Method and system for identifying basalt construction environment

Abstract

The invention discloses a method and system for identifying a basalt construction environment, and the method comprises the steps: calculating a shape coefficient in an REE element composition curve according to the REE abundance of a basalt sample in a to-be-evaluated target region; projecting a first data point formed by a slope coefficient and a curvature coefficient in the shape coefficient into a slope and curvature cross plot, and projecting a second data point formed by an REE logarithmic mean coefficient in the shape coefficient and the magnesium oxide content into a logarithmic mean coefficient and magnesium oxide content cross plot, respectively obtaining projection positions of the two data points; and according to the current projection position, judging the basalt type of the current to-be-evaluated target area. According to the invention, quantitative characterization of construction environment type identification is realized.

Description

Technical Field

[0001] This invention relates to the field of basalt geological environment identification technology, and in particular to a method and system for identifying basalt tectonic environments. Background Technology

[0002] The abundance of rare earth elements (REEs) is typically characterized using REE composition profiles. This involves first normalizing the abundance using chondrite abundance data, then plotting the elemental abundances as curves in atomic number order. The slope of the REE composition profile curve reveals the differentiation between light and heavy REEs, thus providing a semi-quantitative tracing of petrogenesis. For example, mid-ocean ridge basalts (MORBs) are characterized by a flat distribution of medium and heavy REEs, while ocean island basalts (OIBs) exhibit enrichment in light REEs and depletion in heavy REEs. However, this semi-quantitative method of curve slope alone is insufficient to fully uncover REE abundance characteristics and petrogenesis information.

[0003] In existing basalt tectonic environment identification technologies, there is currently no scheme to further distinguish basalts from different tectonic environments, such as mid-ocean ridge basalt (MORB), back-arc basalt (BABB), continental arc basalt (CAB), and island arc basalt (IAB), based on REE abundance.

[0004] In addition, existing technologies also provide methods that use several parameters such as λ0, λ1 and λ2 in orthogonal polynomial decomposition to quantitatively characterize the REE partitioning pattern, making it easier to analyze and compare large amounts of REE data and providing more possibilities for tracing its causes.

[0005] In summary, there is an urgent need to provide a scheme for identifying basaltic tectonic environments using the shape parameters λ0, λ1, and λ2 of the REE composition pattern diagram. Summary of the Invention

[0006] The purpose of this invention is to provide a scheme for identifying basaltic tectonic environments using the shape parameters λ0, λ1, and λ2 of the REE composition pattern diagram.

[0007] To address the aforementioned technical problems, this invention provides a method for identifying basaltic tectonic environments, comprising: calculating the shape coefficient in the REE elemental composition curve based on the REE abundance of basalt samples within the target area to be evaluated; projecting a first data point formed by the slope coefficient and curvature coefficient in the shape coefficient onto a slope-curvature intersection graph, and projecting a second data point formed by the logarithmic mean coefficient of REE and magnesium oxide content in the shape coefficient onto a logarithmic mean coefficient and magnesium oxide content intersection graph, thereby obtaining the projection positions of the two data points respectively; and determining the basalt type of the current target area to be evaluated based on the current projection position.

[0008] Preferably, the step of determining the basalt type of the current sample based on the diagnostic results includes: diagnosing whether the current sample has a garnet signal based on the position of the first data point in the slope and curvature intersection diagram; determining the basalt type of the current sample based on the diagnostic results regarding the garnet signal, wherein, when the current sample has a garnet signal, the current sample is determined to be a first-type basalt sample based on the projection position of the first data point, wherein the first-type basalt sample is selected from continental arc basalt and ocean island basalt; when the current sample does not have a garnet signal, the current sample is determined to be a second-type basalt sample based on the projection position of the second data point, wherein the second-type basalt sample is selected from mid-ocean ridge basalt, island arc basalt, and back-arc basalt.

[0009] Preferably, the slope and curvature intersection diagram also has a signal differentiation line for characterizing spinel signals and garnet signals, wherein when the first data point is located in the garnet phase mantle melting region below the signal differentiation line, it is determined that the current sample has a garnet signal; when the first data point is located in the spinel phase mantle melting region above the signal differentiation line, it is determined that the current sample does not have a garnet signal.

[0010] Preferably, the method further includes: constructing the slope-curvature intersection map and the logarithmic mean coefficient-magnesium oxide content intersection map, including: collecting basalt samples from different tectonic environments within a first geographical area to form a sample set for constructing the intersection map; calculating the shape coefficient of each sample in the sample set; establishing a slope coefficient-curvature coefficient relationship chart, plotting the data points formed by the slope coefficient and curvature coefficient of each sample on the slope coefficient-curvature coefficient relationship chart, and dividing the plotting chart according to the plotting positions of different samples to distinguish between spinel signals and garnet signals, and so on. Garnet-phase mantle melting regions, spinel-phase mantle melting regions, and continental arc basalt regions and ocean island basalt regions located within garnet-phase mantle melting regions are used to form the slope and curvature intersection map; a logarithmic mean coefficient and magnesium oxide content relationship chart is established, and the data points formed by the logarithmic mean coefficient of REE and magnesium oxide content of each sample are plotted on the logarithmic mean coefficient and magnesium oxide content relationship chart. According to the plotting position of different samples, the mid-ocean ridge basalt region, island arc basalt region, and back-arc basalt region are divided on the plotting chart to form the logarithmic mean coefficient and magnesium oxide content intersection map.

[0011] Preferably, the logarithmic mean coefficient corresponding to the upper edge of the mid-ocean ridge basalt region in the cross diagram of the logarithmic mean coefficient and the magnesium oxide content is higher than the logarithmic mean coefficient corresponding to the upper edge of the back-arc basalt region, and the logarithmic mean coefficient corresponding to the lower edge of the island arc basalt region is lower than the logarithmic mean coefficient corresponding to the lower edge of the back-arc basalt region.

[0012] Preferably, the method further includes: collecting multiple original basalt samples from the target area, and performing major and trace element tests on the multiple original basalt samples respectively to obtain corresponding element test results; based on the element test results of each original basalt sample, removing samples with a loss on ignition higher than a preset loss on ignition ratio to obtain multiple basalt samples to be evaluated.

[0013] Preferably, the shape factor of each sample is calculated through the following steps: chondrite normalization is performed on the REE abundance of the current sample; the logarithm of the ratio between the REE abundance of the current sample and the corresponding chondrite normalization result is calculated; and the orthogonal polynomial curve of the REE elemental ion radii is fitted based on the logarithm calculation result of the current sample to obtain the shape factor in the fitted curve, wherein the shape factor includes the REE logarithmic mean coefficient, slope coefficient, and curvature coefficient.

[0014] Preferably, the orthogonal polynomial curve of the sample is represented by the following expression:

[0015]

[0016] f1 = [REE] - 1.05477

[0017] f2=([REE]-1.00533)×([REE]-1.2824)

[0018] f3=([REE]-0.99141)×([REE]-1.06055)×([REE]-1.4552)

[0019] f4=([REE]-0.98482)×([REE]-1.03052)×([REE]-1.10441)×([REE]-1.15343)

[0020] Where [REE] represents the abundance of the sample, [REE] CI The results of the chondrite-normalized treatment of the sample are represented. λ0, λ1, λ2, λ3, and λ4 all represent the shape coefficients of the curves. Among them, λ0 represents the logarithmic mean coefficient of the REE in the shape coefficient, λ1 represents the slope coefficient in the shape coefficient, and λ2 represents the slope coefficient in the shape coefficient.

[0021] On the other hand, embodiments of the present invention provide a system for identifying basaltic tectonic environments, comprising: a sample shape coefficient calculation module configured to calculate the shape coefficient in the REE elemental composition curve based on the REE abundance of basalt samples in the target area to be evaluated; a petrogenesis analysis module configured to project a first data point formed by the slope coefficient and curvature coefficient in the shape coefficient onto a slope-curvature intersection graph, and project a second data point formed by the logarithmic mean coefficient of REE and magnesium oxide content in the shape coefficient onto a logarithmic mean coefficient and magnesium oxide content intersection graph, thereby obtaining the projection positions of the two data points respectively; and a type identification module configured to determine the basalt type in the current target area to be evaluated based on the current projection position.

[0022] In addition, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method described above.

[0023] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0024] This invention proposes a method and system for identifying basaltic tectonic environments. The method and system utilize shape parameters through polynomial decomposition to quantitatively characterize REE distribution patterns. Furthermore, it analyzes the REE array characteristics of basalts in different tectonic environments from two petrogenesis perspectives: mantle melting depth and water content, and constructs REE discrimination diagrams for basaltic tectonic environments. This invention utilizes the shape parameters of basaltic REE composition pattern diagrams to identify tectonic environments, making it easier to extract and analyze REE data from a large number of basalt samples, thus providing a possibility for genetic tracing. In addition, this invention can effectively distinguish basalts in mid-ocean ridges, ocean islands, oceanic island arcs, continental arcs, and back-arc environments, solving the problem that conventional REE composition pattern diagrams cannot distinguish between oceanic island arcs, continental arcs, and back-arc basalts. This discrimination method has the advantages of streamlined data processing and standardized calculation results, and has significant theoretical and applied value.

[0025] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0026] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0027] Figure 1 This is a schematic diagram illustrating the steps of a method for identifying basaltic tectonic environments according to an embodiment of this application.

[0028] Figure 2 This is a schematic diagram illustrating the specific process of a method for identifying basalt tectonic environments according to an embodiment of this application.

[0029] Figure 3 This is a schematic diagram illustrating the tectonic environment identification principle in the method for identifying basaltic tectonic environments according to an embodiment of this application.

[0030] Figure 4 This is an example diagram of the tectonic environment identification result in the method for identifying basaltic tectonic environments according to an embodiment of this application.

[0031] Figure 5 This is a system module block diagram for identifying basalt tectonic environments according to an embodiment of this application. Detailed Implementation

[0032] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that, as long as there is no conflict, the various embodiments and features in the various embodiments of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0033] Furthermore, the steps illustrated in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than that shown here.

[0034] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.

[0035] To address the problems mentioned above, this application of the present invention provides a method and system for identifying basaltic tectonic environments. This method and system obtains shape coefficients that accurately characterize REE composition curves by processing REE data of basalts under different tectonic environments. Furthermore, it analyzes the REE array characteristics of basalts under different tectonic environments from two petrogenesis perspectives: mantle melting depth and magma water content, thereby providing a graphical scheme for REE discrimination of basaltic tectonic environments.

[0036] Example 1

[0037] Figure 1 This is a schematic diagram illustrating the steps of a method for identifying basaltic tectonic environments according to an embodiment of this application. Figure 2 This is a schematic flowchart illustrating the method for identifying basaltic tectonic environments according to an embodiment of this application. The following is in conjunction with... Figure 1 and Figure 2 The specific steps of the porosity calculation method described in the embodiments of the present invention will be explained.

[0038] To avoid the high degree of alteration experienced by the basalt samples to be evaluated, which could affect the accuracy of the identification results, the collected samples need to be screened before implementing step S110 in this embodiment of the invention.

[0039] In the sample screening process, firstly, multiple original basalt samples from the target area to be evaluated were collected, and major and trace element tests were performed on these multiple original basalt samples respectively. Corresponding element test results were obtained for each original basalt sample. Then, based on the element test results of each original basalt sample, samples with loss on ignition higher than the preset loss on ignition ratio were removed, and multiple basalt samples to be evaluated were obtained.

[0040] In this embodiment of the invention, the preset burn-off ratio is the threshold value at which the burn-off amount reaches the allowable degree of alteration during elemental testing. For example, the preset burn-off ratio is 5 wt.%.

[0041] Specifically, such as Figure 2 As shown, the major and trace elements of the collected basalt samples were first tested, and samples with a loss on ignition (LOI) higher than 5 wt.% were removed to ensure that the samples had undergone a low degree of alteration.

[0042] Thus, through the sample screening process, this embodiment of the invention obtains multiple basalt samples that have undergone a low degree of alteration and are to be evaluated, thereby proceeding to step S110.

[0043] Step S110: Calculate the shape factor in the REE element composition curve based on the abundance data of rare earth elements (REE) in the basalt sample to be evaluated within the target area to be evaluated.

[0044] In step S110, the REE abundance data of each basalt sample to be evaluated is first subjected to chondrite normalization. Then, the ratio of the REE abundance of the current sample to the corresponding chondrite normalization result is calculated, and the logarithm of the ratio is obtained. Finally, the orthogonal polynomial curve of the ion radius of the REE elements is fitted according to the logarithm calculation result of the current sample, so as to obtain the shape coefficient in the fitted curve.

[0045] In this embodiment of the invention, the shape factors required to identify the constructed environment include: REE log-mean coefficient, slope coefficient, and curvature coefficient.

[0046] Specifically, the REE abundance data of the samples were first standardized by chondrite, then the logarithm of the REE abundance after chondrite standardization was taken and multinomial expansion was performed.

[0047] In one embodiment, the orthogonal polynomial curve of the sample is represented by the following expression:

[0048]

[0049] f1 = [REE] - 1.05477

[0050] f2=([REE]-1.00533)×([REE]-1.2824)

[0051] f3=([REE]-0.99141)×([REE]-1.06055)×([REE]-1.4552)

[0052] f4=([REE]-0.98482)×([REE]-1.03052)×([REE]-1.10441)×([REE]-1.15343)

[0053] Where [REE] represents the abundance of the sample; [REE] CI The result represents the chondrite-normalized result of the sample; λ0, λ1, λ2, λ3, and λ4 all represent the shape coefficients of the curves. Among them, λ0 represents the logarithmic mean coefficient of the REE in the shape coefficient, λ1 represents the slope coefficient in the shape coefficient, and λ2 represents the slope coefficient in the shape coefficient.

[0054] Therefore, in this embodiment of the invention, the REE abundance data after chondrite normalization is fitted into an orthogonal polynomial curve of REE ion radius. Accordingly, λ0 represents the logarithmic mean abundance of REE after chondrite normalization; λ1 represents the slope, when λ1>0, the REE abundance of light rare earth elements is greater than that of heavy rare earth elements, i.e., LREE>HREE, thus indicating the enrichment of light rare earth elements; λ2 represents the curvature, when λ2>0, the REE line shows an upward concave trend.

[0055] Therefore, in this embodiment of the invention, parameters λ0, λ1, and λ2 are used as shape coefficients required for identifying basaltic tectonic environments.

[0056] After calculating the shape factor of each basalt sample to be evaluated, proceed to step S120.

[0057] Step S120: Project the first data point formed by the slope coefficient and curvature coefficient in the shape coefficient of the basalt sample to be evaluated onto the slope and curvature intersection diagram, and project the second data point formed by the logarithmic mean coefficient of REE and magnesium oxide content in the shape coefficient of the basalt sample to be evaluated onto the logarithmic mean coefficient and magnesium oxide content intersection diagram, and obtain the projection positions of the two data points respectively.

[0058] In step S120, two data points are first generated for each basalt sample to be evaluated, namely the first data point and the second data point.

[0059] The first data point is formed based on the slope and curvature coefficients of the shape factor of the current basalt sample to be evaluated. The second data point is formed based on the logarithmic mean coefficient of the REE in the shape factor of the current basalt sample to be evaluated, combined with the magnesium oxide content data of the current sample.

[0060] In this embodiment of the invention, the magnesium oxide content data of the basalt sample to be evaluated can be obtained from the sample element determination.

[0061] After obtaining two data points for each basalt sample to be evaluated, the first data point of each basalt sample to be evaluated is plotted on a pre-constructed slope and curvature intersection diagram, and the second data point of each basalt sample to be evaluated is plotted on a pre-constructed logarithmic mean coefficient and magnesium oxide content intersection diagram, thus obtaining the projection positions of two data points for each basalt sample to be evaluated.

[0062] In this embodiment of the invention, the slope-curvature intersection diagram is a chart model with the slope coefficient as the abscissa and the curvature coefficient as the ordinate, such as... Figure 3As shown in (a), this slope-curvature intersection plot is a chart used to differentiate samples from the perspective of mantle melting depth and petrogenesis. The logarithmic mean coefficient-magnesium oxide content intersection plot is a chart model with magnesium oxide content on the x-axis and the logarithmic mean coefficient of REE on the y-axis, as shown in (a). Figure 3 As shown in (c), this logarithmic mean coefficient versus magnesium oxide content cross-plot is a chart used to distinguish and identify samples from the perspective of magma water content and petrogenesis.

[0063] The following describes the construction process of the slope-curvature intersection plot and the logarithmic mean coefficient-magnesium oxide content intersection plot described in the embodiments of the present invention.

[0064] The first step is to collect basalt samples from different tectonic environments within the first geographical area to form a sample set for constructing cross-plots.

[0065] The second step is to calculate the shape factor of each sample in the above sample set according to the method described in step S110.

[0066] The third step involves establishing a slope coefficient versus curvature coefficient relationship chart with the slope coefficient as the abscissa and the curvature coefficient as the ordinate. The (first) data points formed by the slope coefficient and curvature coefficient of each sample are plotted on the slope coefficient versus curvature coefficient relationship chart. Based on the plotting positions of the first data points for different samples, signal differentiation lines for distinguishing spinel signals from garnet signals, garnet-phase mantle melting regions, spinel-phase mantle melting regions, and continental arc basalt regions and ocean island basalt regions located within the garnet-phase mantle melting regions are delineated on the plotting chart to form a slope versus curvature intersection map. (See [link to relevant documentation]). Figure 3 (b). Thus, the slope and curvature intersection diagram delineates the outlines of garnet-phase mantle melting regions and spinel-phase mantle melting regions, representing different depths of mantle melting, and also forms signal differentiation lines. Furthermore, it further delineates the outlines of continental arc basalt regions and ocean island basalt regions within the garnet-phase mantle melting regions.

[0067] The third step involves establishing a graph with magnesium oxide content as the abscissa and the relationship between the logarithmic mean coefficient of REE and magnesium oxide content. The (second) data points formed by the logarithmic mean coefficient of REE and magnesium oxide content for each sample are plotted on the graph. Based on the plotting locations of the second data points for different samples, the graph is divided into mid-ocean ridge basalt regions, island arc basalt regions, and back-arc basalt regions. A cross-plot of the logarithmic mean coefficient and magnesium oxide content is then created. (See attached graph.) Figure 3(d). Thus, the cross-plot of logarithmic mean coefficient and magnesium oxide content delineates the regional ranges of three types of second-class basalt samples used to characterize the different magma water content levels of samples without garnet signals, forming the outline ranges of mid-ocean ridge basalt regions, island arc basalt regions, and back-arc basalt regions.

[0068] like Figure 3 As shown in (d), the logarithmic mean coefficient corresponding to the upper edge of the mid-ocean ridge basalt region in the cross diagram of logarithmic mean coefficient and magnesium oxide content is higher than that corresponding to the upper edge of the back-arc basalt region. Furthermore, the logarithmic mean coefficient corresponding to the lower edge of the island arc basalt region is lower than that corresponding to the lower edge of the back-arc basalt region.

[0069] For example, REE data from 616 MORB samples, 120 OIB samples, 317 BABB samples, 218 IAB samples, and 183 CAB samples worldwide were collected. Following the method described in step S110 above, the shape factors λ0, λ1, and λ2 of each sample were calculated and represented using scatter plots to obtain different basalt data arrays, such as... Figure 3 As shown in (a) and 3(c), λ1 and λ2 indicate the presence of garnet in the mantle source region. OIB and MORB are characterized by garnet and spinel phase mantle melting, respectively, forming two distinct arrays in the λ2-λ1 diagram. IAB and BABB also originate from spinel phase mantle melting; therefore, in Figure 3 (a) is in the same array as MORB. The continental margin arc is dominated by garnet phase mantle melting, but if extensional processes such as plate tearing occur, spinel phase mantle melting may also occur. Therefore, for CAB, garnet signals are indispensable, and spinel signals may also exist.

[0070] Therefore, λ1 and λ2 can be used to distinguish OIB, CAB, and basalts formed in other tectonic environments. MORB, IAB, and CABB require further differentiation. The trend of λ0 with MgO can indicate the magma water content. An increase in magma water content promotes olivine crystallization while inhibiting plagioclase crystallization, leading to a rapid decrease in MgO in the magma while the total REE (characterized by λ0) remains almost unchanged. Therefore, the higher the magma water content, the gentler the trend of the sample points in the scatter plot. Figure 3 (c) MORB, BABB and IAB show different trends. The magma water content of IAB is greater than that of BABB, which is greater than that of MORB. Therefore, MORB is located in the uppermost region and IAB is located in the lowermost region. Although the data ranges of the three overlap to some extent, they can still be distinguished.

[0071] Based on this, we used the scatter plot as a basis to delineate the extent of basalt in different tectonic environments, forming a discriminant diagram, namely, the intersection of slope and curvature (see...). Figure 3 (b) and the cross-plot of the logarithmic mean coefficient and magnesium oxide content (see [link]). Figure 3 (d) It should be noted that, due to the limited amount of original sample data that constitutes this discriminant diagram, it is unavoidable that sample points fall outside the range of each array region when using the diagram, but the principle used to distinguish MORB, OIB, IAB, CAB and BABB will not change.

[0072] Thus, after obtaining the projection positions of the corresponding two data points for each basalt sample to be evaluated, the process proceeds to step S130.

[0073] Step S130: Based on the projection positions of the first and second data points of each basalt sample to be evaluated obtained in step S120, determine the basalt type of the current sample, thereby obtaining the tectonic environment type identification result of each basalt sample to be evaluated.

[0074] In step S130, firstly, based on the position of the first data point of each basalt sample to be evaluated in the slope and curvature intersection diagram, it is diagnosed whether the current sample has a garnet signal.

[0075] In the first embodiment, when the first data point of the current sample is located in the garnet phase mantle melting region below the signal differentiation line, it is determined that the current sample has a garnet signal.

[0076] In the first embodiment, when the first data point of the current sample is located in the spinel phase mantle melt region above the signal differentiation line, it is determined that the current sample does not have a garnet signal.

[0077] Then, based on the diagnostic results of the garnet signal for each basalt sample to be evaluated, the basalt type of the corresponding sample was determined.

[0078] When the basalt sample to be evaluated exhibits a garnet signal, the sample is identified as a Type I basalt sample based on the region of the Type I basalt sample where the projection location of the first data point is located, thus distinguishing whether the sample is a continental arc basalt or an ocean island basalt. The Type I basalt sample is selected from either continental arc basalt or ocean island basalt.

[0079] When the basalt sample to be evaluated does not have a garnet signal, the sample is determined to be a type II basalt sample based on the region of the type II basalt sample where the projection location of the second data point is located, thus distinguishing whether the sample is a mid-ocean ridge basalt, island arc basalt, or back-arc basalt. The type II basalt sample is selected from one of the following: mid-ocean ridge basalt, island arc basalt, or back-arc basalt.

[0080] Continue to refer to Figure 2 The λ1 and λ2 values ​​of the basalt sample were plotted in... Figure 3 In (b), it is determined whether the sample has a garnet signal. If the sample has a garnet signal, CAB and OIB are further distinguished based on their data range. If the sample does not have a garnet signal, the λ0 and MgO of the basalt sample are then added to the sample. Figure 3 (d) In the scatter plot, further distinguish MORB, IAB and BABB.

[0081] More specifically, in step S130, the basalt tectonic environment type of the current target area to be evaluated can be determined based on at least one basalt sample area where the vast majority of basalt samples in the target area to be evaluated are located.

[0082] Example 2

[0083] The following describes the implementation process of the method for identifying the tectonic environment of basalt, as described in Example 1, using the Late Carboniferous basalt in Inner Mongolia, China as an example.

[0084] Step 1: Major and trace element tests were performed on Late Carboniferous basalt samples;

[0085] Step 2: Remove samples with a loss on ignition (LOI) higher than 5 wt.% to ensure that the samples have undergone a lower degree of alteration;

[0086] Step 3: Calculate the corresponding λ0, λ1 and λ2 values ​​based on the REE data of the sample;

[0087] Step 4: Plot the λ1 and λ2 of the basalt sample onto the slope and curvature intersection diagram. The plotting results are as follows: Figure 4 As shown in (a), from Figure 4 As can be seen in (a), most of the samples are located on the left side, that is, they do not have garnet signals. Therefore, CAB and OIB are excluded, and the second data point is directly plotted.

[0088] Step 5: Plot the λ0 and MgO values ​​of the basalt sample in the cross plot of the logarithmic mean coefficient and magnesium oxide content. The plotting results are as follows: Figure 4 As shown in (b), from Figure 4 As can be seen in (b), most of the samples fall within the BABB range, thus suggesting that Inner Mongolia in my country was in a back-arc extensional environment during the Late Carboniferous.

[0089] Example 3

[0090] Based on the methods for identifying basalt tectonic environments described in Embodiments 1 and 2 above, this invention also provides a system for identifying basalt tectonic environments. This system is used to implement the aforementioned methods for identifying basalt tectonic environments.

[0091] Figure 5 This is a system module block diagram for identifying basaltic tectonic environments, as described in an embodiment of this application. Figure 5 As shown, the system for identifying basalt tectonic environments according to an embodiment of the present invention includes: a sample shape coefficient calculation module 51, a rock genetic analysis module 52, and a type identification module 53.

[0092] Specifically, the sample shape factor calculation module 51 is implemented according to the method described in step S110 above, and is configured to calculate the shape factor in the REE elemental composition curve based on the REE abundance of the basalt sample in the target area to be evaluated; the petrogenesis analysis module 52 is implemented according to the method described in step S120 above, and is configured to project the first data point formed by the slope coefficient and curvature coefficient in the shape factor onto the slope and curvature intersection diagram, and project the second data point formed by the logarithmic mean coefficient of REE in the shape factor and the magnesium oxide content onto the logarithmic mean coefficient and magnesium oxide content intersection diagram, respectively obtaining the projection positions of the two data points; the type identification module 53 is implemented according to the method described in step S130 above, and is configured to determine the basalt type in the current target area to be evaluated based on the current projection position.

[0093] Example 4

[0094] Based on the methods for identifying basaltic tectonic environments described in Embodiments 1 and 2 above, this invention provides a computer-readable storage medium. The storage medium stores a computer program, which is executed to run a method for identifying basaltic tectonic environments. The computer program is capable of executing computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable files, or some intermediate form.

[0095] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0096] It should be noted that the contents of computer-readable storage media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, the contents may be appropriately increased or decreased according to the requirements of legislation and patent practice. In other jurisdictions, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0097] This invention discloses a method and system for identifying basaltic tectonic environments. The method and system utilize shape parameters to quantitatively characterize REE distribution patterns through polynomial decomposition. Furthermore, it analyzes the REE array characteristics of basalts in different tectonic environments from two petrogenesis aspects: mantle melting depth and water content, and constructs REE discrimination diagrams for basaltic tectonic environments. This invention utilizes the shape parameters of basaltic REE composition pattern diagrams to identify tectonic environments, making it easier to extract and analyze REE data from a large number of basalt samples, thus providing possibilities for genetic tracing. In addition, this invention can effectively distinguish basalts in mid-ocean ridges, ocean islands, oceanic island arcs, continental arcs, and back-arc environments, solving the problem that conventional REE composition pattern diagrams cannot distinguish between oceanic island arcs, continental arcs, and back-arc basalts. This discrimination method has the advantages of streamlined data processing and standardized calculation results, and has significant theoretical and applied value.

[0098] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0099] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0100] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0101] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0102] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0103] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for identifying basaltic tectonic environments, characterized in that, include: Based on the REE abundance of basalt samples within the target area to be evaluated, calculate the shape factor in the REE elemental composition curve; The first data point formed by the slope coefficient and curvature coefficient in the shape coefficient is projected onto the slope and curvature intersection graph, and the second data point formed by the logarithmic mean coefficient of REE in the shape coefficient and magnesium oxide content is projected onto the logarithmic mean coefficient and magnesium oxide content intersection graph, so as to obtain the projection positions of the two data points respectively. Based on the current projection location, determine the type of basalt in the target area to be evaluated.

2. The method according to claim 1, characterized in that, The step of determining the basalt type of the current sample based on the diagnostic results includes: Based on the position of the first data point in the slope and curvature intersection diagram, it is determined whether the current sample has a garnet signal; Based on the diagnostic results regarding the garnet signal, the basalt type of the current sample was determined, among which, When the current sample has a garnet signal, the current sample is determined to be a type basalt sample based on the projection position of the first data point, wherein the type basalt sample is selected from one of continental arc basalt and ocean island basalt. When the current sample does not have a garnet signal, the current sample is determined to be a type II basalt sample based on the projection position of the second data point. The type II basalt sample is selected from one of mid-ocean ridge basalt, island arc basalt, and back-arc basalt.

3. The method according to claim 2, characterized in that, The slope and curvature intersection diagram also includes signal differentiation lines used to characterize spinel and garnet signals, wherein... When the first data point is located in the garnet phase mantle melting region below the signal differentiation line, it is determined that the current sample has a garnet signal; When the first data point is located in the spinel phase mantle melting region above the signal differentiation line, it is determined that the current sample does not have a garnet signal.

4. The method according to claim 3, characterized in that, The method further includes: Constructing the slope-curvature intersection plot and the logarithmic mean coefficient-magnesium oxide content intersection plot includes: Basalt samples from different tectonic environments were collected within the first geographic area to form a sample set for constructing cross-plots; Calculate the shape factor for each sample in the sample set; A slope coefficient and curvature coefficient relationship chart is established. The data points formed by the slope coefficient and curvature coefficient of each sample are plotted on the slope coefficient and curvature coefficient relationship chart. According to the plotting position of different samples, the chart is divided into signal differentiation lines for distinguishing spinel signals and garnet signals, garnet phase mantle melting region, spinel phase mantle melting region, and continental arc basalt region and ocean island basalt region located in garnet phase mantle melting region, so as to form the slope and curvature intersection map. A graph showing the relationship between the logarithmic mean coefficient and magnesium oxide content was established. Data points formed by the logarithmic mean coefficient of REE and magnesium oxide content for each sample were plotted on the graph. Based on the plotting locations of different samples, the graph was divided into mid-ocean ridge basalt regions, island arc basalt regions, and back-arc basalt regions, thus forming the intersection map of the logarithmic mean coefficient and magnesium oxide content.

5. The method according to claim 4, characterized in that, The logarithmic mean coefficient corresponding to the upper edge of the mid-ocean ridge basalt region in the cross diagram of the logarithmic mean coefficient and magnesium oxide content is higher than the logarithmic mean coefficient corresponding to the upper edge of the back-arc basalt region, and the logarithmic mean coefficient corresponding to the lower edge of the island arc basalt region is lower than the logarithmic mean coefficient corresponding to the lower edge of the back-arc basalt region.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Multiple pristine basalt samples were collected from the target area, and major and trace element tests were performed on the multiple pristine basalt samples to obtain the corresponding element test results. Based on the elemental analysis results of each original basalt sample, samples with a loss on ignition exceeding the preset loss on ignition ratio were removed, resulting in multiple basalt samples to be evaluated.

7. The method according to any one of claims 1 to 6, characterized in that, The shape factor of each sample is calculated using the following steps: The REE abundance of the current sample was chondrite normalized. The logarithm of the ratio is calculated based on the ratio of the REE abundance of the current sample to the corresponding chondrite normalization result. The orthogonal polynomial curve of the ion radius of REE elements is fitted based on the logarithmic calculation results of the current sample to obtain the shape coefficients in the fitted curve. The shape coefficients include the REE logarithmic mean coefficient, slope coefficient, and curvature coefficient.

8. The method according to claim 7, characterized in that, The orthogonal polynomial curve of the sample can be represented by the following expression: f1 = [REE] - 1.05477 f2=([REE]-1.00533)×([REE]-1.2824) f3=([REE]-0.99141)×([REE]-1.06055)×([REE]-1.4552) f4=([REE]-0.98482)×([REE]-1.03052)×([REE]-1.10441)×([REE]-1.15343) Where [REE] represents the abundance of the sample, [REE] CI The results of the chondrite-normalized treatment of the sample are represented. λ0, λ1, λ2, λ3, and λ4 all represent the shape coefficients of the curves. Among them, λ0 represents the logarithmic mean coefficient of the REE in the shape coefficient, λ1 represents the slope coefficient in the shape coefficient, and λ2 represents the slope coefficient in the shape coefficient.

9. A system for identifying basaltic tectonic environments, characterized in that, include: The sample shape factor calculation module is configured to calculate the shape factor in the REE element composition curve based on the REE abundance of basalt samples in the target area to be evaluated. The rock genesis analysis module is configured to project the first data point formed by the slope coefficient and curvature coefficient in the shape coefficient onto the slope and curvature intersection diagram, and project the second data point formed by the logarithmic mean coefficient of REE in the shape coefficient and magnesium oxide content onto the logarithmic mean coefficient and magnesium oxide content intersection diagram, thereby obtaining the projection positions of the two data points respectively. The type identification module is configured to determine the type of basalt in the target area to be evaluated based on the current projection position.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 8.

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