pressure; correcting the first porosity by combining the temperature change and the pressure change with the coefficient of thermal expansion and the pore compressibility coefficient to obtain the second porosity; wherein, the expression for the second porosity is: φT=φ0(1-CpΔP+αTΔT); where φT represents the second porosity, φ0 represents the first porosity, Cp represents the pore compressibility coefficient, ΔP represents the pressure change, αT represents the coefficient of thermal expansion, and ΔT represents the temperature change.

[0049] For example, in some specific embodiments, Cp is the pore compressibility coefficient, which can be obtained by normalizing βP. The sources of other coefficient parameters are described in the foregoing embodiments and will not be repeated here.

[0050] Step S500: Determine the effective total volume based on the first solid volume, the first liquid volume, and the pore compression volume. It should be noted that in some embodiments, step S500 may include the following steps: adding the first solid volume to the first liquid volume, and then subtracting the pore compression volume to obtain the effective total volume.

[0051] For example, in some specific embodiments, considering the influence of formation temperature and pressure changes on pore compression and total volume, the following formula is used for correction: Vt=Vs+Vl-Vp where Vt is the corrected total volume; Vs is the solid phase volume; Vl is the liquid phase volume; and Vp is the pore compression volume.

[0052] Step S600: Based on the effective total volume, the second porosity, and the water content, the corrected second solid volume and the second liquid volume are obtained. It should be noted that in some embodiments, step S600 may include the following steps: multiplying the complementary value of the second porosity to 1 by the effective total volume to obtain the second solid volume; multiplying the effective total volume, the second porosity, and the water content to obtain the second liquid volume.

[0053] For example, in some specific embodiments, the solid volume can be calculated based on the porosity φ as: Vs = Vt × (1-φ); the liquid volume Vl can be determined based on the porosity φ and the water content ω as: Vl = Vt × φ × ω.

[0054] Step S700: The solid mass is obtained by multiplying the second solid volume and the solid density, and the liquid mass is obtained by multiplying the second liquid volume and the liquid density. The total sample mass is then obtained by summing these results. For example, in some specific embodiments, the solid mass is calculated by combining the corrected solid volume with the solid density ρs: Ms = Vs × ρs; In addition, the liquid mass is obtained by combining the corrected liquid volume with the liquid density ρl: Ml = Vl × ρl; The total sample mass Mt is determined by the sum of the solid mass and the liquid mass: Mt = Ms + Ml.

[0055] Step S800: The mud flow density is obtained based on the ratio of the total sample mass to the effective total volume; exemplarily, in some specific embodiments, ρ m =Mt / Vt,ρ m This is the mud flow density.

[0056] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0057] Given the shortcomings of existing technologies, there is an urgent need for a dynamic density calculation method that can comprehensively consider the solid-liquid ratio, pore structure, and temperature and pressure changes to achieve a high-precision characterization of the physical properties of mudflows at the bottom of the sea. Based on this, the aim is to provide a method for calculating the density of mudflows at the bottom of the sea. By introducing multi-source parameters such as the volume and density of solid and liquid components, porosity, temperature, and pressure, the density of the mudflow can be calculated accurately in real time during dynamic flow. As shown in Figure 6, the technical solution of this invention can be implemented through the following process: Collecting mudflow samples: Obtain mudflow samples through seabed drilling or remote sensing technology, and record their sampling depth, geographical location, and environmental parameters. (A comprehensive survey of the study area was conducted using seabed drilling and multi-source remote sensing technologies (including acoustic detection, side-scan sonar, and multibeam bathymetry) to identify the spatial distribution characteristics of seabed diapiric structures and mudflow channels, and to determine typical mudflow development zones. Based on the remote sensing identification results, sampling points were deployed in the target area as the basis for subsequent in-situ sampling and parameter monitoring. After positioning, in-situ sampling was carried out using ROVs (Remotely Operated Vehicles) or grab samplers equipped with sampling devices to collect representative seabed mudflow samples. During the sampling process, key parameters such as geographic coordinates, sampling depth, ambient temperature, pore pressure, and sedimentary strata were recorded simultaneously. These environmental parameters were used as dynamic input variables in sampling and subsequent calculations, and were updated in real time to the monitoring and calculation system, providing basic data support for the subsequent coupled analysis of multiple physical parameters such as mudflow density, pore pressure, and flow characteristics.)

[0058] Step 1. Sample collection and parameter recording: Collect mudflow samples through seabed drilling, coring or remote sensing technology, and record their sampling depth, geographic coordinates and environmental parameters (temperature, pressure, sedimentary layer, etc.).

[0059] Step 2. Solid phase volume and density measurement: The volume distribution and density of solid particles were determined using a laser particle size analyzer and a hydrometer. The weighted average density of the solid phase was determined in conjunction with the mineral composition spectrum. .

[0060] The volume distribution and density of each particle size fraction of the slurry solid particles were determined using a laser particle size analyzer and a hydrometer. Combined with mineral composition spectra obtained from X-ray diffraction (XRD) or energy dispersive spectroscopy (EDS), the volume fraction and density values ​​of each mineral component were determined. The weighted average density ρs of the solid fraction was calculated based on the volume weighting principle of particle size distribution. The calculation formula is as follows:

[0061]

[0062] Where ρi is the density of the i-th mineral or particle size fraction, and Vi is its corresponding volume fraction. This weighted average density ρs reflects the overall density characteristics of solid particles in the mud flow and is used for subsequent calculations of the overall mud flow density and flow characteristics.

[0063] Step 3. Liquid phase volume and density measurement: The volume and density of the liquid phase, including seawater, pore water and organic liquid components, are determined by centrifugation and a densitometer to obtain ρl and Vl.

[0064] The solid and liquid phases in the mudflow sample were separated using centrifugation, and the volume and density parameters of the liquid fraction were measured using a hydrometer. The liquid phase mainly consisted of a multi-component system including seawater, pore water, and organic liquids. First, the volume fraction V of each component was measured separately. With density Then, based on the principle of volume weighting of mixed fluids, the weighted average density of the overall liquid phase is calculated. With total volume The calculation formula is as follows:

[0065]

[0066] in, Let Vj be the density of the j-th liquid component, and Vj be its corresponding volume fraction. This mixed density formula can accurately reflect the comprehensive density characteristics of multiphase liquids in submarine mudflows, providing input parameters for subsequent calculations of the overall mudflow density.

[0067] Step 4. Temperature and pressure acquisition: The temperature T and pressure P of the mudflow environment are monitored in real time using a deep-sea multi-parameter sensor as input parameters for volume correction.

[0068] Step 5. Porosity and water content calculation: Obtain images of the pore structure of mud flow using CT scans or nuclear magnetic resonance imaging (NMR), and calculate porosity φ and water content ω using image inversion algorithms.

[0069] (1) Inversion algorithm based on threshold segmentation: Histogram analysis is performed on CT or NMR grayscale images, and a suitable grayscale threshold (e.g., Otsu adaptive thresholding method) is selected to divide the image into solid phase and porous phase regions. The porosity φ is obtained by statistically analyzing the proportion of porous pixels to the total pixels. Combined with the liquid signal intensity or contrast agent distribution, the proportion of liquid in the pores, i.e., water content ω, can be further calculated.

[0070] (2) Machine learning-based inversion algorithm: The convolutional neural network (CNN) or random forest algorithm is used to learn from the labeled CT / NMR training samples to achieve automatic identification and segmentation of solid phase, liquid phase and gas phase. The porosity φ and water content ω are obtained through voxel statistics, which has higher accuracy.

[0071] Step 6. Solid mass calculation: Based on the porosity φ (which can be the porosity obtained in Step 5, or preferably, the porosity φT corrected in Step 9), calculate the solid volume: Vs = Vt × (1-φ); where Vt is the total volume of the sample (i.e., the effective total volume, the specific source of which will be explained in Step 9). Then, combining this with the solid density ρs, calculate the solid mass: Ms = Vs × ρs; where Ms is the solid mass, Vs is the solid volume, and ρs is the solid density.

[0072] Step 7. Liquid mass calculation: Based on the porosity φ and water content ω, determine the liquid volume Vl: Vl = Vt × φ × ω; then combine with the liquid density ρl to obtain the liquid mass: Ml = Vl × ρl; where Ml is the liquid mass, Vl is the liquid volume, and ρl is the liquid density.

[0073] Step 8. Total mass calculation: The total mass of the sample, Mt, is determined by the sum of the solid mass and the liquid mass: Mt = Ms + Ml.

[0074] Step 9. Effective Volume Correction (In this step, the accuracy of porosity φ and water content ω directly affects the calculation result of the total mass): As shown in Figure 7, considering the pore compression and volume change under temperature and pressure effects, the following formula is used for correction: Vt=Vs+Vl-Vp, where Vp is the pore compression volume, calculated according to the temperature and pressure correction model: Vp=V0×(αTΔT+βPΔP), where αT is the coefficient of thermal expansion and βP is the compressibility coefficient.

[0075] Considering the effects of temperature and pressure on pore compressibility and volume change, the total volume is corrected. The corrected porosity can be expressed as: φT=φ0(1-CpΔP+αTΔT), where Cp is the pore compressibility coefficient, αT is the thermal expansion coefficient, and ΔP and ΔT are the pressure and temperature changes, respectively. Specifically, the solid volume Vs, liquid volume Vl, and effective total volume can be recalculated based on the corrected porosity.

[0076] This allows us to obtain the effective volume and density results under actual formation temperature and pressure conditions, ensuring the accuracy and applicability of mud flow density calculation. (1) Parameter definition and calculation method: V0 (initial pore volume) refers to the initial pore volume of the sample under the reference temperature T0 and pressure P0 conditions. The calculation formula is: V0=φ0×Vsmp where φ0 is the initial porosity obtained in step 5, and Vsmp is the total volume of the sample (a fixed specification sample can be directly sampled to determine its total volume when sampling mud flow samples). The porosity φ0 can be obtained from CT or NMR inversion results, so V0 can be directly calculated based on the measured porosity and sample volume.

[0077] αT (coefficient of thermal expansion): a coefficient representing the relative change in pore volume of a mud flow system under temperature variations, measured in Kelvin. - ¹. Its value can be obtained through the following methods: experimental determination: measuring the pore volume change at different temperatures under constant pressure and fitting the result to obtain αT; literature reference method: empirical parameters (1.0 × 10⁻⁶) of similar mud or sediments can be cited. -4 ~3.0×10 -4 K -1 This coefficient is specific to mudflow materials and should be corrected based on the sample's moisture content and mineral composition.

[0078] βP (pressure compressibility coefficient) is the compressibility response coefficient of mud flow pore volume as a function of pressure, with units of MPa. - ¹. Its value can be obtained through: experimental method: using isothermal loading experiments to measure the change in pore volume under different pressures; literature method: taking empirical values ​​from typical seabed mud or soft clay (approximately 1.0 × 10⁻⁶). -3 ~5.0×10 -3  MPa -1 ); βP also has material properties and should be adjusted according to the regional mudflow characteristics.

[0079] ΔT and ΔP (temperature and pressure changes): ΔT = T - T0, which is the difference between the sample temperature and the reference temperature; ΔP = P - P0, which is the difference between the actual pore pressure of the sample and the reference pressure. Reference conditions T0 and P0 are usually taken as sea surface temperature and hydrostatic pressure, or as the average value of sampling points. The specific values ​​of ΔT and ΔP can be calculated based on the sensor monitoring data in step 4.

[0080] (2) Explanation of the scope of application of the correction item: The correction effect in this step only applies to the volume change of the pore part, that is, the temperature and pressure correction of the Vp item. The solid volume Vs and the liquid volume Vl can be regarded as weakly dependent on temperature and pressure conditions, and the thermal expansion correction of Vl is only performed when the accuracy requirement is high. The corrected porosity calculation formula is: φT=φ0(1-CpΔP+αTΔT) where Cp is the pore compressibility coefficient, which can be obtained by normalization of βP. Based on this, the corrected volume and density are recalculated: Vt=Vs+Vl-Vp Step 10. Calculation of mud flow density ρ m =Mt / Vt Step 11. Error and Sensitivity Analysis Perform Monte Carlo error propagation calculation on the input parameters, output density values ​​and confidence intervals, and generate density curves that vary with depth or time.

[0081] In some specific application scenarios, mudflow samples were collected from the land slope area of ​​a certain sea area. The solid volume was 0.8 m³, density 2.65 g / cm³, and the liquid volume was 0.5 m³, density 1.03 g / cm³. The porosity was 18%, the temperature was 4°C, and the pressure was 35 MPa. After correction, the calculated mudflow density was 1.89 g / cm³, with a deviation of less than 2% from the measured value.

[0082] Furthermore, in a deep-sea basin, the method of this invention was compared with the traditional empirical formula. The results showed that in a region with high porosity (>20%), the error of the traditional empirical method was about 0.25 g / cm³, while the error of the method of this invention was reduced to 0.16 g / cm³, a reduction of about 35%; and the accuracy of flow path simulation was improved by about 28%.

[0083] Figure 8 shows an example of a simulated curve illustrating the change in mud flow density with depth. Referring to Figure 8, it should be noted that as depth increases, pressure rises, pores compress, and the mud flow density gradually increases; increased temperature causes liquid volume expansion, resulting in a slight decrease in density. This model curve can be dynamically adjusted based on measured T and P data. Furthermore, it should be noted that the density curve generally shows an increasing trend with depth. The density of the near-surface mud layer is approximately 1.2 g / cm³, and with sediment compaction and pore water drainage, the density can reach approximately 1.7 g / cm³ at a depth of 200 m. This curve can be used to estimate the stability and potential slip risk of mud flows at different burial depths.

[0084] In summary, this invention discloses a method for calculating the density of diapiric flows during the process of seafloor mud flow, belonging to the fields of marine geological engineering and seafloor sedimentary dynamics. This method addresses the errors caused by neglecting solid-liquid heterogeneity, pore compression, and temperature-pressure coupling in existing mudflow density calculations, proposing a multi-parameter coupled dynamic density calculation model. The method includes: sample collection and environmental parameter recording, solid and liquid phase volume and density measurement, real-time temperature and pressure monitoring, porosity and water content acquisition, solid-liquid mass calculation, temperature-pressure corrected volume calculation, density output, and error analysis. By introducing the coefficient of thermal expansion and compressibility to correct the effective volume, and combining it with Monte Carlo method for error propagation analysis, the dynamic and accurate calculation of mudflow density over time, depth, and flow path is achieved. This method can comprehensively reflect the true rheological characteristics of mudflows under high pressure, high temperature, and heterogeneous conditions, providing high-precision parameter support for seafloor mudflow dynamics modeling, landslide and collapse disaster prediction, oil and gas reservoir distribution assessment, and diapiric tectonic evolution research. Compared with traditional empirical formulas, this invention reduces density calculation errors by 30%–40% in multi-regional field data verification, significantly improving the reliability and applicability of submarine mudflow process simulation and geological risk assessment. Specifically, this invention belongs to the fields of marine geological engineering and submarine sedimentary dynamics, and is particularly suitable for mudflow dynamics modeling, geological hazard prediction, and oil and gas resource evaluation in deep-sea basins, continental slope areas, and tectonically active areas.

[0085] As shown in Figure 9, this embodiment of the invention also provides a density detection device 900 for the flow of seabed mudflows, which can implement the above-mentioned method. The device may include: a first module 910, used to acquire environmental parameters and sample parameters corresponding to the mudflow sample; wherein the environmental parameters include the ambient temperature and ambient pressure of the environment where the mudflow sample is located, and the sample parameters include solid density, first solid volume, liquid density, and first liquid volume; a second module 920, used to acquire a mudflow pore structure image of the mudflow sample through an imaging device, and obtain the first porosity and water content based on the mudflow pore structure image through image inversion; a third module 930, used to determine the initial pore volume based on the product of the first porosity and the total sample volume of the mudflow sample, and based on the environmental parameters... The initial pore volume is corrected to obtain the pore compressible volume; the fourth module 940 is used to correct the first porosity based on environmental parameters to obtain the second porosity; the fifth module 950 is used to determine the effective total volume based on the first solid volume, the first liquid volume, and the pore compressible volume; the sixth module 960 is used to obtain the corrected second solid volume and second liquid volume based on the effective total volume, the second porosity, and the water content; the seventh module 970 is used to obtain the solid mass based on the product of the second solid volume and the solid density, and the liquid mass based on the product of the second liquid volume and the liquid density, and then sum them to obtain the total sample mass; the eighth module 980 is used to obtain the mud flow density based on the ratio of the total sample mass to the effective total volume.

[0086] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0087] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0088] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0089] As shown in Figure 10, which illustrates the hardware structure of an electronic device 1000 according to another embodiment, the electronic device 1000 includes: a processor 1001, which can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, for executing related programs to implement the technical solutions provided in the embodiments of the present invention; and a memory 1002, which can be implemented using a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RaM), etc. The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. The input / output interface 1003 is used to implement information input and output. The communication interface 1004 is used to realize communication interaction between this device and other devices. Communication can be realized through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.). The bus 1005 transmits information between various components of the device (such as processor 1001, memory 1002, input / output interface 1003 and communication interface 1004). The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device through the bus 1005.

[0090] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0092] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0093] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0094] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0095] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0096] The present invention provides a method, apparatus, electronic device, storage medium, and program product for detecting density during the flow of seabed mudflows. This method acquires environmental parameters and sample parameters corresponding to the mudflow sample. The environmental parameters include the ambient temperature and pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, first solid volume, liquid density, and first liquid volume. An imaging device is used to acquire an image of the mudflow pore structure of the mudflow sample. Based on the mudflow pore structure image, a first porosity and water content are obtained through image inversion. An initial pore volume is determined by multiplying the first porosity by the total sample volume of the mudflow sample. The initial pore volume is corrected based on environmental parameters to obtain the pore compressible volume; the first porosity is corrected based on environmental parameters to obtain the second porosity; the effective total volume is determined based on the first solid volume, the first liquid volume, and the pore compressible volume; the corrected second solid volume and second liquid volume are obtained based on the effective total volume, the second porosity, and the water content; the solid mass is obtained by multiplying the second solid volume by the solid density, and the liquid mass is obtained by multiplying the second liquid volume by the liquid density, and then the total sample mass is obtained by summing them; the mud flow density is obtained by the ratio of the total sample mass to the effective total volume. This invention, through acquiring environmental temperature and pressure parameters of mudflow samples and dynamically correcting the initial pore volume and porosity, effectively quantifies the influence of pore compression and thermo-baric effects under high pressure and high temperature environments. This overcomes the problem of large estimation deviations in traditional models under extreme conditions, making the density results more closely reflect the actual seabed conditions. Furthermore, this invention utilizes imaging equipment to acquire images of the mudflow pore structure and obtains porosity and water content through image inversion. This intuitively reflects the microscopic non-uniformity of the mudflow, avoiding the simplistic assumption of homogeneous solid-liquid mixing in traditional methods, thus more accurately describing the actual composition and flow behavior of the mudflow. Simultaneously, this invention, through volume and porosity correction, can adapt to the spatiotemporal changes in solid-liquid ratio, porosity, temperature, and pressure during mudflow flow. Specifically, this method integrates environmental parameters, sample parameters, and image data, and through multi-step correction and quantification of solid and liquid volumes, ensures the completeness of the calculation of the total sample mass and effective total volume. The resulting mudflow density is more reliable, providing more accurate data support for geological hazard risk assessment.

[0097] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0098] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0099] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0101] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for detecting density during the flow of a mudflow on the seabed, characterized in that, The method includes the following steps: acquiring environmental parameters and sample parameters corresponding to the mudflow sample; wherein, the environmental parameters include the ambient temperature and ambient pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, first solid volume, liquid density, and first liquid volume; acquiring a mudflow pore structure image of the mudflow sample using an imaging device, and obtaining a first porosity and water content based on the mudflow pore structure image through image inversion; determining an initial pore volume based on the product of the first porosity and the total sample volume of the mudflow sample, and correcting the initial pore volume based on the environmental parameters to obtain the pore volume. The compression volume is determined as follows: the first porosity is corrected based on the environmental parameters to obtain the second porosity; the effective total volume is determined based on the first solid volume, the first liquid volume, and the pore compression volume; the corrected second solid volume and second liquid volume are obtained based on the effective total volume, the second porosity, and the water content; the solid mass is obtained by multiplying the second solid volume by the solid density, and the liquid mass is obtained by multiplying the second liquid volume by the liquid density, and then the total sample mass is obtained by summing them; the mud flow density is obtained by the ratio of the total sample mass to the effective total volume.

2. The method according to claim 1, characterized in that, The acquisition of environmental and sample parameters corresponding to the mudflow sample includes the following steps: Based on the mudflow sample, the density and volume fraction of various solid components in the mudflow sample are measured using a laser particle size analyzer and a hydrometer; a weighted average density of the solid phase is obtained based on the density and volume fraction of each solid component, which is used as the solid density; the volume fractions of all solid components are summed to obtain the first solid volume; Based on the centrifuged mudflow sample, the density and volume fraction of various liquid components in the mudflow sample are measured using a hydrometer; a weighted average density of the liquid phase is obtained based on the density and volume fraction of each liquid component, which is used as the liquid density; the volume fractions of all liquid components are summed to obtain the first liquid volume; the environmental parameters are acquired from a deep-sea multi-parameter sensor; wherein the deep-sea multi-parameter sensor is located in the environment where the mudflow sample is situated.

3. The method according to claim 1, characterized in that, The method of obtaining the first porosity and water content based on the mudflow pore structure image through image inversion includes the following steps: performing grayscale processing on the mudflow pore structure image to obtain a grayscale image; performing histogram analysis on the grayscale image using an adaptive thresholding method to statistically obtain the pore pixel ratio as the first porosity, and then quantifying the water content by combining the liquid signal intensity or contrast agent distribution; or, inputting the mudflow pore structure image or the grayscale image into a pre-trained machine learning model to process and obtain the first porosity and the water content; wherein, the machine learning model is trained based on an image training set labeled with porosity and water content.

4. The method according to claim 1, characterized in that, The step of correcting the initial pore volume based on the environmental parameters to obtain the pore compressible volume includes the following steps: determining the temperature change based on the ambient temperature and the reference temperature, and determining the pressure change based on the ambient pressure and the reference pressure; correcting the initial pore volume by combining the temperature change and the pressure change with the coefficient of thermal expansion and the coefficient of pressure compressibility to obtain the pore compressible volume; wherein, the expression for the pore compressible volume is: Vp=V0×(αTΔT+βPΔP); where Vp represents the pore compressible volume, V0 represents the initial pore volume, αT represents the coefficient of thermal expansion, ΔT represents the temperature change, βP represents the coefficient of pressure compressibility, and ΔP represents the pressure change.

5. The method according to claim 1, characterized in that, The step of correcting the first porosity based on the environmental parameters to obtain the second porosity includes the following steps: determining the temperature change based on the ambient temperature and the reference temperature, and determining the pressure change based on the ambient pressure and the reference pressure; correcting the first porosity by combining the temperature change and the pressure change with the coefficient of thermal expansion and the pore compressibility coefficient to obtain the second porosity; wherein, the expression for the second porosity is: φT=φ0(1-CpΔP+αTΔT); where φT represents the second porosity, φ0 represents the first porosity, Cp represents the pore compressibility coefficient, ΔP represents the pressure change, αT represents the coefficient of thermal expansion, and ΔT represents the temperature change.

6. The method according to claim 1, characterized in that, The method of determining the effective total volume based on the first solid volume, the first liquid volume, and the pore compression volume includes the following steps: adding the first solid volume to the first liquid volume, and then subtracting the pore compression volume to obtain the effective total volume.

7. The method according to claim 1, characterized in that, The method of quantifying the corrected second solid volume and second liquid volume based on the effective total volume, the second porosity, and the water content includes the following steps: multiplying the complementary value of the second porosity to 1 by the effective total volume to obtain the second solid volume; and multiplying the effective total volume, the second porosity, and the water content to obtain the second liquid volume.

8. A device for detecting the density of a mudflow during its flow on the seabed, characterized in that, The device includes: a first module for acquiring environmental parameters and sample parameters corresponding to a mudflow sample; wherein the environmental parameters include the ambient temperature and ambient pressure of the environment in which the mudflow sample is located, and the sample parameters include solid density, a first solid volume, liquid density, and a first liquid volume; a second module for acquiring a mudflow pore structure image of the mudflow sample through an imaging device, and obtaining a first porosity and water content based on the mudflow pore structure image through image inversion; a third module for determining an initial pore volume based on the product of the first porosity and the total sample volume of the mudflow sample, and performing volume correction on the initial pore volume based on the environmental parameters to obtain a pore compression volume; and a fourth module. The first module is used to correct the first porosity based on the environmental parameters to obtain the second porosity; the fifth module is used to determine the effective total volume based on the first solid volume, the first liquid volume, and the pore compression volume; the sixth module is used to quantify the corrected second solid volume and second liquid volume based on the effective total volume, the second porosity, and the water content; the seventh module is used to obtain the solid mass based on the product of the second solid volume and the solid density, and the liquid mass based on the product of the second liquid volume and the liquid density, and then summarize them to obtain the total sample mass; the eighth module is used to obtain the mud flow density based on the ratio of the total sample mass to the effective total volume.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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