WO2015022806A1 - Crust data analysis method, crust data analysis program, and crust data analysis device - Google Patents

Abstract

A crust data analysis device (1) is provided with: an acquisition unit (41) for acquiring a plurality of data items indicating the crustal abundance of a prescribed element or compound at a plurality of locations; a generation unit (42) for using differing first thresholds to delete, from the plurality of data items, data for which the crustal abundance is greater than or equal to the first threshold corresponding to the data item and generating a plurality of data items for which deletion has been completed; a determination unit (43) for determining whether the data composing the data items for which deletion has been completed conforms to a normal distribution having the average crustal abundance of the prescribed element or compound within an area larger than the area including the plurality of positions as the mean thereof; and a setting unit (44) for setting a geochemical anomaly threshold to the maximum crustal abundance value in the data item for which deletion has been completed having the smallest amount of data from among the plurality of data items for which deletion has been completed that have been determined to not conform to the normal distribution.

Description

Crustal data analysis method, crustal data analysis program, and crustal data analysis apparatus

The present invention relates to a crustal data analysis method, a crustal data analysis program, and a crustal data analysis apparatus used for geochemical exploration.

In geochemical exploration data analysis, it is known that the substance content of multiple samples collected from the crust follows a normal distribution with a common logarithm. FIG. 9 is a diagram showing an example of a frequency distribution of a dorsal value corresponding to a general content rate of contained elements in each rock and an abnormal value suggesting the presence of mineral occurrence in a plurality of samples collected from the crust. is there. In FIG. 9, the horizontal axis represents the common logarithm of substance content, and the vertical axis represents the number of samples corresponding to each substance content. FIG. 9 shows a dorsal value frequency distribution FD1 and an abnormal value frequency distribution FD2. These dorsal value frequency distribution FD1 and abnormal value frequency distribution FD2 tend to follow a normal distribution independently.

Conventionally, in order to separate a plurality of samples collected from the crust into a population of dorsal values and a population of abnormal values, the threshold values of the contained elements of the multiple samples with respect to the frequency distribution of the contained elements of the multiple samples Has been set up.

For example, paying attention to the fact that a plurality of data belonging to one population that follows the normal distribution in the common logarithm among the data indicating the substance content of each of a plurality of samples shows a straight line in the cumulative frequency distribution, the straight line in the cumulative frequency distribution A method of reading a threshold value between a population of dorsal values and a population of abnormal values from an inflection point is disclosed (for example, see Non-Patent Document 1).

FIG. 10 is a cumulative frequency distribution diagram of analysis values of each element in each of a plurality of samples. In FIG. 10, the horizontal axis indicates the content of each element, and the vertical axis indicates the normal distribution probability. In FIG. 10, for example, it can be confirmed that the straight line indicating the cumulative distribution of molybdenum (Mo) is bent at about 2 ppm and about 8 ppm. Thus, a line segment of about 2 ppm or less is formed by the population of dorsal values, and a line segment from about 2 ppm to about 8 ppm is formed of the population of dorsal values and the population of abnormal values, and a line of about 8 ppm or more is formed. It can be confirmed that the minute is formed by the population of abnormal values. Therefore, the threshold value of the population corresponding to the back value in Mo and the population corresponding to the abnormal value can be set between about 2 ppm and about 8 ppm.
As another method for determining the threshold value, a method is disclosed in which the threshold value is determined by a multiple of a value obtained by adding the standard deviation to the average value in the common logarithm (see, for example, Non-Patent Document 2).

However, in many cases, the inflection point is not clearly shown as shown in FIG. 9 even when the threshold values of the population of the back value and the population of the abnormal value are determined based on the substance contents of a plurality of samples. Therefore, even if the method disclosed in Non-Patent Document 1 is used, it may be difficult to set the threshold value.

Moreover, even if the method disclosed in Non-Patent Document 2 is used, by giving a value that is half the detection limit value to data less than the detection limit of geochemical analysis, the original average value and standard deviation can be reduced. It may not be obtained. Furthermore, there is a problem that a threshold cannot be set appropriately even if the method disclosed in Non-Patent Document 2 is applied to a population that clearly does not follow the normal distribution.

Therefore, it is desired to be able to appropriately set a threshold value for geochemical abnormality even for a population that does not follow a normal distribution and a plurality of data including data that is less than the detection limit of geochemical analysis.

The object of the present invention is to provide a crustal data analysis method, a crustal data analysis program, and a crustal data analysis apparatus that can appropriately set a threshold value for a geochemical abnormality.

The crustal data analysis method according to the present invention includes a step of obtaining a plurality of data indicating the crustal abundance of a predetermined element or compound at a plurality of positions, and deleting some data from the plurality of data, Based on the determination result, the step of generating deleted data, the step of determining whether or not the plurality of data constituting the deleted data follow a normal distribution for each of the plurality of deleted data, Determining a threshold for geochemical anomaly.

Further, in the crustal data analysis method according to the present invention, in the step of generating the plurality of deleted data, the crustal presence of the plurality of data is calculated from the plurality of data by using different first threshold values. An area where a plurality of data constituting the deleted data is wider than an area including the plurality of positions in the determining step by deleting data equal to or greater than the first threshold to generate a plurality of deleted data. In the step of determining whether or not to follow a normal distribution with an average crustal abundance of the predetermined element or compound in the mean value, and determining the threshold, a plurality of the deleted that has been determined not to follow the normal distribution Among the data, a value within a predetermined range from the maximum value of the crustal abundance in the deleted data with the smallest number of data may be determined as the threshold for geochemical abnormality. .

Further, in the crustal data analysis method according to the present invention, in the step of generating the plurality of deleted data, a crustal presence degree of the plurality of data is determined from the plurality of data by using different first threshold values. Deleting data equal to or greater than one threshold, and using a second threshold lower than the first threshold, deleting data equal to or less than the second threshold, and generating the plurality of deleted data Also good.

Further, in the crustal data analysis method according to the present invention, the second threshold value is a detection limit value of the crustal presence, and in the step of generating the plurality of deleted data, data equal to or less than the second threshold value. The deleted data may be generated by deleting data that is equal to or greater than the first threshold after deleting.

Further, in the crustal data analysis method according to the present invention, in the step of generating the plurality of deletion data, the first threshold value is sequentially decreased to generate the plurality of deleted data, and in the determination step The determination may be performed in response to the generation of the one deleted data.

In the crustal data analysis method according to the present invention, in the step of generating the plurality of deletion data, the first threshold is determined based on a binary search method, and the deletion is performed based on the determined first threshold. Completed data may be generated.

The crustal data analysis program according to the present invention includes a computer that acquires a plurality of data indicating the crustal abundance of predetermined elements or compounds at a plurality of positions, and deletes some data from the plurality of data. A generation unit that generates a plurality of deleted data, a determination unit that determines whether or not a plurality of pieces of data constituting the deleted data follow a normal distribution for each of the plurality of deleted data, and the determination It is made to function as a determination part which determines the threshold value of geochemical abnormality based on a result.

The crustal data analysis apparatus according to the present invention includes an acquisition unit that acquires a plurality of data indicating the crustal abundances of a predetermined element or compound at a plurality of positions, a part of the plurality of data is deleted, and a plurality of data is deleted. A generation unit that generates the deleted data, a determination unit that determines whether or not a plurality of pieces of data constituting the deleted data follow a normal distribution, and a result of the determination And a determination unit that determines a threshold for geochemical abnormality.

According to the present invention, the threshold for geochemical abnormality can be set appropriately.

It is a functional lineblock diagram of the crust data analysis device concerning a 1st embodiment. It is a flowchart which shows an example of the flow of a process until the threshold value of geochemical abnormality is determined in the crustal data analysis apparatus which concerns on 1st Embodiment. It is a figure which shows the collection position of the sample in the existing area applied to the 1st area which concerns on 1st Embodiment. It is a frequency distribution map of the analytical value of lead. It is a cumulative frequency distribution diagram of the analytical value of lead. It is a figure which shows the sampling position determined to be an abnormal value based on the threshold value of the geochemical abnormality of the crustal abundance of lead. It is a frequency distribution map of the analytical value of zinc. It is a cumulative frequency distribution figure of the analytical value of zinc. It is a figure which shows the sampling position determined to be an abnormal value based on the threshold value of the geochemical abnormality of the crustal abundance of zinc. It is a frequency distribution map of the analytical value of silver. It is a cumulative frequency distribution map of the analytical value of silver. It is a figure which shows the sampling position determined to be an abnormal value based on the threshold value of the geochemical abnormality of the degree of silver crust. It is a frequency distribution map of the analytical value of sulfur. It is a cumulative frequency distribution map of the analytical value of sulfur. It is a figure which shows the sampling position determined to be an abnormal value based on the threshold of the geochemical abnormality of the degree of sulfur crust presence. It is a flowchart which shows an example of the flow of a process until the threshold value of a geochemical abnormality is determined in the crustal data analysis apparatus which concerns on 2nd Embodiment. It is a figure which shows the example of the frequency distribution of the back value and the abnormal value in the some sample extract | collected from the crust. It is a cumulative frequency distribution figure of the analysis value of each element in each of a plurality of samples. It is a frequency distribution map of the analytical value of Au (gold) in the Kushikino deposit, which is a typical deposit zone in the northern and southern areas. It is a cumulative frequency distribution figure of the analysis value of Au. It is a figure which shows the collection position (black circle in a figure) determined to be an abnormal value by this method. It is a figure which shows the collection position (black circle in a figure) determined with the abnormal value by the conventional method.

<First Embodiment>
[Configuration example of the crustal data analysis apparatus 1]
FIG. 1 is a functional configuration diagram of a crustal data analysis apparatus 1 according to the present embodiment.
The crustal data analyzing apparatus 1 is a plurality of data indicating the crustal abundance of each of a plurality of elements or compounds (hereinafter also referred to as elements) obtained by analyzing samples collected at a plurality of positions in the first area. Threshold values (hereinafter referred to as geochemical anomaly threshold values) for categorizing the crustal abundance information, which are composed of a population of dorsal values and a population of abnormal values, are determined. Hereinafter, the plurality of data is referred to as target data.

Specifically, the crustal data analysis apparatus 1 deletes data whose crustal presence is equal to or higher than the first threshold value from the target data and creates deleted data. Then, the crustal data analyzing apparatus 1 changes the first threshold value until the deleted data follows a normal distribution having an average value of the average crustal presence in the second area wider than the first area, and among the deleted data, The maximum crustal abundance value in the deleted data with the smallest number of data is determined as the geochemical abnormality threshold. For example, Rudnick, RL and Gao, S. (2003) The Composition of the Continental Crust, p1-64. In The Crust (ed. RL Rudnick) Vol. 3, Treatise on Geochemistry Average chemical composition data of the upper continental crust shown in (eds. HD Holland and 上部 KK 大陸 Turekian), Elsevier-Pergamon, Oxford can be used.

Hereinafter, a specific configuration of the crustal data analysis apparatus 1 will be described.
The crustal data analysis apparatus 1 includes a display unit 10, an input unit 20, a storage unit 30, and a control unit 40.
The display unit 10 is configured by a liquid crystal display, for example. The display unit 10 displays various information according to the control of the control unit 40.
The input unit 20 is configured by a mouse or a keyboard, for example. The input unit 20 receives input of various types of information from the user and outputs the received information to the control unit 40.

The storage unit 30 includes, for example, a ROM and a RAM, and a hard disk. The storage unit 30 stores various programs (not shown) for causing the crustal data analysis apparatus 1 to function. Specifically, the storage unit 30 stores a crustal data analysis program for causing the control unit 40 to function as an acquisition unit 41, a generation unit 42, a determination unit 43, and a determination unit 44 described later. The crustal data analysis program may be stored in a storage medium such as a CD-ROM or a hard disk, and the storage unit 30 may store the crustal data analysis program acquired from the storage medium.

Further, the storage unit 30 stores, for example, target data. In addition, the storage unit 30 stores average chemical composition data indicating average crust abundances of a plurality of elements and the like measured at a plurality of positions in a second area wider than the first area. In the present embodiment, the second area includes the first area.

The control unit 40 is constituted by a CPU, for example. The control unit 40 comprehensively controls functions related to the crustal data analysis device 1 by executing various programs stored in the storage unit 30 for causing the crustal data analysis device 1 to function. Specifically, the control unit 40 executes a crustal data analysis program stored in the storage unit 30 to obtain a computer, an acquisition unit 41, a generation unit 42, a determination unit 43, and a determination unit 44. To function as. Hereinafter, the acquisition unit 41, the generation unit 42, the determination unit 43, and the determination unit 44 will be described.

The acquisition unit 41 acquires target data from the storage unit 30, for example. That is, the acquisition unit 41 acquires a plurality of pieces of data indicating the crustal abundance of predetermined elements or compounds at a plurality of positions, which are stored in advance in the storage unit 30. Here, the predetermined element or compound is an element or compound that is a target for finding a threshold value between the population of the back value and the population of the abnormal value.

The generation unit 42 deletes some data from a plurality of data and generates a plurality of deleted data. Specifically, the generation unit 42 deletes data having a crust presence degree equal to or higher than the first threshold from the target data acquired by the acquisition unit 41 using different first threshold values, and deletes a plurality of deleted data. Generate data. More specifically, the generation unit 42 sets the highest crust presence level among the crust presence levels indicated by the target data as the first threshold value, and deletes data that is equal to or higher than the first threshold value from the target data and has been deleted. Generate data. Thereafter, when the determination unit 43 determines that the first threshold value is reset, the generation unit 42 sets the second highest crust presence level as the first threshold value among the crust presence levels indicated by the target data, Data that is equal to or greater than the first threshold is deleted from the target data to generate deleted data.

Further, a plurality of data indicating a plurality of different values are stored in advance in the storage unit 30 as a first threshold data set, and the generation unit 42 extracts data from the data set in descending order of values, The value of the extracted data may be set as the first threshold value.

Here, the generation unit 42 deletes data whose crustal presence of the plurality of data is equal to or higher than the first threshold from the target data using different first thresholds, and is lower than the first threshold. A plurality of deleted data may be generated by deleting data equal to or lower than the second threshold from the target data using the second threshold. The second threshold is a detection limit value of the crustal presence in the target data.
The generation unit 42 may generate deleted data by deleting data that is equal to or higher than the first threshold after deleting data that is equal to or lower than the second threshold from the target data.

The determination unit 43 determines, for each of the plurality of deleted data, whether or not the plurality of data constituting the deleted data follows a normal distribution. As shown below, the determination unit 43 determines whether or not a plurality of data constituting the deleted data follow a normal distribution having an average value of the average crust presence of the predetermined element in the second area. Here, the predetermined element refers to an element or a compound for which a threshold value between the population of the back value and the population of the abnormal value is found.

First, the determination unit 43 acquires the average crustal abundance μ of a predetermined element from the average chemical composition data stored in the storage unit 30. Then, the determination unit 43 establishes a null hypothesis that the average value of the crust presence of the plurality of data constituting the deleted data is the average crust presence μ of the predetermined element in the second area. Subsequently, the determination unit 43, based on the following formulas (1) to (3), the average value x _bar of the crust presence of a plurality of data constituting the deleted data, the variance S ² , and the test statistic t Is calculated. Here, it is assumed that there are n pieces of data constituting the deleted data, and the crustal presence of each piece of data is x _i (where i is an integer between 1 and n).

Subsequently, the determination unit 43 specifies the boundary value t _(n−1) (0.05) of the t distribution with n−1 degrees of freedom from the t distribution table stored in the storage unit 30 in advance, and the test statistics It is determined whether or not the absolute value of the quantity t is larger than the boundary value. When the determination unit 43 determines that the absolute value of the test statistic t is larger than the boundary value, the determination unit 43 adopts the alternative hypothesis, and the deleted data uses the average crust presence of the predetermined element in the second area as the average value. It is determined that the normal distribution is not followed. Further, when the determination unit 43 determines that the absolute value of the test statistic t is equal to or less than the boundary value, the determination unit 43 rejects the alternative hypothesis, and the deleted data indicates that the average crust presence of the predetermined element in the second area is the average value. It is determined to follow a normal distribution.

The determination unit 44 determines a threshold for geochemical abnormality based on the determined result. Specifically, the determination unit 44 determines the maximum crustal abundance value in the deleted data with the least data among the plurality of deleted data determined not to follow the normal distribution as the geochemical abnormality threshold value. To do.

[Processing flow]
Next, the process flow of the control unit 40 will be described. FIG. 2 is a flowchart illustrating an example of a processing flow until the geochemical abnormality threshold value is determined in the crustal data analysis apparatus 1 according to the first embodiment.

First, the acquisition unit 41 acquires target data stored in the storage unit 30 (S1).
Subsequently, the generation unit 42 deletes data whose crust presence is equal to or less than the second threshold from the target data acquired in S1 (S2).

Subsequently, the generation unit 42 determines a first threshold value (S3). For example, when the number of execution times of the process is 1, that is, when the process is executed for the first time, the generation unit 42 determines the highest crust presence in the target data as the first threshold value. In addition, when the number of execution times of the process is two or more, the generation unit 42 sets a new crust presence level next to the crust presence level corresponding to the first threshold value determined immediately before in the target data. The first threshold is determined.

Subsequently, the generation unit 42 deletes data having a first threshold value or more from the target data to generate deleted data (S4).
Subsequently, the determination unit 43 determines whether or not the deleted data follows a normal distribution having an average value of the average crust presence of the predetermined element in the second area (S5). If the determination unit 43 determines to follow the normal distribution (if the determination in S5 is Yes), the determination unit 43 moves the process to S6. Moreover, the determination part 43 transfers a process to S1, when it determines with not following normal distribution (when determination of S5 is No).

Subsequently, the determination unit 44 deletes the deleted data having the smallest number of data among the plurality of deleted data generated in S4 and determined not to follow the normal distribution, that is, the deleted data generated immediately before. Identify. Subsequently, the determination unit 44 determines the maximum value of the crustal presence in the specified deleted data as the threshold for geochemical abnormality (S6).

[Example of application to actual data]
FIG. 3 is a diagram illustrating a sampling position of a sample in an existing area (Toyoha vein deposit) applied to the first area according to the first embodiment. The sampling position shown in FIG. 3 is 184 points of the Toyoha vein deposit described in Non-Patent Document 2. Analytical values of rock samples collected on the surface of those points described in Non-Patent Document 2 were used as target data to which the method according to this embodiment is applied. The black dots shown in FIG. 3 are sample collection positions. Here, based on the content of lead (Pb), zinc (Zn), silver (Ag), and sulfur (S) obtained by analyzing a plurality of rock samples collected at the above points, each element The threshold of geochemical anomaly was determined.

FIG. 4A is a frequency distribution diagram of analysis values of lead (Pb). The horizontal axis in FIG. 4A indicates the value of the common logarithm of the crustal abundance of Pb. FIG. 4B is a cumulative frequency distribution diagram of lead analysis values. The horizontal axis of FIG. 4B shows the crustal abundance of Pb, and the vertical axis shows the inverse function of the normal distribution probability. FIG. 4C is a diagram illustrating a sampling position determined to be an abnormal value based on a geochemical anomaly threshold value of lead crustal abundance. In FIG. 4C, a black spot having the same size as the black spot shown in FIG. 3 indicates a sampling position where the crustal abundance of Pb was 1.5 to 16 ppm, and a white circle represents a crustal abundance of Pb of 17 to 24 ppm. A black circle larger than the black point indicates a sampling position where the crustal abundance of Pb was 25 to 70 ppm.

As for the crustal abundance of Pb in the first area, the maximum value is 70 ppm and the minimum value is 1.5 ppm, and the average value x _bar of the plurality of data is 5.2 ppm. The average value of the crustal abundance of Pb in the average chemical composition data stored in the storage unit 30 is 17 ppm. Based on these results, the crustal data analysis apparatus 1 calculated the threshold value of the geochemical abnormality, and as a result, the threshold value was 25 ppm. In the first area, 10 points were obtained as geochemical abnormality points exceeding 25 ppm. In addition, the point where the sample of the crustal abundance having the same value as the threshold value of the geochemical abnormality may be taken as the point of the geochemical abnormality. Thus, even when the cumulative frequency distribution diagram shows a substantially straight line as shown in FIG. 4B, it can be seen that according to the method according to the present embodiment, a point indicating a geochemical abnormality can be extracted.

FIG. 5A is a frequency distribution diagram of analytical values of zinc (Zn). FIG. 5B is a cumulative frequency distribution diagram of analytical values of zinc. FIG. 5C is a diagram showing a sampling position determined as an abnormal value based on a geochemical abnormality threshold value of the crustal abundance of zinc. The black dots in FIG. 5C indicate the sampling positions where the crustal abundance of Zn was 0.5 to 66 ppm, the white circles indicate the sampling positions where the crustal abundance of Zn was 67 to 96 ppm, and the black circles represent Zn. The sampling position where the crust abundance was 97 to 370 ppm is shown.

As for the crustal abundance of Zn in the first area, the maximum value is 370 ppm and the minimum value is 0.5 ppm, and the average value x _bar of the plurality of data is 45.9 ppm. In addition, the average value of Zn crust presence in the average chemical composition data stored in the storage unit 30 is 67 ppm. Based on these results, the crustal data analysis apparatus 1 calculated the threshold value for geochemical abnormality, and as a result, the threshold value was 97 ppm. In the first area, 23 points were obtained as geochemical anomalies exceeding 97 ppm.

FIG. 6A is a frequency distribution diagram of analysis values of silver (Ag). FIG. 6B is a cumulative frequency distribution diagram of analysis values of silver. FIG. 6C is a diagram illustrating a sampling position determined as an abnormal value based on a threshold value of a geochemical abnormality of the degree of silver crust presence. The black dots in FIG. 6C indicate the sampling positions where the crustal abundance of Ag was 0.1 ppm or less, and the black circles indicate the sampling positions where the crustal abundance of Ag was 0.2 to 6.2 ppm.

As for the crustal abundance of Ag in the first area, the maximum value is 6.2 ppm and the minimum value is 0.1 ppm, and the average value x _bar of the plurality of data is 0.2 ppm. The detection limit value of Ag is 0.2 ppm. Moreover, the average value of Ag crustal abundance in the average chemical composition data stored in the storage unit 30 is 53 ppb. Based on these results, the crustal data analysis apparatus 1 calculates the threshold value of the geochemical anomaly. As a result, the deleted data when the 0.2 ppm data is not deleted is the normal value of the crustal abundance with an average crust abundance of 53 ppb. Since it did not follow the distribution, the detection limit value of 0.2 ppm was determined as the geochemical abnormality threshold. As a result, in the first area, 35 points were obtained as points of geochemical abnormality exceeding the detection limit value of 0.2 ppm.

FIG. 7A is a frequency distribution diagram of analysis values of sulfur (S). FIG. 7B is a cumulative frequency distribution diagram of analysis values of sulfur. FIG. 7C is a diagram illustrating a sampling position determined as an abnormal value based on a threshold of geochemical abnormality of sulfur crustal abundance. A black dot in FIG. 7C indicates a sampling position where the crustal abundance of S was 0.005 to 0.05 ppm, and a white circle indicates a sampling position where the crustal abundance of S was 0.06 to 0.09 ppm. The black circles indicate the sampling positions where the crustal abundance of S was 0.1 to 4.41 ppm.

The maximum crustal abundance of sulfur in the first area is 4.41% and the minimum value is 0.005%, and the average value x _bar of the plurality of data is 0.2%. Moreover, the average content of the crustal abundance of S in the average chemical composition data stored in the storage unit 30 is 0.06%. Based on these results, the crustal data analysis apparatus 1 calculated the threshold value for geochemical abnormality, and as a result, 0.1% was determined as the threshold value for geochemical abnormality, and 109 points were obtained as points for the geochemical abnormality. .

[Comparative example]
Subsequently, with respect to the content of each element and the like in the sample collected at 163 points in the existing area applied to the first area, the threshold for geochemical abnormality was determined using another method.
Here, Pb, Zn, and Ag are determined by a method (hereinafter referred to as the method of the Ministry of International Trade and Industry) proposed by the Ministry of International Trade and Industry in 1986 to determine a threshold value by a multiple of a value obtained by adding a standard deviation to an average value of common logarithms. An example in which the threshold value for geochemical abnormality of S is determined will be described.

Pb has an average value of 5.2 ppm, an average value + standard deviation of 13.2 ppm, and an average value + 2 × standard deviation of 33.4 ppm. In this case, the appearance of geochemical anomalies is not much different between the method of the crustal data analysis apparatus 1 and the method of the Ministry of International Trade and Industry that determines the threshold value by a multiple of the value obtained by adding the standard deviation to the average value in the common logarithm. It is considered a thing.

Zn has an average value of 45.9 ppm, an average value + standard deviation of 146.6 ppm, and an average value + 2 × standard deviation of 468.0 ppm. A value greater than the average value + 2 × standard deviation does not exist in the target data. The average value of 45.9 ppm is smaller than 67 ppm, which is the average value of the crustal abundance indicated by the average chemical composition data of the upper continental crust. Therefore, the method of the Ministry of International Trade and Industry cannot detect a population of abnormal values that can be detected by the method using the crustal data analysis apparatus 1.

Ag has an average value of 0.2 ppm, an average value + standard deviation of 0.4 ppm, and an average value + 2 × standard deviation of 0.9 ppm. Here, there are 25 points greater than 0.4 ppm and 9 points greater than 0.9 ppm. Even with the method of the Ministry of International Trade and Industry, a potential portion of a geochemical anomaly vein can be obtained, but the threshold value for the geochemical anomaly is determined without considering the detection limit value, so that the threshold value may not be appropriately determined. Therefore, compared with the method of the Ministry of International Trade and Industry, it is considered that the method for determining the threshold value of the geochemical abnormality by the crustal data analysis apparatus 1 more accurately indicates the presence of the geochemical abnormality.

S has an average value of 0.2%, an average value + standard deviation of 1.1%, and an average value + 2 × standard deviation of 6.1%. This value of 6.1% is the crustal abundance that is not present in the sample, and is far above the average value of the crustal abundance shown in the average chemical composition data. Therefore, it can be said that the method of the Ministry of International Trade and Industry cannot appropriately determine the threshold for S geochemical abnormality. On the other hand, in the method for determining the threshold value of the geochemical abnormality by the crustal data analyzing apparatus 1, 109 points are obtained as the points of the geochemical abnormality as described above. Therefore, also from this result, compared with the method of the Ministry of International Trade and Industry, the method of determining the threshold value of the geochemical abnormality by the crustal data analysis apparatus 1 can accurately detect the point where the geochemical abnormality is occurring. Conceivable.

[Verification of normal distribution]
Next, the results of testing whether the average chemical composition of the main igneous rocks in the crust conforms to a normal distribution with the crustal abundance as an average value will be described. Specifically, assuming that the average chemical composition of the main igneous rocks follows the normal distribution of the average crustal abundance μ in the second area, a confidence interval with a 95% confidence coefficient of population variance is obtained, and μ + σ by its maximum standard deviation σ The value of was calculated. For example, SiO ₂ showed a result that 93.1% was close to the maximum value in a normal population having an average value of crustal abundance. This value roughly corresponds to the value of strong silicified rock. In addition, MgO showed a result that 25.9% was close to the maximum value in the normal population with the average crustal abundance. Also regarding this, since the MgO contained in the partially melted liquid of the mantle reaches 20% or more, the numerical value of 25.9 is also considered appropriate. Therefore, it was shown that the average chemical composition of the main igneous rocks in the crust follows a normal distribution with the average crustal abundance.

[Effect in the first embodiment]
As described above, the crustal data analysis apparatus 1 according to the present embodiment determines whether or not the plurality of data constituting the deleted data follows a normal distribution for each of the plurality of deleted data from the plurality of data. Based on the determination result, a threshold for geochemical abnormality is determined. Therefore, the crustal data analysis apparatus 1 classifies the plurality of deleted data into deleted data that conforms to the normal distribution and deleted data that does not conform to the normal distribution, and a population of dorsal values and a population of abnormal values And the threshold value of the geochemical abnormality can be appropriately set based on the boundary values of these populations.

In addition, the crustal data analyzing apparatus 1 deletes data having a crustal abundance of the plurality of data equal to or higher than the first threshold from a plurality of data by using different first threshold values. To determine whether or not the plurality of data constituting the deleted data follow a normal distribution having an average value of the average crust presence of the predetermined element in the second area wider than the first area. Among the plurality of deleted data determined not to be followed, the maximum value of the crustal presence in the deleted data with the smallest number of data is determined as the threshold for geochemical abnormality.

Therefore, the crustal data analyzing apparatus 1 is configured to analyze a plurality of data from a population that follows a normal distribution having an average value of the average crustal abundance of a predetermined element in a second area wider than the first area, and a mother that does not follow the normal distribution. It is possible to appropriately set a threshold of geochemical abnormality for classifying into a group.

In addition, in the step of generating a plurality of deleted data, the crustal data analysis apparatus 1 uses a first threshold value that is different from each other, and the crustal presence of the plurality of data is greater than or equal to the first threshold value. While deleting the data, the second threshold lower than the first threshold is used to delete data equal to or lower than the second threshold to generate a plurality of deleted data. That is, the crustal data analyzing apparatus 1 deletes the data that has been deleted from the deleted data by deleting the data that is equal to or lower than the second threshold value indicating the detection limit value of the crustal presence, and then deletes the data that is equal to or higher than the first threshold value. Is generated. In this way, the crustal data analysis apparatus 1 determines whether or not to follow the normal distribution using the deleted data obtained by deleting the crustal abundance level lower than the detection limit value. Can be determined.

<Second Embodiment>
[Determine first threshold using binary search]
Next, the second embodiment will be described.
Unlike the first embodiment, the generation unit 42 of the present embodiment determines the first threshold based on the binary search method, and generates deleted data based on the determined first threshold. The point is the same.

Specifically, the generation unit 42 rearranges the deleted data in descending order of crust presence. Subsequently, the generation unit 42 sets the search range of the first threshold from data corresponding to the crustal presence closest to the average crustal presence to data having the highest crustal presence. Subsequently, the generation unit 42 determines an intermediate value of the search range as an initial value of the first threshold value. Subsequently, the generation unit 42 generates deleted data by deleting data that is equal to or greater than the determined first threshold value from the target data.
Subsequently, the determination unit 43 determines whether or not the deleted data follows a normal distribution having the average crustal presence of the predetermined element in the second area as an average value.

Based on the determination result of the determination unit 43, the generation unit 42 reduces the new search range of the first threshold to half the previous search range, and determines a new first threshold.
Specifically, when the determination unit 43 determines that the normal distribution is followed, the generation unit 42 sets a range larger than the first threshold in the current search range as a new search range. Further, when the determination unit 43 determines that the normal distribution is not followed, the generation unit 42 sets a range smaller than the first threshold in the current search range as a new search range. Subsequently, the generation unit 42 determines the intermediate value of the newly set search range as a new first threshold value. Subsequently, the generation unit 42 deletes data that is equal to or more than the determined first threshold value and generates deleted data.

The generation unit 42 repeats the process of determining the first threshold and generating deleted data until the search range cannot be reduced.
The determination unit 44, after the generation unit 42 finishes generating the deleted data, among the plurality of deleted data determined not to follow the normal distribution, the largest crust in the deleted data with the smallest number of data. The abundance value is determined as the geochemical anomaly threshold.

[Processing flow]
Next, the process flow of the control unit 40 will be described. FIG. 8 is a flowchart illustrating an example of a processing flow until the geochemical abnormality threshold value is determined in the crustal data analysis apparatus 1 according to the second embodiment.

First, the acquisition unit 41 acquires target data stored in the storage unit 30 (S11).
Subsequently, the generation unit 42 deletes data whose crust presence is equal to or less than the second threshold from the target data acquired in S11 (S12).

Subsequently, the generation unit 42 determines the search range for the first threshold and the first threshold (S13). For example, the generation unit 42 executes the process once, that is, when the process is executed for the first time, the search range of the first threshold is data corresponding to the crustal presence closest to the average crustal presence. To the data with the highest crustal abundance. In addition, when the number of execution times of the process is two or more, the generation unit 42 sets the search range to half of the previous search range based on the determination result immediately before by the determination unit 43. In addition, the generation unit 42 determines the median value of the set search range as the first threshold value.

Subsequently, the determination unit 43 determines whether or not the deleted data follows a normal distribution having an average value of the average crust presence of the predetermined element in the second area (S15).
Subsequently, the generation unit 42 determines whether or not the search range of the first threshold can be reduced (S16). When the generation unit 42 determines that the search range can be reduced (when the determination of S16 is Yes), the process proceeds to S11, and when it is determined that the search range cannot be reduced (when the determination of S16 is No), S17. Move processing to.

Subsequently, the determination unit 44 identifies the deleted data with the smallest data among the plurality of deleted data generated in S14 and determined not to follow the normal distribution, that is, the deleted data generated immediately before. To do. Subsequently, the determination unit 44 determines the maximum value of the crustal presence in the specified deleted data as the threshold for geochemical abnormality (S17).

[Effects of Second Embodiment]
As described above, according to the crustal data analysis apparatus 1, the generation unit 42 determines the first threshold value based on the binary search method, and generates deleted data based on the determined first threshold value. Thus, the first threshold value can be determined, and the threshold value of the geochemical abnormality can be calculated at high speed.

As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the above embodiment.

For example, in the above embodiment, among the plurality of deleted data determined not to follow the normal distribution, the maximum crustal abundance value in the deleted data with the smallest number of data is determined as the geochemical abnormality threshold value. However, another value within a predetermined range may be determined as the threshold value from the maximum crustal abundance value. For example, the maximum crustal presence value in the deleted data having the largest number of data among the plurality of deleted data determined to follow the normal distribution may be determined as the geochemical abnormality threshold value. In addition, among a plurality of deleted data determined not to follow the normal distribution, a threshold value that is larger by a predetermined value is determined from the maximum crustal presence value in the deleted data with the smallest number of data. May be.

<Third Embodiment>
In the first embodiment, as a partial test, the test is repeated by deleting the upper and lower data centering on the value of the crustal abundance until the normal distribution is followed, and the highest value of the population of the minimum number not following the normal distribution A method for obtaining the value as a threshold has been described. In the present embodiment, the threshold value is determined by the following method so that it can be applied even when the number of data increases.

When the number of data items is n, the determination unit 43 calculates the standard deviation σ as follows, assuming that n items of data follow a χ ² distribution with n degrees of freedom.

Subsequently, the determination unit 43 reads a value from the χ ² distribution table and calculates σ ² . However, the determination unit 43, when n> 100,

Σ ² is calculated as approximately following the normal normal distribution N (0,1).

The determination unit 43 uses a minimum standard deviation obtained in this way, and follows a normal distribution in which the value obtained by adding the average value and the minimum standard deviation to the real number is returned to the real value. And the most recent value is set as the threshold value. The reason why the maximum standard deviation is not used is that the upper part of the dorsal value population and the lower order of the abnormal value population are considered to be overlapped, so that the overlap is minimized.

[Example of application to actual data]
Based on the contents of Pb, Zn, and S obtained by analyzing the rock sample collected in the collection area shown in FIG. 3 with the technique according to the present embodiment, the threshold of geochemical abnormality in each element is calculated. Made a decision.

Regarding Pb, the threshold value was 45 ppm, and two points were obtained as points of geochemical abnormality.
Regarding Zn, the threshold value was 166 ppm, and 5 points were obtained as points of geochemical abnormality.
For S, the threshold was 0.4%, and 70 points were obtained as geochemical abnormality points.

The method according to the present embodiment is particularly effective when the number of data exceeds 1000.
FIG. 11A is a frequency distribution diagram of the analysis value of Au (gold) in the Kushikino deposit, which is a typical deposit in the northern and southern areas. 2331 data were used. FIG. 11B is a cumulative frequency distribution diagram of Au analysis values. FIG. 11C is a diagram showing a sampling position (black circle in the figure) determined as an abnormal value based on the threshold value of the geochemical abnormality of the crustal abundance of Au obtained by this method. FIG. 11D is a diagram showing sampling positions (black circles in the figure) determined as abnormal values by the method of the Ministry of International Trade and Industry.

According to this method, the average value of crustal abundance was 1.5 ppb, the threshold value was 14 ppb, and 132 geochemical anomaly points were obtained. On the other hand, according to the method of the Ministry of International Trade and Industry, the average value of crustal abundance was 2 ppb, the average value + 2 × standard deviation was 75 ppb, and 65 geochemical abnormalities were obtained. As is clear when FIG. 11C and FIG. 11D are compared, by using this method, it was possible to grasp more geochemical abnormalities in the vicinity of the vein deposit.

A similar comparative analysis was conducted in the Kitaka area, which is a typical deposit area in Akita and Aomori.
When 3666 pieces of Pb data were used, according to this method, the average value of the crustal abundance was 17 ppm, the threshold value was 57 ppm, and 11 geochemical anomaly points were obtained. On the other hand, according to the method of the Ministry of International Trade and Industry, the average value of crustal abundance was 12 ppm, the average value + 2 × standard deviation was 161 ppm, and 8 geochemical anomaly points were obtained. Therefore, by using this method, more geochemical abnormalities could be grasped in the vicinity of the vein deposit.

For Zn, the same comparison was performed using 3743 pieces of data. According to this method, the average value of crustal abundance was 67 ppm, the threshold was 176 ppm, and 12 geochemical anomaly points were obtained. On the other hand, in the method of the Ministry of International Trade and Industry, the average value of the crustal abundance was 65 ppm, the average value + 2 × standard deviation was 534 ppm, and two geochemical anomaly points were obtained. Therefore, by using this method, more geochemical abnormalities could be grasped in the vicinity of the vein deposit.

[Effect in the third embodiment]
As described above, according to the method according to the third embodiment, even when the number of data is large, for example, 1000 or more, a threshold value can be obtained with high accuracy. There is an effect that it can be grasped in the vicinity of the deposit.

DESCRIPTION OF SYMBOLS 1 ... Crustal data analysis apparatus, 10 ... Display part, 20 ... Input part, 30 ... Memory | storage part, 40 ... Control part, 41 ... Acquisition part, 42 ... Generation part , 43 ... determination part, 44 ... determination part

Claims (8)

1. Obtaining a plurality of data indicating the crustal abundance of a predetermined element or compound at a plurality of positions;
   Deleting some data from the plurality of data to generate a plurality of deleted data;
   For each of the plurality of deleted data, determining whether or not the plurality of data constituting the deleted data follows a normal distribution;
   Determining a geochemical abnormality threshold based on the determined result;
   A crustal data analysis method comprising:
2. In the step of generating the plurality of deleted data, by using different first threshold values, from the plurality of data, deleting the data having a crustal presence of the plurality of data equal to or more than the first threshold, Generate multiple deleted data,
   In the determining step, the plurality of data constituting the deleted data follow a normal distribution having an average value of the average crust abundance of the predetermined element or compound in an area wider than an area including the plurality of positions. Determine whether or not
   In the step of determining the threshold value, a value within a predetermined range from the maximum value of the crust presence in the deleted data having the smallest number of data among the plurality of deleted data determined not to follow the normal distribution To be the threshold for geochemical anomalies,
   The crustal data analysis method according to claim 1.
3. In the step of generating the plurality of deleted data, the data having a crustal presence of the plurality of data having a first threshold value or more is deleted from the plurality of data by using different first threshold values. Using a second threshold value lower than the first threshold value, deleting data equal to or less than the second threshold value to generate the plurality of deleted data items;
   The crustal data analysis method according to claim 2.
4. The second threshold is a detection limit value of the crustal presence,
   In the step of generating the plurality of deleted data, after deleting the data below the second threshold, the deleted data is generated by deleting the data above the first threshold,
   The crustal data analysis method according to claim 3.
5. In the step of generating the plurality of deleted data, the first threshold value is sequentially reduced to generate the plurality of deleted data,
   In the determining step, the determination is performed in response to the generation of the one deleted data.
   The crustal data analysis method according to any one of claims 2 to 4.
6. In the step of generating the plurality of deleted data, the first threshold is determined based on a binary search method, and the deleted data is generated based on the determined first threshold.
   The crustal data analysis method according to any one of claims 2 to 4.
7. Computer
   An acquisition unit that acquires a plurality of data indicating the crustal abundance of a predetermined element or compound at a plurality of positions,
   A generator that deletes some data from the plurality of data to generate a plurality of deleted data;
   For each of the plurality of deleted data, a determination unit that determines whether or not the plurality of data constituting the deleted data follows a normal distribution, and a determination that determines a threshold value for a geochemical abnormality based on the determined result Part,
   Crustal data analysis program to function as.
8. An acquisition unit for acquiring a plurality of data indicating the crustal abundance of a predetermined element or compound at a plurality of positions;
   A generation unit that deletes some data from the plurality of data and generates a plurality of deleted data;
   For each of the plurality of deleted data, a determination unit that determines whether or not a plurality of data constituting the deleted data follows a normal distribution;
   Based on the determined result, a determination unit that determines a threshold value for geochemical abnormality,
   A crust data analysis device.

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