Prediction system and method using gamma ray for rare earth resource amount of deep sea deposit

Abstract

To provide a system and a method, capable of not only confirming segmentation information for a rare earth rock phase of deep sea deposits by using natural gamma ray data, but predicting a rare earth resource amount.SOLUTION: A prediction system for a rare earth resource amount of deep sea deposits by using a gamma ray, includes: a data collection unit that collects gamma ray data for the deep sea deposits; a data processing unit that normalizes the gamma ray data collected by the data collection unit to process the resulting data; and a prediction modeling unit that generates a model for predicting segmentation information for a rare earth rock phase and the rare earth resource amount according to a linear regression method by using the data normalized by the data processing unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a system and method for predicting lithology or resource abundance, and more particularly to a system and method for predicting rare earth lithology and resource abundance in deep-sea sediments by modeling that analyzes the relationships of gamma ray data. [Background technology]

[0002] Rare earth elements (REEs) are mineral resources that are currently widely used in cutting-edge industries, including IT-related products such as mobile phones and personal computers, ceramic products, capacitors, filters, sensors, rare earth permanent magnets, hydrogen storage alloy batteries, automobile exhaust gas catalysts, glass abrasives, UV absorbers for automotive glass, and colorants for cathode ray tube glass.

[0003] There are over 200 types of minerals that contain rare earth elements, and currently, the minerals that can be commercially mined include bastnaesite ((Ce,La)(Co3)F), monazite ((Ce,La,ND,Th)PO4), xenotime (YPO4), fergusonite ((REE)(Nb,Ti)O4), and ion-adsorbing clay.

[0004] Rare earths are a total of 17 elements, including 15 elements ranging from lanthanum (La) with atomic number 57 to ruthenium (Lu) with atomic number 71, as well as scandium (Sc, atomic number 21) and yttrium (Y, atomic number 31), which have similar chemical properties. They are mainly used in cutting-edge industries and are one of the most important raw material resources in modern industry.

[0005] However, since prediction of resource or estimated amount is very important in the exploration of rare earth minerals, there is a demand for estimation and prediction technology for rare earth resource or reserves in ground or deep sea deposits, which can dramatically reduce time and cost.

[0006] On the other hand, sedimentological interpretation using high-resolution gamma-ray logging data can go beyond simple lithofacies differentiation and can be used to analyze depositional environments and interpret stratigraphy by correlating with nearby boreholes.

[0007] These interpretations are generally carried out using natural potential, gamma rays, resistivity, density, velocity, or neutron logging values, which are all used to compare lithologies or stratigraphy. Of these, gamma ray logging is primarily used to interpret sedimentary layers, as it is possible to predict changes in sediment grain size and depositional environment from the perspective of changes in clay content.

[0008] Gamma ray logging is a technique to measure natural gamma rays (NGR) that are naturally emitted in sediments or rocks. These natural gamma ray emissions are caused by the decay of potassium (K), thorium (Th), and uranium (U), and the distribution and content of these elements have different geological significance.

[0009] Generally, elements that emit gamma rays are found mostly in rocks, but they are particularly abundant in potassium feldspar and mica, which are the main products of weathering, and are deposited together with clay minerals and shale.

[0010] Therefore, shale layers have relatively high gamma ray logging values, while sandstone and limestone that contain almost no clay minerals show low logging values. Although there are some sections where the changes in gamma ray logging values ​​are influenced by highly radioactive materials (organic matter, heavy minerals, feldspar, rock fragments, etc.) regardless of the clay content, changes in gamma ray logging values ​​generally show a high correlation with changes in clay content.

[0011] For this reason, gamma ray logs are called indicators of shale or clay content. Typically, when the gamma ray log reading increases from a steadily low to a steadily high value, the clay content is considered to be increasing, and conversely, when the log reading decreases steadily, the clay content is considered to be decreasing.

[0012] However, although information from these natural gamma rays can be used to infer information about the minerals contained in sediments, the reality is that there is currently no technology that can effectively estimate and predict the amount of rare earth mineral resources contained in deep-sea sediments, etc. [Prior art documents] [Patent documents]

[0013] [Patent Document 1] Korean Patent Publication No. 10-1982297 (Registration Date: May 20, 2019) [Patent Document 2] Korean Patent Publication No. 10-2020-0010897 (Publication date: January 31, 2020) Summary of the Invention [Problem to be solved by the invention]

[0014] In order to solve the above-mentioned problems, the object of the present invention is to provide a system and method that can not only confirm rare earth rock facies classification information of deep-sea sediments using natural gamma ray data, but also predict rare earth resource amounts.

[0015] Another object of the present invention is to provide a system and method that, when integrated with a data system that has already collected or will collect natural gamma ray data, can quickly and effectively, at low cost, identify rare earth rock facies classification information, and predict rare earth resource amounts. [Means for solving the problem]

[0016] In order to achieve the above-mentioned object, the system for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention is characterized in that it includes a data collection unit that collects gamma ray data for deep-sea sediments, a data processing unit that normalizes and processes the gamma ray data collected by the data collection unit, and a predictive modeling unit that generates a model for predicting rare earth rock facies classification information and rare earth resource amounts using a linear regression method based on the data normalized by the data processing unit.

[0017] In addition, in the system for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention, the gamma ray data includes natural gamma ray (NGR) data and total gamma ray (SGR) data.

[0018] In addition, in the prediction system for rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention, the data processing unit is characterized by including a normalization unit that normalizes the gamma ray data collected by the data collection unit, and a shale volume correction unit that corrects the shale volume of the gamma ray data using XRD data.

[0019] In addition, in the system for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention, the predictive modeling unit is characterized by including: a lithology information unit that analyzes gamma ray spectrum data and generates rare earth lithology classification information that classifies clay types; and a linear regression modeling unit that generates a clay-specific linear regression model based on the lithology classification information and normalized and corrected data.

[0020] In addition, in the system for predicting rare earth resources in deep-sea sediments using gamma rays according to the present invention, the lithology information is characterized in that it is generated by analyzing gamma ray spectrum data, classifying clay types based on the ratios of Th, K, and U, and generating rare earth lithology classification information.

[0021] In addition, the system for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention is characterized in that it further includes a resource amount prediction unit that estimates and predicts the rare earth content by clay and the total rare earth resource amount using the least squares method based on the model generated by the prediction modeling unit.

[0022] In order to achieve the above-mentioned object, the method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention is characterized in that it includes the steps of (a) a data collection unit collecting gamma ray data for deep-sea sediments, (b) a data processing unit normalizing and processing the gamma ray data collected by the data collection unit, and (c) a predictive modeling unit generating a model for predicting rare earth resource amounts using a linear regression method based on the data normalized by the data processing unit.

[0023] In the method for predicting rare earth resources in deep-sea sediments using gamma rays according to the present invention, the step (a) is characterized in that the data collection unit includes a step of collecting natural gamma ray (NGR) data and total gamma ray (SGR) data for deep-sea sediments.

[0024] In the method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention, the step (b) is characterized by including: (b1) a step in which the data processing unit normalizes the gamma ray data collected by the data collection unit; and (b2) a step in which the data processing unit corrects the shale volume of the gamma ray data using XRD data.

[0025] In the method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to the present invention, the step (c) includes the steps of: (c1) the predictive modeling unit analyzing gamma ray spectrum data to generate rare earth rock facies classification information that classifies the clay types of the gamma ray data; and (c2) the predictive modeling unit generating a clay-specific linear regression model based on the rock facies classification information and the normalized and corrected data.

[0026] In the method for predicting rare earth resources in deep-sea sediments using gamma rays according to the present invention, the lithology information is generated by analyzing gamma ray spectrum data, classifying clay types based on the ratios of Th, K, and U, and generating rare earth lithology classification information.

[0027] In the method for predicting rare earth resources in deep-sea sediments using gamma rays according to the present invention, the prediction unit further includes a step of estimating and predicting the rare earth content by clay and the total rare earth resource amount using a least squares method based on the model generated by the prediction modeling unit.

[0028] Other specific details of the embodiments are included in the "Description of Embodiments" and the accompanying "Drawings".

[0029] The advantages and / or features of the present invention and the manner in which they are achieved will become apparent from the following detailed description of various embodiments in conjunction with the accompanying drawings.

[0030] However, the present invention is not limited to the configurations of the embodiments disclosed below, and may be embodied in various different forms. However, each embodiment disclosed in this specification is provided to complete the disclosure of the present invention and to fully inform those skilled in the art of the present invention of the scope of the present invention, and it should be understood that the present invention is defined only by the scope of each claim. Effect of the Invention

[0031] According to the present invention, a system and method can be provided that can not only confirm rare earth rock facies classification information of deep-sea sediments using natural gamma ray data, but also predict rare earth resource amounts.

[0032] The present invention also provides a system and method that uses non-invasive analysis and prediction techniques to predict rare earth resource abundance without damaging or altering the sediments being analyzed, and while preserving the natural state of the sediments.

[0033] In addition, according to the present invention, since obtaining natural gamma ray (NGR) data is a standard procedure in various deep-sea drilling operations, additional or special equipment may not be required, and when the data is integrated with data systems that are already collecting or will be collecting, a system and method can be provided that can not only quickly and effectively confirm rare earth lithology classification information at low cost, but also predict rare earth resource amounts.

[0034] Furthermore, in accordance with the present invention, the use of NGR data in deep sea drilling not only allows for the use of vast amounts of gamma ray data, but also allows for extensive preliminary assessment of rare earth potential.

[0035] In addition, according to the present invention, a system and method can be provided that can predict rare earth resource amounts, which provides a fast prediction and allows for faster decision-making in exploration operations, whereas existing methods for predicting and evaluating rare earth content are labor-intensive and time-consuming.

[0036] The present invention is also useful in distinguishing between various types of clays, such as bioapatite and Fe-Mn zeolite clays, in gamma ray spectrum data, and these specificities enhance the accuracy of rare earth predictions.

[0037] In addition, the present invention provides a system and method for predicting rare earth resource amounts by integrating various linear regression models suited to various types of clay, thereby improving prediction accuracy for various sedimentary layers.

[0038] The present invention also makes it possible to not only predict the occurrence of rare earths, but also to gauge the economics of a field, which greatly aids in decision making considering exploration and potential extraction costs. [Brief description of the drawings]

[0039] [Figure 1]1 is a diagram showing a block diagram of a system for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a schematic diagram illustrating the process of a method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention. [Diagram 3] 2 is a detailed flow chart of a method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention. [Figure 4] 1 is a graph showing the results of a comparison of rare earth measurement values ​​against Vsh of natural gamma ray data for applying a method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention. [Diagram 5] This is a distribution graph of rare earth measurement values ​​against Vsh of natural gamma ray data to predict the distribution of rare earth rock facies based on gamma ray data using a method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention. [Figure 6] This is a graph showing the proportional relationship between bioapatite and Fe-Mn zeolite in uranium, and the division spectrum of Th and K rock facies, using the method for predicting rare earth resources in deep-sea sediments according to an embodiment of the present invention. [Figure 7] This is a graph showing the proportional relationship between bioapatite and Fe-Mn zeolite in uranium, and the division spectrum of Th and K rock facies, using the method for predicting rare earth resources in deep-sea sediments according to an embodiment of the present invention. [Figure 8] This is a graph showing the proportional relationship between bioapatite and Fe-Mn zeolite in uranium, and the division spectrum of Th and K rock facies, using the method for predicting rare earth resources in deep-sea sediments according to an embodiment of the present invention. [Figure 9] FIG. 1 is a schematic diagram showing the relationship between uranium-based bioapatite and Fe-Mn zeolite, which exhibits high rare earth content. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] Before describing the present invention in detail, the terms and words used in this specification should not necessarily be interpreted as being limited to their ordinary or dictionary meanings, and the inventors of the present invention may appropriately define and use the concepts of various terms in order to explain their invention in the best possible manner, and further, these terms and words should be interpreted as having meanings and concepts that correspond to the technical ideas of the present invention.

[0041] In other words, the terms used in this specification are used only to describe preferred embodiments of the present invention, and are not intended to specifically limit the content of the present invention. It should be understood that these terms are defined in consideration of various possibilities of the present invention.

[0042] Furthermore, in this specification, unless the context clearly indicates otherwise, singular expressions may include plural expressions, and similarly, even if a plural is indicated, it should be understood to include the singular meaning.

[0043] Throughout this specification, when a component is described as "comprising" another component, unless specifically stated to the contrary, this does not mean to exclude any other components, but rather that the component may further include any other components.

[0044] Furthermore, when a certain component is described as being "located inside or connected to" another component, this component may be directly connected to the other component, or may be installed in contact with the other component, or may be installed at a certain distance apart. In the case where the component is installed at a certain distance apart, a third component or means may be present to fix or connect the component to the other component, and a description of this third component or means may be omitted.

[0045] On the other hand, when an element is described as being "directly coupled" or "directly connected" to another element, it must be understood that no third element or means is present.

[0046] Similarly, other expressions describing the relationship between components, such as "between" and "immediately between," or "adjacent to" and "directly adjacent to," should be construed to have a similar meaning.

[0047] Furthermore, in this specification, terms such as "one side," "other side," "one side," "other side," "first," "second," etc., if used, are used to describe one component so as to clearly distinguish the one component from other components, and it should be understood that these terms are not used to limit the meaning of the components.

[0048] Additionally, terms relating to positions such as "top," "bottom," "left," "right," and the like, if used herein, should be understood to indicate relative positions in the drawings with respect to the components, and should not be understood to refer to absolute positions unless absolute positions are specified for these positions.

[0049] Furthermore, in this specification, when assigning a reference number to each component in each drawing, the same component will have the same reference number even if the component is shown in another drawing, i.e., the same reference number will indicate the same component throughout the entire specification.

[0050] In the drawings accompanying this specification, the size, position, connection relationship, etc. of each component constituting the present invention may be exaggerated, reduced, or omitted in order to fully and clearly convey the idea of ​​the present invention or for the convenience of explanation, and therefore the proportions and scales may not be strictly accurate.

[0051] In addition, in the following description of the present invention, configurations that are deemed to make the gist of the present invention unclear, such as detailed descriptions of publicly known technologies including conventional technologies, may be omitted.

[0052] In the following, preferred embodiments of the present invention will be described in detail with reference to the drawings.

[0053] FIG. 1 is a block diagram showing a system 100 for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention.

[0054] As shown in FIG. 1, a system 100 for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention can be configured to include a data collection unit 110 for collecting gamma ray data, a data processing unit 120 for normalizing and processing the data, and a predictive modeling unit 130 for generating a model for estimating and predicting rare earth resource amounts based on the processed data.

[0055] More specifically, as shown in FIG. 1, the data collection unit 110 is configured to measure natural gamma rays emitted from deep-sea strata using a natural gamma ray (NGR) device installed in a deep-sea drilling device, etc., and collect the measured natural gamma ray (NGR) data.

[0056] These data collection units 110 are equipped with communication devices and can collect data online in conjunction with the NGR server 200 in which external natural gamma ray data is stored, or the data collection units 110 can directly store and collect the data.

[0057] That is, the data collecting unit 110 may include a communication device capable of collecting gamma ray data online, and a database (DB) for storing and managing the received data.

[0058] The data processing unit 120 can be configured to include a normalization unit 121 that normalizes the gamma ray data collected by the data collection unit 110, and a shale volume correction unit 123 that corrects the shale volume of the gamma ray data using XRD data.

[0059] The normalization unit 121 can adjust the gamma ray reading value with the gamma ray data using the following formula 1, and can create a gamma ray total value, SGR (Sum of Gamma Ray), using formula 2, and compare it after normalization (IGR: index of SGR).

[0060]

number

[0061] Here, IGR stands for index of Gamma ray.

[0062]

number

[0063] Here, SGR stands for Sum of Gamma Ray.

[0064] Thus, in the system 100 for predicting rare earth resource abundance in deep-sea sediments using gamma rays according to an embodiment of the present invention, it is preferable to normalize the original NGR data because it has variability depending on the equipment and the human effort used to collect the data.

[0065] As shown in FIG. 1, a volume correction unit can correct the shale volume of the gamma ray data using the XRD data.

[0066] In general, unconsolidated deposits are characterized by an overestimation of the volume of shale by gamma rays, but the actual quantitative clay analysis results by XRD show that this is not the case and appropriate correction is required.

[0067] That is, unconsolidated deposits show an overestimation of shale volume in gamma ray data, and therefore shale volume must be corrected using quantitative X-ray diffraction (XRD) analysis.

[0068] Therefore, the volume correction unit of the system 100 for predicting rare earth resource amounts in deep-sea deposits using gamma rays according to an embodiment of the present invention corrects the shale volume in which the gamma ray data is distributed using the XRD data.

[0069] That is, the shale volume of the formation (V sh ) and, if the formation is not integrated, integrate X-ray diffraction (XRD) data to correct the gamma-ray estimated shale volume. This correction allows us to confirm that the gamma-ray estimated shale volume is consistent with the actual clay content.

[0070] As shown in FIG. 1, the predictive modeling unit 130 can generate a model for predicting rare earth rock facies classification information and rare earth resource amount by linear regression method based on the data normalized by the data processing unit 120.

[0071] In addition, the predictive modeling unit 130 can be configured to include a lithology information unit 131 that analyzes gamma ray spectrum data and generates rare earth lithology classification information that classifies clay types, and a linear regression modeling unit 133 that generates clay-specific linear regression models based on the lithology classification information and normalized and corrected data.

[0072] The lithology information unit 131 classifies clay types based on the ratios of Th, K, and U in the gamma ray spectrum, and the linear regression modeling unit 133 can generate clay-specific linear regression models according to shale volume based on the normalized and corrected data.

[0073] Here, the lithology information may be rare earth lithology classification information that classifies clay types based on the proportions of thorium (Th), potassium (K) and uranium (U) by analyzing gamma ray spectrum data.

[0074] As shown in FIG. 1, the resource amount prediction unit 140 may further include a configuration for estimating and predicting the clay-specific rare earth content and the total rare earth resource amount using the least squares method based on the model generated by the predictive modeling unit 130.

[0075] Thus, the system 100 for predicting rare earth resource abundance in deep-sea sediments using gamma rays according to an embodiment of the present invention provides a system that can predict the presence and potential abundance of rare earth elements in marine sediments by combining pristine gamma ray data, shale volume correction, and linear regression modeling.

[0076] FIG. 2 is a schematic diagram illustrating the process of a method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention, and FIG. 3 is a detailed flow chart of the method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention.

[0077] As shown in Figure 3, the process of the method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention first involves performing a data pre-processing process to normalize natural gamma ray data and total gamma ray (SGR) data, and correct the shale volume in the gamma ray data using XRD data.

[0078] Based on the gamma-ray spectrum data, rock facies is classified according to the distribution of U, TH, and K content, and a linear regression model is generated from the relationship between this rock facies information and rare earth (REE) measurement values. Based on the generated model, the clay-specific REE content and total resource volume are predicted.

[0079] More specifically, as shown in FIG. 3, a method for predicting rare earth resource amounts in deep-sea sediments using gamma rays according to an embodiment of the present invention can include the steps of (a) collecting gamma ray data (S100), (b) normalizing and processing the gamma ray data (S200), (c) generating a model for predicting rare earth resource amounts using a linear regression method based on the normalized data (S300), and (d) predicting rare earth resource amounts based on the generated model (S400).

[0080] Here, step (a) (S100) is a step of collecting gamma ray data, in which natural gamma ray (NGR) data measured from a natural gamma ray measuring device installed in a deep sea drilling device, etc. is collected from an external system via an online network, etc., in which the data is stored.

[0081] Step (b) (S200) is a step of normalizing and preprocessing the gamma ray data, and may include a step (b1) of the data processing unit 120 normalizing the gamma ray data collected by the data collection unit 110 (S210), and a step (b2) of the data processing unit 120 correcting the shale volume in the gamma ray data using XRD data (S230).

[0082] Step (b1) (S210) may include a step of normalizing the natural gamma ray (NGR) data collected by the data collection unit 110 using the above-mentioned Equation 1, and a step of calculating a sum of the raw data of the natural gamma ray (NGR) using Equation 2 and normalizing the calculated value.

[0083] Step (b2) (S230) is a step of correcting the shale volume in the gamma ray data, and the shale volume (V sh) and, if the formation is not integrated, integrating X-ray diffraction (XRD) data to correct the gamma ray estimated shale volume. These correction steps can confirm that the gamma ray estimated shale volume is consistent with the actual clay content.

[0084] Thus, natural gamma ray (NGR) data was used to estimate the shale volume (V sh ) is estimated or converted to a shale index, which is suitable for unconsolidated sediments, after converting the NGR and SGR data to a shale index to exclude non-clay elements from the clay and to see only the clay ratio. sh This is because we use the formula to calculate

[0085] Step (c) (S300) is a step of generating a model for predicting rare earth resource amounts, in which a linear regression model is generated by analyzing the relationship between actual rare earth (REE) measurement values ​​and shale volume and rock facies classification information.

[0086] More specifically, step (c) (S300) may include: (c1) a step (S310) in which the predictive modeling unit 130 analyzes the gamma ray spectrum data and generates rare earth rock facies classification information that classifies the clay type of the gamma ray data; and (c2) a step (S330) in which the predictive modeling unit 130 generates a clay-specific linear regression model based on the rock facies classification information and the normalized and corrected data.

[0087] Step (c1) (S310) is a step of examining the ratios of thorium (Th), potassium (K), and uranium (U) based on the gamma-ray spectrum data, thereby generating rock facies classification information that classifies clay types, and step (c2) is a step (S330) of generating a clay-specific linear regression model for classified rock facies.

[0088] That is, in stage (c) (S300), the sum of gamma ray (SGR) data and the shale volume (V sh) values ​​to determine the most likely clay type (e.g., apatite-rich clay, Fe-Mn-rich clay, or Fe-rich zeolitic clay, etc.) and generate a linear regression model.

[0089] Based on actual rare earth element (REE) measurements and shale volume and lithology classification information, a linear regression model can be generated as follows:

[0090] 1) Bio-apatite enriched zeolite clay: y=3488.36×x-605.448

[0091] 2) Fe-Mn-rich zeolite clay: y = 1271.5 x + 375.136

[0092] 3) Iron-rich clay: y = 814.688 x + 303.721

[0093] where "x" is the normalized gamma ray reading or V sh value.

[0094] Step (d) (S400) may be a step of predicting rare earth resource amounts, and may be a step of predicting rare earth resource amounts in deep-sea deposits based on the model generated in step (c) (S300).

[0095] That is, step (d) (S400) is a step of predicting rare earth resource amount based on the model generated in step (c) (S300), and a calculation formula for rare earth resource amount can be shown as Equation 3 by applying the total rare earth estimation model.

[0096]

number

[0097] where Y is the estimated rare earth resource and "X" is the V to obtain a comprehensive estimate of the total rare earth resource. sh and normalized natural gamma radiation (NGR) values.

[0098] FIG. 4 shows the V of natural gamma ray data for applying the method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention. sh FIG. 5 is a graph showing a comparison of rare earth measurements against V of natural gamma ray data to predict rare earth rock facies distribution based on gamma ray data by a method for predicting rare earth resource amount in deep sea sediments using gamma rays according to an embodiment of the present invention. sh FIG. 6 to FIG. 8 show the proportional relationship between bioapatite and Fe-Mn zeolite in uranium, and the results of the Th and K rock facies division spectrum, when the method for predicting rare earth resources in deep-sea sediments according to an embodiment of the present invention is applied. FIG. 9 is a schematic diagram showing the relationship between bioapatite and Fe-Mn zeolite in uranium with a high rare earth content.

[0099] As shown in Figure 4, the rare earth (REE) measurements and V sh Value (V sh The results of comparing the shale volume with that of the Fe-Mn zeolite clay (NGR [%)) clearly show that for values ​​with a shale volume of 40% or more (this may vary depending on the results of correction to the XRD data), there are bioapatites and Fe-Mn zeolite clays with 950 ppm or more of rare earths, which shows a proportional relationship with the rare earths.

[0100] And as shown in Figure 5, V sh From the results of the rare earth measurements, the domains were divided to analyze the rock facies classification information showing the tendency according to the clay classification. Domain 1 shows rock facies of zeolite clay and pelagic clay, and V sh The wide range of values ​​indicates that there is no correlation.

[0101] Domain 2, which represents the lithology of ferruginous clay and bioapatite clay, is prominent in the Exp. 329 area and has relatively high rare earth concentrations and low V. sh(above 900 ppm, below 0.5), showing a proportional relationship, with bio-apatite clay showing the highest concentration.

[0102] And, as shown in Figures 6 to 8, when considering the gamma ray spectrum data, V sh NGR, Th, and K form crusts around the background, which can be distinguished from bioapatite and Fe-Mn zeolite clay, and the proportional relationship can be confirmed by overlapping with U.

[0103] As shown in Figure 9, the rare earth (REE) measurements and V sh As a result of comparing the NGR data with the shale data, a proportional relationship between bioapatite and Fe-Mn zeolite was found at values ​​with high correlation to low shale (50%), and it was shown that below 800 ppm of rare earths, most of the rock facies is either pelagic clay or shows zeolite that does not contain Mn-Fe.

[0104] According to the rock facies classified in this way, the calculation formula for predicting or evaluating rare earth resource amount using a linear regression model by the least squares method for bioapatite and Fe-Mn zeolite clay with rare earth of 950 ppm or more can be shown as the above-mentioned Equation 3. The correlation coefficient is 0.768721 and the r2 value is 0.590931975841.

[0105] Thus, the integrated sieving apparatus for sorting soil sample particle size according to embodiments of the present invention provides a balance between functionality, efficiency and user convenience, simplifying the sieving process and ensuring more consistent results, making it a highly useful apparatus in soil analysis and similar applications.

[0106] Various preferred embodiments of the present invention have been described above using some examples. However, the description of the various embodiments described in this section "Form for carrying out the invention" is merely illustrative, and a person having ordinary knowledge in the technical field to which the present invention pertains can understand from the above description that the present invention can be implemented in various modified forms or in an equivalent manner to the present invention.

[0107] Furthermore, the present invention may be embodied in various other forms and is not limited by the above description. The above description is provided to complete the disclosure of the present invention and to fully inform those skilled in the art of the scope of the present invention, and the present invention is defined only by the claims. [Explanation of symbols]

[0108] 100 Rare Earth Resource Prediction System 110 Data Collection Unit 120 Data Processing Unit 121 Normalization section 123 Shale volume correction section 130 Predictive Modeling Department 131 Lithology Information Department 133 Linear Regression Modeling Section 140 Resource Prediction Department 200 NGR Server

Claims

1. a data collection unit for collecting gamma ray data for deep-sea sediments; a data processing unit that normalizes and processes the gamma ray data collected by the data collecting unit; a prediction modeling unit that generates a model for predicting rare earth rock facies classification information and rare earth resource amount by a linear regression method based on the data normalized by the data processing unit; Characterized in that it comprises A system for predicting rare earth resource abundance in deep-sea sediments using gamma rays.

2. The gamma ray data includes Natural gamma ray (NGR) data and total gamma ray (SGR) data are included. A system for predicting rare earth resource amounts in deep-sea sediments using gamma rays as described in claim 1.

3. The data processing unit a normalization unit that normalizes the gamma ray data collected by the data collection unit; a shale volume correction unit that corrects the shale volume of the gamma ray data using the XRD data; Characterized in that it comprises A system for predicting rare earth resource amounts in deep-sea sediments using gamma rays as described in claim 1.

4. The predictive modeling unit includes: a lithology information unit that analyzes the gamma ray spectrum data and generates rare earth lithology classification information for classifying clay types; A linear regression modeling unit that generates a clay-specific linear regression model based on the rock facies classification information and the normalized and corrected data; Characterized in that it comprises A system for predicting rare earth resource amounts in deep-sea sediments using gamma rays as described in claim 1.

5. The lithology information is The method is characterized in that the gamma ray spectrum data is analyzed to classify clay types based on the ratios of Th, K, and U, and rare earth rock facies classification information is generated. A system for predicting rare earth resource amounts in deep-sea sediments using gamma rays as described in claim 4.

6. The method further includes a resource amount prediction unit that estimates and predicts the clay-specific rare earth content and the total rare earth resource amount using the least squares method based on the model generated by the prediction modeling unit. A system for predicting rare earth resource amounts in deep-sea sediments using gamma rays as described in claim 1.

7. (a) a data collection unit collecting gamma ray data for deep-sea sediments; (b) a data processing unit normalizing and processing the gamma ray data collected by the data collecting unit; (c) generating a model for predicting rare earth resource amount by a linear regression method using the data normalized by the data processing unit; Characterized in that it comprises A method for predicting rare earth resources in deep-sea sediments using gamma rays.

8. The above step (a) The data collection unit includes a step of collecting natural gamma ray (NGR) data and total gamma ray (SGR) data for deep-sea sediments. A method for predicting rare earth resource amounts in deep-sea sediments using gamma rays as claimed in claim 7.

9. The above step (b) (b1) normalizing the gamma ray data collected by the data collecting unit by the data processing unit; (b2) the data processor corrects the gamma ray data for shale volume using XRD data; Characterized in that it comprises A method for predicting rare earth resource amounts in deep-sea sediments using gamma rays as claimed in claim 7.

10. The above step (c) (c1) the predictive modeling unit analyzes the gamma ray spectrum data to generate rare earth rock facies classification information for classifying clay types of the gamma ray data; (c2) generating a clay-specific linear regression model based on the rock facies classification information and the normalized and corrected data by the predictive modeling unit; Characterized in that it comprises A method for predicting rare earth resource amounts in deep-sea sediments using gamma rays as claimed in claim 7.

11. The lithology information is The method is characterized in that the gamma ray spectrum data is analyzed to classify clay types based on the ratios of Th, K, and U, and rare earth rock facies classification information is generated. A method for predicting rare earth resource amounts in deep-sea sediments using gamma rays as claimed in claim 10.

12. The method further includes a step of estimating and predicting the clay-specific rare earth content and the total rare earth resource amount by using a least squares method according to the model generated by the prediction modeling unit, A method for predicting rare earth resource amounts in deep-sea sediments using gamma rays as claimed in claim 7.

Images