JP4887261B2 - Tephra identification method - Google Patents

Description

  The present invention relates to a tephra identification method for identifying tephra that are volcanic debris such as volcanic ash and pyroclastic flows released by volcanic activity.

  Conventionally, when constructing a building, a facility, or the like, the ground of the land to be constructed, that is, the geology, the stratum, or the like is sometimes investigated in advance (see, for example, Patent Document 1). In particular, important facilities such as nuclear power plants need to be constructed on a strong ground to ensure safety with high reliability. This is because if it is constructed on a solid ground, it is possible to avoid damage to the building in the event of a natural disaster such as an earthquake. In particular, in the case of nuclear power plants, accidents such as radioactivity leakage can be avoided by avoiding damage to the facilities.

  Furthermore, when designing facilities that require a high level of safety, such as nuclear power plants, information on the scale and timing of earthquakes that occurred in the past is required. This is because the design strength required for a nuclear power plant and the like can be grasped with high accuracy if the magnitude and timing of past earthquakes are known. As a method for examining the magnitude and timing of earthquakes in the past, there is a method for examining tephra stacked in the formation, that is, volcanic ash and pyroclastic flows released by volcanic activity. In this method, it is possible to identify the magnitude and timing of earthquakes that occurred in the past with high accuracy by identifying tephra in the formation.

  Conventionally, as a technique for identifying a tephra, a method based on component analysis of glass (magma essential substance volcanic glass) in the tephra has been proposed. On the other hand, in the weathered tephra, the volcanic glass of the magmatic essential material does not remain, so it is often impossible to clarify the descriptive characteristics of the rocks of the glass. In particular, in the case of wide-area tephra in Japan, there are many examples in which weathering is remarkable and no glass remains.

  Conventionally, as a method for identifying a tephra in which no glass remains, a method using characteristics such as the composition and refractive index of phenocryst minerals has been proposed. For example, in the Oyama Kurayoshi pumice layer (hereinafter referred to as DKP) originating from Oyama volcano, an identification method based on crystal habits and refractive index of orthopyroxene contained in tephra has been tried. And by this tephra identification method, DKP is identified in the wide area from western Japan to the southern part of Tohoku district (refer nonpatent literature 1).

  For example, tephras originating from the new Oyama volcano, including DKP, are classified into the lowermost, lower, middle and upper volcanic ash from the lower level (see Non-Patent Document 2). Among these tephra, the Oyama Ikutake pumice layer (hereinafter referred to as DNP), the Oyama Sekikin pumice layer (hereinafter referred to as DSP), and DKP are sandwiched between the uppermost part of the lower volcanic ash and the lowermost part of the central volcanic ash. Stratiform tephra show similar lithology on the outcrop and similar mineral combinations.

  Machida and Arai are based on the morphology and refractive index of orthopyroxene and amphibole for the above-mentioned three-layer quasi-pumice layers that are stratigraphically close and have very similar lithology and mineral composition. The pumice layer is identified (see Non-Patent Document 3). In addition, Furusawa and Umeda will grasp in detail the vertical refractive index change in the tephra monolayer at a well-preserved point and examine whether each pumice layer can be identified from amphibole and orthopyroxene alone. Is going. Here, Furusawa et al. Have revealed that there is a certain change in the refractive index of the mineral even in the same tephra, and that the refractive index partially overlaps between DKP and DNP (Non-Patent Document 4). reference.).

  Upper Pleistocene Takano Formation, distributed in Takano, Shinsara-cho, Nagano City, northern Nagano Prefecture, preserves the wide-area tephra that have fallen in this area. Upper Pleistocene Takano Formation is a wide-area tephra such as Aso 3 (Aso-3), Kikai Kuzuhara (K-Tz), Aso 4 (Aso-4), Kurayoshi Oyama (DKP), Aira Tn (AT), and Kiso Mitake Tephras originating from volcanoes or Tateyama volcanoes are sandwiched between them, and are known to be important strata in tephra stratigraphy.

  However, the conventional tephra identification method based on the composition and refractive index of the phenocryst mineral has the following problems. That is, the identification of tephra based on the composition and refractive index of the phenocryst mineral has a problem that the identification accuracy is remarkably lowered as compared with identification of tephra by component analysis of glass or the like. Due to the low identification accuracy, for example, research examples suggesting the possibility of comparison with DSP (possibly the same tephra as DSP) have been reported for the DKP of the Upper Pleistocene Takano Formation (non-patent literature). 5).

JP 2004-121523 A “Japanese Quaternary Studies”, The University of Tokyo Press, 1977, p. 59-68 “The 4th system of the San'in Kaigan area. The 4th system of Japan”, JDS Special Report, 15, 1969, p. 354-376 "Oyamakurakishi pumice stone-wide area of distribution and significance in the Quaternary edition", Geographical Journal, 1979, 88, p. 33-50 “Reexamination of Rock Descriptive Features of New Oyama Tephra DNP, DSP, DKP”, Quaternary Research, Japan Quaternary Society, 2002, Vol. 41, p. 123-129 “Tephrostratigraphy of the Takano Formation Boring Core, Northern Nagano Prefecture,” Abstracts of the Quaternary Society of Japan, 2005, 35, p. 86-87

  The present invention has been made in view of the above-described conventional problems, and an object of the present invention is to provide a tephra identification technique for accurately identifying a weathered tephra in which no volcanic glass remains sufficiently.

The present invention is a method for identifying tephra that are volcanic debris, such as volcanic ash and pyroclastic flow,
To identify whether the first tephra whose source volcano and eruption period cannot be specified depending on the sedimentary site and sedimentary layer can be compared with the second tephra whose source volcano and eruption period are specified For the purpose of
A specific mineral extraction step for extracting a specific type of mineral from the tephra;
A glass inclusion analysis step for analyzing the glass inclusions contained in the mineral extracted by the specific mineral extraction step to determine the content ratio of the specific component that is not a volatile component ;
A statistical processing step of calculating a mean value and a standard deviation by performing statistical processing on the content ratio of the specific component;
Based on the average value and the standard deviation obtained by the statistical processing step, the dispersion ranges of the content ratios of the specific components in the first and second tephra are respectively specified, and both are determined according to the overlapping degree of these dispersion ranges. And a tephra comparison step for determining whether or not can be compared with each other .

  The glass inclusions (also referred to as glass inclusions, which are part of melt inclusions) included in the minerals that make up the tephra are less affected by weathering than volcanic glass, and are contained in minerals in a good state. There is a high possibility of remaining. However, as is clear from the fact that glass inclusions in phenocryst minerals are analyzed for the purpose of grasping the magma composition change at the time of tephra ejection, the components of glass inclusions are crystals in the magma chamber. It can change over time in the process of volcanic eruption, such as depth and magma mixing.

  For many years, the inventor has studied the relationship between the change width of glass inclusion components over time in the volcanic eruption process and the change width (difference) of glass inclusion components due to differences in tephra. As a result of the research, the inventor has obtained new knowledge that overturns the conventional common knowledge of volcanology that the change in glass inclusion due to the difference in tephra is larger than the change in glass inclusion over time.

  The tephra identification method according to the present invention is an invention which has been devised and created by utilizing the new knowledge discovered by the inventors as described above. The tephra identification method of the present invention is an epoch-making method of analyzing tephra by analyzing the glass inclusion contained in the mineral in the tephra. According to the glass inclusion that is contained in mineral and preserves the characteristics at the time of eruption, the tephra can be identified with high reliability. In addition, even if the volcanic glass is weathered and a tephra that cannot be identified by the volcanic glass, high-precision identification is possible by using the glass inclusion. The tephra identification method of the present invention can be applied to a tephra in which volcanic glass has been weathered, and is an excellent technique with a wide range of applications.

  As described above, the tephra identification method of the present invention is a completely new method for identifying a tephra based on a glass inclusion component, and is excellent in that it can accurately identify a tephra by using glass inclusion that is less affected by weathering. Identification method.

  In the tephra identification method of the present invention, as a method for analyzing the glass inclusion, for example, the concentration (ratio) of each constituent element of a substance is determined based on a spectrum of characteristic X-rays emitted by the substance in response to electron beam irradiation. There is a method using EPMA (Electron Probe Micro Analyzer) which is an analysis apparatus. As the EPMA, various types of EPMA such as an energy dispersive (EDX) EPMA and a wavelength dispersive (WDX) EPMA can be used.

Further, the first tephra and the second tephra are compared by comparing the statistical value which is the statistical processing result of the statistical processing step between the first tephra and the second tephra that are identification targets. Including a tephra comparison step for determining whether or not it is possible to compare, and the statistical value is an average value and a standard deviation regarding a content ratio of the specific component .

  In this case, the range of variation in the content ratio of the specific component can be specified by a combination of the average value and the standard deviation. And if the range of variation in the content ratio of the specific component can be specified, whether or not the first tephra and the second tephra can be compared, that is, whether or not the same tephra is further determined, It becomes possible to perform with high accuracy. As the range of variation, for example, a range of 1σ width centered on the average value, a range of ± 1σ, and the like can be set. The statistical processing step and the tephra comparison step can be realized by a calculation process such as a computer.

  Here, the fact that the first tephra and the second tephra can be compared means that it is possible to determine that the two tephras are the same tephra. The fact that the first tephra and the second tephra cannot be compared means that the two tephra are judged to be different.

It also includes a specific mineral extraction step that extracts specific types of minerals from tephra,
The glass inclusion analysis step is a step of analyzing the glass inclusion contained in the mineral extracted by the specific mineral extraction step .
When the specific type of mineral is targeted, the difference (variation) in the components of the glass inclusion due to the difference in the type of mineral can be suppressed. Therefore, if the target is a specific type of mineral as described above, the tephra identification accuracy can be further improved.

Moreover, the specific mineral extraction step is preferably a step of extracting a mineral in the range of particle size 1 / 16~1 / 8mm (claim 2).
In this case, by targeting the mineral having the particle size range as described above, the tephra identification method of the present invention can be easily implemented, and the applicable range can be expanded. Minerals having a particle size of less than 1/16 mm may be difficult to handle, for example, because it becomes difficult to identify the mineral with an optical microscope or the like. On the other hand, a mineral having a particle size exceeding 1/8 mm has a narrow scattering range at the time of volcanic eruption, and is difficult to use, for example, for identification of a wide-area tephra.

The mineral extracted in the specific mineral extraction step is preferably at least one of quartz and plagioclase (Claim 3 ).
Since quartz has a stable crystal structure, the component of the glass inclusion it contains is stable. Therefore, if the tephra identification method of the present invention is applied to quartz, the tephra can be identified with higher accuracy. In addition, plagioclase has a lighter specific gravity than, for example, orthopyroxene, and therefore, it may be scattered farther during volcanic eruptions. Therefore, if plagioclase is the target, the application range of the tephra identification method of the present invention can be further expanded, which is more advantageous in the identification of wide-area tephra.

Moreover, it is preferable that the said glass inclusion analysis step is a step which analyzes the said glass inclusion using the sample formed by grind | polishing the resin hardened in the state which embedded the said mineral (Claim 4 ).
In this case, embedding in the resin makes it easier to handle the mineral and facilitates polishing to obtain a cross section of the glass inclusion.

The embodiment of the present invention will be specifically described with reference to the following examples.
Example 1
This example is an example relating to a tephra identifying method for identifying tephra that are volcanic debris such as volcanic ash and pyroclastic flows released by volcanic activity. The contents will be described with reference to FIGS.
In the tephra identification method of this example, as shown in FIG. 1, a specific mineral extraction step S101 for extracting a specific type of mineral from the tephra, and a glass inclusion contained in the mineral extracted by the specific mineral extraction step are analyzed and specified. A glass inclusion analysis step S102 for obtaining the component content ratio and a statistical process step S103 for performing a statistical process regarding the content ratio of the specific component are performed.
This will be described in detail below.

  The specific mineral extraction step S101 is a step of extracting plagioclase set as a specific type of mineral from a tephra sample. In this example, plagioclase was extracted by magnetic separation after pretreatment for removing minerals with a particle size of 1/16 to 1/8 mm from the tephra. The specific type of mineral may be quartz or a combination of quartz and plagioclase.

The glass inclusion analysis step S102 is a step of analyzing glass inclusions contained in plagioclase extracted from the tephra sample. In the glass inclusion analysis step of this example, main components such as SiO ₂ , TiO ₂ , Al ₂ O ₃ , FeO, CaO, Na ₂ O, and K ₂ O are set as the specific components, and the content ratio of each specific component is set. Looking for.

  In this example, prior to the execution of the glass inclusion analysis step S102, a flake sample for analyzing glass inclusion was prepared. This flake sample was obtained by embedding plagioclase in an epoxy resin coated on the surface of a slide glass, and then polishing with a carburundum having a roughness of 3000 and mirror polishing with a diamond paste of 1 μm. In this example, this thin sample was observed using a scanning electron microscope (SEM), and components were analyzed for glass inclusions having a diameter of 6 to 10 μm.

  In this example, glass inclusions were analyzed using an energy dispersive X-ray microanalyzer (Hitachi S2150 and HORIBA, Ltd. EMAX5770). As measurement conditions, an acceleration voltage of 15 kV, a sample current of 0.3 nA, and a beam diameter of 150 nm were set. The range of 4 μm × 4 μm in the thin sample was scanned with a beam for 200 seconds, and component analysis was performed. In this example, the correction calculation of the main component composition is performed by the ZAF method.

In addition, as a measurement standard, high-purity artificial oxide crystals (SiO ₂ , Al ₂ O ₃ , TiO ₂ , MnO, MgO having a purity of 99.99% or more), high-purity natural oxide crystals (purity 99.99% Quartz, Fe ₂ O ₃ ), and artificial single crystal NaCl, KCl, CaF ₂ having a purity of 99.99% or more were used. Based on these standards, American Standard Bureau NIST620 glass and glass-D (Sawada et al., “Drifting pumice derived from early Holocene volcanic activity on Ulleungdo discovered from the drilling core in Ota City, Shimane Prefecture”, Quaternary Research, Japan Quaternary Society, 1997, Vol. 36, p.1-16), measuring each tephra sample after confirming that the difference from the nominal value is within 0.2% did. In addition, accuracy was ensured by confirming the accuracy with so-called EPMA standard materials such as ASTIMEX and Taylor. Furthermore, NIST620 glass was used as a working standard as a standard for checking accuracy for each measurement.

  The statistical processing step S103 is a step of performing statistical processing relating to the content ratio of the specific component. In this example, as shown in FIGS. 12 to 14 referred to in Example 2, the average value and standard deviation (σ) of the content ratio of each specific component are obtained as statistical values. In addition, about FIGS. 3-11 referred in this example, it is the figure which plotted the data regarding each specific component, and illustration of the concrete value of the average value calculated | required as a statistical value and a standard deviation is abbreviate | omitted. .

  Next, the analysis result by the tephra identification method of this example will be described. In this example, the tephra identification method of this example was applied to DNP, DSP, and DKP that are thickly deposited on the foot of Mt. Oyama (shown as Daisen Volcano in FIG. 2). As tephra samples used for verification, Furusawa and Umeda ("Reexamination of New Oyama Tephra DNP, DSP, DKP Rock Descriptive Features", Quaternary Research, Japan Quaternary Society, 2002, Vol. 41, , P.123-129) was used at Oyamaike. As each tephra sample, a block-like sample having a height of 10 cm belonging to the lowest, uppermost, and middle layer levels of the tephra stacked in the vertical direction was used.

A list of tephra samples is shown. These tephra samples are all tephra deposited near the volcanic source.
Sample # 1 Top DNP Tephra Sample # 2 Middle DNP Tephra Sample # 3 Bottom DNP Tephra Sample # 4 Top DSP Tefler Sample # 5 Middle DSP Tephra Sample # 6 Bottom DSP Tephra Sample # 7 Top Upper DKP tephra Sample # 8 Middle DKP tephra Sample # 9 Bottom DKP tephra

The component analysis results of glass inclusions contained in plagioclase of DKP, DSP, and DNP tephra according to the tephra identification method of this example are shown in FIGS. Each figure is a graph in which the content ratio of SiO ₂ is defined on the horizontal axis and the content ratio of other specific components is defined on the vertical axis.
As shown in FIGS. 3 to 5, according to the comparison of samples # 1 to # 3, the content ratio of SiO ₂ , Al ₂ O ₃ , FeO, CaO and K ₂ O in DNP is such that the change in the main component is in the vertical direction. It can be seen. SiO ₂ increases to about 72 to 73% at the bottom (sample # 3), 72 to 75% at the middle (sample # 2), and 74 to 75% at the top (sample # 1). There is a trend. Al ₂ O ₃ tends to decrease toward the top, with 15-16% at the bottom (sample # 3) and middle (sample # 2), and about 14.5-15.5% at the top (sample # 1). Is seen. FeO is 2.2 to 2.6% at the bottom (sample # 3), 2.0 to 2.6% at the middle (sample # 2), and 1.6 to 2.3% at the top (sample # 1). There is a tendency to decrease at the upper part of about 2%. CaO is 2.4 to 2.7% at the bottom (sample # 3), 2.2 to 2.5% at the middle (sample # 2), and 1.9 to 2.3% at the top (sample # 1). There is a tendency to decrease at the upper part of about 3%. K ₂ O is about 2.4 to 2.7% at the bottom (sample # 3) and middle (sample # 2), and about 2.6 to 2.7% at the top (sample # 1). There is an increasing trend.

As shown in FIGS. 6 to 8, component changes are also observed in the vertical direction in the DSPs of the samples # 4 to 6 as in the DNP of the samples # 1 to # 3. The content ratio of SiO ₂ , Al ₂ O ₃ , FeO, CaO, and K ₂ O changes from the lower part to the upper part. There is a tendency for SiO ₂ to increase at the top, about 70.5-73.5% at the bottom (sample # 6) and middle (sample # 5), 74-76% at the top (sample # 4). . Al ₂ O ₃ tends to decrease at the top, with 15-16% at the bottom (sample # 6) and middle (sample # 5) and about 14.5-15.0% at the top (sample # 4). Is seen. FeO decreases at the top, with 2.0 to 3.5% at the bottom (sample # 6) and middle (sample # 5), and about 1.4 to 2.4% at the top (sample # 4). There is a trend. CaO decreases from 2.5 to 3.3% at the bottom (sample # 6) and middle (sample # 5) and from 1.6 to 2.2% at the top (sample # 4) and decreases at the top. There is a trend. K ₂ O is 2.2 to 2.7% at the bottom (sample # 6) and middle (sample # 5) and about 2.7 to 3.0% at the top (sample # 4). There is an increasing trend.

As shown in FIGS. 9 to 11, the DKPs of the samples # 7 to 9 have few vertical component changes, unlike the DNPs of the samples # 1 to # 3 and the DSPs of the samples # 4 to # 6. SiO ₂ is about 74 to 76%, Al ₂ O ₃ is about 14.5 to 15.5%, FeO is about 1.5 to 2.0%, and CaO is only the lowermost part (sample # 9). Slightly high 2.1 to 2.3%, and the middle (sample # 8) and the top (sample # 7) are about 1.7 to 2.1%. K ₂ O is about 2.6 to 3.2%.

Based on the analysis results of FIGS. 3 to 11, the usefulness of the tephra identification method of the present example can be verified through the following discussion.
First, it can be easily imagined that the composition of the glass inclusions in plagioclase changes over time during the eruption process due to the crystallization depth in the magma chamber and the mixing of magmas. If the width of the composition change of the glass inclusion over time is larger than the width of the composition change of the glass inclusion due to the difference in the tephra, the tephra cannot be identified with high accuracy by the tephra identification method of this example. Conversely, if it can be demonstrated that the change width of the composition change of the glass inclusion over time is sufficiently smaller than the fluctuation width of the composition of the glass inclusion due to the difference of the tephra, the usefulness of the tephra identification method of this example is demonstrated. .

  The range of change in composition of glass inclusion over time is related to Samples # 1 to 3 (DNP), Samples 4 to 6 (DSP), or Samples # 7 to 9 (DKP) collected near the volcanic source. It can be verified by comparing the analysis results.

  As described above with reference to FIGS. 9 to 11, with respect to the DKP of Samples # 7 to 9, almost no change over time (change in the vertical direction) was observed as described in the analysis results. On the other hand, as described above with reference to FIGS. 3 to 8, the DNP of the samples # 1 to 3 and the DSP of the samples # 4 to 6 are particularly the top (samples # 1 and # 4) and the others ( Samples # 2, # 3, # 5, and # 6), that is, those ejected at the end of time (samples # 1 and # 4) and those earlier than this in time (samples # 2 and # 3) , # 5 and # 6).

As for DNP and DSP, for example, when comparing the relationship between SiO ₂ and FeO, CaO, and Na ₂ O, some values are close to each other due to changes in the vertical direction (change over time). There is a clear difference in the range (variation range) in which the values of are dispersed. Based on this fact, it can be determined that DNP and DSP can be easily distinguished regardless of changes in composition over time (changes in the vertical direction).

  Based on the above discussion, the tephra identification method of this example is not affected by the compositional change over time of the glass inclusions contained in plagioclase and can accurately identify each of the DNP, DSP, and DKP tephras. To be judged. That is, the tephra identification method of this example is a very useful technique that can identify DNP, DSP, and DKP by analyzing glass inclusions.

  The tephra measured by the method of Arai (“Identification of tephra by refractive index of orthopyroxene and amphibole—Basic research of tephrochronology”, Quaternary study, 1972, 11, p.254-269) The Tephrochronology established by Machida and Arai's “Volcanic Ash Atlas” (University of Tokyo Press, 1992, p. 276) based on this is the progress of analytical instruments and analytical materials. Review is required due to the accumulation of However, in the weathered tephra, no volcanic glass remains, and there is no discussion about the main component of volcanic glass, which is the basis for tephra identification. For example, the identification problem of weathered tephra introduced in Example 2, such as DKP of the Takano Formation, is largely caused by the change in the rock description in the formation and the lack of analysis data of the volcanic glass.

(Example 2)
In this example, a tephra comparison step S104 is added in addition to steps S101 to S103 constituting the tephra identification method of the first embodiment. In this example, a tephra identification method to which a tephra comparison step is added is used to examine whether or not a DKP flying to a remote place can be compared with a DSP. The contents will be described with reference to FIGS.

  The tephra comparison step S104 of this example is a step of identifying the tephra using the statistical value obtained in the statistical processing step S103 as shown in FIG. Specifically, in the tephra comparison step S104 of this example, as shown in FIGS. 13 to 15 described later, first, the content ratio of each specific component is 1σ width centered on the average value (σ is a standard deviation). ) Variation range. Then, by comparing this variation range between the first tephra and the second tephra that are identification targets, it is determined with high accuracy whether or not they can be compared.

  In this example, as a sample of DKP that flew to a remote area, it was shown as Odahara volcanic ash in Echizen City (former Odamachi), Fukui Prefecture (shown as Oda in Fig. 2. Nakagawa et al. Tara volcanic ash layer and Oyamakurayoshi pumice layer and their significance found from ”, Quaternary study, 1995, 34, 1, p.49-53), and Takano Formation in Nagano Prefecture (in Figure 2, Takano and Adopted). Then, it is determined whether or not the DKP flying to these remote locations can be compared with the DSP.

A list of tephra samples is as follows.
Sample # 10 Oda-cho tephra
Sample # 11 Tephra from the Takano Formation

In particular, sample # 11 is a tephra that has been compared with a DKP tephra in a conventional research example, but has a research report that suggests a possibility of comparison with a DSP (tephra identification problem). Therefore, if sample # 11 can be identified, it is very significant academically. The analysis results of samples # 10 and # 11 are shown in FIGS. Each figure is a graph in which the content ratio of SiO ₂ is defined on the horizontal axis and the content ratio of other specific components is defined on the vertical axis. For comparison, the analysis results of sample # 4 (DSP top) and sample # 7 (DKP top) of Example 1 are shown in FIG.

In sample # 10, SiO ₂ is about 74 to 76%, Al ₂ O ₃ is about 14.5 to 15.5%, FeO is about 1.6 to 1.9%, and CaO is 1.7. about to 2.1%, K ₂ O is of the order of 2.5 to 2.9%.
In sample # 11, SiO ₂ is about 74 to 76%, Al ₂ O ₃ is about 14.5 to 15.0%, FeO is about 1.5 to 1.8%, and CaO is 1 About 0.8 to 2.2%, and K ₂ O is about 2.6 to 2.9%.

Tephras that can be compared with sample # 11 include DSP (samples # 4 to 6 in Example 1) and DKP (samples # 7 to 9 in Example 1).
First, it is examined whether or not it can be compared with a DSP. Component analysis results of plagioclase glass inclusions contained in the bottom tephra (# 6) and middle tephra (sample # 5) of the DSP are DKP (sample # 7-9), sample # 10 and sample # 11. Is completely different. On the other hand, the component analysis result of the glass inclusion contained in the uppermost part of the DSP (sample # 4) is slightly similar to DKP (samples # 7 to 11).

  As is apparent from FIGS. 13 to 15, it can be determined that Sample # 10 and Sample # 11 have almost identical component analysis results of plagioclase glass inclusion. When observing this figure in detail, there is little difference in the component analysis results of the glass inclusion between the DKP in the remote area such as Samples # 10 and 11 and the DKP at the foot of Oyama (Sample # 7). Absent. The component analysis result of the tephra (sample # 4) at the top of the DSP is partially similar to that of DKP (sample # 7).

  However, for each component of the tephra (sample # 4) at the top of the DSP, a range D (range indicated by a dotted line in FIGS. 13 to 15) centered on the average value of 1σ is DKP (sample # 4). It does not overlap with the same range C of 7) (the range indicated by the alternate long and short dash line in the figure). On the other hand, the range C for the DKP (sample # 7) is the same as the range A for the remote DKP (sample # 10) (the range indicated by the solid line in the figure) and the remote DKP (sample # 11). ) Is overlapped with a similar range B (a range indicated by a broken line in the figure).

Here, a part of the statistical values obtained in this example is illustrated. For example, the average value of SiO ₂ for sample # 7 is 75.07%, and the range of 1σ centered on the average value is 74.84 to 75.29%. Further, the average value of Al ₂ O ₃ for sample # 7 is 14.74%, and the range of 1σ centered on the average value is in the range of 14.57 to 14.91%. Further, for example, the average value of TiO ₂ for sample # 10 is 0.20%, and the range of 1σ centered on the average value is 0.17 to 0.24%.

As shown in FIGS. 13 to 15, the DSP wide-area tephra varies in the main component of plagioclase glass inclusions even if it is assumed that only the uppermost layer near the foot of the mountain has come (see range D). ) And there is no overlap with the DKP range C and the remote DKP ranges A and B. On the other hand, as is known from FIGS. 13 to 15, the ranges A, B, and C of DKP overlap each other. Based on this, it can be determined that the remote DKP (range A, B) is a different tephra that cannot be compared with the DSP (range D) and can be compared with the DKP (range C). .
In this way, if a certain fixed width (for example, 1σ, 2σ, etc.) is set and the ranges are compared to determine whether or not there is an overlap, whether or not the target tephra can be compared. Can be determined with high accuracy.

  Regarding the tephra identified as DKP in Oyama Volcano, Hokuriku, Chubu Mountains, and the southern Tohoku region, including Sample # 10, there are no research reports other than Sample # 11 that have been reviewed for comparison with DSP. It is an announcement. Sample # 10 and sample # 11 have the same refractive index according to existing research results, and the main components of glass inclusions contained in plagioclase as described above, and the contrast is very rational. Conceivable. From the viewpoint of its rationality, the component analysis results of DKP (samples # 7 to # 9) by Yamagata Oyama clearly suggest that it can be compared with sample # 11.

  As described above, the tephra (sample # 10) identified as DKP in Echizen City (former Odamachi), Fukui Prefecture, and the tephra (sample # 11) identified as DKP in the Takano Formation, Nagano Prefecture, are component analysis of glass inclusions. The results were consistent. In addition, the feature matches the feature of DKP, but only partially matches the feature of DSP. Therefore, the sample # 11 (DKP of the Takano Formation) can be compared with the DKP of the foot of Oyama (Samples # 7 to 9 in Example 1).

  Thus, according to the tephra identification method of the present example, it is possible to obtain a conclusion that the sample # 11, which has conventionally been suspicious of being DKP or DSP, can be compared with DKP. Unlike the conventional tephra identification method, the tephra identification method of this example is an excellent technique that is not affected by the weathering of volcanic glass.

In this example, a variation range of 1σ width (± 0.5σ) centered on the average value is set for the component analysis result (specific component content ratio) of each specific component. It is also possible to set a variation range such as width (± 1σ) and 4σ width (± 2σ).
Furthermore, in this example, the comparison of the tephra contrast is made by comparing the variation ranges of the content ratios of SiO ₂ , TiO ₂ , Al ₂ O ₃ , FeO, CaO, Na ₂ O, and K ₂ O set as specific components. Is going. The specific component for comparing the variation range may be only one of the components of SiO ₂ , TiO ₂ , Al ₂ O ₃ , FeO, CaO, Na ₂ O, and K ₂ O. Moreover, the contrast which added other main components, such as MnO and MgO, may be sufficient.

Further, in this example, the variation range is compared two-dimensionally on a two-dimensional graph in which the content ratio of SiO ₂ is defined on the horizontal axis and the content ratio of other specific components is defined on the vertical axis. Alternatively, the content ratio of other specific components other than SiO ₂ can be defined on the horizontal axis. Furthermore, it is also possible to individually compare the variation range of each specific component one-dimensionally.
Other configurations and operational effects are the same as those in the first embodiment.

(Example 3)
In this example, as shown in FIGS. 16 and 17, the tephra identification method of Example 1 is an example in which changes in specific components in glass inclusions due to the types of minerals and the difference in particle diameter are examined.
This example is based on the following two findings. The first finding is that when the crystal growth process is considered, the component composition of the glass inclusions in plagioclase may change due to the change in grain size. The second finding is that orthopyroxene, amphibole, etc. are different from plagioclase because the environment in which crystals grow is different from plagioclase. It is knowledge.

  The examination of this example is aimed at tephra samples collected from the DKP central layer at the foot of Oyama. As tephra samples, plagioclase, orthopyroxene and amphibole with a particle size of 1/8 mm or more on one side and plagioclase with a particle size of 1/16 to 1/8 mm on one side were prepared. A particle size having a side of 1/8 mm or more is generally a sample that is rarely used for tephra identification. A particle size of 1/16 to 1/8 mm on a side is a sample having a general particle size in tephra identification. A particle diameter of 1/16 to 1/8 mm on a side is also used in Example 1.

A list of tephra samples in this example is as follows.
Sample # 12 Plagioclase with one side of 1/8 mm or more Sample # 13 Orthopyroxene with one side of 1/8 mm or more Sample # 14 Amphibole with one side of 1/8 mm or more Sample # 15 One side of 1/16 to 1/8 mm Plagioclase of particle size

In this example, the analysis results of the glass inclusions for the samples # 12 to # 15 are shown in FIGS. Each figure is a graph in which the content ratio of SiO ₂ is defined on the horizontal axis and the content ratio of other specific components is defined on the vertical axis. In addition, in the same figure, the analysis result of the volcanic glass (Volcanic Glass, (triangle | delta) plot in a figure) is plotted for reference.

  First, when comparing sample # 15, which is plagioclase with a particle size of 1/16 to 1/8 mm, and sample # 12, which is plagioclase with a particle size of 1/8 mm or more, sample # 12 is more in glass inclusions. The variation in the content ratio of each specific component is large. The variation in the content ratio of the specific component can be a factor that lowers the identification accuracy of the tephra. In addition, the change due to the difference in mineral is also large, and the difference in mineral as well as the particle size can be a factor of decreasing accuracy.

From the distribution characteristics of the tephra, whose particle size decreases with increasing distance from the source volcano, the finer particles are more likely to be distributed over a wider area. Furthermore, as described above, finer particles have a more comprehensive component analysis result of glass inclusion, and the tephra may be identified with high accuracy. In this example, a large difference was found in the component analysis results of glass inclusion between the amphibole and orthopyroxene (see FIGS. 16 and 17). In addition, there was a great difference between amphibole and other minerals, especially in plagioclase, in the content of K ₂ O. Amphibolite is heavier than plagioclase, and it is easy to imagine that it is less likely to fly far than plagioclase.

From the above, in order to identify the tephra efficiently and with high precision, plagioclase with a particle size range of about 1/16 to 1/8 mm generally used for tephra identification is used, and the included glass inclusions are included. It is good to analyze.
If plagioclase is extremely diffused in the formation and only orthopyroxene or amphibole can be detected, the glass inclusions of these minerals may be analyzed.

FIG. 3 is a flowchart showing a flow of identification processing by a tephra identification method in the first embodiment. The Japan map which shows the collection place of the tephra in Example 1. FIG. 2 is a graph showing the analysis result of DNP in Example 1. 2 is a graph showing the analysis result of DNP in Example 1. 2 is a graph showing the analysis result of DNP in Example 1. 3 is a graph showing the DSP analysis results in Example 1. FIG. 3 is a graph showing the DSP analysis results in Example 1. FIG. 3 is a graph showing the DSP analysis results in Example 1. FIG. 3 is a graph showing the result of DKP analysis in Example 1. 3 is a graph showing the result of DKP analysis in Example 1. 3 is a graph showing the result of DKP analysis in Example 1. FIG. 9 is a flowchart showing a flow of identification processing by a tephra identification method in Embodiment 2. The graph which shows the analysis result of the tephra in Example 2. FIG. The graph which shows the analysis result of the tephra in Example 2. FIG. The graph which shows the analysis result of the tephra in Example 2. FIG. 10 is a graph showing a tephra analysis result in Example 3. FIG. 10 is a graph showing a tephra analysis result in Example 3. FIG.

Claims (4)

A method for identifying tephra that are volcanic debris, such as volcanic ash and pyroclastic flows,
To identify whether the first tephra whose source volcano and eruption period cannot be specified depending on the sedimentary site and sedimentary layer can be compared with the second tephra whose source volcano and eruption period are specified For the purpose of
A specific mineral extraction step for extracting a specific type of mineral from the tephra;
A glass inclusion analysis step for analyzing the glass inclusions contained in the mineral extracted by the specific mineral extraction step to determine the content ratio of the specific component that is not a volatile component ;
A statistical processing step of calculating a mean value and a standard deviation by performing statistical processing on the content ratio of the specific component;
Based on the average value and the standard deviation obtained by the statistical processing step, the dispersion ranges of the content ratios of the specific components in the first and second tephra are respectively specified, and both are determined according to the overlapping degree of these dispersion ranges. And a tephra comparison step for determining whether or not can be compared . 2. The tephra identification method according to claim 1, wherein the specific mineral extraction step is a step of extracting a mineral having a particle size in the range of 1/16 to 1/8 mm. 3. The tephra identification method according to claim 1, wherein the mineral extracted in the specific mineral extraction step is at least one of quartz and plagioclase. 4. The glass inclusion analysis step according to claim 1, wherein the glass inclusion analysis step is a step of analyzing the glass inclusion using a sample obtained by polishing a resin cured in a state where the mineral is embedded. 5. A tephra identification method characterized by the above.

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