A method for exploring heavy mineral sand resources in shallow sea and related equipment

Abstract

This invention discloses a method and related equipment for exploring heavy mineral placer resources in shallow seas. The method includes: acquiring preliminary research data of a target shallow sea area; predicting an exploration plan based on the preliminary research data; deploying seafloor seismometers in the target shallow sea area based on the exploration plan; acquiring passive source micromotion data using the seafloor seismometers; extracting raw data from the passive source micromotion data based on preset data quality standards; constructing horizontal and vertical spectral ratio curves based on the raw data; performing spectral separation on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak; constructing a paleotopographic map based on the low-frequency curve through bedrock surface depth inversion; acquiring a regional geological model of the target shallow sea area; and overlaying the paleotopographic map with the regional geological model to obtain an assessment result of the heavy placer resource potential. This invention, by identifying changes in ore-controlling geomorphology, can effectively improve the targeting of exploration and the accuracy of resource potential assessment, and can be widely applied in the field of data processing technology.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and related equipment for exploring shallow sea heavy mineral placer resources. Background Technology

[0002] Heavy minerals such as zircon sand, rutile, and ilmenite are mainly concentrated in shallow marine geomorphic units formed by marine dynamic screening, including coastal dunes and backshore areas, shallow marine sandbars and underwater sand ridges, submerged paleochannels and drowned valleys. The high-energy environments in these areas (such as waves and currents) effectively wash away light minerals, causing heavy minerals to accumulate and become concentrated. However, exploration work in these high-potential areas faces significant challenges: the shallow marine environment severely impacts the quality of geophysical survey data; existing survey techniques (single-channel seismic, shallow seismic profiling) for heavy placer bodies have limited identification capabilities; bottlenecks exist in the processing and fusion of multi-source data; and exploration standards and evaluation criteria for marine placer deposits are still incomplete. These factors collectively restrict the accurate detection and evaluation of seafloor zircon-titanium placer resources.

[0003] In existing technologies, exploration of heavy sand minerals in shallow coastal waters mainly relies on active source acoustic methods such as single-channel seismic surveys and shallow seismic profiling. These methods face significant limitations in practical applications: complex sea conditions and intensive human activities interfere with construction and reduce data quality; it is difficult to balance detection depth and resolution; shallow seismic profiling lacks sufficient penetration; and single-channel seismic surveys have limited ability to identify thin or deep sand bodies; the small difference in acoustic impedance between sand and mud interfaces and ore body boundaries leads to multiple interpretations; under the constraint of sparse boreholes, two-dimensional profiling cannot accurately depict the three-dimensional morphology of sand bodies (such as paleochannels), limiting the accuracy of resource assessment. While combined applications can partially complement each other, the aforementioned bottlenecks remain fundamentally unresolved. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for exploring shallow sea heavy mineral placer resources, aiming to solve at least one problem of the prior art.

[0005] To achieve the above objectives, one aspect of this invention proposes a method for exploring shallow-sea heavy mineral placer resources, the method comprising:

[0006] Obtain preliminary research data of the target shallow sea area, predict exploration plans based on the preliminary research data, and deploy seabed seismometers in the target shallow sea area based on the exploration plans; Passive source micro-motion data were collected using a seabed seismograph, and raw data was extracted from the passive source micro-motion data based on preset data quality standards. Based on the original data, horizontal and vertical spectral ratio curves are constructed, and spectral separation is performed on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. Based on low-frequency curves, paleotopographic maps are constructed by inverting bedrock surface depth. A regional geological model of the target shallow sea area is obtained, and the paleotopographic map is overlaid with the regional geological model to obtain the assessment results of the heavy mineral resource potential.

[0007] In some embodiments, the exploration plan includes the density of the seafloor seismograph network. The exploration plan is predicted based on preliminary research data, and includes the following steps: Based on preliminary research data, a sedimentary-metallogenic model of the target shallow sea area was obtained, and the minimum horizontal scale of the target sand body was determined through the sedimentary-metallogenic model. The minimum horizontal scale is calibrated using the a priori distribution scale of mineralization anomalies as the initial predicted value for the measurement network density. The lower limit of the size of the favorable mineralization body is used as a conservative value to constrain the initial prediction value, and the grid density is determined accordingly. Among them, the lower limit of the scale of the favorable mineralization body is determined by preliminary research data, the line spacing of the surveying network density is less than or equal to the first proportion of the minimum horizontal scale, and the point spacing of the surveying network density is less than or equal to the second proportion of the line spacing.

[0008] In some embodiments, predicting exploration plans based on preliminary research data further includes the following steps: The hydrodynamic intensity of each area in the target shallow sea region was determined based on preliminary study data; Regions with hydrodynamic intensity greater than the first threshold are marked as strong hydrodynamic regions, and regions with hydrodynamic intensity less than the second threshold are marked as weak hydrodynamic regions. The density of the measuring network in the strong hydrodynamic zone is increased, while the density of the measuring network in the weak hydrodynamic zone is decreased.

[0009] In some embodiments, the exploration plan includes the density of the seafloor seismograph network in various areas of the target shallow sea area. Deploying seafloor seismographs in the target shallow sea area based on the exploration plan includes the following steps: The location of all seabed seismographs in the target shallow sea area is determined based on the density of the seismic network. The seabed seismometer is deployed to the designated location using remote control equipment; the seabed seismometer is deployed using a gravity-coupled base and a seabed anchoring device. Waveforms are collected and recorded by deployed seabed seismometers, and judgment and verification features are extracted from the recorded waveforms. Among them, the verification characteristics include waveform integrity and stability, signal response sensitivity, spectral response rationality, and positioning stability; Based on the judgment and verification characteristics, the coupling qualification of each seabed seismograph and seabed sediment is quantified using the preset survey specifications, thereby determining the coupling qualification rate of all seabed seismographs. When the coupling pass rate is less than the third threshold, the seabed seismometers with unqualified coupling are re-deployed, and the process of collecting and recording waveforms on the deployed seabed seismometers is repeated until the coupling pass rate is greater than or equal to the third threshold.

[0010] In some embodiments, each data acquisition in the passive source micro-motion data is marked with a timestamp. The raw data is extracted from the passive source micro-motion data based on a preset data quality standard, including the following steps: The first root mean square value of the signal amplitude in the target frequency band and the second root mean square value of the amplitude in the background noise frequency band are quantized based on the passive source micro-motion data. The signal-to-noise ratio corresponding to the target frequency band is obtained according to the ratio of the first root mean square value to the second root mean square value. The data continuity of passive source micro-motion data is obtained by quantizing the timestamp of each collected data. Data integrity is quantified based on the component types and corresponding data formats contained in the passive source micro-motion data; The passive source micro-motion data is preprocessed, and the noise interference control result is obtained by quantification based on the data preprocessing result. Based on the signal-to-noise ratio, data continuity, data integrity, and noise interference control results, effective data segments in passive source micro-motion data are selected using preset definition criteria. When the proportion of valid data segments in passive source micro-motion data is greater than or equal to the fourth threshold, the passive source micro-motion data within the preset acquisition period will be used as the raw data.

[0011] In some embodiments, the component types in the original data include a first horizontal component, a second horizontal component, and a vertical component. A horizontal and vertical spectral ratio curve is constructed based on the original data. Spectral separation is performed on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. This includes the following steps: The original data is preprocessed to obtain preprocessed data; the data preprocessing includes mean averaging, detrending, denoising based on inverse short-time average or long-time average ratio algorithm, and spectral smoothing. The preprocessed data is subjected to Fast Fourier Transform, and the horizontal and vertical spectral ratio curves are generated by comparing the composite value of the horizontal component vector with the spectral ratio of the vertical component; wherein, the composite value of the horizontal component vector is obtained based on the square root of the sum of the squares of the first horizontal component and the second horizontal component. Wavelet packet decomposition was performed on the horizontal and vertical spectral ratio curves to obtain the energy distribution results of each frequency band and the energy proportion of each frequency band. Based on the energy distribution results, the preset high-frequency band and preset low-frequency band spectrum segments are obtained by bandpass filtering; Based on the energy percentage of each spectral segment, the first frequency band corresponding to the water resonance peak and the second frequency band corresponding to the bedrock resonance peak are obtained by combining energy threshold determination and spectral morphology verification. By using the first frequency band as the separation band and the second frequency band as the retained band, the low-frequency curve of the bedrock resonance peak is obtained.

[0012] In some embodiments, paleotopographic maps are constructed based on low-frequency curves through bedrock surface depth inversion, including the following steps: The frequency value corresponding to the main peak is extracted from the low-frequency curve as the resonance peak frequency of the sedimentary layer-bedrock interface; Based on the pre-set verification boreholes in the target shallow sea area, geological logging and sonic logging data were collected; Based on geological logging, the depth of the top surface of the bedrock is determined through core logging. Based on acoustic logging data, the average shear wave velocity of sedimentary layers in the exploration area was calibrated using the linear regression method. The target coefficients are derived using empirical formulas based on the resonant peak frequency, the depth of the bedrock top surface, and the average shear wave velocity of the sedimentary layer. Bedrock surface burial depth inversion data were obtained based on target coefficients and empirical formulas. Based on the bedrock surface burial depth inversion data, spatial interpolation was performed using the Kriging interpolation method to generate a paleotopographic map of the seabed bedrock surface.

[0013] In some embodiments, the paleotopographic map is overlaid with a regional geological model to obtain an assessment of the potential of heavy mineral resources, including the following steps: Import ancient topographic maps and regional geological models into a preset topographic analysis tool to obtain preliminary topographic analysis data through digital topographic analysis; In response to the correction command of the target object, the preliminary terrain analysis data is corrected for local deviations to obtain the target terrain analysis data; Based on the target terrain analysis data, favorable mineralization structures are delineated by pre-set terrain screening thresholds; among them, favorable mineralization structures include ancient river channels, erosion depressions, and ancient beaches. Heavy minerals were identified and graded from core samples within favorable mineralized structures to assess the resource potential of heavy minerals. The core samples were collected from verification boreholes deployed within the corresponding areas of the favorable mineralized structures.

[0014] To achieve the above objectives, another aspect of the present invention provides a shallow-sea heavy mineral placer resource exploration device, the device comprising: The first module is used to acquire preliminary research data of the target shallow sea area, predict exploration plans based on the preliminary research data, and deploy seabed seismometers in the target shallow sea area based on the exploration plans. The second module is used to collect passive source micro-motion data using a seabed seismograph and extract raw data from the passive source micro-motion data based on preset data quality standards. The third module is used to construct horizontal and vertical spectral ratio curves based on the original data, and to perform spectral separation on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. The fourth module is used to construct paleotopographic maps based on low-frequency curves and by inverting bedrock surface depth. The fifth module is used to obtain a regional geological model of the target shallow sea area. The paleotopographic map is overlaid with the regional geological model to obtain the assessment results of the heavy sand mineral resource potential.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] The embodiments of this invention include at least the following beneficial effects: This invention provides a method, apparatus, electronic device, storage medium, and program product for exploring shallow-sea heavy mineral placer resources. This scheme involves acquiring preliminary research data of a target shallow-sea area, predicting an exploration plan based on the preliminary research data, and deploying seafloor seismometers in the target shallow-sea area based on the exploration plan. Passive-source micromotion data is collected using the seafloor seismometers, and raw data is extracted from the passive-source micromotion data based on preset data quality standards. Horizontal and vertical spectral ratio curves are constructed based on the raw data, and spectral separation is performed on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. Based on the low-frequency curve, a paleotopographic map is constructed by inverting the bedrock surface depth. A regional geological model of the target shallow-sea area is obtained, and the paleotopographic map is overlaid and analyzed with the regional geological model to obtain an assessment result of the heavy placer resource potential. The embodiments of this invention effectively overcome the shortcomings of existing technologies by deploying seafloor seismometers to collect passive-source micromotion data, using its spectral ratio curves to invert the bedrock surface depth, and constructing a paleotopographic map. The beneficial effects of this invention are as follows: it utilizes natural field sources, is not hindered by active source construction obstacles or human noise interference, and is adaptable to complex coastal and shallow marine environments; it non-destructively detects deep interfaces through bedrock resonance characteristics, has strong penetration capabilities, can reveal buried paleogeographic units, and indirectly indicate areas rich in heavy mineral deposits; by combining regional geological models for overlay analysis, it can achieve the transformation from "directly detecting sand bodies" to "identifying ore-controlling geomorphologies," thereby effectively improving the accuracy of exploration direction and resource potential assessment. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an implementation environment for a method for exploring shallow-sea heavy mineral placer resources provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for exploring shallow-sea heavy mineral placer resources provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the unfolding process of a predictive exploration scheme provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of another unfolding process of the predictive exploration scheme provided in the embodiment of the present invention; Figure 5 This is a schematic diagram of the process for extracting and expanding raw data provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the unfolding process of step S300 provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the unfolding process of step S400 provided in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the overall application process of the shallow sea heavy mineral placer resource exploration method provided in this embodiment of the invention; Figure 9This is a schematic diagram of the structure of a shallow sea heavy mineral placer resource exploration device provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] 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.

[0021] 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.”

[0022] 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.

[0023] 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.

[0024] In related technologies, complex sea conditions and intensive human activities interfere with construction and reduce data quality; it is difficult to balance detection depth and resolution, shallow seismic profiles lack sufficient penetration, and single-channel seismic analysis has limited ability to identify thin or deep sand bodies; the small difference in wave impedance at sand-mud interfaces and orebody boundaries leads to multiple interpretations; under the constraint of sparse boreholes, two-dimensional profiles cannot accurately depict the three-dimensional morphology of sand bodies (such as paleochannels), which restricts the accuracy of resource assessment. Although joint applications can partially complement each other, the above bottlenecks have not been fundamentally resolved.

[0025] In view of this, this invention provides a method and related equipment for exploring shallow-sea heavy mineral placer resources. This method involves acquiring preliminary research data of a target shallow-sea area, predicting an exploration plan based on the preliminary research data, and deploying seafloor seismometers in the target shallow-sea area based on the exploration plan. Passive-source micromotion data is collected using the seafloor seismometers, and raw data is extracted from the passive-source micromotion data based on preset data quality standards. Horizontal and vertical spectral ratio curves are constructed based on the raw data, and spectral separation is performed on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. Based on the low-frequency curve, a paleotopographic map is constructed by inverting the bedrock surface depth. A regional geological model of the target shallow-sea area is obtained, and the paleotopographic map is overlaid with the regional geological model for analysis to obtain an assessment result of the heavy placer resource potential. This invention effectively overcomes the shortcomings of existing technologies by deploying seafloor seismometers to collect passive-source micromotion data, using its spectral ratio curves to invert the bedrock surface depth, and constructing a paleotopographic map. The beneficial effects of this invention are as follows: it utilizes natural field sources, is not hindered by active source construction obstacles or human noise interference, and is adaptable to complex coastal and shallow marine environments; it non-destructively detects deep interfaces through bedrock resonance characteristics, has strong penetration capabilities, can reveal buried paleogeographic units, and indirectly indicate areas rich in heavy mineral deposits; by combining regional geological models for overlay analysis, it can achieve the transformation from "directly detecting sand bodies" to "identifying ore-controlling geomorphologies," thereby effectively improving the accuracy of exploration direction and resource potential assessment.

[0026] It is understood that the shallow-sea heavy mineral placer resource exploration method provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of 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.

[0027] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a method for exploring shallow sea heavy mineral placer resources. The following description uses the application of this method in server 101 as an example. It can be understood that this method can also be applied in terminal 102.

[0032] Reference Figure 2 , Figure 2 This is an optional flowchart of a shallow-sea heavy mineral placer resource exploration method provided in an embodiment of the present invention. The subject executing this shallow-sea heavy mineral placer resource exploration method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S500.

[0033] Step S100: Obtain preliminary research data of the target shallow sea area, predict the exploration plan based on the preliminary research data, and deploy seabed seismometers in the target shallow sea area based on the exploration plan. It should be noted that the exploration scheme includes the density of the seafloor seismograph network. In some embodiments, such as... Figure 3As shown, predicting an exploration scheme based on preliminary research data may include the following steps: S110, quantifying the sedimentary-mineralization model of the target shallow sea area based on the preliminary research data, and determining the minimum horizontal scale of the target sand body through the sedimentary-mineralization model; S120, calibrating the minimum horizontal scale using the distribution scale of the a priori mineralization anomaly zone as the initial predicted value of the survey network density; S130, using the lower limit of the scale of the favorable mineralization body as a conservative value to constrain the initial predicted value, and determining the survey network density; wherein, the lower limit of the scale of the favorable mineralization body is determined through the preliminary research data, the line spacing of the survey network density is less than or equal to the first proportion of the minimum horizontal scale, and the point spacing of the survey network density is less than or equal to the second proportion of the line spacing.

[0034] For example, in some specific implementations, a two-dimensional or three-dimensional survey network is designed in the target shallow sea area (water depth 5-100m) based on preliminary research data (regional geological maps, multibeam bathymetry data, and previous reports on mineralization anomalies). A reasonable two-dimensional line spacing or three-dimensional grid density is selected according to the planar distribution scale of the anomaly area, ensuring that the survey network covers known anomaly areas and surrounding potential mineralization zones. The layout density of the two-dimensional survey lines or the three-dimensional OBS (Ocean Bottom Seismometer) station grid should first be determined by the minimum horizontal scale of the target sand body.

[0035] Specifically, the formation of sand bodies is controlled by the sedimentary environment, and their size and distribution patterns can be derived through regional sedimentary-mineralization models. This is the primary basis for the design phase because the preliminary study phase often lacks direct drilling data from the target area and relies on theoretical models and analogies from neighboring areas. Specifically, this can be achieved as follows: 1. Derivation of sedimentary facies-controlled laws: Sand bodies in different sedimentary facies zones have typical size ranges, which can be inferred through the following steps: Step 1: Determine the sedimentary system type of the target stratum by using regional historical data (literature or previous large-scale survey data).

[0036] Step 2: Based on the tectonic background of the target area, narrow down the prediction range of sand body size.

[0037] Step 3: Refer to the actual scale of similar sand bodies in neighboring areas (analogy method) to determine the initial predicted value of the minimum horizontal scale of the target sand body.

[0038] 2. Metallogenic model constraints (for ore-bearing sand bodies): The mineralization model further constrains the effective size of the sand body. The mineralization model clarifies the lower limit of the size of the "metallogenic favorable sand body", that is, the lower limit of the size given by the mineralization model is used as a conservative value. This is a key constraint for setting the measurement network density.

[0039] The principles for setting the density of the survey network are as follows: the line spacing ≤ 1 / 3 of the minimum width of the sand body, and the point spacing ≤ 1 / 2 of the line spacing, to ensure that at least 3 survey lines and 5 sampling points cover a single minimum sand body and avoid missed judgments.

[0040] It should be noted that in some embodiments, such as Figure 4 As shown, the exploration plan based on preliminary research data can also include the following steps: S140, determining the hydrodynamic intensity of each area in the target shallow sea area based on the preliminary research data; S150, marking areas with hydrodynamic intensity greater than a first threshold as strong hydrodynamic areas and areas with hydrodynamic intensity less than a second threshold as weak hydrodynamic areas; S160, increasing the density of the survey network in strong hydrodynamic areas and decreasing the density of the survey network in weak hydrodynamic areas.

[0041] For example, in some specific implementations, auxiliary calibration can also be performed: verification and correction of the known size of the anomaly region. Specifically, this can be achieved as follows: In the preliminary research phase of shallow coastal heavy sand exploration, data such as heavy sand mineral anomalies, geophysical anomalies (e.g., shallow seismic profile reflection anomalies, magnetic anomalies), and remote sensing data on sand body distribution anomalies in shallow water areas are typically collected. The scale of these anomaly zones can serve as a calibration basis for sand body size. If the heavy mineral anomaly zone (such as a zone with high monazite or zircon content) coincides with the reflection axis of the sand body identified by the shallow stratigraphic profile, the minimum distribution width of the anomaly zone can be directly used as the reference value for the minimum horizontal scale of the sand body. If the anomalous area is only locally enriched, it is necessary to combine the hydrodynamic diffusion model to estimate the actual distribution range of the sand body corresponding to the anomalous area.

[0042] Hydrodynamic intensity correction: In areas with strong hydrodynamics (such as tidal channels), sand bodies are prone to migration and have irregular shapes, so the density of the survey network needs to be increased; in areas with weak hydrodynamics, the density can be appropriately reduced.

[0043] It should be noted that the exploration plan includes the density of the seafloor seismometer network in each area of ​​the target shallow sea region. In some embodiments, the deployment of seafloor seismometers in the target shallow sea region based on the exploration plan may include the following steps: determining all deployment points of seafloor seismometers in the target shallow sea region based on the network density; deploying the seafloor seismometers to the deployment points using remote control equipment; wherein the seafloor seismometers are deployed using gravity coupling bases and seafloor anchoring devices; collecting and recording waveforms through the deployed seafloor seismometers, and extracting judgment and verification features from the recorded waveforms; wherein the judgment and verification features include waveform integrity and stability, signal response sensitivity, spectral response rationality, and positioning stability; based on the judgment and verification features, quantifying the coupling qualification of each seafloor seismometer with seabed sediments using preset survey specifications, thereby determining the coupling qualification rate of all seafloor seismometers; when the coupling qualification rate is less than a third threshold, the seafloor seismometers with unqualified coupling are re-deployed, and the step of collecting and recording waveforms for the deployed seafloor seismometers is returned until the coupling qualification rate is greater than or equal to the third threshold.

[0044] For example, in some specific implementations, the OBS deployment can be achieved by selecting a short-period OBS (sampling rate 50-200Hz, frequency response range 0.01-100Hz) conforming to GB / T 41520-2022 "Specifications for Marine Geophysical Surveys", with a built-in three-component seismometer (sensitivity ≥2000V / (m / s)) and hydrophone. A gravity-type coupling base (weight ≥50kg) is used in conjunction with a seabed anchoring device to ensure that the OBS coupling qualification rate with seabed sediments is ≥95%, and the positioning error is controlled within ±5m using shipborne GNSS and underwater acoustic positioning fusion technology. Specifically, the specific technical criteria for determining the "qualified coupling" of the OBS can be configured as follows: Referring to GB / T 41520-2022 "Specifications for Marine Geophysical Surveys", the core criteria for determining "coupling qualification" of OBS are based on the following characteristics of the recorded waveforms: 1. Waveform integrity and stability: The environmental noise waveform recorded by the three-component seismometer showed no obvious distortion, clipping or discontinuity. The waveform amplitude fluctuations during the continuous recording period conformed to the natural variation law of marine environmental noise (without sudden amplitude changes due to poor coupling). Clipping refers to the distortion phenomenon where the waveform amplitude recorded by the OBS exceeds the maximum dynamic range of the equipment or data acquisition system, resulting in the top or bottom of the signal waveform being "truncated." In the OBS coupling qualification assessment, "no obvious clipping" is one of the core indicators of waveform integrity, because clipping causes signal distortion, affecting the accuracy of signal-to-noise ratio calculation and spectrum analysis, and consequently interfering with subsequent exploration work such as bedrock resonance peak identification and bedrock depth inversion.

[0045] 2. Signal response sensitivity: The horizontal components (H1, H2) and the vertical component (V) respond to environmental noise in the same way, the waveform signal-to-noise ratio (effective signal amplitude / background noise amplitude) is ≥3:1, and the correlation of the three-component waveforms conforms to the vibration propagation characteristics of the seabed medium (no single component signal is missing or abnormally attenuated). 3. Rationality of Spectral Response: The waveform spectrum covers the rated frequency response range of OBS (0.01-100Hz), with no high-frequency signal attenuation or low-frequency signal distortion caused by loose coupling, and the peak distribution of the spectrum matches the noise field characteristics of the seabed sedimentary environment; 4. Positioning stability: Monitoring by shipborne GNSS and underwater acoustic positioning fusion technology shows that the position offset after OBS deployment is ≤ ±5m, with no positioning drift caused by the slippage of the coupling base, indirectly proving the reliability of the coupling.

[0046] 5. The hardware guarantee scheme of "gravity coupling base (weight ≥ 50kg) + seabed anchoring device" is adopted, and the final requirement is "coupling qualification rate ≥ 95%". The above waveform and positioning characteristics are the core technical basis for judging qualification.

[0047] Step S200: Use a seabed seismograph to collect passive source micro-motion data, and extract raw data from the passive source micro-motion data based on preset data quality standards; It should be noted that each data acquisition in the passive source micro-motion data is marked with a timestamp. In some embodiments, such as... Figure 5 As shown, extracting raw data from passive source micro-motion data based on preset data quality standards may include the following steps: S210, quantizing the first root mean square value of the signal amplitude in the target frequency band and the second root mean square value of the amplitude in the background noise frequency band based on the passive source micro-motion data, and obtaining the signal-to-noise ratio corresponding to the target frequency band based on the ratio of the first root mean square value to the second root mean square value; S220, quantizing the data continuity of the passive source micro-motion data based on the timestamp of each acquired data; S230, quantizing the data integrity based on the component types and corresponding data formats contained in the passive source micro-motion data; S240, performing data preprocessing on the passive source micro-motion data, and quantizing the noise interference control result based on the data preprocessing result; S250, using preset definition standards to screen the effective data segments in the passive source micro-motion data based on the signal-to-noise ratio, data continuity, data integrity, and noise interference control result; S260, when the proportion of the effective data segment in the passive source micro-motion data is greater than or equal to the fourth threshold, the passive source micro-motion data within the preset acquisition period is used as the raw data.

[0048] For example, in some specific implementations, passive source micro-motion data is synchronously acquired: multiple OBSs are synchronized via an underwater acoustic beacon synchronization system (synchronization error < 0.01ms), acquiring continuous time-series data for a duration of no less than 24 hours (verified by a case study in the northern South China Sea, this duration can capture stable environmental noise fields such as waves and tides). The data storage format adopts the SEG-Y standard to ensure compatibility with subsequent processing, while simultaneously monitoring data quality in real time (valid data segments account for ≥ 90%). Specifically, the specific definition criteria for "valid data segments" (based on marine geological and geophysical survey specifications) can be achieved as follows: Based on GB / T 41520-2022 "Specifications for Marine Geophysical Surveys" and industry practices for marine passive source data acquisition, the definition criteria for "valid data segments" are as follows: 1. Signal-to-noise ratio threshold: The signal-to-noise ratio of the three-component raw data must be ≥2:1 (the signal-to-noise ratio is calculated as: the root mean square value of the signal amplitude in the target frequency band / the root mean square value of the amplitude in the background noise frequency band). The signal-to-noise ratio of the low-frequency band (0.1-5Hz, corresponding to the bedrock response signal) must be ≥3:1, and the signal-to-noise ratio of the high-frequency band (5-20Hz, corresponding to the water resonance signal) must be ≥2:1. 2. Data continuity: The continuous duration of a single valid data segment is ≥1 hour, and there is no signal interruption of more than 10 seconds in the continuous recording (brief interruptions caused by equipment failure or extreme marine environment must be ≤1% of the total duration). 3. Data integrity: The valid data segment must contain the complete three-component signal (H1, H2, V), with no missing single component or data anomalies (such as constant value, clipping, large jump, etc.), and the data storage format must conform to the SEG-Y standard, with no format errors or data loss; 4. Noise interference control: After processing with mean removal, trend removal, and inverse STA / LTA algorithms, there is no obvious human interference (such as shipboard equipment vibration, acoustic interference) or extreme environmental interference (such as abnormal pulses caused by strong storms) in the data. High-frequency random noise can be effectively suppressed after Konno-Ohmachi smoothing. 5. Percentage requirement: During the entire acquisition period (no less than 24 hours), the cumulative duration of valid data segments accounts for ≥90%, which meets the basic data requirements for subsequent HVSR curve calculation and spectrum separation.

[0049] Step S300: Construct horizontal and vertical spectral ratio curves based on the original data, and perform spectral separation on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak; It should be noted that the component types in the raw data include a first horizontal component, a second horizontal component, and a vertical component. In some embodiments, such as... Figure 6As shown, step S300 may include the following steps: S310, preprocessing the original data to obtain preprocessed data; wherein, the data preprocessing includes mean averaging, detrending, denoising based on the inverse short-time average or long-time average ratio algorithm, and spectral smoothing; S320, performing a fast Fourier transform on the preprocessed data, generating a horizontal and vertical spectral ratio curve by comparing the composite value of the horizontal component vector with the spectral ratio of the vertical component; wherein, the composite value of the horizontal component vector is obtained based on the square root of the sum of the squares of the first horizontal component and the second horizontal component; S33 0. Perform wavelet packet decomposition on the horizontal and vertical spectral ratio curves to obtain the energy distribution results and energy proportion of each frequency band; S340. Based on the energy distribution results, obtain the preset high-frequency band and preset low-frequency band spectral segments through bandpass filtering; S350. Based on the energy proportion of each spectral segment, combine energy threshold judgment and spectral morphology verification to obtain the first frequency band corresponding to the water resonance peak and the second frequency band corresponding to the bedrock resonance peak; S360. Use the first frequency band as the separated frequency band and the second frequency band as the retained frequency band to obtain the low-frequency curve of the bedrock resonance peak.

[0050] For example, in some specific implementations, the calculation of the HVSR characteristic curve and spectrum separation of the marine environment can be achieved as follows: Data preprocessing: The three-component raw data (horizontal components H1 and H2, vertical component V) are sequentially subjected to the mean removal, detrending, and inverse STA / LTA (short / long window average ratio) algorithms for noise reduction. The Konno-Ohmachi smoothing function (smoothing coefficient α=40) is used for spectral smoothing to eliminate high-frequency random noise interference.

[0051] HVSR curve generation: Perform Fast Fourier Transform (FFT) on the preprocessed data, calculate the ratio of the combined value of the horizontal component vector (H=√(H12+H22)) to the spectrum of the vertical component, and generate the initial HVSR curve. √ represents the square root operation.

[0052] Innovative Spectrum Separation Processing: A combined algorithm of wavelet packet decomposition (5 decomposition layers) and bandpass filtering (5-20Hz high-frequency band, 0.1-5Hz low-frequency band) is employed to accurately identify and separate two types of resonance peaks in the frequency domain: high-frequency water resonance peaks caused by sea surface waves (energy percentage ≤15%) and low-frequency resonance peaks caused by the seabed sediment-bedrock interface (energy percentage ≥60%). Water interference signals are eliminated using an energy thresholding method to ensure the purity of the low-frequency bedrock response signal.

[0053] The inverse STA / LTA algorithm, short for "Inverse Short-Term Average / Long-Term Average Ratio Algorithm," is a commonly used method in signal processing for suppressing sudden interference and identifying effective signal intervals. Its core principle is to eliminate interference by calculating the energy ratio of two sliding time windows. STA (Short Time Average): The signal is slid across a short window (e.g., 0.1-1 seconds) and the average value of the signal energy within that window is calculated, reflecting the instantaneous changes in the signal. LTA (Long-Time Average): The signal is slid across a long window (e.g., 10-30 seconds) and the average signal energy within that window is calculated, reflecting the background energy level of the signal.

[0054] The "anti" logic is reflected in the following: when the STA / LTA ratio is significantly higher than the threshold (indicating a sudden strong interference, such as a strong storm pulse or ship noise), the algorithm will mark the interval and suppress or remove it; only the signal segment with a stable STA / LTA ratio within the normal range will be retained, thereby improving data purity.

[0055] Step S400: Based on the low-frequency curve, construct a paleotopographic map by inverting the bedrock surface depth; It should be noted that in some embodiments, such as Figure 7 As shown, step S400 may include the following steps: S410, extracting the frequency value corresponding to the main peak from the low-frequency curve as the resonance peak frequency of the sedimentary layer-bedrock interface; S420, collecting geological logging and sonic logging data based on the preset verification borehole in the target shallow sea area; S430, determining the bedrock top surface depth through core logging based on the geological logging; S440, calibrating the average shear wave velocity of the sedimentary layer in the exploration area using the linear regression method based on the sonic logging data; S450, deriving the target coefficient using empirical formulas based on the resonance peak frequency, the bedrock top surface depth, and the average shear wave velocity of the sedimentary layer; S460, obtaining the bedrock surface burial depth inversion data based on the target coefficients and empirical formulas; S470, generating a paleotopographic map of the seabed bedrock surface using the Kriging interpolation method based on the bedrock surface burial depth inversion data.

[0056] For example, in some specific implementations, bedrock surface depth inversion and paleotopographic map construction can be achieved as follows: Resonance frequency extraction: Extract the resonance peak frequency f0 of the sedimentary layer-bedrock interface from the separated low-frequency HVSR curve (take the frequency value corresponding to the main peak of the curve).

[0057] Shear wave velocity calibration: Using geological logging and sonic logging data from 3-5 verification boreholes (hole depth ≥ 5m below bedrock surface), the average shear wave velocity Vs of the sedimentary layer in the exploration area is calibrated using the linear regression method, with the calibration error controlled within ±10% (e.g., Vs = 450-600m / s in a mining area in the South China Sea).

[0058] Bedrock burial depth calculation: The bedrock surface burial depth is calculated using the empirical formula H=Vs / (k·f0), where k is a coefficient related to the Poisson's ratio of the sediment (the Poisson's ratio of shallow marine sandy and muddy sediments is 0.25-0.3, and k is taken as 4). The inversion error of this formula in the offshore Pearl River Estuary is <12%.

[0059] Paleotopographic map generation: Kriging interpolation (using a spherical model for the semi-variogram) was used to spatially interpolate the bedrock depth data of all OBS stations to generate a 10m×10m resolution paleotopographic map of the seabed bedrock surface. The interpolation accuracy was evaluated using cross-validation (root mean square error < 4m).

[0060] The algorithm for identifying the resonance peaks between water and bedrock, and the judgment process, are as follows: I. Basis of the recognition algorithm: Formant identification employs a comprehensive algorithm based on "frequency range division + energy percentage threshold + spectral morphology features": 1. Frequency Range Matching: Based on the physical characteristics of marine environmental noise, the typical frequency bands of two types of resonance peaks are pre-defined—the high-frequency band (5-20Hz) corresponding to water resonance peaks and the low-frequency band (0.1-5Hz) corresponding to bedrock resonance peaks. The target frequency band is initially locked through bandpass filtering. (<0.1Hz extremely low frequencies are easily interfered with by Earth solid tides, distant storms, etc., resulting in low signal-to-noise ratios and difficulty in effectively reflecting the geological characteristics of shallow layers (shallow marine sedimentary layers); >20Hz ultra-high frequency signals attenuate rapidly, have shallow penetration depths (usually <10m), cannot reach the bedrock-sedimentary interface related to heavy mineral occurrence, and are easily masked by marine high-frequency noise (such as wave breaking and ship interference), limiting the value of extracted geological information. Eliminating these frequency bands does not affect the core requirements of heavy mineral exploration; on the contrary, it can reduce noise interference and improve the analysis accuracy of effective signals.) 2. Energy percentage threshold: The energy percentage of each frequency band is extracted using wavelet packet decomposition (5-level decomposition). The energy percentage of the water resonance peak is ≤15%, and the energy percentage of the bedrock resonance peak is ≥60%, which is used as the core distinguishing threshold. 3. Spectral morphology: The spectral morphology of the bedrock resonance peak is "single-peak dominant and symmetrically distributed" (the main peak is sharp and the side lobes have low energy), corresponding to the single resonance response of the sedimentary layer-bedrock interface; the spectral morphology of the water resonance peak is "multi-peak dispersed and asymmetrically distributed" (there is no obvious dominant main peak, and the energy is dispersed in multiple secondary peaks), corresponding to the complex vibration superposition of sea surface waves.

[0061] II. Specific judgment process from "obtaining the HVSR curve" to "determining the frequency band to be separated": 1. Generate the initial HVSR curve — Perform FFT transformation on the preprocessed three-component data, calculate the spectral ratio of the composite value of the horizontal component vector (H=√(H1²+H2²)) to the vertical component, and obtain the initial HVSR curve containing full-band information; 2. Spectral Energy Scan – Perform 5-level wavelet packet decomposition on the initial HVSR curve to obtain the energy distribution results of different frequency bands, and calculate the energy proportion of each frequency band (the total energy is the sum of the energy of the entire frequency band). 3. Preliminary frequency band screening – Based on the frequency range division, screen out the corresponding spectrum segments of the high frequency band (5-20Hz) and the low frequency band (0.1-5Hz), and exclude the frequency bands (such as extremely low frequencies <0.1Hz and ultra-high frequencies >20Hz) as invalid noise bands. 4. Energy threshold verification – Calculate the energy proportion of the high-frequency band and the low-frequency band respectively. If the energy proportion of the high-frequency band is ≤15%, it is determined to be the frequency band corresponding to the water resonance peak; if the energy proportion of the low-frequency band is ≥60%, it is determined to be the frequency band corresponding to the bedrock resonance peak. 5. Spectrum morphology verification – Spectrum morphology analysis is performed on the selected frequency bands to confirm that there are no sharp dominant peaks and energy dispersion in the high-frequency bands, and that there are clear and symmetrical peaks in the low-frequency bands, further verifying the accuracy of the frequency band division; 6. Determine the separation frequency bands – Ultimately, the high-frequency band (5-20Hz) is identified as the separation frequency band for the water body resonance peak, and the low-frequency band (0.1-5Hz) is identified as the retention frequency band for the bedrock resonance peak, thus completing the frequency band separation and definition.

[0062] Among them, the FFT transform, or Fast Fourier Transform, is a highly efficient computational algorithm for the Discrete Fourier Transform (DFT), which significantly improves the efficiency of signal spectrum analysis by simplifying the computational steps. Its core role here is to convert the time-domain waveform signal (vibration amplitude data that changes over time) acquired by the OBS into a frequency-domain spectrum signal. Specifically, after performing an FFT transform on the preprocessed three-component data, the signal energy distribution corresponding to different frequencies can be obtained. This allows for the calculation of the spectral ratio (HVSR curve) between the horizontal and vertical components, providing fundamental spectral data for subsequent identification of water-bedrock resonance peaks (such as extracting the low-frequency 0.1-5Hz bedrock resonance peak frequency).

[0063] It should also be noted that the method for determining the coefficient k and the calibration steps can be implemented as follows: I. The core logic for determining the coefficient k: The coefficient k is determined by a combination of "theoretical value + verification borehole inversion calibration". The document mentions that "k is related to the Poisson's ratio of sediments (the Poisson's ratio of shallow sea sandy and muddy sediments is 0.25-0.3, so k is taken as 4)" as the theoretical basis. In actual implementation, it is necessary to verify the accuracy by inversion calibration through borehole data.

[0064] II. Specific procedures for calibration: 1. Preparation of verification borehole data - Select 3-5 verification boreholes covering different structural units in the exploration area. The borehole depth should be ≥ 5m below the bedrock surface. Obtain two types of key data: (1) The actual bedrock surface burial depth H_true revealed by the borehole (the depth of the top bedrock surface is determined by core logging); (2) The average shear wave velocity Vs_cal of the sedimentary layer at the location of the borehole (calibrated by sonic logging data combined with linear regression method, with an error ≤ ±10%). 2. Measured resonance frequency f0 - At each OBS station corresponding to the verification borehole, the bedrock surface resonance peak frequency f0_meas (the frequency value corresponding to the main peak) is extracted from the low-frequency HVSR curve through the spectrum separation processing in step S3. 3. Initial k back calculation – Based on the empirical formula H=Vs / (k·f0) (representing the operational relationship between various parameters; in the empirical formula, symbols without suffixes only represent the conceptual meaning of the parameter, while symbols with suffixes represent the numerical values ​​collected, calculated, or derived for the corresponding parameter), the k back calculation formula is derived as: k_cal=Vs_cal / (H_true·f0_meas). For each verification borehole, a single back calculation value k_i is calculated (i=1,2,...,n, where n is the number of verification boreholes). 4. k-mean calibration and outlier removal—Calculate the arithmetic mean of all k_i, remove outliers that deviate from the mean by more than ±15% (due to borehole data errors or OBS signal interference), and obtain the calibrated average k value k_avg; 5. Accuracy Verification – Substitute k_avg into the empirical formula to calculate the bedrock surface burial depth H_cal=Vs_cal / (k_avg·f0_meas) for each verification borehole, and compare it with the actual burial depth H_true to verify whether the inversion error is <12% (the standard applied in the Pearl River Estuary offshore area in the document). 6. Final determination of k value - If the error meets the requirements, k_avg is the final coefficient of the exploration area; if the error does not meet the requirements, the number of verification boreholes needs to be increased (to 5-7), and steps 1-5 are repeated until the inversion error is ≤12%, and the actual value of k is finally determined.

[0065] Step S500: Obtain the regional geological model of the target shallow sea area, overlay the paleotopographic map with the regional geological model, and obtain the assessment results of the heavy sand mineral resource potential. It should be noted that in some embodiments, the overlay analysis of paleotopographic maps and regional geological models to obtain the assessment results of heavy mineral placer resource potential may include the following steps: importing the paleotopographic maps and regional geological models into a preset topographic analysis tool to obtain preliminary topographic analysis data through digital topographic analysis; responding to the correction instructions of the target object, performing local deviation correction on the preliminary topographic analysis data to obtain target topographic analysis data; based on the target topographic analysis data, delineating favorable mineral-forming structures through a preset topographic screening threshold; wherein, favorable mineral-forming structures include paleochannels, erosion depressions, and paleotides; performing heavy mineral identification and grade analysis on core samples within the favorable mineral-forming structures to obtain the assessment results of heavy mineral placer resource potential; wherein, the core samples are collected based on verification boreholes deployed within the corresponding area of ​​the favorable mineral-forming structures.

[0066] For example, in some specific embodiments, the comprehensive evaluation of heavy mineral resource potential can be achieved as follows: Delineation of favorable metallogenic structures: By overlaying the paleotopographic map of the bedrock surface with regional geological models (fault structure distribution map, paleoriver evolution model, sediment diffusion path model), three types of favorable metallogenic structures were delineated: (1) ancient river channels (width ≥ 50m, longitudinal slope < 5°, lateral undulation < 3m); (2) erosion depressions (area ≥ 1000m²). 2 (3) Ancient beaches (slope < 2°, extension length ≥ 500m), such structures contribute more than 75% to shallow sea heavy sand deposits.

[0067] Verification and Modeling: Within the delineated favorable zone, verification boreholes were deployed at intervals of 500-1000m to collect core samples for heavy mineral identification (using a panning-magnetic separation-microscopy method) and grade analysis (e.g., zircon grade ≥ 0.1 kg / m). 3 Ilmenite grade ≥ 0.5 kg / m³ 3 (Defined as mineralization anomaly). Combining borehole data and HVSR inversion results, a three-dimensional geological model is constructed using Surfer or Micromine software to achieve resource estimation and potential classification evaluation (divided into Level A: proven resources, Level B: controlled resources, and Level C: inferred resources).

[0068] Specifically, the methods and tools / algorithms for delineating ancient river channels, depressions, and other structures can be implemented as follows: I. Defining the core approach: The delineation adopts a method of "digital terrain analysis algorithm as the main approach and manual interpretation as a supplement." The core relies on terrain analysis tools in GIS to achieve automated extraction, while manual interpretation is only used to correct local deviations in the algorithm results.

[0069] II. Specific tool or algorithm name used: 1. Basic tool platform: Utilizes professional GIS software such as ArcGIS and MapGIS, combined with Surfer or Micromine 3D modeling software; 2. Core Digital Terrain Analysis Algorithm: Ancient riverbed delineation: (1) Hydrological analysis algorithms (flow direction analysis, runoff accumulation calculation) identify potential water system channels; (2) Slope analysis algorithm (calculate longitudinal slope and lateral undulation); (3) Buffer analysis and width measurement tools to screen areas that meet the criteria of "width ≥ 50m, longitudinal slope < 5°, and lateral undulation < 3m"; The threshold was selected based on the following: First, the regional geological background constraints. Referring to the development characteristics of paleochannels in the Pearl River Estuary, the width of paleochannels in secondary bays such as the Lion Ocean is generally between 50-2800m, with 50m being the lower limit for distinguishing between the main channel and tributary systems. Second, the exploration target orientation of heavy minerals. Paleochannels with a width ≥50m have sufficient space for sediment transport and deposition, making it easy to form heavy mineral enrichment layers, while tributaries narrower than this value cannot meet the requirements for large-scale mineral accumulation. Third, the matching of topographic and dynamic conditions. A longitudinal slope <5° is consistent with the channel morphology characteristics of shallow coastal low-energy sedimentary environments (for example, the average slope of the main channel of the Qingyi River, a first-level tributary of the Yangtze River, is about 1‰ (i.e., 0.057°), and the slope of identified paleochannels in nearshore areas such as the western Bohai Sea is generally in the range of 0.5‰-2‰). The adoption of a 5° threshold is mainly based on two considerations: First, to adapt to the resolution of the paleotopographic map of the bedrock surface retrieved by OBS (1 The design incorporates several key features: 1) a 0-20m grid to avoid misjudging gentle slopes due to data accuracy limitations; 2) coverage of minor local undulations in ancient river channels during geological history (such as short-term slope increases due to tectonic uplifts), ensuring no potential main river channel areas are overlooked, while the actual selected target ancient river channels still primarily exhibit gentle longitudinal slopes of <1‰; 3) a lateral undulation threshold of <3m ensures the identification of gentle river channel main bodies, excluding interfering terrain such as steep gullies; 4) technical feasibility, combining the resolution of the bedrock surface paleotopographic map derived from OBS data (typically 10-20m grid), a 3m lateral undulation threshold effectively distinguishes between real terrain changes and data noise, improving delineation accuracy; and 5) industry experience reference, in near-shore ancient river channel surveys in the western Bohai Sea, similar thresholds (width ≥50m, slope <5°) have proven effective in screening target structures related to heavy mineral mineralization.

[0070] Delineation of erosion depressions: (1) Depression extraction algorithm (based on local minimum identification of digital elevation model DEM). (2) Area and depth measurement tool (deepness is calculated by the difference in elevation between the terrain surface and the bottom of the depression), and screen closed negative terrain with "area ≥ 1000m² and depth ≥ 8m"; Ancient beach demarcation: (1) Slope analysis algorithm (calculating terrain slope); (2) Length measurement and shape recognition tool (to identify gently extending terrain) to filter gentle areas with "slope < 2° and extension length ≥ 500m"; 3. Auxiliary analysis tools: (1) Spatial overlay analysis tool (overlaying the paleotopographic map of the bedrock surface with the regional geological model and the paleo-river system evolution model to constrain the tectonic boundary); (2) Visual verification tool (through three-dimensional terrain rendering, manually correcting the tectonic boundary error extracted by the algorithm to ensure consistency with the regional geological background).

[0071] 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.

[0072] In order to address the shortcomings of existing technologies, this invention aims to overcome two deficiencies in the geophysical survey of nearshore and shallow marine heavy mineral resources, namely: (1) the serious impact of nearshore and shallow marine environment on the quality of geophysical survey data; and (2) the limited identification capabilities of single-channel seismic and shallow stratigraphic profile survey techniques.

[0073] Correspondingly, the technical problems that need to be solved are: (1) how to overcome the problem of low data quality by changing the construction method, deploying the signal receiving array, changing the nature of the received waveform, and avoiding strong noise interference in the complex environment of shallow coastal waters; (2) the problems of limited detection depth, insufficient accuracy of ore body boundary identification, and limited bedrock surface identification capability.

[0074] In view of this, such as Figure 8 As shown, this invention provides a method for exploring shallow-sea heavy mineral placer resources. This invention belongs to the field of marine geological exploration and mineral resource exploration technology, specifically involving a rapid and low-cost exploration solution for detecting the thickness of shallow seabed sedimentary layers and identifying bedrock surfaces using passive source seismic waves (environmental noise). The method of this invention can be specifically implemented as follows: Step S1: Exploration Scheme Design: Survey Network Design: Based on preliminary research data (regional geological maps, multibeam bathymetry data, and previous reports on mineralization anomalies), design a two-dimensional or three-dimensional survey network in the target shallow sea area (water depth 5-100m). Select a reasonable two-dimensional line spacing or three-dimensional grid density according to the planar distribution scale of the anomaly area, ensuring that the survey network covers known anomaly areas and surrounding potential mineralized zones. The layout density of the two-dimensional survey lines or three-dimensional OBS (Ocean Bottom Seismometer) station grid should first be determined by the minimum horizontal scale of the target sand body: according to the spatial sampling theorem, the station spacing should not exceed half of this scale. For example, when delineating a medium-thick lenticular sand layer approximately 500m wide and 5-15m thick, the line spacing or grid side length should be controlled within 250m, corresponding to no less than 16 stations per square kilometer; if the exploration target is a sand ridge 1-5km wide and more than 15m thick, the survey lines or grid can be widened to 500m, corresponding to 4 stations / km. 2 This can meet the accuracy requirements for boundary localization in subsequent HVSR (Horizontal-to-Vertical Spectral Ratio) and surface wave imaging; for large structures such as bedrock slopes with a width exceeding 5 km, the line spacing can be increased to 1-2 km, and the grid density can be reduced to 1 unit / km. 2 This approach allows for control of deep interfaces while avoiding excessive investment. The station density determined in this way has been proven in multiple placer test areas both domestically and internationally to control the inversion errors of sedimentary layer thickness, ore body boundaries, and bedrock surfaces to within 15%.

[0075] OBS Deployment: A short-period OBS (sampling rate 50-200Hz, frequency response range 0.01-100Hz) conforming to GB / T 41520-2022 "Specifications for Marine Geophysical Surveys" was selected, with a built-in three-component seismometer (sensitivity ≥2000V / (m / s)) and hydrophone. A gravity-type coupling base (weight ≥50kg) was used in conjunction with a seabed anchoring device to ensure that the coupling qualification rate between the OBS and seabed sediments is ≥95%, and the positioning error is controlled within ±5m through the fusion technology of shipborne GNSS and underwater acoustic positioning.

[0076] Step S2: Synchronous acquisition of passive source micro-motion data: Multiple OBS units are synchronized using an underwater acoustic beacon synchronization system (synchronization error < 0.01ms) to collect continuous time-series data for a duration of no less than 24 hours (verified by a case study in the northern South China Sea, this duration can capture stable environmental noise fields such as ocean waves and tides). The data storage format adopts the SEG-Y standard to ensure compatibility with subsequent processing, while data quality is monitored in real time (valid data segment ratio ≥ 90%).

[0077] Step S3: Calculation of HVSR characteristic curves and spectrum separation for marine environment: Data preprocessing: The three-component raw data (horizontal components H1 and H2, vertical component V) are sequentially subjected to the mean removal, detrending, and inverse STA / LTA (short / long window average ratio) algorithms for noise reduction. The Konno-Ohmachi smoothing function (smoothing coefficient α=40) is used for spectral smoothing to eliminate high-frequency random noise interference.

[0078] HVSR curve generation: Perform Fast Fourier Transform (FFT) on the preprocessed data, calculate the ratio of the combined value of the horizontal component vector (H=√(H12+H22)) to the spectrum of the vertical component, and generate the initial HVSR curve. √ represents the square root operation.

[0079] Innovative Spectrum Separation Processing: A combined algorithm of wavelet packet decomposition (5 decomposition layers) and bandpass filtering (5-20Hz high-frequency band, 0.1-5Hz low-frequency band) is employed to accurately identify and separate two types of resonance peaks in the frequency domain: high-frequency water resonance peaks caused by sea surface waves (energy percentage ≤15%) and low-frequency resonance peaks caused by the seabed sediment-bedrock interface (energy percentage ≥60%). Water interference signals are eliminated using an energy thresholding method to ensure the purity of the low-frequency bedrock response signal.

[0080] Step S4: Bedrock surface depth inversion and paleotopographic map construction: Resonance frequency extraction: Extract the resonance peak frequency f0 of the sedimentary layer-bedrock interface from the separated low-frequency HVSR curve (take the frequency value corresponding to the main peak of the curve).

[0081] Shear wave velocity calibration: Using geological logging and sonic logging data from 3-5 verification boreholes (hole depth ≥ 5m below bedrock surface), the average shear wave velocity Vs of the sedimentary layer in the exploration area is calibrated using the linear regression method, with the calibration error controlled within ±10% (e.g., Vs = 450-600m / s in a mining area in the South China Sea).

[0082] Bedrock burial depth calculation: The bedrock surface burial depth is calculated using the empirical formula H=Vs / (k·f0), where k is a coefficient related to the Poisson's ratio of the sediment (the Poisson's ratio of shallow marine sandy and muddy sediments is 0.25-0.3, and k is taken as 4). The inversion error of this formula in the offshore Pearl River Estuary is <12%.

[0083] Paleotopographic map generation: Kriging interpolation (using a spherical model for the semi-variogram) was used to spatially interpolate the bedrock depth data of all OBS stations to generate a 10m×10m resolution paleotopographic map of the seabed bedrock surface. The interpolation accuracy was evaluated using cross-validation (root mean square error < 4m).

[0084] Step S5: Comprehensive evaluation of heavy mineral resource potential: Delineation of favorable metallogenic structures: By overlaying the paleotopographic map of the bedrock surface with regional geological models (fault structure distribution map, paleoriver evolution model, sediment diffusion path model), three types of favorable metallogenic structures were delineated: (1) ancient river channels (width ≥ 50m, longitudinal slope < 5°, lateral undulation < 3m); (2) erosion depressions (area ≥ 1000m²). 2 (3) Ancient beaches (slope < 2°, extension length ≥ 500m), such structures contribute more than 75% to shallow sea heavy sand deposits.

[0085] Verification and Modeling: Within the delineated favorable zone, verification boreholes were deployed at intervals of 500-1000m to collect core samples for heavy mineral identification (using a panning-magnetic separation-microscopy method) and grade analysis (e.g., zircon grade ≥ 0.1 kg / m). 3 Ilmenite grade ≥ 0.5 kg / m³ 3 (Defined as mineralization anomaly). Combining borehole data and HVSR inversion results, a three-dimensional geological model is constructed using Surfer or Micromine software to achieve resource estimation and potential classification evaluation (divided into Level A: proven resources, Level B: controlled resources, and Level C: inferred resources).

[0086] In some specific embodiments, a certain sea area is used as an example to illustrate the application of the shallow sea heavy mineral placer resource exploration method of the present invention: The target sea area is a shallow water zone (20-80m deep), where zircon heavy mineralization is known to exist. Traditional exploration methods (single-channel seismic + random sampling) have an error of up to 30% in delineating the mineralization zone. Therefore, the method of this invention is required for precise exploration. The implementation process is as follows: S1-S2: Design a 2km×2km two-dimensional measurement network, deploy 15 short-period OBS units, and synchronously collect 24-hour micro-motion data; S3: Through preprocessing and spectral separation, the 10-15Hz wave resonance peak and the 1-5Hz sedimentary layer-bedrock resonance peak are separated, and the effective peak detection rate of the HVSR curve reaches 86.7%; S4: Based on data from 12 verification boreholes (Vs=500m / s), the bedrock surface burial depth range was calculated to be 20-80m. The paleotopographic map generated by interpolation showed that there were ancient river channels (50-100m wide) in the study area. S5: The ancient riverbed and surrounding erosion depressions were delineated as favorable areas, and five verification boreholes were deployed. Three of these boreholes confirmed that the mineralized bodies were industrial. Compared with traditional methods, the exploration cost was reduced by 40%, the efficiency was increased by 2 times, and the area of ​​environmental disturbance was reduced by 80%.

[0087] Referring to Table 1 below, this invention is compared with other methods: Table 1

[0088] In summary, this invention aims to overcome the shortcomings of existing technologies and provide a low-cost, high-efficiency, and environmentally friendly exploration method for rapid identification and resource evaluation of shallow marine heavy placer deposits. The technical content of this invention includes: exploration scheme design; passive source microseismic data acquisition; drilling data acquisition; core geochemical data acquisition; HVSR processing of passive source microseismic data; and computer discrimination of the above three types of data. External data includes: HVSR data, borehole physical properties and stratigraphic data, and core geochemical data. This invention can solve key problems in HVSR technology such as water noise interference and seabed coupling of underwater seismometers, and can thus be deeply integrated with geological exploration standard procedures. The mineralization zone delineated by the method of this invention has a high degree of agreement with the borehole verification results, significantly better than traditional methods (60%); the bedrock surface inversion error is <10%, and the paleotopographic map resolution meets the requirements for identifying mineralized structures, providing an efficient technical solution for rapid exploration of shallow marine heavy placer deposits.

[0089] Compared with the prior art, the embodiments of the present invention achieve at least the following beneficial effects: Unique signal processing and separation algorithms: A "water resonance and sediment layer resonance spectrum separation technique" was proposed for the marine environment. This is a core technological breakthrough for accurate interpretation of marine HVSR, effectively solving the problem of severe interference of marine-specific noise on target signals.

[0090] An integrated exploration workflow: A complete and closed-loop technical process has been creatively constructed, encompassing "OBS grid deployment → passive data acquisition → spectral separation and processing → bedrock mapping → mineral evaluation." This process closely integrates geophysical methods with mineral exploration objectives, forming a proprietary technical system.

[0091] Direct application of exploration models: The results of HVSR (bedrock paleotopography) are directly applied to the "basement ore-controlling model", realizing an efficient and direct conversion from geophysical parameters to mineral resource evaluation, which greatly improves the targeting and success rate of exploration.

[0092] like Figure 9 As shown, this embodiment of the invention also provides a shallow-sea heavy mineral placer resource exploration device 900, which can implement the above-mentioned method. The device may include: The first module 910 is used to acquire preliminary research data of the target shallow sea area, predict exploration plans based on the preliminary research data, and deploy seabed seismometers in the target shallow sea area based on the exploration plans. The second module 920 is used to collect passive source micro-motion data using a seabed seismograph and extract raw data from the passive source micro-motion data based on a preset data quality standard. The third module 930 is used to construct horizontal and vertical spectral ratio curves based on the original data, and to perform spectral separation on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. Module 4, 940, is used to construct paleotopographic maps based on low-frequency curves and through bedrock surface depth inversion. Module 5, 950, is used to obtain a regional geological model of the target shallow sea area. The paleotopographic map is overlaid with the regional geological model to obtain the assessment results of the heavy sand mineral resource potential.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. 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. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., 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 via bus 1005.

[0097] 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.

[0098] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0099] 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.

[0100] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0101] 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.

[0102] 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.

[0103] The present invention provides a method, apparatus, electronic device, storage medium, and program product for exploring shallow-sea heavy mineral placer resources. This method involves acquiring preliminary research data of a target shallow-sea area, predicting an exploration plan based on the data, and deploying seafloor seismometers in the target area based on the plan. The seafloor seismometers collect passive-source micromotion data, extracting raw data from this data based on preset data quality standards. Horizontal and vertical spectral ratio curves are constructed based on the raw data, and spectral separation is performed on these curves to obtain low-frequency curves of the bedrock resonance peak. Based on these low-frequency curves, a paleotopographic map is constructed by inverting the bedrock surface depth. A regional geological model of the target shallow-sea area is obtained, and the paleotopographic map is overlaid with the regional geological model to obtain an assessment of the heavy placer resource potential. The present invention effectively overcomes the shortcomings of existing technologies by deploying seafloor seismometers to collect passive-source micromotion data, using the spectral ratio curves to invert the bedrock surface depth, and constructing a paleotopographic map. The beneficial effects of this invention are as follows: it utilizes natural field sources, is not hindered by active source construction obstacles or human noise interference, and is adaptable to complex coastal and shallow marine environments; it non-destructively detects deep interfaces through bedrock resonance characteristics, has strong penetration capabilities, can reveal buried paleogeographic units, and indirectly indicate areas rich in heavy mineral deposits; by combining regional geological models for overlay analysis, it can achieve the transformation from "directly detecting sand bodies" to "identifying ore-controlling geomorphologies," thereby effectively improving the accuracy of exploration direction and resource potential assessment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 exploring shallow-sea heavy mineral placer resources, characterized in that, The method includes the following steps: Acquire preliminary research data of the target shallow sea area, predict exploration plans based on the preliminary research data, and deploy seabed seismometers in the target shallow sea area based on the exploration plans. The passive source micro-motion data is collected using the seabed seismograph, and raw data is extracted from the passive source micro-motion data based on a preset data quality standard. Based on the original data, horizontal and vertical spectral ratio curves are constructed, and spectral separation is performed on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. Based on the low-frequency curve, a paleotopographic map is constructed by inverting the bedrock surface depth. A regional geological model of the target shallow sea area is obtained, and the paleotopographic map is overlaid with the regional geological model to obtain the assessment results of the heavy sand mineral resource potential.

2. The method according to claim 1, characterized in that, The exploration plan includes the density of the seabed seismograph network. The step of predicting the exploration plan based on the preliminary research data includes the following steps: Based on the preliminary research data, a sedimentary-metallogenic model of the target shallow sea area was quantified, and the minimum horizontal scale of the target sand body was determined through the sedimentary-metallogenic model. The minimum horizontal scale is calibrated using the a priori scale of the mineralization anomaly zone as the initial predicted value for the measurement network density; The lower limit of the size of the favorable mineralization body is used as a conservative value to constrain the initial predicted value, and the density of the survey network is determined. Wherein, the lower limit of the scale of the favorable mineralization body is determined by the preliminary research data, the line spacing of the surveying network density is less than or equal to a first proportion of the minimum horizontal scale, and the point spacing of the surveying network density is less than or equal to a second proportion of the line spacing.

3. The method according to claim 2, characterized in that, The method of predicting exploration plans based on the preliminary research data also includes the following steps: The hydrodynamic intensity of each area in the target shallow sea region was determined based on the aforementioned preliminary study data. The region with hydrodynamic intensity greater than the first threshold is marked as a strong hydrodynamic region, and the region with hydrodynamic intensity less than the second threshold is marked as a weak hydrodynamic region. The density of the measuring network in the strong hydrodynamic zone is increased, and the density of the measuring network in the weak hydrodynamic zone is decreased.

4. The method according to claim 1, characterized in that, The exploration plan includes the density of the seabed seismograph network in each area of ​​the target shallow sea area. The deployment of seabed seismographs in the target shallow sea area based on the exploration plan includes the following steps: Based on the density of the seismic network, determine all the deployment points of the seabed seismograph in the target shallow sea area; The seabed seismograph is deployed to the deployment point using a remote control device; wherein the seabed seismograph is deployed using a gravity coupling base and a seabed anchoring device. Waveforms are collected and recorded by the deployed seabed seismometers, and judgment and verification features are extracted from the recorded waveforms; The judgment and verification features include waveform integrity and stability, signal response sensitivity, spectral response rationality, and positioning stability. Based on the aforementioned judgment and verification characteristics, the coupling qualification of each seabed seismograph with seabed sediments is quantified using preset survey specifications, thereby determining the coupling qualification rate of all seabed seismographs. When the coupling pass rate is less than the third threshold, the seabed seismometers with unqualified coupling are re-deployed, and the step of collecting and recording waveforms on the deployed seabed seismometers is returned to be executed until the coupling pass rate is greater than or equal to the third threshold.

5. The method according to claim 1, characterized in that, Each data point in the passive source micro-motion data is marked with a timestamp. The extraction of raw data from the passive source micro-motion data based on a preset data quality standard includes the following steps: Based on the first root mean square value of the signal amplitude in the target frequency band and the second root mean square value of the amplitude in the background noise frequency band, the signal-to-noise ratio corresponding to the target frequency band is obtained according to the ratio of the first root mean square value to the second root mean square value. The data continuity of the passive source micro-motion data is obtained by quantizing the timestamp of each of the collected data. Data integrity is quantified based on the component types and corresponding data formats contained in the passive source micro-motion data; The passive source micro-motion data is preprocessed, and the noise interference control result is obtained by quantification based on the result of the data preprocessing. Based on the signal-to-noise ratio, the data continuity, the data integrity, and the noise interference control results, effective data segments in the passive source micro-motion data are screened using preset definition criteria. When the proportion of the effective data segment in the passive source micro-motion data is greater than or equal to the fourth threshold, the passive source micro-motion data within the preset acquisition period is used as the original data.

6. The method according to claim 1, characterized in that, The original data contains components of a first horizontal component, a second horizontal component, and a vertical component. The process of constructing horizontal and vertical spectral ratio curves based on the original data, and then performing spectral separation on these curves to obtain the low-frequency curves of the bedrock resonance peak, includes the following steps: The original data is preprocessed to obtain preprocessed data; wherein, the data preprocessing includes mean averaging, detrending, denoising based on inverse short-time average or long-time average ratio algorithm, and spectral smoothing. The preprocessed data is subjected to a Fast Fourier Transform, and the horizontal and vertical spectral ratio curve is generated by comparing the composite value of the horizontal component vector with the spectral ratio of the vertical component; wherein, the composite value of the horizontal component vector is obtained based on the square root of the sum of the squares of the first horizontal component and the second horizontal component. Wavelet packet decomposition is performed on the horizontal and vertical spectral ratio curves to obtain the energy distribution results of each frequency band and the energy proportion of each frequency band. Based on the energy distribution results, spectrum segments of a preset high-frequency band and a preset low-frequency band are obtained by bandpass filtering. Based on the energy percentage of each spectral segment, the first frequency band corresponding to the water resonance peak and the second frequency band corresponding to the bedrock resonance peak are obtained by combining energy threshold determination and spectral morphology verification. By using the first frequency band as the separated frequency band and the second frequency band as the retained frequency band, the low-frequency curve of the bedrock resonance peak is obtained.

7. The method according to claim 1, characterized in that, The process of constructing a paleotopographic map based on the low-frequency curve through bedrock surface depth inversion includes the following steps: The frequency value corresponding to the main peak is extracted from the low-frequency curve as the resonance peak frequency of the sedimentary layer-bedrock interface; Based on the pre-set verification boreholes in the target shallow sea area, geological logging and sonic logging data were collected; Based on the geological logging, the depth of the top surface of the bedrock is determined by core logging. Based on the sonic logging data, the average shear wave velocity of the sedimentary layer in the exploration area was calibrated using the linear regression method. Based on the resonant peak frequency, the depth of the top surface of the bedrock, and the average shear wave velocity of the sedimentary layer, the target coefficient is derived using empirical formulas. Based on the target coefficients and the empirical formula, the bedrock surface burial depth inversion data is obtained; Based on the bedrock surface burial depth inversion data, spatial interpolation is performed using the Kriging interpolation method to generate the paleotopographic map of the seabed bedrock surface.

8. The method according to claim 1, characterized in that, The process of overlaying the paleotopographic map with the regional geological model to obtain an assessment of the potential of heavy mineral resources includes the following steps: The paleotopographic map and the regional geological model are imported into a preset topographic analysis tool, and preliminary topographic analysis data are obtained through digital topographic analysis. In response to the correction command of the target object, the preliminary terrain analysis data is corrected for local deviations to obtain the target terrain analysis data; Based on the target terrain analysis data, favorable mineralization structures are delineated by using a preset terrain screening threshold; wherein, the favorable mineralization structures include ancient river channels, erosion depressions, and ancient beaches; Heavy minerals were identified and graded on core samples within the favorable mineral-forming structure to obtain the assessment results of the heavy mineral resource potential; wherein the core samples were obtained based on verification boreholes deployed in the area corresponding to the favorable mineral-forming structure.

9. A device for exploring shallow-sea heavy mineral placer resources, characterized in that, The device includes: The first module is used to acquire preliminary research data of the target shallow sea area, predict exploration plans based on the preliminary research data, and deploy seabed seismometers in the target shallow sea area based on the exploration plans. The second module is used to collect passive source micro-motion data using the seabed seismograph and extract raw data from the passive source micro-motion data based on a preset data quality standard. The third module is used to construct horizontal and vertical spectral ratio curves based on the original data, and to perform spectral separation on the horizontal and vertical spectral ratio curves to obtain the low-frequency curve of the bedrock resonance peak. The fourth module is used to construct paleotopographic maps based on the low-frequency curves through bedrock surface depth inversion; The fifth module is used to obtain a regional geological model of the target shallow sea area, and to overlay the paleotopographic map with the regional geological model to obtain an assessment result of the heavy sand mineral resource potential.

10. 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 8.

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