Rare earth resource quantity prediction system and method for deep-sea sediments

Abstract

The purpose of the present invention is to provide a system and method for predicting the amount of rare earth resources in deep-sea sediments, which can determine rare earth lithofacies analysis information of deep-sea sediments by using natural gamma ray data, and in addition, can predict the amount of rare earth resources. In order to achieve the purpose, the system for predicting the amount of rare earth resources in deep-sea sediments using gamma rays, according to the present invention, comprises: a data collection unit for collecting gamma ray data relating to deep-sea sediments; a data processing unit that normalizes and processes the gamma-ray data collected by the data collection unit; and a prediction modeling unit for generating a model for predicting rare earth lithofacies analysis information and the amount of rare earth resources by a linear regression method on the basis of the data normalized by the data processing unit.

Description

Technical Field

[0001] The present invention relates to a system and method for distinguishing lithofacies or predicting resource quantity, and more particularly, to a system and method for distinguishing rare earth lithofacies of deep-sea sediments and predicting resource quantity by modeling the relationship between gamma-ray data. Background Art

[0002] Rare earth elements (REE) are widely used in current collection, personal computers and other IT-related products, ceramic products, capacitors, filters, sensors, rare earth permanent magnets, hydrogen storage alloy batteries, automobile exhaust catalysts, glass abrasives, automobile glass ultraviolet absorbers, cathode ray tube glass colorants and other high-tech industries.

[0003] There are more than 200 kinds of minerals containing rare earths. 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 earth elements include 15 elements ranging from lanthanum (La) with atomic number 57 to lutetium (Lu) with atomic number 71, as well as a total of 17 elements with similar chemical properties, such as scandium (Sc, atomic number 21) and yttrium (Y, atomic number 31). They are mainly used in high-tech industries and are one of the most important raw material resources for modern industry.

[0005] However, in the exploration process of rare earth minerals, the prediction of resource quantities or estimated quantities is very important, and therefore a technology for estimating and predicting the amount of rare earth resources or reserves in underground or deep-sea sediments, etc., which can significantly reduce time and cost, is needed.

[0006] On the other hand, sedimentological analysis using high-resolution gamma-ray logging data goes beyond simple lithofacies differentiation and can be used for differentiation of depositional environments and for bottom layer analysis by comparison with nearby boreholes.

[0007] The above analysis is usually carried out using natural potential, gamma ray, resistivity, density, velocity or neutron logging values ​​for comparing lithofacies or strata. In the case of double gamma ray logging, the particle size and sedimentary environment changes of the sediments can be predicted from the perspective of changes in clay content. Therefore, it is mainly used for sedimentary layer analysis.

[0008] Gamma ray logging is a method of measuring natural gamma rays (NGR) naturally released from sediments or rocks. The release of this natural gamma ray is caused by the decay of potassium (K), thorium (Th) and uranium (U), and its geological significance varies depending on the distribution and content of these elements.

[0009] Generally, elements emitting gamma rays are mostly included in rocks, and in particular, exist in large quantities in potassium feldspar and mica which are major weathering products and are deposited together with clay minerals and shales.

[0010] Therefore, shale phases have relatively high gamma-ray logging values, while sandstone or limestone containing a small amount of clay minerals have low logging values. Although there are some intervals where the change in gamma-ray logging values ​​is affected by highly radioactive substances (organic matter, heavy minerals, feldspar, rock fragments, etc.) regardless of the clay content, in general, the change in gamma-ray logging values ​​is highly correlated with the change in clay content.

[0011] For this reason, gamma ray logging is called an indicator of shale or clay content. Generally, when the gamma ray logging value increases from a specified small value to a larger value, it is considered that the clay content is increasing. Conversely, when the logging value decreases steadily, it is considered that the clay content is decreasing.

[0012] However, even if the information on minerals contained in sediments etc. can be estimated from the above-mentioned natural gamma ray information, there is no method to effectively estimate and predict the resource amount of rare earth minerals contained in deep-sea sediments etc.

[0013] Prior art literature

[0014] Patent Literature

[0015] Patent Document 1: Korean Patent Gazette No. 10-1982297 (Grant Date: May 20, 2019)

[0016] Patent Document 2: Korean Patent Publication No. 10-2020-0010897 (Publication Date: January 31, 2020) Summary of the invention

[0017] The purpose of the present invention for solving the above-mentioned conventional problems is to provide a system and method for confirming rare earth facies differentiation information of deep-sea sediments using natural gamma-ray data, and in addition, predicting the amount of rare earth resources.

[0018] Furthermore, the purpose of the present invention is to provide the following system and method, which, when integrated with a data system that has collected or is collecting natural gamma-ray data, can quickly and effectively confirm rare earth facies differentiation information at a low cost, and not only that, but also predict the amount of rare earth resources.

[0019] In order to achieve the above-mentioned purpose, the rare earth resource prediction system of deep-sea sediments using gamma rays of the present invention is characterized in that it includes: a data collection unit, which collects gamma ray data related to deep-sea sediments; a data processing unit, which normalizes and processes the gamma ray data collected by the above-mentioned data collection unit; and a prediction modeling unit, which generates a model for predicting rare earth lithology analysis information and rare earth resource quantities by a linear regression method using the data normalized in the above-mentioned data processing unit.

[0020] Furthermore, the rare earth resource prediction system for deep-sea sediments using gamma rays of the present invention is characterized in that the gamma ray data includes natural gamma ray (NGR) data and total gamma ray (SGR) data.

[0021] Furthermore, the rare earth resource prediction system for deep-sea sediments using gamma rays of the present invention is characterized in that the above-mentioned data processing unit includes: a normalization unit, which normalizes the gamma ray data collected by the above-mentioned data collection unit; and a shale volume correction unit, which uses X-ray diffraction data to correct the shale volume of the above-mentioned gamma ray data.

[0022] Furthermore, the rare earth resource prediction system for deep-sea sediments using gamma rays of the present invention is characterized in that the above-mentioned prediction modeling unit includes: a facies information unit, which analyzes gamma-ray spectral data to generate rare earth facies analysis information for distinguishing clay types; and a linear regression modeling unit, which generates a linear regression model for each type of clay based on the above-mentioned facies distinction information and normalized and corrected data.

[0023] Furthermore, the rare earth resource prediction system for deep-sea sediments using gamma rays of the present invention is characterized in that, for the above-mentioned lithofacies information, gamma-ray spectrum data is analyzed and clay types are distinguished based on Th, K, and U ratios to generate rare earth lithofacies analysis information.

[0024] Furthermore, the rare earth resource prediction system for deep-sea sediments using gamma rays of the present invention is characterized in that it also includes a resource prediction unit, which uses the least squares method to estimate the rare earth content of each clay and the total rare earth resources through the model generated by the above-mentioned prediction modeling unit.

[0025] Moreover, in order to achieve the above-mentioned purpose, the method for predicting rare earth resources in deep-sea sediments using gamma rays of the present invention is characterized in that it includes: step (a), a data collection unit collects gamma ray data related to deep-sea sediments; step (b), a data processing unit normalizes and processes the gamma ray data collected by the above-mentioned data collection unit; and step (c), a prediction modeling unit generates a model for predicting rare earth facies analysis information and rare earth resources by linear regression method using the data normalized in the above-mentioned data processing unit.

[0026] The method for predicting rare earth resources in deep-sea sediments using gamma rays of the present invention is characterized in that the above step (a) includes the following steps: the above data collection unit collects natural gamma ray data and total gamma ray data related to deep-sea sediments.

[0027] The method for predicting rare earth resources in deep-sea sediments using gamma rays of the present invention is characterized in that the above-mentioned step (b) includes: step (b1), the above-mentioned data processing unit normalizes the gamma ray data collected by the above-mentioned data collection unit; and step (b2), the above-mentioned data processing unit uses X-ray diffraction data to correct the shale volume of the above-mentioned gamma ray data.

[0028] The method for predicting rare earth resources in deep-sea sediments using gamma rays of the present invention is characterized in that the above-mentioned step (c) includes: step (c1), the above-mentioned prediction modeling unit analyzes gamma-ray spectral data to generate rare earth facies analysis information for distinguishing the clay type of the above-mentioned gamma-ray data; and step (c2), the above-mentioned prediction modeling unit generates a linear regression model for each type of clay based on the above-mentioned facies distinction information and the normalized and corrected data.

[0029] The method for predicting rare earth resources in deep-sea sediments using gamma rays of the present invention is characterized in that, for the above-mentioned lithofacies information, gamma-ray spectrum data is analyzed and clay types are distinguished based on Th, K, and U ratios to generate rare earth lithofacies analysis information.

[0030] The method for predicting rare earth resources in deep-sea sediments using gamma rays of the present invention is characterized in that it also includes the following steps: the resource prediction unit uses the least squares method to estimate the rare earth content of each clay and the total rare earth resources through the model generated by the above-mentioned prediction modeling unit.

[0031] Specific matters of other embodiments are included in the “Detailed Description” and “Drawings”.

[0032] The advantages and / or features of the present invention and the methods for achieving these will be described with reference to the accompanying drawings. Figure 1 This will become more apparent with reference to various embodiments described in detail later.

[0033] However, the present invention is not limited to the structures of the embodiments disclosed below, but can be embodied in a variety of different forms. Only the embodiments disclosed in this specification make the disclosure of the present invention complete and are provided to inform ordinary technicians in the technical field to which the present invention belongs of the full scope of the present invention. The present invention should be defined by the scope of the invention protection claims.

[0034] According to the present invention, the present invention can provide the following system and method, which can use natural gamma ray data to confirm rare earth facies analysis information of deep-sea sediments, and not only that, but also can predict the amount of rare earth resources.

[0035] Furthermore, according to the present invention, the present invention can provide the following system and method, which, as a non-invasive analysis and prediction technology, will not cause damage or change to the sediments to be analyzed, thereby maintaining the natural state of the sediments and predicting the amount of rare earth resources.

[0036] Furthermore, according to the present invention, the present invention can provide the following system and method, wherein ensuring natural gamma ray (NGR) data is a standard procedure in many deep-sea drilling operations, and therefore, there may be no need for additional or special equipment. When combined with a data system that has collected or is collecting data, rare earth lithofacies analysis information can be quickly and efficiently confirmed at low cost. Not only that, the amount of rare earth resources can be predicted.

[0037] Furthermore, according to the present invention, the utilization of natural gamma-ray data in deep-sea drilling can use a large amount of gamma-ray data, and not only that, but also a wide range of preliminary evaluations of rare earth potential can be performed.

[0038] Furthermore, according to the present invention, the present invention can provide the following system and method, which can provide rapid prediction to predict the amount of rare earth resources so that decisions can be made more quickly in exploration operations, compared to the labor-intensive and time-consuming existing methods of predicting and evaluating rare earth content.

[0039] Furthermore, according to the present invention, it is helpful to distinguish raw apatite from various types of clays such as Fe-Mn zeolite clay in gamma-ray spectrum data, and this specificity improves the accuracy of rare earth prediction.

[0040] Furthermore, according to the present invention, the present invention can provide the following system and method, which can predict the amount of rare earth resources by combining multiple linear regression models for various types of clays, and the above-mentioned rare earth resources can improve the prediction accuracy of various sedimentary layers.

[0041] Furthermore, according to the present invention, the presence of rare earth elements can be predicted, and not only that, the economic feasibility of the deposit can be determined, which greatly helps in decision-making considering exploration and potential mining costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a diagram showing the block structure of a system for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention.

[0043] Figure 2 This is a diagram schematically showing the process of a method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention.

[0044] Figure 3 This 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.

[0045] Figure 4 V is the natural gamma ray data used in the method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention. sh This is a graph showing the comparison results of the rare earth element measurement values.

[0046] Figure 5 The method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention predicts the V of natural gamma ray data of rare earth lithofacies distribution based on gamma ray data. sh Distribution chart of rare earth element measured values.

[0047] Figures 6 to 8 The diagram shows the ratio of raw material apatite and Fe-Mn zeolite in uranium and the spectral result of Th and K lithofacies differentiation in the method for predicting rare earth resources in deep-sea sediments according to the embodiment of the present invention.

[0048] Fig. 9 Schematic diagram of the relationship between raw material apatite (Bioapatite) and Fe-Mn zeolite in uranium with a higher rare earth content.

[0049] Description of Reference Numerals

[0050] 100: Rare earth resource prediction system

[0051] 110: Data Collection Department

[0052] 120: Data Processing Department

[0053] 121: Regularization Department

[0054] 123: Shale Volume Correction Department

[0055] 130: Predictive Modeling Department

[0056] 131: Petrographic Information Department

[0057] 133: Linear Regression Modeling Department

[0058] 140: Resource Prediction Department

[0059] 200: Natural Gamma Ray Server DETAILED DESCRIPTION

[0060] Before explaining the present invention in detail, the terms or words used in this specification do not have to be limited to the usual or dictionary meanings. The inventors of the present invention may appropriately define the concepts of various terms for use in order to explain their method in the most preferred way. Furthermore, the meanings of these terms or words should be consistent with the meanings and concepts of the technical ideas of the present invention.

[0061] That is, the terms used in this specification are for explaining the preferred embodiments of the present invention rather than specifically limiting the contents of the present invention, and these terms are defined in consideration of various functionalities of the present invention.

[0062] Also, in this specification, unless clearly indicated otherwise in the context, an expression in the singular may include an expression in the plural, and similarly, even if expressed in the plural, it may include a singular meaning.

[0063] Throughout the specification, when it is described that a structural element “includes” other structural elements, unless there is a special description to the contrary, it may mean that any other structural elements may also be included, and does not mean that other structural elements are excluded.

[0064] Furthermore, when a structural element is recorded as existing "inside" or "connected to" other structural elements, the structural element may be directly connected or in contact with the other structural elements, or may be separated from the other structural elements by a specified distance. In the case of being separated from the other structural elements by a specified distance, there may be a third structural element or unit that fixes or connects the corresponding structural element to the other structural elements, and the description of the third structural element or unit may be omitted.

[0065] In contrast, when it is described that a structural element is “directly connected to” or “directly coupled to” another structural element, it should be understood that there is no third structural element or unit.

[0066] Similarly, other expressions describing the relationship between structural elements, that is, "between" and "just between" or "adjacent to" and "directly adjacent to" etc., should also be interpreted as having the same subject matter.

[0067] Furthermore, in this specification, when terms such as "one side", "the other side", "one side", "the other side", "first", and "second" are used, this is to clearly distinguish between two structural elements, and the meaning of the corresponding structural elements is not limited to the above terms.

[0068] Furthermore, in this specification, when terms related to positions such as "up", "down", "left", and "right" are used, these represent the relative positions of the corresponding structural elements in the corresponding drawings, and unless the absolute positions of these positions are specified, these position-related terms should not be interpreted as representing absolute positions.

[0069] Furthermore, in the present specification, when assigning figure marks to each structural element in each drawing, the same structural element is assigned the same figure mark as much as possible even if the structural element appears in different drawings, that is, the same figure mark represents the same structural element throughout the specification.

[0070] In the drawings attached to this specification, the size, position, connection relationship, etc. of each structural element constituting the present invention may be enlarged or reduced or omitted so as to fully and clearly convey the idea of ​​the present invention or facilitate explanation. Therefore, the proportion or scale may not be strict.

[0071] Furthermore, in the following description of the present invention, a detailed description of a structure that is judged to be likely to unnecessarily obscure the gist of the present invention, for example, including a known technology of the related art, may be omitted.

[0072] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0073] Figure 1 It is a diagram showing the block structure of a system for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention.

[0074] like Figure 1 As shown, the rare earth resource prediction system 100 of deep-sea sediments using gamma rays according to an embodiment of the present invention may include: a data collection unit 110 for collecting gamma ray data; a data processing unit 120 for normalizing and processing the data; and a prediction modeling unit 130 for generating a model for estimating and predicting rare earth resources based on the processed data.

[0075] More specifically, if Figure 1 As shown, the data collection unit 110 measures natural gamma rays emitted from deep-sea formations using a natural gamma ray (NGR) device installed in a deep-sea drilling rig or the like, and collects the measured natural gamma ray (NGR) data.

[0076] The data collection unit 110 may include a communication device and cooperate with a natural gamma ray server 200 storing external natural gamma ray data online to collect data, or the data collection unit 110 may directly store and collect data.

[0077] That is, the data collection 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.

[0078] The data processing unit 120 may include: a normalization unit 121 for normalizing the gamma ray data collected by the data collection unit 110; and a shale volume correction unit 123 for correcting the shale volume of the gamma ray data using the X-ray diffraction data.

[0079] The normalization unit 121 can adjust the gamma ray reading value as the gamma ray data by the following equation 1, obtain the total gamma ray (SGR, Sum of Gamma Ray) as the gamma ray sum value by equation 2 and normalize it (IGR: Index of SGR) for comparison.

[0080] IGR=(NGR raw -1) / (30-1)(Mathematical formula 1)

[0081] Here, IGR refers to the Index of Gamma Ray.

[0082] SGR[API]=16*K[%]+8*U[ppm]+Th[ppm]

[0083] IGR = (SGR - 26) (114 - 26) (Formula 2)

[0084] Here, SGR refers to the total gamma ray.

[0085] As described above, preferably, in the rare earth resource prediction system 100 for deep-sea sediments using gamma rays according to an embodiment of the present invention, the original natural gamma ray data varies depending on the device and the manpower for collecting the data, and therefore needs to be normalized.

[0086] Moreover, if Figure 1 As shown, the shale volume corrector 123 may correct the shale volume of the gamma-ray data using the X-ray diffraction data.

[0087] Generally, for loose sediments, the gamma-ray shale volume or shale amount is overestimated (Over Estimation), but according to the quantitative analysis result value of clay by actual X-ray diffraction, it is not overestimated, so the shale volume needs to be appropriately adjusted (Correction).

[0088] That is, for unconsolidated sediments, the shale volume presented in the gamma-ray data is overestimated, and therefore, quantitative analysis using X-ray diffraction (XRD) is needed to correct the shale volume.

[0089] Therefore, the shale volume correction unit 123 of the system 100 for predicting rare earth resources in deep-sea sediments using gamma rays according to the embodiment of the present invention uses X-ray diffraction data to correct the distributed shale volume of gamma ray data.

[0090] That is, the shale volume (V sh ), when the formation is loose, the X-ray diffraction (XRD) data is combined to correct the gamma-ray estimated shale volume. Through the above correction, it can be confirmed whether the gamma-ray estimated shale volume is consistent with the actual clay content.

[0091] Moreover, if Figure 1 As shown, the prediction modeling unit 130 can generate a model for predicting rare earth petrographic analysis information and rare earth resource quantity by a linear regression method using the data normalized in the data processing unit 120 .

[0092] Furthermore, the prediction modeling unit 130 may include: a facies information unit 131 that analyzes gamma-ray spectrum data to generate rare earth facies analysis information for distinguishing clay types; and a linear regression modeling unit 133 that generates a linear regression model for each clay based on the facies distinction information and the normalized and corrected data.

[0093] The facies information unit 131 may distinguish clay types based on the ratio of Th, K, and U in the gamma-ray spectrum, and the linear regression modeling unit 133 may generate a linear regression model for each clay according to the shale volume based on the normalized and corrected data.

[0094] Among them, for the petrographic information, the gamma-ray spectrum data is analyzed and the clay types are distinguished based on the ratios of thorium (Th), potassium (K) and uranium (U) to generate rare earth petrographic analysis information.

[0095] Moreover, if Figure 1 As shown, the resource prediction unit 140 may further include a structure for estimating the rare earth content of each clay and the total rare earth resource amount by using the least square method through the model generated by the prediction modeling unit 130 .

[0096] As described above, the rare earth resource prediction system 100 for deep-sea sediments using gamma rays according to an embodiment of the present invention can provide a system that combines gamma ray data, shale volume correction, and linear regression modeling to predict whether rare earth elements exist in seabed sediments and their potential amount.

[0097] Figure 2 This is a diagram schematically showing the process of a method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention. Figure 3This 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.

[0098] like Figure 3 As shown, in the process of the rare earth resource prediction method of deep-sea sediments using gamma rays in an embodiment of the present invention, first, the natural gamma ray data and the total gamma ray (SGR) data are normalized, and the X-ray diffraction data is used to perform a data preprocessing process to correct the shale volume in the gamma ray data.

[0099] Based on gamma-ray spectral data, lithofacies are distinguished according to the distribution of U, TH and K contents. Based on this lithofacies information, a linear regression model is generated through the relationship with the measured values ​​of rare earth elements (REE). Based on the generated model, the rare earth content and total resource volume of each clay are predicted.

[0100] In more detail, Figure 3 As shown, the method for predicting rare earth resources in deep-sea sediments using gamma rays according to an embodiment of the present invention may include: step a (S100), collecting gamma ray data; step b (S200), normalizing and processing the gamma ray data; step c (S300), generating a model for predicting rare earth resources by linear regression using the normalized data; and step d (S400), predicting rare earth resources based on the generated model.

[0101] Among them, step a (S100) is a step of collecting gamma ray data, which is a step of collecting from an external system storing natural gamma ray (NGR) data measured by a natural gamma ray measuring device installed in a deep-sea drilling rig, etc. through an online network, etc.

[0102] Step b (S200) is a step of normalizing and processing the gamma-ray data, which may include: step b1 (S210), the data processing unit 120 normalizes the gamma-ray data collected by the above-mentioned data collection unit 110; and step b2 (S230), the data processing unit 120 uses X-ray diffraction data to correct the shale volume in the above-mentioned gamma-ray data.

[0103] Step b1 (S210) may include the following steps: normalizing the natural gamma ray (NGR) data collected by the data collection unit 110 by the above-mentioned mathematical formula 1; and calculating the value of the total natural gamma ray (NGR) raw data using mathematical formula 2 and normalizing the calculated value.

[0104] Step b2 (S230) is a step of correcting the shale volume in the gamma ray data, which may include the following steps: estimating the shale volume (V sh); and when the formation is loose, the gamma-ray estimated shale volume is corrected by incorporating X-ray diffraction (XRD) data. The above correction process can be used to confirm whether the gamma-ray estimated shale volume is consistent with the actual clay content.

[0105] As mentioned above, natural gamma ray (NGR) data are used to estimate or convert shale volume (V sh ) is because the natural gamma ray data and the total gamma ray data are converted into the shale index (Shale Index), in order to exclude elements other than clay in the clay and only look at the clay ratio, and then use the V for finding the appropriate sedimentary layer sh formula.

[0106] Step c (S300) is a step of generating a linear regression model by analyzing actual rare earth element (REE) measurement values, shale volume, and lithofacies differentiation information as a model for predicting rare earth resources.

[0107] More specifically, step c (S300) may include: step c1 (S310), the predictive modeling unit 130 analyzes the gamma-ray spectral data to generate rare earth lithology analysis information for distinguishing the clay type of the gamma-ray data; and step c2 (S330), the predictive modeling unit 130 generates a linear regression model for each clay based on the lithology distinction information and the normalized and corrected data.

[0108] In step c1 (S310), the ratios of thorium (Th), potassium (K) and uranium (U) are checked based on gamma-ray spectrum data and based on this, facies differentiation information for distinguishing clay types is generated, and in step c2 (S330), a linear regression model for each clay whose facies is differentiated is generated.

[0109] That is, step c (S300) may be the following step: based on the total gamma ray (SGR) data and the shale volume (V sh ) values ​​to distinguish and determine the clay type with the highest probability (e.g., apatite-rich clay, Fe-Mn-rich clay, or Fe-rich zeolite clay, etc.) to generate a linear regression model.

[0110] The following linear regression model can be generated based on the actual rare earth element (REE) measured values ​​and shale volume and lithofacies differentiation information:

[0111] 1) Zeolite clay rich in raw material apatite: y = 3488.36 × x - 605.448;

[0112] 2) Fe-Mn-rich zeolite clay: y = 1271.5 × x + 375.136;

[0113] 3) Iron-rich clay: y = 814.688 × x + 303.721;

[0114] wherein, "x" is the normalized gamma-ray reading value or V sh value.

[0115] Step d (S400), as a step of predicting the rare earth resource amount, may be the following step of predicting the rare earth resource amount of deep-sea sediments based on the model generated in step c (S300).

[0116] That is, step d (S400), as a step of predicting the rare earth resource amount based on the generated model, as shown in Mathematical Formula 3, may be a mathematical formula for the rare earth resource amount applicable to the entire rare earth estimation model.

[0117] Y = 2239.41 * X(V sh NGR) - 29.8112 (800 < X ≤ 3500) (Mathematical Formula 3)

[0118] wherein, Y is the predicted rare earth resource amount, and "X" is V for obtaining a comprehensive estimation value related to the total rare earth resource amount sh and the normalized natural gamma-ray (NGR) value.

[0119] Figure 4 is a chart of the comparison results of the natural gamma-ray data of the V of the rare earth element measurement values for the method for predicting the rare earth resource amount of deep-sea sediments using gamma rays according to the embodiments of the present invention sh of, Figure 5 is a distribution chart of the rare earth element measurement values of the natural gamma-ray data for predicting the rare earth lithofacies distribution based on the gamma-ray data according to the method for predicting the rare earth resource amount of deep-sea sediments using gamma rays according to the embodiments of the present invention sh of, Figures 6 to 8 is a graph of the proportional relationship between the raw material apatite and Fe-Mn zeolite in uranium and the Th, K lithofacies discrimination spectrum results for the method for predicting the rare earth resource amount of deep-sea sediments according to the embodiments of the present invention Fig. 9 is a schematic diagram of the relationship between the raw material apatite (Bioapatite) and Fe-Mn zeolite in uranium showing a relatively high rare earth content.

[0120] As Figure 4 shown, comparing the results of the rare earth element (REE) measurement values and the shale volume content V sh value (V sh NGR [%]) from the natural gamma-ray data, in the values where the shale volume is 40% or more (changed according to the results corrected by X-ray diffraction data), there is clay rich in iron-manganese and raw material apatite with a rare earth content of 950 ppm or more, and it can be clearly known that there is a proportional relationship with the rare earth. r 2is the coefficient of determination, which represents the explanatory power in regression analysis.

[0121] Moreover, if Figure 5 As shown, in order to sh The analysis of the results of the determination of rare earth elements shows the facies differentiation information based on the tendency of clay differentiation. Domain 1 represents the zeolitic clay and pelagic clay facies. sh It appears in a large range at various rare earth concentrations (2000ppm to 10000ppm), so it can be seen that the lithology distribution has no correlation with rare earths. The domain represents the area where the rock is distributed.

[0122] Moreover, domain 2 represents the iron-bearing clay and apatite clay facies, which can be seen to be clearly present in the Exp.329 area, due to the relatively high rare earth concentration and low V sh (above 900 ppm and below 0.5), it can be seen that the distribution of biogenic zeolite and ferromanganese zeolite clays has a linear relationship with rare earths and apatite clay presents the highest concentration. That is, domain 2 shows the distribution of iron-bearing clay and biogenic apatite clay lithologies, among which Exp.329 is dominant, with relatively high rare earth concentrations, V sh The bioapatite clay concentration is the highest and is linearly proportional.

[0123] Moreover, if Figures 6 to 8 As shown, selectively through gamma-ray spectroscopy data, V sh Natural gamma rays, Th, and K form crusts around the background (i.e., the distribution of paleoclays and zeolitic clays), which can distinguish raw apatite and Fe-Mn zeolitic clays, and the proportional relationship can be repeatedly confirmed by U, that is, the Uranium [ppm] value and V in the distribution of bioapatite and Fe-Mn zeolitic clays. sh The proportional relationship between the NGR values ​​can be confirmed again.

[0124] Moreover, if Figures 6 to 8 As shown, the rare earth element (REE) measurement values ​​and V sh The results show that the raw apatite (Bioapatite) and Fe-Mn zeolite show a correlation from the high correlation (High Correlation) of the original gamma ray to the value with lower shale (50%). When the rare earth concentration is below 800 ppm, most of the lithofacies are marine clay or zeolite clay without Fe-Mn.

[0125] Based on the lithology classified as above, the raw material apatite and Fe-Mn zeolite clay with a rare earth concentration of 950 ppm or more are used as the object, and the mathematical formula for predicting or evaluating the amount of rare earth resources using the linear regression model based on the least squares method can be shown in the above mathematical formula 3. The correlation coefficient value (Correlation Coefficient) is 0.768721, r 2 The value is 0.590931975841.

[0126] As described above, the integrated screening device for screening the particle size of soil samples according to the embodiment of the present invention provides a harmonious balance between functionality, effectiveness and ease of use, and simplifies the screening process, thereby ensuring consistent results. Therefore, the present invention is a very useful device in soil analysis and similar application fields.

[0127] In the above, a plurality of preferred embodiments of the present invention are described through some examples. The descriptions related to the plurality of embodiments recorded in the “Specific Implementation Method” item are only for illustration. Any person skilled in the art in the technical field to which the present invention belongs can clearly understand from the following description that the present invention can be subjected to various modifications and implementations to perform implementations equivalent to the present invention.

[0128] Furthermore, the present invention can be embodied in a variety of different forms. Therefore, the present invention is not limited to the above description. The above description makes the disclosure of the present invention complete and is provided to inform the ordinary technicians in the technical field to which the present invention belongs of the full scope of the present invention. The present invention is defined by each claim in the scope of protection required by the invention.

Claims

1. A system for predicting rare earth resources in deep-sea sediments using gamma rays, characterized in that: include: the Data Collection Division, which collects gamma-ray data related to deep-sea sediments; a data processing unit that normalizes and processes the gamma ray data collected by the data collection unit; as well as The prediction modeling unit generates a model for predicting rare earth petrographic analysis information and rare earth resource quantity by a linear regression method from the data normalized in the data processing unit.

2. The system for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 1, characterized in that: The above gamma ray data includes natural gamma ray data and total gamma ray data.

3. The system for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 1, characterized in that: The data processing department includes: a normalization unit that normalizes the gamma-ray data collected by the data collection unit; and The shale volume correction unit corrects the shale volume of the gamma-ray data using the X-ray diffraction data.

4. The system for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 1, characterized in that: The above-mentioned predictive modeling section includes: A petrographic information unit that analyzes gamma-ray spectroscopy data to generate rare earth petrographic information that distinguishes clay types; and The linear regression modeling unit generates a linear regression model for each clay based on the facies differentiation information and the normalized and corrected data.

5. The system for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 4, characterized in that: For the above petrographic information, gamma-ray spectrum data is analyzed and clay types are distinguished based on the ratios of thorium Th, potassium K, and uranium U to generate rare earth petrographic analysis information.

6. The system for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 1, characterized in that: The system further includes a resource prediction unit for estimating and predicting the rare earth content of each type of clay and the total rare earth resource amount using a least square method based on the model generated by the prediction modeling unit.

7. A method for predicting rare earth resources in deep-sea sediments using gamma rays, characterized in that: include: Step (a), a data collection unit collects gamma ray data related to deep-sea sediments; Step (b), the data processing unit normalizes and processes the gamma ray data collected by the data collection unit; as well as In step (c), the prediction modeling unit generates a model for predicting rare earth petrographic analysis information and rare earth resource quantity by a linear regression method using the data normalized in the data processing unit.

8. The method for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 7, characterized in that: The above step (a) includes the following steps: the above data collection unit collects natural gamma ray data and total gamma ray data related to deep-sea sediments.

9. The method for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 7, characterized in that: The above step (b) comprises: Step (b1), the data processing unit normalizes the gamma ray data collected by the data collection unit; and In step (b2), the data processing unit uses the X-ray diffraction data to correct the shale volume in the gamma-ray data.

10. The method for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 7, characterized in that: The above step (c) comprises: Step (c1), the prediction modeling unit analyzes the gamma-ray spectrum data to generate rare earth petrographic analysis information for distinguishing clay types of the gamma-ray data; and In step (c2), the prediction modeling unit generates a linear regression model for each clay based on the facies differentiation information and the normalized and corrected data.

11. The method for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 10, characterized in that: For the above petrographic information, gamma-ray spectrum data is analyzed and clay types are distinguished based on the ratios of thorium Th, potassium K, and uranium U to generate rare earth petrographic analysis information.

12. The method for predicting rare earth resources in deep-sea sediments using gamma rays according to claim 7, characterized in that: The method further includes a step in which a resource prediction unit estimates and predicts the rare earth content of each clay and the total rare earth resource amount using a least square method based on the model generated by the prediction modeling unit.

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