Fault identification method and system based on multivariate spectral mineral anomaly analysis

Abstract

The application provides a fault identification method and system based on multi-element spectral mineral anomaly analysis, relates to the fields of geology, spectroscopy, petrology and engineering geology, and a specific scheme comprises the following steps: in-situ testing of tunnel surrounding rock is carried out by using a visible light-near infrared, mid-infrared and laser Raman spectrometer, and mixed spectral variation characteristics are extracted; based on the mixed spectral variation characteristics, the mineral composition of the tunnel surrounding rock is identified according to the spectral identification standard of the abnormal mineral combination of the fault zone, quantitative inversion of the minerals is carried out, and the mineral content is obtained; according to the mineral anomaly mode of the fault zone, mineral anomaly analysis is carried out on the mineral composition, and the mineral anomaly type is obtained; based on the differential change of the mineral content and the mineral anomaly type, mineralogical anomaly characteristic analysis is carried out, and the fault identification result is output; the application replaces the experience knowledge of traditional geologists with quantitative information of multi-element spectral mineral anomaly, and further improves the accuracy and efficiency of fault identification in tunnels and underground engineering.

Description

Technical Field

[0001] This invention belongs to the fields of geology, spectroscopy, petrology and engineering geology, and in particular relates to a fault identification method and system based on multi-spectral mineral anomaly analysis. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As the focus of tunnel engineering construction shifts towards areas with complex geological environments and conditions, tunnel construction constantly faces challenges brought about by complex geological conditions. Faults, as common disaster-causing structures, often trigger geological disasters such as water and mud inrushes, collapses, and large deformations of surrounding rock due to the fractured, low-strength, and fissure-developed surrounding rock within their affected zones. Accurate and timely fault identification during the excavation of deep-buried tunnels is of great engineering significance for safe tunnel construction and geological disaster prevention and control.

[0004] Traditional methods for fault geological identification mainly utilize stratigraphic and structural markers. For example, by observing the repetition and absence of strata and considering the relationship between the attitude of strata and the attitude of faults, the movement mode of faults can be identified. Structural markers include discontinuities in linear or planar geological bodies, slickensides and steps, joints and cleavages, traction structures, tectonic lenses, and fault rocks, which serve as important evidence of fault existence. Secondly, geomorphic markers such as fault scarps and fault triangular facets, and hydrological markers such as the distribution of springs in belts and the formation of beaded lakes and depressions are also used. However, in tunnel engineering, geomorphic and hydrological markers for fault identification are difficult to apply, especially in cases of complex geological structures and deep tunnels. Therefore, stratigraphic and structural markers are mainly used for fault identification in tunnel engineering, such as the increase in the number of joint and fracture groups near faults, traction folds, and the appearance of fault rocks.

[0005] In stratigraphy and tectonic markers, minerals are the result of the dispersion, aggregation, migration, and movement of various chemical elements in the Earth's crust. The geochemical effects of fault tectonics inevitably lead to changes in mineral composition and content. Fault activity and changes in the elemental and mineral information of the surrounding rocks are highly correlated. Therefore, changes in the geochemical and mineralogical characteristics of the surrounding rocks caused by fault activity can serve as identification markers for faults. As a result, methods for identifying unfavorable geological conditions have emerged based on elemental and mineral anomaly analysis, using the anomalous information of elements and minerals in the surrounding rocks along the tunnel as identification markers for unfavorable geological conditions.

[0006] Existing fault identification methods for anomaly analysis still have the following problems: 1) There are many types of metamorphic minerals, and the types of metamorphic minerals that can be used to identify faults are unclear; 2) The means of collecting rock and mineral information in tunnel engineering cannot meet the needs of rapid tunnel construction; 3) Different mineral information collection technologies have different identification effects, and multiple mineral combinations affect anomaly analysis and thus hinder fault identification. Therefore, existing fault identification methods are difficult to meet the needs of accurate and rapid identification of faults and other adverse geological conditions based on geological analysis methods in modern tunnel engineering. Summary of the Invention

[0007] To overcome the shortcomings of the existing technologies and address the need for quantitative identification of faults in tunnel engineering, this invention provides a fault identification method and system based on multi-spectral mineral anomaly analysis. The method performs multi-spectral mineral anomaly analysis on the surrounding rock, and based on the obtained mineral anomaly types and mineral contents, faults are identified. This method replaces the traditional experience and knowledge of geological experts with quantitative information on multi-spectral mineral anomalies, further improving the accuracy and efficiency of fault identification in tunnels and underground engineering.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] The first aspect of this invention provides a fault identification method based on multivariate spectral mineral anomaly analysis.

[0010] Fault identification methods based on multivariate spectral mineral anomaly analysis include:

[0011] In-situ testing of the surrounding rock of the tunnel was conducted using visible-near-infrared, mid-infrared, and laser Raman spectrometers to extract mixed spectral variation characteristics;

[0012] Based on the characteristics of mixed spectral variation, and according to the spectral identification criteria of abnormal mineral assemblages in fault zones, the mineral composition of the surrounding rock of the tunnel is identified, and the mineral content is obtained by quantitative inversion.

[0013] Based on the mineral anomaly pattern of the fault zone, mineral composition is analyzed to obtain the mineral anomaly type;

[0014] Based on the differential changes in mineral content and the types of mineral anomalies, mineralogical anomaly characteristics are analyzed, and fault identification results are output.

[0015] The spectral identification standard for the abnormal mineral assemblage in the fault zone defines the relationship between the absorption characteristics of visible-near infrared light, the reflection peaks of mid-infrared light, and the Raman peaks of laser Raman spectroscopy and the mineral composition. This standard is used to identify the mineral composition based on the absorption characteristics, reflection peaks, and Raman peaks in the mixed spectral variation characteristics.

[0016] Furthermore, the mixed spectral variation characteristics are divided into visible-near-infrared mixed spectral variation characteristics, mid-infrared mixed spectral variation characteristics, and laser Raman mixed spectral variation characteristics;

[0017] The visible-near-infrared mixed spectral variation features are absorption features extracted from the visible-near-infrared reflectance spectrum, including the position and depth of the absorption bands in the spectrum.

[0018] The mid-infrared mixed spectral variation characteristics are the reflection peaks extracted from the mid-infrared reflectance spectrum, including the position and height of the reflection peaks in the spectrum;

[0019] The laser Raman mixed spectrum variation characteristics are Raman peaks extracted from the laser Raman spectrum, including the Raman peak position and peak height.

[0020] Furthermore, the identification of the mineral composition of the tunnel surrounding rock specifically includes:

[0021] Based on the relationship between the absorption characteristics of visible light and near-infrared light and mineral composition, the mineral composition of the tunnel surrounding rock is obtained according to the position and depth of the absorption bands in the spectrum.

[0022] Based on the relationship between mid-infrared reflection peaks and mineral composition, the mineral composition of the tunnel surrounding rock is obtained according to the position and height of the reflection peaks in the spectrum.

[0023] Based on the relationship between Raman peaks and mineral composition obtained from laser Raman spectroscopy, the mineral composition of the tunnel surrounding rock is determined according to the position and height of the Raman peaks in the spectrum.

[0024] Furthermore, the quantitative inversion of the minerals is based on the identified mineral components, and the mineral content is obtained by selecting the corresponding mineral mixture spectral unmixing method.

[0025] Furthermore, the fault zone mineral anomaly pattern, based on the formation mode of clay minerals within the fault zone, uses mineral composition as the basis for anomaly classification, and is divided into four major categories: felsic anomalies, mafic anomalies, magnesian anomalies, and argillaceous anomalies.

[0026] Furthermore, the felsic anomalies include illite-type anomalies and kaolinite-type anomalies;

[0027] The magnesian anomalies include chlorite-type anomalies;

[0028] The magnesium-related anomalies include montmorillonite-type anomalies, serpentine + talc-type anomalies, vermiculite-type anomalies, and palygorskite + sepiolite-type anomalies;

[0029] The mud-type anomalies include mixed-layer anomalies.

[0030] Furthermore, the mineralogy anomaly characteristic analysis based on the differential changes in mineral content and the types of mineral anomalies specifically includes:

[0031] The degree of mineral anomaly is characterized by the differential variation in mineral content. The absence of mineral anomaly indicates a normal surrounding rock section. Based on the degree of mineral anomaly, the fault is divided into the fault core and the fault fracture zone. The fault fracture zone is further divided into the hanging wall fracture zone and the footwall fracture zone. The degree of mineral anomaly in the hanging wall fracture zone is higher than that in the footwall fracture zone.

[0032] A second aspect of the present invention provides a fault identification system based on multivariate spectral mineral anomaly analysis.

[0033] A fault identification system based on multivariate spectral mineral anomaly analysis includes a feature extraction module, a mineral identification module, an anomaly analysis module, and a fault identification module.

[0034] The feature extraction module is configured to: use visible-near infrared, mid-infrared and laser Raman spectrometers to conduct in-situ tests on the surrounding rock of the tunnel and extract mixed spectral variation features;

[0035] The mineral identification module is configured to: identify the mineral composition of the tunnel surrounding rock based on the mixed spectral variation characteristics and the spectral identification standard of abnormal mineral assemblages in the fault zone, and perform quantitative inversion of minerals to obtain the mineral content;

[0036] The anomaly analysis module is configured to perform mineral anomaly analysis on mineral composition based on the mineral anomaly pattern of the fault zone to obtain the mineral anomaly type;

[0037] The fault identification module is configured to perform mineralogical anomaly feature analysis based on the differential changes in mineral content and the types of mineral anomalies, and output the fault identification results.

[0038] The spectral identification standard for the abnormal mineral assemblage in the fault zone defines the relationship between the absorption characteristics of visible-near infrared light, the reflection peaks of mid-infrared light, and the Raman peaks of laser Raman spectroscopy and the mineral composition. This standard is used to identify the mineral composition based on the absorption characteristics, reflection peaks, and Raman peaks in the mixed spectral variation characteristics.

[0039] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the fault identification method based on multivariate spectral mineral anomaly analysis as described in the first aspect of the present invention.

[0040] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the fault identification method based on multi-spectral mineral anomaly analysis as described in the first aspect of the present invention.

[0041] The above one or more technical solutions have the following beneficial effects:

[0042] This invention proposes four categories and eight types of mineral anomaly models in fault zones, revealing the geological factors and processes that influence the anomalous enrichment of clay minerals within fault zones, and providing a geological basis for the study of mixed spectral variation characteristics and fault identification methods.

[0043] This invention constructs a mineral assemblages based on fault zone mineral anomaly patterns, studies the influence of mineral composition and relative content variations on mixed spectral characteristics, proposes visible-near-infrared, mid-infrared, and Raman mixed spectral variation characteristics of fault zone mineral anomaly patterns, and analyzes the impact of mineral mixed spectral variation effects on mineral identification and quantitative inversion.

[0044] This invention proposes a spectral standard for identifying anomalous mineral assemblages within fault zones, constructs a fault identification method based on multi-spectral mineral anomaly analysis, and further improves the accuracy and efficiency of anomalous mineral assemblages identification by integrating visible-near-infrared, mid-infrared, and Raman spectroscopy techniques. Engineering verification has demonstrated the practicality and accuracy of this identification method.

[0045] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0046] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0047] Figure 1 This is a technical roadmap for the first embodiment.

[0048] Figure 2 This is a schematic diagram of mineral anomalies in the fault zone in the first embodiment.

[0049] Figure 3 This is a schematic diagram illustrating the selection of visible-near-infrared, mid-infrared, and laser Raman spectroscopy techniques for identifying minerals or mineral assemblages in the first embodiment.

[0050] Figure 4 The flowchart illustrates the implementation of the tomography identification method in the first embodiment. Detailed Implementation

[0051] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0052] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0053] Example 1

[0054] One embodiment of this disclosure provides a fault identification method based on multivariate spectral mineral anomaly analysis, comprising the following steps:

[0055] Step S1: Use visible-near infrared, mid-infrared and laser Raman spectrometers to conduct in-situ tests on the surrounding rock of the tunnel and extract the mixed spectral variation characteristics;

[0056] Step S2: Based on the mixed spectral variation characteristics and according to the spectral identification criteria of abnormal mineral assemblages in fault zones, identify the mineral composition of the tunnel surrounding rock and perform quantitative inversion of minerals to obtain the mineral content;

[0057] Step S3: Based on the mineral anomaly pattern of the fault zone, perform mineral anomaly analysis on the mineral composition to obtain the mineral anomaly type;

[0058] Step S4: Based on the differences in mineral content and the types of mineral anomalies, perform mineralogical anomaly characteristic analysis and output the fault identification results;

[0059] The spectral identification standard for the abnormal mineral assemblage in the fault zone defines the relationship between the absorption characteristics of visible-near infrared light, the reflection peaks of mid-infrared light, and the Raman peaks of laser Raman spectroscopy and the mineral composition. This standard is used to identify the mineral composition based on the absorption characteristics, reflection peaks, and Raman peaks in the mixed spectral variation characteristics.

[0060] The following is a detailed description of the implementation process of the fault identification method based on multivariate spectral mineral anomaly analysis in this embodiment.

[0061] To more clearly illustrate this embodiment, we will begin with its technical approach. Addressing the need for quantitative identification of faults in tunnel engineering, this embodiment focuses on the research of a "fault identification method based on multivariate spectral mineral anomaly analysis," such as... Figure 1As shown, firstly, based on the analysis of typical engineering cases, a mineral anomaly model of fault zones is proposed, providing a geological basis for the study of mixed spectral variation characteristics and fault identification methods. Secondly, a symbiotic assemblage based on the mineral anomaly model of fault zones is constructed, and the mixed spectral variation characteristics of visible-near-infrared, mid-infrared, and laser Raman are studied. The influence of changes in mineral composition, relative mineral content, and endmember number on the mixed spectral characteristics is analyzed, and the impact of mixed spectral variation effects on mineral identification and quantitative inversion is investigated, providing a theoretical basis for the proposal of fault identification methods. Finally, the spectral identification criteria for anomalous mineral assemblages within fault zones are summarized, and a fault identification method based on multivariate spectral mineral anomaly analysis is proposed. These will be explained in detail below.

[0062] I. Mineral Anomaly Patterns in Fault Zones

[0063] The phenomenon of secondary minerals forming in geological bodies with different mineral compositions from the host rocks, influenced by tectonic stress and driven by geochemical processes, is called mineral anomaly. Some minerals are mainly found in specific rocks, such as clay minerals in sedimentary rocks and graphite and epidote in metamorphic rocks. Therefore, if minerals such as clay, graphite, and epidote are found in igneous rocks (usually in metamorphic and sedimentary rocks), this is considered a mineral anomaly, which may be caused by specific geological processes.

[0064] like Figure 2 The diagram shows a simplified geological map of mineral anomalies in a fault zone. Influenced by fault stress and the geochemical processes of the fracture structure, the interaction between fluids and rocks in the fracture zone and core leads to the easy transformation of protolith minerals (feldspar, mica, amphibole, pyroxene, and olivine, etc.), while corresponding secondary clay minerals (montmorillonite, illite, kaolinite, chlorite, serpentine, and talc, etc.) tend to accumulate. This is a typical mineral anomaly phenomenon in fault zones. Clearly defining mineral anomalies and typical mineral anomalies in fault zones is more conducive to the study of the patterns and models of mineral anomalies in fault zones.

[0065] This embodiment analyzes a typical engineering case: the authigenic clay minerals in the fault zone are formed by the transformation of minerals in the original rock. Therefore, this embodiment uses the mineral composition of the original rock as the basis for anomaly classification. Illite and kaolinite are mainly formed by the transformation of protolith minerals feldspar and mica, therefore illite-type and kaolinite-type are classified as felsic. Chlorite is mainly formed by the transformation of iron- and magnesium-rich dark minerals amphibole and biotite, or by the replacement of feldspar by iron- and magnesium-rich fluids released from dark minerals, therefore chlorite-type is classified as magnesian-ferrous. Montmorillonite, serpentine + talc, vermiculite, palygorskite + sepiolite are mostly formed by the transformation of magnesium-rich protolith minerals such as olivine, pyroxene, dolomite, and magnesite, or by the interaction of magnesium-rich fluids and aluminosilicates, therefore montmorillonite-type, serpentine + talc type, vermiculite type, and palygorskite + sepiolite type are classified as magnesian. Illite-montmorillonite mixed layer and chlorite-montmorillonite mixed layer are intermediate products of the mutual transformation between illite and montmorillonite, and chlorite and montmorillonite, respectively, therefore mixed-layer anomalies are classified as argillaceous.

[0066] This results in four categories and eight types of fault zone mineral anomaly models, specifically:

[0067] Felsic anomalies (illite-type anomalies, kaolinite-type anomalies);

[0068] Magnesian anomaly (chlorite-type anomaly);

[0069] Magnesium-based anomalies (montmorillonite-type anomaly, serpentine + talc-type anomaly, vermiculite-type anomaly, palygorskite + sepiolite-type anomaly);

[0070] Muddy anomaly (mixed-layer anomaly);

[0071] II. Constructing hybrid spectral variation characteristics based on the symbiotic assemblage of mineral anomaly patterns in fault zones.

[0072] After the mineral anomaly pattern of the fault zone has given the correlation between mineral composition and anomaly type, the anomaly type can be predicted by the mineral composition in the sample to be tested. However, the mixing of minerals in the sample interferes with the accurate extraction of mineral composition. Therefore, this embodiment estimates the mineral composition by identifying the mixed spectral variation characteristics.

[0073] The mixed spectral variation characteristics of the mineral anomaly pattern in the fault zone here are a regularity of spectral parameters changing with different mineral compositions and contents. Based on different mineral testing and analysis methods, they are divided into visible-near infrared mixed spectral variation characteristics, mid-infrared mixed spectral variation characteristics, and laser Raman mixed spectral variation characteristics.

[0074] 1. Visible-near-infrared mixed spectral variation characteristics are absorption characteristics extracted from the visible-near-infrared reflectance spectrum, including the position and depth of the absorption bands in the spectrum, and establishing the correlation between absorption characteristics and mineral composition.

[0075] Based on the mineral anomaly model of the fault zone, 24 binary, ternary, and quaternary mineral assemblages were constructed under the felsic, mafic, and magnesian anomaly models. The visible-near-infrared spectral behavior of the mineral mixtures was explored, the influence of mineral species and content variations on the mixture spectral parameters was studied, the visible-near-infrared spectral variation characteristics of the mineral anomaly model within the fault zone were revealed, and the influence of mineral spectral mixing effects on quantitative mineral inversion was analyzed.

[0076] Based on absorption characteristics, the variation characteristics of the visible-near-infrared mixed spectrum are divided into three categories: (1) no interference; (2) superimposed interference; (3) annihilation.

[0077] Table 1. Visible-near-infrared mixed spectral variation characteristics under mineral anomaly patterns within the fault zone.

[0078]

[0079]

[0080] Quantitative inversion of minerals estimates mineral content using spectral unmixing methods. However, the co-occurrence and mixing of minerals can lead to spectral variation effects, which can affect the accuracy of quantitative inversion. Specifically:

[0081] (1) Minerals with “no significant spectral absorption characteristics” such as sodium feldspar and potassium feldspar will increase the error in abundance estimation. In addition, the accuracy of unmixing is also related to whether the absorption characteristics overlap.

[0082] (2) The unmixing accuracy is related to the preprocessing method, but it is also highly controlled by the characteristics of the mineral mixing spectrum itself. That is, with the same preprocessing method, the unmixing accuracy of different mineral mixing spectra is different.

[0083] (3) Envelope removal - first-order derivative pretreatment method has good advantages in the linear unmixing of quaternary mineral mixtures, and can better reduce the problem of unmixing failure and the occurrence of 0 value.

[0084] Therefore, the mixed spectral variation effects of different mineral assemblages are different, and have different impacts on the quantitative inversion of mineral content. The estimation of mineral content should be based on the selection of appropriate preprocessing methods and unmixing methods.

[0085] 2. The mid-infrared mixed spectral variation characteristics are the reflection peaks extracted from the mid-infrared reflectance spectrum, including the position and height of the reflection peaks in the spectrum, and the correlation between the reflection peak information and the mineral composition is established.

[0086] This embodiment of the study still uses mineral mixed samples to explore the mid-infrared mixed spectral behavior under mineral anomaly patterns in fault zones, revealing the influence of changes in mineral type and content on the changes in mixed spectral parameters, studying the mid-infrared mixed spectral variation characteristics under mineral anomaly patterns in fault zones, and analyzing the impact of mineral spectral mixing effects on mineral quantitative inversion.

[0087] Based on the reflection peak information, the mid-infrared mixed spectral variation characteristics are divided into four categories: ① superposition interference; ② "camouflage" enhancement; ③ annihilation; ④ shift, as shown in Table 2.

[0088] Table 2. Mid-infrared mixed spectral variation characteristics of mineral anomaly patterns within fault zones.

[0089]

[0090]

[0091] Based on the linear unmixing results of binary and ternary mineral mixed spectra under different anomaly types in fault zone mineral anomaly modes under different preprocessing methods, the influence of visible-near-infrared mixed spectrum variation effect on mineral quantitative inversion is analyzed. Similar to the visible-near-infrared band, the mid-infrared unmixing accuracy is related to the preprocessing method, but is also highly controlled by the characteristics of the mineral mixed spectrum itself. That is, with the same preprocessing method, the unmixing accuracy of different mineral mixed spectra is also different.

[0092] Because minerals such as feldspar possess diagnostic characteristics in the mid-infrared band, mineral identification using the mid-infrared band has unique advantages. However, strong interference in the 8μm–12μm vibrational band of the mid-infrared band causes variations in the spectra of mixed minerals. Consequently, when the end-member mineral composition is complex, the near-infrared band provides better unmixing performance than the mid-infrared band.

[0093] Due to strong interference between the 8μm to 12μm vibrational bands of various silicate minerals, the height or area of ​​the reflection peaks in the mid-infrared spectrum do not have a linear relationship with the mineral content. Therefore, it is difficult to estimate the mineral abundance in the mid-infrared band using the relative intensity of the reflection peaks, as is the case in the near-infrared band.

[0094] 3. The laser Raman mixed spectrum variation characteristics are Raman peaks extracted from the laser Raman spectrum, including the Raman peak position and peak height, and the correlation between Raman peaks and mineral composition is established.

[0095] The effectiveness of laser Raman spectroscopy in identifying minerals depends on their crystal structure and chemical composition: it is most effective for carbonate minerals; followed by island-like, chain-like, and framework-like silicates; and it is least effective for layered silicates.

[0096] Unlike the visible-near-infrared mixed spectral variation characteristics of mineral anomaly patterns within fault zones, in addition to superposition interference and annihilation, laser Raman mixed spectra also exhibit shift interference. This is mainly manifested in the presence of one mineral causing the Raman peak of another mineral to shift towards longer or shorter wavelengths, as shown in Table 3.

[0097] Table 3 shows the variation characteristics of laser Raman mixed spectra under mineral anomaly patterns within the fault zone.

[0098]

[0099]

[0100] Based on the mixed Raman spectral characteristics of mineral anomalies in fault zones, it is evident that directly applying Raman spectroscopy to the identification and quantitative analysis of anomalous mineral assemblages in fault zones presents the following two challenges:

[0101] ① Noise and Spectral Shift: Firstly, there is the issue of significant mineral noise, most notably in illite, whose Raman spectrum essentially lacks Raman peaks. This makes it difficult to identify illite-type mineral anomalies using Raman spectroscopy. Illite also interferes with the Raman peaks of other minerals. For example, in the mixed spectrum of muscovite and illite, due to the influence of illite noise, the Raman peaks of muscovite cannot be identified when the illite content is greater than or equal to 50%. Similarly, in the mixed spectrum of kaolinite and potassium feldspar, the Raman peaks of kaolinite cannot be identified when the potassium feldspar content is greater than or equal to 50%. Secondly, there is the issue of spectral shift, primarily caused by the mixing of potassium feldspar with kaolinite or chlorite, which results in peak shift. This inherent, difficult-to-eliminate noise and peak shift significantly alter the difference between the spectrum of a particular mineral and its corresponding endmember spectrum in the mixed spectrum, thus affecting mineral identification and quantitative analysis.

[0102] ② Raman peak characteristics exhibit crystal orientation: Raman spectra of mixtures of albite and kaolinite, amphibole and chlorite, olivine and serpentine, and dolomite and serpentine in different wavelength ranges show that the area or height of the Raman peak is not directly proportional to the abundance of the mineral. For example, in the Raman spectra of a mixture of olivine and serpentine in different wavelength ranges, the mixed spectrum is at 386 cm⁻¹. -1 The area or height of the Raman peaks on the left and right sides is not directly proportional to the abundance of serpentine, but is largest when the serpentine content is 80%, with the mixed spectrum at 690 cm⁻¹. -1 The Raman peaks on the left and right also exhibit a similar pattern, but the Raman peak here is not affected by olivine. The mixed spectrum is at 822 cm⁻¹. -1 and 854cm -1The area or height of the Raman peaks on the left and right sides are not directly proportional to the abundance of olivine, but are largest when the olivine content is 60%. The mixed spectrum of dolomite and serpentine is at 386 cm⁻¹. -1 690cm -1 and 1098cm -1 The area or height of the Raman peaks on the left and right are not directly proportional to the abundance of minerals.

[0103] Therefore, due to the significant noise, offset interference, and crystal orientation of Raman spectral features in some layered silicates, especially the crystal orientation problem, mineral abundance estimation based on spectral unmixing still faces enormous challenges.

[0104] Visible-near-infrared, mid-infrared, and laser Raman spectroscopy techniques differ in their ability or effectiveness in identifying minerals or mineral assemblages, such as... Figure 3 As shown, felsic anomalies and mafic anomalies should be identified using a combination of visible-near-infrared and mid-infrared spectroscopy techniques because: ① Visible-near-infrared spectroscopy is effective in identifying clay minerals, but it cannot identify the presence of feldspar; ② Mid-infrared spectroscopy can identify feldspar, thus compensating for the shortcomings of visible-near-infrared spectroscopy; ③ Laser Raman spectroscopy cannot identify illite-type anomalies in felsic anomalies, and it is also ineffective in identifying mineral assemblages related to potassium feldspar in mafic anomalies.

[0105] Magnesium-related anomalies should be identified using a combination of mid-infrared and laser Raman spectroscopy because: ① due to the obliteration of the diagnostic absorption characteristics of dolomite and magnesite by serpentine, visible-near-infrared spectroscopy is difficult to identify carbonate minerals in magnesium-related anomalies; ② mid-infrared and laser Raman spectroscopy are effective in identifying magnesium-related anomalies.

[0106] III. Spectral Identification Criteria for Anomalous Mineral Assemblages within Fault Zones

[0107] Based on the study of visible-near-infrared, mid-infrared, and laser Raman mixed spectral variation characteristics of fault zone mineral anomaly patterns, and assuming that the spectral parameters have been extracted and vary with different mineral compositions and contents, spectral identification criteria for fault zone anomalous mineral assemblages (felsic, mafic, and magnesian) are proposed to predict anomalous mineral assemblages (i.e., mineral composition). The specific spectral criteria are shown in the table below:

[0108] Table 4. Spectral identification criteria for long-fiber-like anomalies

[0109]

[0110]

[0111]

[0112] Table 5. Spectral Identification Criteria for Magnesium-Iron Anomalies

[0113]

[0114]

[0115] Table 6. Spectral Identification Criteria for Magnesium-Related Anomalies

[0116]

[0117]

[0118] IV. Fault Identification Method Based on Multi-Dimensional Spectroscopic Mineral Anomaly Analysis

[0119] Rock and mineralogical anomalies are used as identification markers for faults. Due to the influence of fault stress and geochemical processes, the interaction between fluids and rocks leads to mineral anomalies within the fault-affected zone. The interaction between fluids and rocks is strong in the fault core, resulting in a high degree of mineral anomalies. The interaction between fluids and rocks is relatively weak in the fault fracture zone, resulting in a relatively low degree of mineral anomalies. In contrast, there is basically no interaction between fluids and rocks in the normal surrounding rock section, and mineral anomalies are generally not present.

[0120] Focusing on the main theme of "mixed spectral variation characteristics - anomalous mineral assemblages (mineral composition) - fault identification," and based on the principles of fault identification and the spectral identification criteria for anomalous mineral assemblages in fault zones, this paper proposes a fault identification method based on multivariate spectral mineral anomaly analysis. Figure 4 As shown, the implementation process of this method is as follows:

[0121] (1) First, based on the geological survey data of the tunnel, including topography, lithological composition, geological structure and hydrogeological conditions, the mileage range of fault exposure in the tunnel is preliminarily determined.

[0122] (2) The surrounding rocks in normal and fault-affected areas were tested in situ using visible-near infrared, mid-infrared and laser Raman spectrometers. Based on the spectral characteristics of end-member minerals, the mixed spectral variation characteristics were extracted.

[0123] (3) Based on the spectral identification criteria proposed by the mixed spectral variation characteristics of mineral anomaly patterns in fault zones, the mineral composition and content of protoliths and fault rocks are identified;

[0124] (4) Based on the mineral anomaly pattern of the fault zone, the anomaly characteristics of protolith minerals and clay minerals in the rock samples are analyzed to obtain the mineral anomaly type;

[0125] (5) Based on the characteristics of "abnormal reduction of protolith minerals and abnormal enrichment of clay minerals," that is, based on the differential changes in mineral content and the types of mineral anomalies, mineralogical anomaly characteristic analysis is performed to identify faults and output the fault identification results, specifically:

[0126] Mileage sections without the aforementioned mineral anomalies are considered normal surrounding rock sections; mileage sections with stronger mineral anomalies are considered fault core sections; and mileage sections with relatively weaker mineral anomalies are considered fault fracture zones, which are divided into hanging wall fracture zones and footwall fracture zones, with the degree of mineral anomalies in the hanging wall fracture zone generally being higher than that in the footwall fracture zone.

[0127] Example 2

[0128] One embodiment of this disclosure provides a fault identification system based on multivariate spectral mineral anomaly analysis, including a feature extraction module, a mineral identification module, an anomaly analysis module, and a fault identification module:

[0129] The feature extraction module is configured to: use visible-near infrared, mid-infrared and laser Raman spectrometers to conduct in-situ tests on the surrounding rock of the tunnel and extract mixed spectral variation features;

[0130] The mineral identification module is configured to: identify the mineral composition of the tunnel surrounding rock based on the mixed spectral variation characteristics and the spectral identification standard of abnormal mineral assemblages in the fault zone, and perform quantitative inversion of minerals to obtain the mineral content;

[0131] The anomaly analysis module is configured to perform mineral anomaly analysis on mineral composition based on the mineral anomaly pattern of the fault zone to obtain the mineral anomaly type;

[0132] The fault identification module is configured to perform mineralogical anomaly feature analysis based on the differential changes in mineral content and the types of mineral anomalies, and output the fault identification results.

[0133] The spectral identification standard for the abnormal mineral assemblage in the fault zone defines the relationship between the absorption characteristics of visible-near infrared light, the reflection peaks of mid-infrared light, and the Raman peaks of laser Raman spectroscopy and the mineral composition. This standard is used to identify the mineral composition based on the absorption characteristics, reflection peaks, and Raman peaks in the mixed spectral variation characteristics.

[0134] Example 3

[0135] The purpose of this embodiment is to provide a computer-readable storage medium.

[0136] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the fault identification method based on multi-dimensional spectral mineral anomaly analysis as described in Embodiment 1 of this disclosure.

[0137] Example 4

[0138] The purpose of this embodiment is to provide an electronic device.

[0139] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the fault identification method based on multi-spectral mineral anomaly analysis as described in Embodiment 1 of this disclosure.

[0140] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for fault identification based on multivariate spectral mineral anomaly analysis, characterized in that, include: In-situ testing of the surrounding rock of the tunnel was conducted using visible-near-infrared, mid-infrared, and laser Raman spectrometers to extract mixed spectral variation characteristics; Based on the characteristics of mixed spectral variation, and according to the spectral identification criteria of abnormal mineral assemblages in fault zones, the mineral composition of the surrounding rock of the tunnel is identified, and the mineral content is obtained by quantitative inversion. Based on the mineral anomaly pattern of the fault zone, mineral composition is analyzed to obtain the mineral anomaly type; Based on the differential changes in mineral content and the types of mineral anomalies, mineralogical anomaly characteristics are analyzed, and fault identification results are output. The spectral identification standard for the abnormal mineral assemblage in the fault zone defines the relationship between the absorption characteristics of visible light and near-infrared light, the reflection peaks of mid-infrared light, and the Raman peaks of laser Raman spectroscopy and the mineral composition. This standard is used to identify the mineral composition based on the absorption characteristics, reflection peaks, and Raman peaks in the mixed spectral variation characteristics. The identification of the mineral composition of the tunnel surrounding rock specifically includes: Based on the relationship between the absorption characteristics of visible light and near-infrared light and mineral composition, the mineral composition of the tunnel surrounding rock is obtained according to the position and depth of the absorption bands in the spectrum. Based on the relationship between mid-infrared reflection peaks and mineral composition, the mineral composition of the tunnel surrounding rock is obtained according to the position and height of the reflection peaks in the spectrum. Based on the relationship between Raman peaks and mineral composition obtained by laser Raman spectroscopy, the mineral composition of the surrounding rock of the tunnel is obtained according to the position and height of the Raman peaks in the spectrum. The aforementioned fault zone mineral anomaly pattern, based on the formation mode of clay minerals within the fault zone, uses mineral composition as the basis for anomaly classification, and is divided into four major categories: felsic anomalies, mafic anomalies, magnesian anomalies, and argillaceous anomalies. Felsic and mafic anomalies are identified by combined visible-near-infrared and mid-infrared spectroscopy, while magnesian anomalies are identified by combined mid-infrared and laser Raman spectroscopy.

2. The fault identification method based on multivariate spectral mineral anomaly analysis according to claim 1, characterized in that, The mixed spectral variation characteristics are divided into visible-near-infrared mixed spectral variation characteristics, mid-infrared mixed spectral variation characteristics, and laser Raman mixed spectral variation characteristics; The visible-near-infrared mixed spectral variation features are absorption features extracted from the visible-near-infrared reflectance spectrum, including the position and depth of the absorption bands in the spectrum. The mid-infrared mixed spectral variation characteristics are the reflection peaks extracted from the mid-infrared reflectance spectrum, including the position and height of the reflection peaks in the spectrum; The laser Raman mixed spectrum variation characteristics are Raman peaks extracted from the laser Raman spectrum, including the Raman peak position and peak height.

3. The fault identification method based on multivariate spectral mineral anomaly analysis according to claim 1, characterized in that, The quantitative inversion of minerals is based on the identified mineral components, and the mineral content is obtained by selecting the corresponding mineral mixture spectral unmixing method.

4. The fault identification method based on multivariate spectral mineral anomaly analysis according to claim 1, characterized in that, The felsic anomalies include illite-type anomalies and kaolinite-type anomalies; The magnesian anomalies include chlorite-type anomalies; The magnesium-related anomalies include montmorillonite-type anomalies, serpentine + talc-type anomalies, vermiculite-type anomalies, and palygorskite + sepiolite-type anomalies; The mud-type anomalies include mixed-layer anomalies.

5. The fault identification method based on multivariate spectral mineral anomaly analysis according to claim 1, characterized in that, The analysis of mineralogical anomaly characteristics based on the differential variations in mineral content and the types of mineral anomalies specifically includes: The degree of mineral anomaly is characterized by the differential variation in mineral content. The absence of mineral anomalies indicates a normal surrounding rock section. Based on the degree of mineral anomaly, the fault core and fault fracture zone are divided into the fault core and the fault fracture zone. The fault fracture zone is further divided into the hanging wall fracture zone and the footwall fracture zone. The degree of mineral anomaly in the hanging wall fracture zone is higher than that in the footwall fracture zone.

6. A fault identification system based on multivariate spectral mineral anomaly analysis, characterized in that, The fault identification method based on multivariate spectral mineral anomaly analysis as described in any one of claims 1-5 includes a feature extraction module, a mineral identification module, an anomaly analysis module, and a fault identification module. The feature extraction module is configured to: use visible-near infrared, mid-infrared and laser Raman spectrometers to conduct in-situ tests on the surrounding rock of the tunnel and extract mixed spectral variation features; The mineral identification module is configured to: identify the mineral composition of the tunnel surrounding rock based on the mixed spectral variation characteristics and the spectral identification standard of abnormal mineral assemblages in the fault zone, and perform quantitative inversion of minerals to obtain the mineral content; The anomaly analysis module is configured to perform mineral anomaly analysis on mineral composition based on the mineral anomaly pattern of the fault zone to obtain the mineral anomaly type; The fault identification module is configured to perform mineralogical anomaly feature analysis based on the differential changes in mineral content and the types of mineral anomalies, and output the fault identification results. The spectral identification standard for the abnormal mineral assemblage in the fault zone defines the relationship between the absorption characteristics of visible-near infrared light, the reflection peaks of mid-infrared light, and the Raman peaks of laser Raman spectroscopy and the mineral composition. This standard is used to identify the mineral composition based on the absorption characteristics, reflection peaks, and Raman peaks in the mixed spectral variation characteristics.

7. An electronic device, characterized in that it comprises: Memory is used to store computer-readable instructions in a non-transitory manner. as well as Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in any one of claims 1-5.

8. A storage medium characterized by, The computer-readable instructions are stored non-transitory, wherein when the non-transitory computer-readable instructions are executed by a computer, the method described in any one of claims 1-5 is performed.

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