Geological exploration data intelligent processing method and device and storage medium

Abstract

The invention relates to the technical field of geological exploration, in particular to an intelligent geological exploration data processing method and device and a storage medium, and the method comprises the steps: collecting geological vibration data and hydrothermal nozzle parameters of a target area in real time, collecting sediment physical property data, building a pressure compensation model and a temperature correction model, and correcting the geological vibration data to obtain a corrected geological exploration result; the method comprises the following steps: constructing a first feature curved surface of a target area, further constructing a second feature value of the target area, performing feature enhancement on the first feature curved surface by using the second feature value, generating geologic structure features of the target area, and constructing third dimension features of the geologic structure features of the target area so as to update the geologic structure features of the target area, and constructing an alarm model based on the geological structure features of the target area, and outputting an alarm result. According to the invention, the efficiency of geological exploration data processing is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a method, device and storage medium for intelligent processing of geological exploration data. Background Art

[0002] Traditional geological exploration systems are mostly designed for land or shallow sea environments, and their data processing methods have significant limitations in extreme deep sea environments. For example, conventional sensors are prone to signal drift in high-pressure environments, and data such as deep sea-specific hydrothermal vent activities and seafloor seismic wave propagation characteristics have not been fully integrated and analyzed.

[0003] Chinese Patent Publication No. CN113608271A discloses a method and system for processing geological exploration data. Obtain induced polarization instrument parameters, raw data and raw data files; calculate the corresponding K value and apparent resistivity of each measuring point; update the raw data file to obtain an updated raw data file; summarize all the raw data to obtain a raw data summary file; perform quality inspection on the raw data according to the quality inspection data and generate a quality inspection data summary file; match the quality inspection data in the quality inspection data summary file with the raw data in the raw data summary file and generate a quality inspection summary file; add plane coordinates to obtain a corresponding file with coordinates. It can be seen that the invention does not analyze geological exploration under special environments. Although it realizes automatic data processing, the micro-electric instrument parameters and apparent resistivity calculations it relies on cannot be directly applied in deep-sea scenarios, and the dynamic environmental adaptability is not considered. There is a problem of low efficiency in processing geological exploration data. Summary of the invention

[0004] The object of the present invention is to provide a method, device and storage medium for intelligent processing of geological exploration data to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, according to one aspect of the present application, a method for intelligent processing of geological exploration data is provided, comprising: Based on the corrected geological vibration data, a first characteristic surface of the target area is constructed, and then a second characteristic value of the target area is constructed by combining the hydrothermal vent parameters and the sediment physical property data, and the first characteristic surface is enhanced by the second characteristic value, so as to generate the geological structure characteristics of the target area; Acquire regional physical parameters of the target area, and construct a third dimension feature of the geological structure characteristics of the target area based on the acquisition results and the hydrothermal vent parameters to update the geological structure characteristics of the target area; An alarm model is constructed based on the geological structure characteristics of the target area, and the alarm results are output.

[0006] Optionally, obtaining a seafloor topographic map of the target area; Establishing the first characteristic surface of the target area based on the seabed topographic map and the corrected geological vibration data; The implementation process of establishing the first characteristic surface of the target area is: Construct the vibration spectrum entropy H(x,y) of each plane coordinate point in the seabed topographic map of the target area, and set ; In the formula, p[fi(x,y)] represents the power spectrum density of the i-th frequency at the point with coordinates (x,y) in the target area, and fi(x,y) represents the frequency value of the i-th frequency; The vibration spectrum entropy of each plane coordinate point in the seabed topographic map of the target area is combined into the first characteristic surface of the target area.

[0007] Optionally, constructing a second characteristic value of the target area based on the fusion of hydrothermal vent parameters and sediment physical property data; The hydrothermal activity index I is constructed based on the temperature gradient △T of the hydrothermal vent, the fluid flow rate and hydrogen sulfide concentration in the target area. ; Where v is the fluid velocity in the target area, and ρ(H2S) is the hydrogen sulfide concentration; The sedimentation state index G is constructed based on the surface porosity φ, surface sand density ρ and shear wave velocity Vs of the target area. ; In the formula, k1 is the flow velocity weight, k2 is the soil density weight, k3 is the wave velocity weight, φmax, ρmax, and Vsmax are the maximum surface porosity, the maximum surface sand density, and the maximum shear wave velocity, respectively; The value of [0.4×I / 100+G×0.6] is taken as the second eigenvalue of the target area.

[0008] Optionally, a result of multiplying the second eigenvalue by the vibration spectrum entropy of each plane coordinate point in the first characteristic surface is used as the first characteristic surface after feature enhancement; A feature point map having a value greater than a preset entropy value is extracted from the first feature surface after feature enhancement, and the feature point map is used as the geological structure feature of the target area; the feature point map includes a plurality of feature plane coordinate points.

[0009] Optionally, obtaining regional physical parameters of the target area; A generation process of constructing a third dimensional feature based on the acquired regional physical parameters and calorific value nozzle parameters of the target area, and updating the geological structure features of the target area according to the third dimensional feature; When c1×μr+c2×σ is less than or equal to I / 100, the third dimension feature is not constructed; when c1×μr+c2×σ is greater than I / 100, c1×μr+c2×σ is set as the third dimension feature; When there is a third dimensional feature, the sum of the third dimensional feature and the second eigenvalue is used as the updated second eigenvalue, and the generation process of the geological structure feature of the target area is updated based on the updated second eigenvalue; Among them, μr is the relative magnetic permeability of the target area, σ is the relative electrical conductivity of the geology and seawater in the target area, c1 is the magnetic field weight, c2 is the conductivity weight, and c1+c2=1.

[0010] Optionally, the complexity of each plane coordinate point of the target area is constructed, and the geological complexity state of the target area is obtained based on the integration of the complexity of each plane coordinate point, and then the updating process of the geological structure characteristics of the target area is iterated; The complexity mean of each plane coordinate point is calculated, and the complexity mean of each plane coordinate point is used as the terrain state index γ of the target area. The geological complexity state of the target area is judged by setting the terrain complexity threshold Y: if γ is less than or equal to Y, the update process of the geological structure characteristics of the target area is not iterated; if γ is greater than Y, the terrain state index γ is used as the fourth dimension feature to iterate the third dimension feature. The iterative process is as follows: When c1×μr+c2×σ is greater than I / 100×η, [c1×μr+c2×σ] / γ is set as a highly active third dimension feature; When c1×μr+c2×σ is less than or equal to I / 100×η and c1×μr+c2×σ is greater than I / 100, [c1×μr+c2×σ] is set as a low-activity third dimension feature; Wherein, η is the preset offset ratio.

[0011] Optionally, the process of building an alarm model is as follows: When the value of the j-th feature plane coordinate point in the feature point map is less than 150% of the preset entropy value, the feature plane coordinate point is marked as a mineral resource point; When the value of the j-th characteristic plane coordinate point in the characteristic point map is greater than or equal to 150% of the preset entropy value, the characteristic plane coordinate point is marked as a geologically active alarm point.

[0012] Optionally, it also includes: real-time collection of geological vibration data and hydrothermal vent parameters of the target area, and also collection of sediment physical property data; Obtain the underwater pressure and actual detection water depth during geological vibration data collection, and construct a pressure compensation model and a temperature correction model, and then correct the geological vibration data based on the pressure compensation model and the temperature correction model; The nonlinear attenuation coefficient α is set according to the underwater pressure and water density, and then the compensation range is set according to the actual detected water depth. When the nonlinear attenuation coefficient α meets the set compensation range, the nonlinear attenuation coefficient α is used as the compensation coefficient of the pressure compensation model; A signal drift correction coefficient β is set based on the real-time collected hydrothermal vent parameters, and the signal drift correction coefficient β is used as the output result of the temperature correction model; the process of setting the signal drift correction coefficient β is the temperature correction model; The geological vibration data is corrected in real time based on the pressure compensation model and the temperature correction model; the above-mentioned real-time correction process of the geological vibration data includes the correction of the amplitude of the low-frequency seismic wave, the correction of the recording delay, and the correction of the amplitude attenuation rate.

[0013] According to another aspect of the present application, there is provided a device for intelligently processing geological exploration data, comprising: The data acquisition module is used to: collect geological vibration data and hydrothermal vent parameters of the target area in real time, and also collect sediment physical property data; The data correction module is used to obtain the underwater pressure and the actual detection water depth during the geological vibration data collection, and to construct a pressure compensation model and a temperature correction model, and then to correct the geological vibration data based on the pressure compensation model and the temperature correction model; A feature extraction module is used to: construct a first characteristic surface of the target area based on the corrected geological vibration data, and then construct a second characteristic value of the target area in combination with the hydrothermal vent parameters and the sediment physical property data, and enhance the first characteristic surface with the second characteristic value, so as to generate the geological structure characteristics of the target area; A geological update module, used to: obtain regional physical parameters of the target area, and construct a third dimension feature of the geological structure characteristics of the target area based on the obtained results and the hydrothermal vent parameters to update the geological structure characteristics of the target area; The alarm model building module is used to: build an alarm model based on the geological structure characteristics of the target area and output the alarm results.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program is used to control the device where the computer-readable storage medium is located to execute the intelligent processing method for geological exploration data during operation.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: through pressure compensation and temperature correction models, the problem of sensor data distortion caused by high pressure and high temperature difference in the deep sea is effectively solved, filling the technical gap of traditional land / shallow sea methods; integrating vibration data, hydrothermal parameters, sediment properties and regional physical parameters, constructing multi-level features, and comprehensively describing the geological structure; based on the complex geological state and preset thresholds, dynamic adjustment of model parameters and feature update are realized to improve analysis accuracy and real-time performance; mineral resource points and geologically active areas are quickly marked through alarm models, taking into account economic benefits and operational safety, and reducing exploration risks; from data collection, correction to feature extraction, alarm output, the entire process is automated, reducing manual intervention and significantly improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the flow of the intelligent processing method of geological exploration data in this embodiment.

[0018] Figure 2 Schematic diagram of the flow of the geological vibration data correction method of this embodiment.

[0019] Figure 3 Schematic diagram of the flow of the geological structure feature generation method of this embodiment.

[0020] Figure 4 Schematic diagram of the process of the third dimension feature analysis method of this embodiment.

[0021] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium according to this embodiment.

[0022] Figure 6 This is a schematic diagram of the structure of the intelligent processing device for geological exploration data in this embodiment. DETAILED DESCRIPTION

[0023] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0024] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0025] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0026] Specifically, the intelligent processing method of geological exploration data described in this embodiment is applied to the intelligent processing of exploration data in deep-sea geological environment. The deep-sea geological environment described in this embodiment is specifically an area with frequent deep-sea geological activities, and there are geological activities and hydrothermal vents in this environment. In this environment, the existing technology lacks a processing solution for geological exploration data in this extreme environment and is unable to complete the task requirements of geological exploration.

[0027] Based on the above application environment, please refer to Figure 1 As shown, it is a schematic diagram of the flow of the intelligent processing method of geological exploration data described in this embodiment, including: Step S101, real-time collection of geological vibration data and hydrothermal vent parameters of the target area, and also collection of sediment physical property data; the geological vibration data includes low-frequency seismic wave amplitude, low-frequency seismic wave frequency, recording delay, and amplitude attenuation rate; the hydrothermal vent parameters include the temperature gradient of the hydrothermal vent, the material thermal expansion coefficient of the in-situ sensor, the fluid flow rate and hydrogen sulfide concentration in the target area; the sediment physical property data includes surface porosity, surface sand density and shear wave velocity; by simultaneously collecting geological vibration data, hydrothermal vent parameters and sediment physical property data, the comprehensiveness of the data is ensured, and multi-dimensional input is provided for subsequent analysis; it is stipulated that the collection interval of sediment data and vibration data shall not exceed 1 month to avoid data failure problems caused by dynamic changes in the deep-sea environment.

[0028] In the present application, the real-time acquisition frequency of the geological vibration data and hydrothermal vent parameters of the target area is simultaneously acquired at a fixed frequency and time; the acquisition time of the sediment physical property data and the time interval between the acquisition time of the geological vibration data and hydrothermal vent parameters of the target area should not exceed 1 month to ensure the timeliness of the data in the target area with frequent activities.

[0029] Exemplarily, the geological vibration data can be collected by a distributed hydrophone array; the hydrothermal vent parameters can be collected in real time by an in-situ sensor; and the sediment physical property data can be collected by a sampler.

[0030] The geological exploration data intelligent processing system described in this embodiment also includes: Step S102, obtaining the underwater pressure and the actual detected water depth when collecting geological vibration data, and constructing a pressure compensation model and a temperature correction model, and then correcting the geological vibration data based on the pressure compensation model and the temperature correction model.

[0031] For example, the correction of geological vibration data is realized to facilitate the subsequent geological analysis of the target area. The specific analysis process in the above step S102 is as follows: Figure 2 The geological vibration data correction method shown comprises: Step S201, setting a nonlinear attenuation coefficient α according to underwater pressure and water density, and then setting a compensation range according to the actual detected water depth. When the nonlinear attenuation coefficient α meets the set compensation range, the nonlinear attenuation coefficient α is used as the compensation coefficient of the pressure compensation model.

[0032] Specifically, the process of setting the nonlinear attenuation coefficient α is: taking the inverse of the ratio of the underwater pressure to the reference pressure as the logarithm of an exponential function with the natural logarithm as the base, and taking the value of the exponential function as the nonlinear attenuation coefficient α.

[0033] Optionally, the compensation range is set in combination with the actual detected water depth. In an exemplary embodiment, the compensation range is set as follows: the range of nonlinear attenuation coefficient α greater than or equal to ln (actual detected water depth) is used as the compensation range, and the range of nonlinear attenuation coefficient α less than ln (actual detected water depth) is used as the non-compensation range; the nonlinear attenuation coefficient α is set based on the underwater pressure and the detected water depth to correct the low-frequency seismic wave amplitude, recording delay, etc., so as to reduce the interference of the high-pressure environment on the sensor data.

[0034] Please continue reading Figure 2 As shown, the geological vibration data correction method also includes: Step S202, setting a signal drift correction coefficient β based on the real-time collected hydrothermal vent parameters, and using the signal drift correction coefficient β as the output result of the temperature correction model; the process of setting the signal drift correction coefficient β is the temperature correction model.

[0035] Specifically, the setting process of the signal drift correction coefficient β is as follows: the product of the temperature gradient △T of the hydrothermal nozzle and the material thermal expansion coefficient of the in-situ sensor is used as the main part of the signal drift correction coefficient β, and 1 plus the corrected main part is used as the signal drift correction coefficient β; the signal drift correction coefficient β is dynamically adjusted through the temperature gradient of the hydrothermal nozzle to solve the signal drift problem of the in-situ sensor caused by the thermal expansion of the material and improve data reliability.

[0036] In order to make the temperature correction model more accurate in a complex environment, in an optional implementation, when implementing the above step S202, it is necessary to set a preset condition, and when the water flow data in the target area meets the preset condition, add the main element of the signal drift correction coefficient; the setting process of the preset condition is: take the product of the standard fluid flow rate and the chemical calorific value component concentration as the judgment threshold of the preset condition, when the product of the fluid flow rate and the chemical calorific value component concentration in the target area collected in real time is greater than or equal to the judgment threshold of the preset condition, it is judged to meet the preset condition, and the proportion of exceeding the judgment threshold of the preset condition is used as the main added element of the signal drift correction coefficient β, and the addition operation is addition.

[0037] It is worth noting that the chemical calorific value component concentration is the concentration of chemical components that affect the water temperature in the target area, such as H2S, Fe 3+ etc.; the concentration of the chemical calorific value component can be a single component or a mixture of multiple components; the standard fluid flow rate is the average fluid flow rate of the target area during a continuous monitoring process of more than 1 month.

[0038] Step S203, performing real-time correction on the geological vibration data based on the pressure compensation model and the temperature correction model; the real-time correction process of the geological vibration data includes correction of the amplitude of the low-frequency seismic wave, correction of the recording delay, and correction of the amplitude attenuation rate.

[0039] Specifically, the process of real-time correction of geological vibration data in step S203 is to correct the corresponding data by mathematical formula: The product of the compensation coefficient of the pressure compensation model and the low-frequency seismic wave amplitude is taken as the corrected low-frequency seismic wave amplitude; The product of the recording delay and the signal drift correction coefficient β is taken as the corrected recording delay; The product of the amplitude attenuation rate and the signal drift correction coefficient β is taken as the corrected amplitude attenuation rate.

[0040] The geological exploration data intelligent processing system described in this embodiment also includes: Step S103, constructing a first characteristic surface of the target area based on the corrected geological vibration data, and then generating geological structural features of the target area by combining the hydrothermal vent parameters and the first characteristic surface.

[0041] In order to analyze the geological structure characteristics of the target area, the specific analysis process of step S103 is as follows: Figure 3 The method for generating geological structure characteristics shown in the figure comprises: Step S301, obtaining a seabed topographic map of the target area; the seabed topographic map is obtained by using a multi-beam sonar system, a laser altimeter and other equipment; the seabed topographic map is a three-dimensional spatial image within the first quadrant.

[0042] Step S302, establishing a first characteristic surface of the target area based on the seabed topography map and the corrected geological vibration data; the first characteristic surface is a spatial surface composed of vibration characteristic values ​​of the target area established based on the geological vibration data, and the first characteristic surface is a spatial surface projected on the seabed topography map of the target area, which is another layer of non-solid, mathematical surface established in three-dimensional space.

[0043] Specifically, the implementation process of establishing the first characteristic surface of the target area is: Construct the vibration spectrum entropy H(x,y) of each plane coordinate point in the seabed topographic map of the target area, and set ; In the formula, p[fi(x,y)] represents the power spectrum density of the i-th frequency at the point with coordinates (x,y) in the target area, and fi(x,y) represents the frequency value of the i-th frequency; The vibration spectrum entropy of each plane coordinate point in the seabed topographic map of the target area is combined into the first characteristic surface of the target area; the vibration spectrum entropy H(x,y) is used to quantify the vibration characteristics of each point in the seabed topographic map to form a spatial surface, which intuitively reflects the distribution law of geological vibration.

[0044] In the present application, taking the point (x, y) as an example, the vibration spectrum entropy of all points in the seabed topographic map of the target area is constructed; at the same time, the points in the seabed topographic map of the target area are set as a plane square matrix, and the side length setting is set by the user, and the side length setting is set based on the user's accuracy requirements; the plane coordinate point is the coordinate point in the area formed by the projection of the seabed topographic map on the horizontal plane.

[0045] It is worth noting that the vibration spectrum entropy of each plane coordinate point described in the present application is a numerical analysis of integer points, and the plane coordinate points of non-integer points are smoothed by data tools in the present application. The processing process is a technical solution fully disclosed in the prior art and is not specifically limited in this embodiment.

[0046] Step S303, constructing a second characteristic value of the target area based on the fusion of hydrothermal vent parameters and sediment physical property data; the specific interpretation of the second characteristic value is the surface composition characteristic value of the target area established based on the sediment physical property data and hydrothermal vent parameters.

[0047] Specifically, the implementation process of constructing the second characteristic value of the target area in step S303 is: The hydrothermal activity index I is constructed based on the temperature gradient △T of the hydrothermal vent, the fluid flow rate and hydrogen sulfide concentration in the target area. ; Where v is the fluid velocity in the target area, and ρ(H2S) is the hydrogen sulfide concentration. The purpose of constructing a logarithmic function based on H2S concentration is to balance the contribution of high concentration and low concentration, avoid numerical explosion, and enhance low concentration sensitivity. The sedimentation state index G is constructed based on the surface porosity φ, surface sand density ρ and shear wave velocity Vs of the target area. ; In the formula, k1 is the flow velocity weight, k2 is the soil density weight, k3 is the wave velocity weight, φmax, ρmax, and Vsmax are the maximum surface porosity, the maximum surface sand density, and the maximum shear wave velocity, respectively; The value of [0.4×I / 100+G×0.6] is taken as the second characteristic value of the target area; the hydrothermal activity index I and the sedimentary state index G are combined to balance the surface and deep geological characteristics and enhance the comprehensiveness of the characteristic expression.

[0048] It is understandable that the reason why H2S is used as the hydrothermal activity index in this application is that H2S is a key marker of hydrothermal vent activity, and its concentration is directly related to the formation of metal sulfide deposits (such as copper and zinc mines). High H2S usually indicates potential mineral-rich areas; it should be noted that the unit of △T is ℃ / m, the unit of v is m / s, and the unit of ρ(H2S) is mg / L; at the same time, the hydrothermal activity index I is only used as a mathematical value and has no physical meaning; at the same time, the specific values ​​of the flow velocity weight k1, the soil density weight k2 and the wave velocity weight k3 are determined by the statistical method of regression analysis in this application. The optimal value of the flow velocity weight k1 is 0.17, the optimal value of the soil density weight k2 is 0.53, and the optimal value of the wave velocity weight k3 is 0.3; the maximum surface porosity, the maximum surface sand density, and the maximum shear wave velocity are set by the user's own interactive input, which reflects the corresponding data thresholds acceptable to the user in actual situations.

[0049] Step S304, based on the first characteristic surface of the target area, the first characteristic surface is enhanced by using the second characteristic value, thereby generating the geological structure characteristics of the target area; the process of enhancing the first characteristic surface by using the second characteristic value is as follows.

[0050] Specifically, the process of generating geological structural characteristics of the target area includes: The result of multiplying the second eigenvalue by the vibration spectrum entropy of each plane coordinate point in the first characteristic surface is used as the first characteristic surface after feature enhancement; A feature point map greater than a preset entropy value is extracted by using the first feature surface after feature enhancement, and the feature point map is used as the geological structure feature of the target area; the feature point map contains multiple feature plane coordinate points; the first feature surface is enhanced by the second feature value, and a significant feature point map is extracted to highlight the geological abnormal area and reduce redundant data interference.

[0051] It can be understood that the process of extracting feature points greater than the preset entropy value using the first feature surface after feature enhancement is to compare the value of each plane coordinate point in the first feature surface after feature enhancement with the preset entropy value, and use the plane coordinate points greater than the preset entropy value as feature plane coordinate points, and use all feature points in the target area as feature point map; the value of each plane coordinate point is "the result of multiplying the second eigenvalue by the vibration spectrum entropy of each plane coordinate point in the first feature surface".

[0052] Specifically, this embodiment does not impose any specific limitation on the value of the preset entropy value, and those skilled in the art can set it freely as long as the value requirements are met. In this embodiment, the optimal value of the preset entropy value can be 110% of the mean value of the historical vibration spectrum entropy. Those skilled in the art can also construct the optimal value through a mathematical model.

[0053] The geological survey data intelligent processing system described in this embodiment also includes: Step S104, obtaining regional physical parameters of the target area, and constructing the third dimension features of the geological structure characteristics of the target area based on the obtained results and the hydrothermal vent parameters to update the geological structure characteristics of the target area. The regional physical parameters include the relative magnetic permeability of the target area and the relative electrical conductivity of the geology of the target area and the seawater; the third dimension features are features that reflect the deep geological structure of the target area.

[0054] See also Figure 4 As shown, it is a schematic diagram of the process of the third dimension feature analysis method of this embodiment, including: Step S401, obtaining regional physical parameters of the target area; the regional physical parameters of the target area are obtained in the same manner and at the same time as the hydrothermal vent parameters and the geological vibration data; Step S402, constructing a third dimensional feature based on the acquired regional physical parameters and calorific value nozzle parameters of the target area, and updating the generation process of the geological structure feature of the target area according to the third dimensional feature.

[0055] Specifically, the process of constructing the third dimension features and updating the geological structure features of the target area is as follows: When c1×μr+c2×σ is less than or equal to I / 100, the third dimension feature is not constructed; when c1×μr+c2×σ is greater than I / 100, c1×μr+c2×σ is set as the third dimension feature; When there is a third dimensional feature, the sum of the third dimensional feature and the second eigenvalue is used as the updated second eigenvalue, and the generation process of the geological structure feature of the target area is updated based on the updated second eigenvalue; Among them, μr is the relative magnetic permeability of the target area, σ is the relative electrical conductivity of the geology and seawater in the target area, c1 is the magnetic field weight, c2 is the electrical conductivity weight, and c1+c2=1. The deep geological characteristics are constructed through regional physical parameters (magnetic permeability, electrical conductivity), and the second eigenvalue is dynamically updated to improve the adaptability of the model to complex geological structures.

[0056] Please continue reading Figure 4 As shown, the third dimension feature analysis method also includes: Step S403, construct the complexity of each plane coordinate point of the target area, and obtain the geological complexity state of the target area based on the complexity integration of each plane coordinate point, and then iterate the updating process of the geological structure characteristics of the target area; the geological complexity state is specifically the terrain complexity of the target area; the step S403 is a step based on the operation when the third dimensional feature exists.

[0057] Specifically, the calculation process of the complexity of each plane coordinate point of the target area is as follows: taking plane coordinate point a as an example, defining the z-axis coordinates of all coordinate points surrounding coordinate point a as a complex data set of coordinate point a, and calculating the coefficient of variation of the complex data set of coordinate point a, and taking the coefficient of variation of the complex data set of coordinate point a as the complexity of coordinate point a; The process of updating the geological structural characteristics of the iterative target area is as follows: The complexity mean of each plane coordinate point is calculated, and the complexity mean of each plane coordinate point is used as the terrain state index γ of the target area. The geological complexity state of the target area is judged by setting the terrain complexity threshold Y: if γ is less than or equal to Y, the update process of the geological structure characteristics of the target area is not iterated; if γ is greater than Y, the terrain state index γ is used as the fourth dimension feature to iterate the third dimension feature. The iterative process is as follows: When c1×μr+c2×σ is greater than I / 100×η, [c1×μr+c2×σ] / γ is set as a highly active third dimension feature; When c1×μr+c2×σ is less than or equal to I / 100×η and c1×μr+c2×σ is greater than I / 100, [c1×μr+c2×σ] is set as a low-activity third dimension feature; Wherein, η is the preset offset ratio.

[0058] Specifically, in the present application, the preset value of the offset ratio η is 0.05. Those skilled in the art can freely set the preset offset ratio η as long as the value requirement of the preset offset ratio η is met; in the step S403, the feature weight of the third dimension is adjusted according to the complex geological state to realize dynamic iteration of the model parameters and improve the precision of the analysis.

[0059] Step S105, constructing an alarm model based on the geological structure characteristics of the target area, and outputting an alarm result; the process of outputting the alarm result is an existing public technology and will not be described in detail in this embodiment.

[0060] Specifically, the process of building an alarm model is as follows: When the value of the j-th feature plane coordinate point in the feature point map is less than 150% of the preset entropy value, the feature plane coordinate point is marked as a mineral resource point; When the value of the j-th characteristic plane coordinate point in the characteristic point map is greater than or equal to 150% of the preset entropy value, the characteristic plane coordinate point is marked as a geologically active alarm point.

[0061] It is worth noting that the geologically active alarm point is also a mineral resource point in the actual environment. This embodiment takes safety as the priority attribute and names it as a geologically active alarm point.

[0062] From the hardware level, this application is to realize the function of the geological exploration data intelligent processing method in a computer. This application also provides a computer-readable storage medium, please refer to Figure 5 As shown, it is arranged in an electronic device, and the electronic device includes: Processor 1 (processor), memory 2 (memory), communication interface 3 (CommunicationsInterface) and bus 4; wherein, data is transmitted between the processor 1 and memory 2, and between the memory 2 and communication interface 3 via the bus 4; the processor is used to process data in the memory and generate commands, the memory is used to store data, the communication interface is used to receive and send data, and the bus is used to realize data transmission between the processor, memory, and communication interface.

[0063] In this embodiment, the method for intelligent processing of geological exploration data can be implemented as an executable computer program. When the computer program is loaded into a processor, when the computer program is loaded into a memory and processed by the processor through a bus, one or more steps of the method for intelligent processing of geological exploration data can be executed.

[0064] Optionally, the processor described in this embodiment can be connected to the computer program through hardware burning or the like to complete the data execution process of the intelligent processing method for geological exploration data.

[0065] See also Figure 6 As shown, it is a structural schematic diagram of a geological exploration data intelligent processing device provided by this embodiment, including: The data acquisition module is used to: collect geological vibration data and hydrothermal vent parameters of the target area in real time, and also collect sediment physical property data; The data correction module is used to obtain the underwater pressure and the actual detection water depth during the geological vibration data collection, and to construct a pressure compensation model and a temperature correction model, and then to correct the geological vibration data based on the pressure compensation model and the temperature correction model; A feature extraction module is used to: construct a first characteristic surface of the target area based on the corrected geological vibration data, and then construct a second characteristic value of the target area in combination with the hydrothermal vent parameters and the sediment physical property data, and enhance the first characteristic surface with the second characteristic value, so as to generate the geological structure characteristics of the target area; A geological update module, used to: obtain regional physical parameters of the target area, and construct a third dimension feature of the geological structure characteristics of the target area based on the obtained results and the hydrothermal vent parameters to update the geological structure characteristics of the target area; The alarm model building module is used to: build an alarm model based on the geological structure characteristics of the target area and output the alarm results.

[0066] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for intelligent processing of geological exploration data, characterized in that: include: Based on the corrected geological vibration data, a first characteristic surface of the target area is constructed, and then a second characteristic value of the target area is constructed by combining the hydrothermal vent parameters and the sediment physical property data, and the first characteristic surface is enhanced by the second characteristic value, so as to generate the geological structure characteristics of the target area; Acquire regional physical parameters of the target area, and construct a third dimension feature of the geological structure characteristics of the target area based on the acquisition results and the hydrothermal vent parameters to update the geological structure characteristics of the target area; An alarm model is constructed based on the geological structure characteristics of the target area, and the alarm results are output.

2. The method for intelligent processing of geological exploration data according to claim 1, characterized in that: Obtain seafloor topographic maps of the target area; Establishing the first characteristic surface of the target area based on the seabed topographic map and the corrected geological vibration data; The implementation process of establishing the first characteristic surface of the target area is: Construct the vibration spectrum entropy H(x,y) of each plane coordinate point in the seabed topographic map of the target area, and set ; In the formula, p[fi(x,y)] represents the power spectrum density of the i-th frequency at the point with coordinates (x,y) in the target area, and fi(x,y) represents the frequency value of the i-th frequency; The vibration spectrum entropy of each plane coordinate point in the seabed topographic map of the target area is combined into the first characteristic surface of the target area.

3. The method for intelligent processing of geological exploration data according to claim 2, characterized in that: The second eigenvalue of the target area is constructed based on the fusion of hydrothermal vent parameters and sediment physical properties data; The hydrothermal activity index I is constructed based on the temperature gradient △T of the hydrothermal vent, the fluid flow rate and hydrogen sulfide concentration in the target area. ; Where v is the fluid velocity in the target area, and ρ(H2S) is the hydrogen sulfide concentration; The sedimentation state index G is constructed based on the surface porosity φ, surface sand density ρ and shear wave velocity Vs of the target area. ; In the formula, k1 is the flow velocity weight, k2 is the soil density weight, k3 is the wave velocity weight, φmax, ρmax, and Vsmax are the maximum surface porosity, the maximum surface sand density, and the maximum shear wave velocity, respectively; The value of [0.4×I / 100+G×0.6] is taken as the second eigenvalue of the target area.

4. The method for intelligent processing of geological exploration data according to claim 3, characterized in that: The result of multiplying the second eigenvalue by the vibration spectrum entropy of each plane coordinate point in the first characteristic surface is used as the first characteristic surface after feature enhancement; A feature point map having a value greater than a preset entropy value is extracted from the first feature surface after feature enhancement, and the feature point map is used as the geological structure feature of the target area; the feature point map includes a plurality of feature plane coordinate points.

5. The method for intelligent processing of geological exploration data according to claim 1, characterized in that: Obtaining regional physical parameters of the target area; A generation process of constructing a third dimensional feature based on the acquired regional physical parameters and calorific value nozzle parameters of the target area, and updating the geological structure features of the target area according to the third dimensional feature; When c1×μr+c2×σ is less than or equal to I / 100, the third dimension feature is not constructed; when c1×μr+c2×σ is greater than I / 100, c1×μr+c2×σ is set as the third dimension feature; When there is a third dimensional feature, the sum of the third dimensional feature and the second eigenvalue is used as the updated second eigenvalue, and the generation process of the geological structure feature of the target area is updated based on the updated second eigenvalue; Among them, μr is the relative magnetic permeability of the target area, σ is the relative electrical conductivity of the geology and seawater in the target area, c1 is the magnetic field weight, c2 is the conductivity weight, and c1+c2=1.

6. The method for intelligent processing of geological exploration data according to claim 5, characterized in that: Constructing the complexity of each plane coordinate point of the target area, and integrating the complexity of each plane coordinate point to obtain the geological complexity state of the target area, and then iterating the updating process of the geological structure characteristics of the target area; The complexity mean of each plane coordinate point is calculated, and the complexity mean of each plane coordinate point is used as the terrain state index γ of the target area. The geological complexity state of the target area is judged by setting the terrain complexity threshold Y: if γ is less than or equal to Y, the update process of the geological structure characteristics of the target area is not iterated; if γ is greater than Y, the terrain state index γ is used as the fourth dimension feature to iterate the third dimension feature. The iterative process is as follows: When c1×μr+c2×σ is greater than I / 100×η, [c1×μr+c2×σ] / γ is set as a highly active third dimension feature; When c1×μr+c2×σ is less than or equal to I / 100×η and c1×μr+c2×σ is greater than I / 100, [c1×μr+c2×σ] is set as a low-activity third dimension feature; Wherein, η is the preset offset ratio.

7. The method for intelligent processing of geological exploration data according to claim 6, characterized in that: The process of building an alarm model is as follows: When the value of the j-th feature plane coordinate point in the feature point map is less than 150% of the preset entropy value, the feature plane coordinate point is marked as a mineral resource point; When the value of the j-th characteristic plane coordinate point in the characteristic point map is greater than or equal to 150% of the preset entropy value, the characteristic plane coordinate point is marked as a geologically active alarm point.

8. The method for intelligent processing of geological exploration data according to claim 7, characterized in that: Also includes: Real-time collection of geological vibration data and hydrothermal vent parameters in the target area, as well as sediment physical property data; Obtain the underwater pressure and actual detection water depth during geological vibration data collection, and construct a pressure compensation model and a temperature correction model, and then correct the geological vibration data based on the pressure compensation model and the temperature correction model; The nonlinear attenuation coefficient α is set according to the underwater pressure and water density, and then the compensation range is set according to the actual detected water depth. When the nonlinear attenuation coefficient α meets the set compensation range, the nonlinear attenuation coefficient α is used as the compensation coefficient of the pressure compensation model; The signal drift correction coefficient β is set based on the real-time collected hydrothermal vent parameters, and the signal drift correction coefficient β is used as the output result of the temperature correction model; The process of setting the signal drift correction coefficient β is the temperature correction model; The geological vibration data is corrected in real time based on the pressure compensation model and the temperature correction model; the above-mentioned real-time correction process of the geological vibration data includes the correction of the amplitude of the low-frequency seismic wave, the correction of the recording delay, and the correction of the amplitude attenuation rate.

9. An intelligent processing device for geological exploration data, characterized in that: include: The data acquisition module is used to: collect geological vibration data and hydrothermal vent parameters of the target area in real time, and also collect sediment physical property data; The data correction module is used to obtain the underwater pressure and the actual detection water depth during the geological vibration data collection, and to construct a pressure compensation model and a temperature correction model, and then to correct the geological vibration data based on the pressure compensation model and the temperature correction model; A feature extraction module is used to: construct a first characteristic surface of the target area based on the corrected geological vibration data, and then construct a second characteristic value of the target area in combination with the hydrothermal vent parameters and the sediment physical property data, and enhance the first characteristic surface with the second characteristic value, so as to generate the geological structure characteristics of the target area; A geological update module, used to: obtain regional physical parameters of the target area, and construct a third dimension feature of the geological structure characteristics of the target area based on the obtained results and the hydrothermal vent parameters to update the geological structure characteristics of the target area; The alarm model building module is used to: build an alarm model based on the geological structure characteristics of the target area and output the alarm results.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent processing method for geological exploration data according to any one of claims 1 to 8 during operation.

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