Land-oriented super-deep volcanic edifice identification and lithofacies space prediction method and device, and electronic equipment

Abstract

The application discloses a land-oriented super-deep volcanic mechanism identification and lithofacies space prediction method and device, constructs a multi-element constraint system of "lithology-lithofacies-logging response-seismic response-geological evolution", that is, fuses core, slice, logging, seismic and regional tectonic evolution data, establishes a corresponding relationship between different volcanic rock lithofacies and logging, seismic facies and seismic attributes, and realizes identification and space distribution prediction of volcanic mechanism units such as volcanic channels, volcanic vents, eruption bodies, explosion accumulation bodies and volcanic sedimentary bodies under the constraint of regional structure and volcanic eruption evolution background, so that the exploration and evaluation precision of deep-super-deep volcanic rock oil and gas reservoirs is improved.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to oil and gas geophysical exploration and volcanic reservoir prediction technology, and particularly to a method and apparatus for identifying ultra-deep volcanic structures and predicting lithofacies space based on geological-geophysical data fusion. Background Technology

[0002] As oil and gas exploration gradually shifts from intermediate to shallow layers to deep to ultra-deep layers, volcanic rocks are becoming an important area for deep to ultra-deep oil and gas exploration. However, due to the intense compaction, hydrothermal fluid alteration, and superposition of multiple tectonic phases that have occurred in ultra-deep volcanic rocks, their original eruptive structures and lithofacies are poorly preserved, and their seismic response characteristics are complex and highly ambiguous, significantly constraining the identification of volcanic structures. Particularly in volcanic systems involving intermediate to acidic volcanic rocks, the high viscosity of the magma and diverse eruption modes easily lead to the formation of volcanic structures with complex internal structures and frequent lithofacies changes. In the context of ultra-deep, high-burial depths, there is still a lack of targeted research on the systematic identification methods, internal structural characteristics, and coupling relationship between these volcanic structures and tectonic evolution; most work remains at the level of qualitative description or local analysis. Therefore, how to accurately identify volcanic structures in deep to ultra-deep layers is one of the key issues in the exploration and development of volcanic oil and gas reservoirs.

[0003] The igneous rock oil and gas exploration method described in application number CN201310057154.X first identifies the crater and lithofacies based on geological information, and then delineates the range of dominant lithofacies using seismic facies analysis. Next, it calculates the strike-slip distance by analyzing the matching relationship of lithofacies on both sides of a fault, and predicts the distribution and thickness of deep lithology by combining natural gamma-ray inversion from well logging data. Then, it identifies reservoir fracture development zones using deep-shallow resistivity difference inversion, and finally determines the location of oil and gas reservoirs within these zones through a comprehensive approach using seismic attribute clustering and two-phase medium hydrocarbon detection technology.

[0004] Application number CN201310695366.0 relates to an oil and gas exploration method based on the identification and classification of volcanic rock weathering crust. This method first organizes basic data and selects wells in the target area. Then, it establishes well logging evaluation models for the secondary porosity of weathering crust in water-bearing strata and oil- and gas-bearing cementite volcanic rock strata, respectively, and obtains the secondary porosity index under oil- and gas-bearing cementite conditions. Based on this, it establishes criteria for the identification and classification of volcanic rock weathering crust structures, and finally evaluates oil and gas reserves based on these structural characteristics.

[0005] Chinese patent application number CN201810398397.2 proposes a method for identifying volcanic rock lithology based on the fractal dimension of electrical imaging logging. It proposes to construct a lithology identification model based on the fractal dimension of different volcanic rock lithologies in electrical imaging logging.

[0006] Chinese Patent Application No. CN201010548395.0 discloses a method and apparatus for identifying the lithology of altered volcanic rocks. This invention establishes a correlation between volcanic rock lithology and corresponding drilling and logging data: The extracted lithological characteristic parameters are fused to obtain a characteristic model of the volcanic rock drilling and logging lithology. The measured parameters of the volcanic rock to be tested are combined with the characteristic parameters from the fused data and matched against the aforementioned characteristic model.

[0007] Application number CN202511614767.8 constructs a stratigraphic framework model of a volcanic structure using multi-source data. First, time-depth conversion is performed using well logging data to complete the first correction. Then, a fluid overpressure factor is introduced and decompaction correction and fault elimination are performed to achieve the second correction. Finally, the original morphology of the volcanic structure is restored through erosion recovery and layer flattening.

[0008] Application number CN202411872530.5 relates to a method for identifying volcanic structures using a combination of well-seismic data and neural networks. First, a two-dimensional image dataset of volcanic structure profiles is constructed using post-stack seismic data, divided into training and testing sets, while simultaneously acquiring well logging data of volcanic rocks. Next, a pre-defined neural network model is trained using the training set data, and the test set data is input to identify volcanic structures in the seismic profiles. Then, the identification results are combined with the well logging data to establish a fused dataset. Finally, a CNN classification network is constructed based on the fused dataset to achieve automatic classification of volcanic structures.

[0009] Currently, existing methods for identifying volcanic structures mostly rely on single seismic attributes or single-well data, which have the following problems: (1) Low identification accuracy; (2) Inaccurate lithofacies classification; (3) Difficulty in predicting overall spatial distribution.

[0010] Therefore, there is an urgent need for an identification method that utilizes geological-geophysical data fusion to quantitatively determine volcanic structures for the identification of ultra-deep volcanic structures and the spatial prediction of lithofacies. Summary of the Invention

[0012] The purpose of this invention is to provide a method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas, in order to solve the problems mentioned in the background art.

[0013] Specifically, the purpose of this invention is to achieve a precise characterization of the distribution of deep volcanic rocks through a method for identifying ultra-deep volcanic structures and predicting lithofacies space in terrestrial areas, by fusing multi-source data. To achieve the above objective, this invention adopts the following technical solution: Firstly, a method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas is provided, including the following steps: Step S1: Obtain core data for the study area. The core data includes thin section data, well logging data, and 3D seismic data. Perform well-seismic calibration on the well logging data and 3D seismic data, and compare them under a unified dimension to establish a unified well-seismic-geological integrated data system. The well logging data includes natural gamma logging (GR), density logging (DEN), sonic transit time (AC), neutron logging (CNL), and deep resistivity logging (RD). Step S2: Based on core observation, thin section identification, and well logging data identification, the volcanic rocks in the study area are classified into lithologies; based on the lithology classification, the lithofacies types of the volcanic rocks are further classified by combining rock structure, tectonic features, grain size variation, and eruption mode. Step S3: Based on the interpretation results of drilled lithofacies, statistically analyze the logging parameter characteristics corresponding to different volcanic rocks, and establish multi-parameter cross plots. The cross plots include: GR-DEN cross plot, AC-DEN cross plot, CNL-AC cross plot, and RD-GR cross plot. Step S4: Establish the correspondence between well-seismic data, extract the seismic response characteristics of different lithofacies, and further establish the correspondence between different volcanic lithofacies and seismic response based on the well-seismic calibration results; Step S5: Combine the seismic response characteristics of different volcanic rock facies to extract sensitive seismic attributes, and realize the prediction and analysis of volcanic rocks in well-free areas based on seismic data; Step S6: Based on seismic multi-attribute fusion processing, and combined with horizontal slices, layer slices and vertical seismic profiles, a comprehensive identification of volcanic structures is carried out.

[0014] Furthermore, step S1 further includes the following steps: Step S11: Perform environmental correction and depth unification on the well logging data; Step S12: Perform seismic data denoising, amplitude preservation, and fine-scale stratigraphic calibration on the seismic data; Step S13: Perform well-to-seismic time-depth conversion; Step S14: Perform scale unification and normalization processing on the thin section data, well logging data, and 3D seismic data.

[0015] Furthermore, step S2 further includes the following steps: Step S21: Classify the volcanic rocks in the study area by lithology, including: volcanic lava, volcanic clastic rocks, volcanic sedimentary rocks, volcanic breccia, and tuff-like rocks; Step S22: Classify the volcanic rock facies types, including: effusive facies, eruptive facies, volcanic conduit facies, and volcanic sedimentary facies; The effusive facies mainly consist of massive lava, flow structures, and directional flow structures, reflecting a relatively stable lava flow environment. The eruptive phase mainly consists of volcanic breccia, tuff and clastic accumulation structures, reflecting a strong explosive eruption process; The volcanic sedimentary facies mainly develops layered sedimentary structures, reflecting the sedimentary environment during the inter-volcanic eruption period; The volcanic conduit facies mainly develop matrix-supported breccia and cryptovolcanic breccia, reflecting magma intrusion and migration conduits; Step S23: In conjunction with the regional tectonic evolution background, further clarify the spatial configuration relationship of different lithofacies in the volcanic eruption cycle. The spatial configuration relationship includes: the volcanic conduit facies and eruptive facies are mainly developed near the deep fault area, while the effusive facies and volcanic sedimentary facies are mainly developed far away from the conduit area, thus establishing the lithofacies combination model inside the volcanic structure.

[0016] Furthermore, step S3 further includes the following steps: Step S31: By analyzing the differences in logging responses of different volcanic rock facies, establish the identification threshold and quantitative discrimination criteria for volcanic rock facies. Basic effusive facies are characterized by low GR, high DEN, and low AC; acidic eruptive facies are characterized by high AC and medium-high GR; volcanic sedimentary facies are characterized by high CNL, low RD, and high continuity; and volcanic conduit facies are characterized by violent fluctuations in logging curves and high resistivity anomalies. Step S32: Combining the coupling relationship between different logging parameters, construct a volcanic rock facies logging identification template to achieve quantitative identification of different volcanic rock facies at the well point.

[0017] Furthermore, step S4 further includes the following steps: Step S41: By extracting the seismic amplitude, frequency, continuity, and reflection structure characteristics of corresponding strata of different lithofacies, a lithofacies-seismic correspondence model is established; the lithofacies-seismic correspondence model includes: volcanic conduit facies exhibiting weak amplitude, low continuity, and chaotic reflection structure; eruptive facies exhibiting medium to weak amplitude, low frequency, and mound-like or lenticular reflection characteristics; effusive facies exhibiting strong amplitude and parallel continuous reflection characteristics; volcanic sedimentary facies exhibiting highly continuous layered strong reflection characteristics; Step S42: Geological constraints are imposed on the seismic response based on the regional volcanic activity evolution patterns; In step S42, the geological constraints include: Near the eruption center, there are disordered weak reflection structures and high-square anomalies, corresponding to the volcanic conduit phase or eruption phase; in areas far from the eruption center, there are more continuous parallel reflection structures, corresponding to lava flows or volcanic sedimentary bodies formed by stable eruptions.

[0018] Furthermore, step S5 further includes the following steps: Step S51: Extract sensitive seismic attributes to highlight the facies characteristics of different volcanic rocks. The sensitive seismic attributes include: variance attribute, root mean square amplitude attribute, and frequency attribute.

[0019] Furthermore, step S6 further includes the following steps: Step S61: Conduct comprehensive identification, which includes: crater identification, volcanic conduit identification, lava flow distribution prediction, eruptive deposit identification, and volcanic sedimentary body identification; Step S62: Combine the regional tectonic evolution background with the characteristics of volcanic eruption cycles to establish a spatial distribution model of volcanic structures; Step S62 further includes the following steps: establishing a spatial distribution model of volcanic structures with the following constraints: early volcanic activity is usually dominated by central eruptions, forming concentrated volcanic conduits and eruption deposits; late volcanic activity gradually evolves into fissure eruptions, forming large-area eruptive lava bodies.

[0020] Step S63: By analyzing the characteristics of volcanic rock facies assemblage at different eruption stages, the longitudinal evolution and planar distribution patterns of the volcanic structure are restored, and finally a three-dimensional spatial model of the volcanic structure and a spatial prediction model of the facies are established for the study area.

[0021] Secondly, a device for identifying structures and predicting lithofacies spatial patterns in ultra-deep volcanoes on land is provided, comprising the following modules: Unit 1: Acquiring core data of the study area, including thin section data, well logging data, and 3D seismic data. Well logging data and 3D seismic data are calibrated using well-seismic methods and compared under a unified dimension to establish a unified well-seismic-geological integrated data system. The well logging data includes natural gamma-ray logging (GR), density logging (DEN), sonic transit time (AC), neutron logging (CNL), and deep resistivity logging (RD). The second unit: Based on core observation, thin section identification, and well logging data identification, the lithology of volcanic rocks in the study area is classified; on the basis of the lithology classification, the lithofacies types of volcanic rocks are further classified by combining rock structure, tectonic features, grain size variation and eruption mode. Unit 3: Based on the interpretation results of drilled lithofacies, statistically analyze the logging parameter characteristics corresponding to different volcanic rocks, and establish multi-parameter cross plots, including: GR-DEN cross plot, AC-DEN cross plot, CNL-AC cross plot, and RD-GR cross plot; Unit 4: Establish the correspondence between well-seismic data, extract the seismic response characteristics of different lithofacies, and further establish the correspondence between different volcanic lithofacies and seismic response based on the well-seismic calibration results; Unit 5: Combining the seismic response characteristics of different volcanic rock facies, extracting sensitive seismic attributes, and realizing the prediction and analysis of volcanic rocks in well-free areas based on seismic data; Unit 6: Based on seismic multi-attribute fusion processing, combined with horizontal slices, layer slices and vertical seismic profiles, a comprehensive identification of volcanic structures is carried out.

[0022] Furthermore, the first unit includes the following: Unit 7: Environmental correction and depth standardization for well logging data; Unit 8: Seismic data denoising, amplitude preservation, and fine-grained stratigraphic calibration; Unit 9: Performing well-to-seismic time-depth conversion; Unit 10: Conduct scale unification and normalization processing of the aforementioned thin section data, well logging data, and 3D seismic data.

[0023] Furthermore, the second unit includes the following: Unit 11: Lithological classification of volcanic rocks in the study area, including: volcanic lava, volcanic clastic rocks, volcanic sedimentary rocks, volcanic breccia, and tuff-like rocks; Unit 12: Classification of volcanic rock facies types, including: effusive facies, eruptive facies, volcanic conduit facies, and volcanic sedimentary facies; The effusive facies mainly consist of massive lava, flow structures, and directional flow structures, reflecting a relatively stable lava flow environment. The eruptive phase mainly consists of volcanic breccia, tuff and clastic accumulation structures, reflecting a strong explosive eruption process; The volcanic sedimentary facies mainly develops layered sedimentary structures, reflecting the sedimentary environment during the inter-volcanic eruption period; The volcanic conduit facies mainly develop matrix-supported breccia and cryptovolcanic breccia, reflecting magma intrusion and migration conduits; Unit 13: In conjunction with the regional tectonic evolution background, further clarify the spatial configuration relationship of different lithofacies in volcanic eruption cycles. The spatial configuration relationship includes: volcanic conduit facies and eruptive facies mainly developed near deep fault areas, while effusive facies and volcanic sedimentary facies mainly developed far from conduit areas, thus establishing a lithofacies assemblage model within the volcanic structure.

[0024] Furthermore, the third unit includes the following: Unit 14: By analyzing the differences in logging responses of different volcanic rock facies, we establish the identification threshold and quantitative discrimination criteria for volcanic rock facies. Basic effusive facies are characterized by low GR, high DEN, and low AC; acidic explosive facies are characterized by high AC and medium-high GR; volcanic sedimentary facies are characterized by high CNL, low RD, and high continuity; and volcanic conduit facies are characterized by violent fluctuations in logging curves and high resistivity anomalies. Unit 15: Combining the coupling relationship between different logging parameters, construct a logging identification template for volcanic rock facies to achieve quantitative identification of different volcanic rock facies at the well point.

[0025] Furthermore, Unit 4 includes the following: Unit 16: By extracting the seismic amplitude, frequency, continuity, and reflection structure characteristics of corresponding strata of different lithofacies, a lithofacies-seismic correspondence model is established. The lithofacies-seismic correspondence model includes: volcanic conduit facies exhibiting weak amplitude, low continuity, and chaotic reflection structure; eruptive facies exhibiting medium to weak amplitude, low frequency, and mound-like or lenticular reflection characteristics; effusive facies exhibiting strong amplitude and parallel continuous reflection characteristics; and volcanic sedimentary facies exhibiting highly continuous layered strong reflection characteristics. Unit 17: Geological constraints on seismic response based on regional volcanic activity evolution patterns; The geological constraints include: Near the eruption center, there are disordered weak reflection structures and high-square anomalies, corresponding to the volcanic conduit phase or eruption phase; in areas far from the eruption center, there are more continuous parallel reflection structures, corresponding to lava flows or volcanic sedimentary bodies formed by stable eruptions.

[0026] Furthermore, Unit 5 includes the following: Unit 18: Extracting sensitive seismic attributes to highlight the facies characteristics of different volcanic rocks. The sensitive seismic attributes include: variance attribute, root mean square amplitude attribute, and frequency attribute.

[0027] Furthermore, Unit 6 includes the following: Unit 19: Conduct comprehensive identification, which includes: crater identification, volcanic conduit identification, lava flow distribution prediction, eruptive deposit identification, and volcanic sedimentary body identification; Unit 20: Establishing a spatial distribution model of volcanic structures by combining the regional tectonic evolution background with the characteristics of volcanic eruption cycles; Step S62 further includes the following steps: establishing a spatial distribution model of volcanic structures with the following constraints: early volcanic activity is usually dominated by central eruptions, forming concentrated volcanic conduits and eruption deposits; late volcanic activity gradually evolves into fissure eruptions, forming large-area eruptive lava bodies.

[0028] Unit 21: Through the analysis of the characteristics of volcanic rock facies assemblage at different eruption stages, the longitudinal evolution and planar distribution patterns of volcanic structures are restored, and finally a three-dimensional spatial model of volcanic structures and a spatial prediction model of facies are established for the study area.

[0029] Thirdly, an electronic device is provided, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of a method for identifying and predicting lithofacies spatial patterns of ultra-deep volcanic structures in terrestrial areas.

[0030] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of a method for identifying terrestrial ultra-deep volcanic structures and predicting lithofacies spatial patterns are implemented.

[0031] By adopting the above technical solution, accurate prediction of volcanic rocks has been achieved, which has good applicability and promotion value in practical production applications.

[0032] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a method for identifying ultra-deep volcanic structures and predicting lithofacies spatial distribution in terrestrial areas. By constructing a multi-constraint system of "lithology-lithofacies-well logging response-seismic response-geological evolution," it achieves precise identification of ultra-deep volcanic structures and lithofacies spatial prediction. This invention integrates core, thin section, well logging, seismic, and regional tectonic evolution data to establish the correspondence between different volcanic rock lithofacies and well logging, seismic facies, and seismic attributes. Under the constraints of regional tectonic and volcanic eruption evolution background, it achieves the identification and spatial distribution prediction of volcanic structural units such as volcanic conduits, craters, effusive bodies, eruptive deposits, and volcanic sedimentary bodies, thereby improving the accuracy of exploration and evaluation of deep-to-ultra-deep volcanic oil and gas reservoirs.

[0033] Specifically, compared with the prior art, the present invention has the following beneficial effects: (1) By integrating core, well logging, seismic and regional tectonic data from multiple sources, the accuracy of volcanic structure identification is significantly improved; (2) A quantitative correspondence between volcanic rock facies and well logging and seismic response was established, reducing the ambiguity of human interpretation; (3) Improve the geological rationality of volcanic structure identification by constraining the regional geological evolution background; (4) It has realized three-dimensional spatial prediction of the lithofacies of ultra-deep volcanic rocks, and improved the prediction capability in well-free areas; (5) It can effectively identify key volcanic structural units such as volcanic conduits, effluents, and eruptive deposits; (6) It is applicable to deep-ultra-deep volcanic rock oil and gas exploration under complex tectonic backgrounds and has high engineering application value. Attached Figure Description

[0034] Figure 1 The flowchart below illustrates the implementation of the method for identifying terrestrial ultra-deep volcanic structures and predicting lithofacies spatial patterns according to an embodiment of the present invention. Figure 2 A flowchart illustrating the implementation of the multi-source data acquisition method provided in this embodiment of the invention; Figure 3 A flowchart illustrating the implementation of the volcanic lithology and lithofacies identification method provided in this embodiment of the invention; Figure 4 A flowchart illustrating the implementation of the volcanic rock logging response correspondence method provided in this embodiment of the invention; Figure 5 A flowchart illustrating the implementation of the seismic response correspondence method for volcanic rocks provided in this embodiment of the invention; Figure 6 A flowchart illustrating the implementation of the comprehensive volcanic rock identification method provided in this embodiment of the invention; Figure 7 This is a structural diagram of a device for identifying structures and predicting lithofacies space in terrestrial ultra-deep volcanoes according to an embodiment of the present invention. Figure 8 This is a structural diagram of a multi-source data acquisition device provided according to an embodiment of the present invention; Figure 9 A structural diagram of a volcanic lithology and lithofacies identification device is provided according to an embodiment of the present invention; Figure 10 This is a structural diagram of a volcanic rock logging response corresponding device provided according to an embodiment of the present invention; Figure 11 This is a structural diagram of a device for responding to seismic responses in volcanic rocks, provided in an embodiment of the present invention. Figure 12 This is a structural diagram of a comprehensive volcanic rock identification device provided according to an embodiment of the present invention; Figure 13 A flowchart for predicting ultra-deep volcanic structures and lithofacies according to an embodiment of the present invention; Figure 14 This is a comprehensive response characteristic diagram of volcanic rock facies provided according to an embodiment of the present invention; Figure 15 This is a lithofacies-electrical property plate of ultra-deep volcanic rocks provided according to an embodiment of the present invention; Figure 16 This is a diagram illustrating the lithofacies-seismic correspondence and identification markers provided according to an embodiment of the present invention. Figure 17 This is a regional volcanic lithofacies prediction map provided according to an embodiment of the present invention; Figure 18 A typical volcanic structure profile predicted according to an embodiment of the present invention; Figure 19 This is a structural diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Figure 1 The flowchart below illustrates the implementation of the method for identifying terrestrial ultra-deep volcanic structures and predicting lithofacies spatial patterns according to an embodiment of the present invention. Figure 2 A flowchart illustrating the implementation of the multi-source data acquisition method provided in this embodiment of the invention; Figure 3 A flowchart illustrating the implementation of the volcanic lithology and lithofacies identification method provided in this embodiment of the invention; Figure 4 A flowchart illustrating the implementation of the volcanic rock logging response correspondence method provided in this embodiment of the invention; Figure 5 A flowchart illustrating the implementation of the seismic response correspondence method for volcanic rocks provided in this embodiment of the invention; Figure 6 A flowchart illustrating the implementation of the comprehensive volcanic rock identification method provided in this embodiment of the invention; Figure 7 This is a structural diagram of a device for identifying structures and predicting lithofacies space in terrestrial ultra-deep volcanoes according to an embodiment of the present invention. Figure 8 This is a structural diagram of a multi-source data acquisition device provided according to an embodiment of the present invention; Figure 9 A structural diagram of a volcanic lithology and lithofacies identification device is provided according to an embodiment of the present invention; Figure 10 This is a structural diagram of a volcanic rock logging response corresponding device provided according to an embodiment of the present invention; Figure 11 This is a structural diagram of a device for responding to seismic responses in volcanic rocks, provided in an embodiment of the present invention. Figure 12 This is a structural diagram of a comprehensive volcanic rock identification device provided according to an embodiment of the present invention; Figure 13 A flowchart for predicting ultra-deep volcanic structures and lithofacies according to an embodiment of the present invention; Figure 14 This is a comprehensive response characteristic diagram of volcanic rock facies provided according to an embodiment of the present invention; Figure 15 This is a lithofacies-electrical property plate of ultra-deep volcanic rocks provided according to an embodiment of the present invention; Figure 16 This is a diagram illustrating the lithofacies-seismic correspondence and identification markers provided according to an embodiment of the present invention. Figure 17 This is a regional volcanic lithofacies prediction map provided according to an embodiment of the present invention; Figure 18 A typical volcanic structure profile predicted according to an embodiment of the present invention; Figure 19 This is a structural diagram of an electronic device provided according to an embodiment of the present invention. For ease of description, only the parts related to the embodiments of the present invention are shown, and are described in detail below: Step S1: Obtain core data, thin section data, well logging data (natural gamma ray GR, density DEN, sonic transit time AC, neutron CNL, resistivity RD) and three-dimensional seismic data in the study area, and perform well-seismic calibration on the well logging and seismic data, and compare them under a unified dimension. The specific steps include (1) environmental correction and depth unification of well logging curves; (2) noise reduction, amplitude preservation and fine calibration of seismic data and stratigraphy; (3) well-seismic time-depth conversion; (4) unification and normalization of different data scales.

[0038] Through the above processing, a unified integrated well-seismic-geological data system is established, providing a foundation for subsequent lithofacies identification and volcanic structure prediction.

[0039] Step S2: Based on core observation, thin section identification, and logging data, the lithology of the volcanic rocks in the study area is classified.

[0040] The volcanic rock lithology includes: (1) volcanic lava; (2) volcanic clastic rocks; (3) volcanic sedimentary rocks; (4) volcanic breccia; and (5) tuff-like rocks.

[0041] Based on lithological identification, and combined with rock structure, tectonic features, grain size variation and eruption mode, volcanic rock facies types are further classified, including: (1) effusive facies; (2) eruptive facies; (3) volcanic conduit facies; and (4) volcanic sedimentary facies.

[0042] Among them: the effusive facies mainly develops massive lava, rhyolite and directional flow structures, reflecting a relatively stable lava flow environment; the explosive facies mainly develops volcanic breccia, tuff and clastic accumulation structures, reflecting a strong explosive eruption process; the volcanic conduit facies mainly develops matrix-supported breccia and cryptovolcanic breccia, representing magma intrusion and migration channels; the volcanic sedimentary facies mainly develops layered sedimentary structures, reflecting the sedimentary environment during the inter-eruption period of volcanic eruptions.

[0043] Based on the regional tectonic evolution background, the spatial configuration relationship of different lithofacies in volcanic eruption cycles was further clarified. Among them, volcanic conduit facies and eruptive facies are mainly developed near deep fault zones, while effusive facies and volcanic sedimentary facies are mainly developed in areas far from the conduits, thus establishing a lithofacies assemblage model within the volcanic structure.

[0044] Step S3: Based on the interpretation results of drilled lithofacies, statistically analyze the logging parameter characteristics corresponding to different volcanic rocks and establish multi-parameter cross plots. Specifically, this includes: (1) GR-DEN cross plot; (2) AC-DEN cross plot; (3) CNL-AC cross plot; (4) RD-GR cross plot.

[0045] By analyzing the differences in logging responses of different lithofacies, lithofacies identification thresholds and quantitative discrimination criteria were established. Among them: basic effusive facies are characterized by low GR, high DEN, and low AC; acidic explosive facies are characterized by high AC and medium-high GR; volcanic sedimentary facies are characterized by high CNL, low RD, and high continuity; and volcanic conduit facies are characterized by violent fluctuations in logging curves and high resistivity anomalies.

[0046] By combining the coupling relationship between different parameters, a volcanic rock facies logging identification template is constructed to achieve quantitative identification of different volcanic rock facies at the well point.

[0047] Step S4: Establish the correspondence between well-seismic data and extract the seismic response characteristics of different lithofacies: Based on the well-seismic calibration results, establish the correspondence between different volcanic lithofacies and seismic response.

[0048] By extracting the seismic amplitude, frequency, continuity, and reflection structure characteristics of corresponding strata of different lithologies, a lithofacies-seismic correspondence model is established. The seismic response characteristics of different lithologies are as follows: (1) Volcanic conduit facies: characterized by weak amplitude, low continuity, and chaotic reflection structure; (2) Eruption facies: characterized by medium to weak amplitude, low frequency, and mound-like or lenticular reflection characteristics; (3) Effusion facies: characterized by strong amplitude and parallel continuous reflection characteristics; (4) Volcanic sedimentary facies: characterized by high continuity and layered strong reflection characteristics. At the same time, combined with the regional volcanic activity evolution law, the seismic response is geologically constrained. For example, near the volcanic eruption center, chaotic weak reflection structures and high-square anomalies are usually developed, corresponding to the volcanic conduit facies or eruption facies; in areas far from the eruption center, more continuous parallel reflection structures are developed, corresponding to lava flows or volcanic sedimentary bodies formed by stable effusive eruptions.

[0049] Based on the above analysis, a unified correspondence between volcanic rock facies and seismic response is established.

[0050] Step S5: Extract seismic attributes: Combine the seismic response characteristics of different volcanic rock facies, select the most sensitive seismic attributes, such as variance attributes, root mean square amplitude attributes, frequency attributes, etc., to highlight the characteristics of different volcanic rock facies, and thus achieve the purpose of predicting and analyzing volcanic rocks in well-free areas with the help of seismic data.

[0051] Step S6: Based on the multi-attribute fusion results, combined with horizontal slices, layer slices, and vertical seismic profiles, a comprehensive identification of volcanic structures is carried out. Specifically, this includes: (1) crater identification; (2) volcanic conduit identification; (3) lava flow distribution prediction; (4) eruptive deposit identification; and (5) volcanic sedimentary body identification. Combining the regional tectonic evolution background and volcanic eruption cycle characteristics, a spatial distribution model of volcanic structures is established. Among them, early volcanic activity is usually dominated by central eruptions, forming concentrated volcanic conduits and eruptive deposits; late volcanic activity gradually evolves into fissure eruptions, forming large-area eruptive lava bodies.

[0052] By analyzing the lithofacies assemblages of volcanic rocks at different eruption stages, the longitudinal evolution and planar distribution patterns of the volcanic structure are reconstructed. Finally, a three-dimensional spatial model of the volcanic structure and a lithofacies spatial prediction model for the study area are established.

[0053] Figure 13 The flowchart for predicting ultra-deep volcanic structures and lithofacies according to embodiments of the present invention is described in detail below: Through comprehensive research involving multiple disciplines such as petrography, geochemistry, and well logging geology, the volcanic rock lithology and lithofacies corresponding to core-rock fragments at different depths have been determined, which can provide a basis for the geological evolution process and characteristics of the entire study area.

[0054] Based on the well logging response characteristics of the target strata and combined with core-cuttings interpretation results, the correspondence between different volcanic rock lithologies and facies and well logging response characteristics in the target strata is determined, and a volcanic rock lithology and facies identification chart based on well logging data is established. Well-seismic calibration is used to perform time-depth conversion processing of seismic data, thereby establishing the correspondence between different volcanic rock lithologies and facies and seismic response characteristics.

[0055] Predicting the lithology and lithofacies of volcanic rocks across the entire region requires the use of seismic data. The correspondence between different volcanic rock lithologies and lithofacies encountered in drilled wells and their seismic response characteristics is transformed into a correspondence between different volcanic rock lithologies and lithofacies and different seismic attributes, thus eliminating the ambiguity caused by using single seismic data.

[0056] Based on the geological evolution characteristics of the entire region, horizontal and vertical seismic slices that can reflect the characteristics of the study area are selected for comprehensive analysis from multiple angles and with multiple seismic attributes, so as to achieve a detailed characterization of volcanic structure and prediction of the regional distribution characteristics of volcanic rocks.

[0057] Figure 14 This is a comprehensive response characteristic chart of volcanic rock facies provided according to an embodiment of the present invention; specifically, it includes the rock structure, conventional logging response, and imaging logging characteristics corresponding to volcanic rock facies. Basic effusive facies generally exhibit massive structures, high density, and low natural gamma characteristics, appearing as relatively uniform bright and dark textures on imaging logging; basic eruptive facies, due to the development of volcanic clastic material, show obvious fragmented structures on imaging; acidic eruptive facies exhibit low density, high acoustic transit time, and chaotic massive imaging characteristics. Different volcanic rock facies show significant differences in logging and imaging responses, and are jointly controlled by the volcanic eruption mode, magma composition, and subsequent alteration. Through the combined constraints of core-logging-imaging logging, a comprehensive identification template for different volcanic rock facies can be established, providing a basis for subsequent well-seismic calibration and seismic facies identification.

[0058] Figure 15 This invention provides an ultra-deep volcanic rock facies-electrical property chart based on embodiments of the present invention. Specifically, different volcanic rock facies exhibit both differences and overlaps in different well logging data, a problem that becomes even more complex for ultra-deep formations. Therefore, a comprehensive analysis combining the response characteristics of different volcanic rock facies in different well logging data is necessary to accurately distinguish between different volcanic rock facies and further construct a corresponding volcanic rock facies identification chart. Basic eruptive facies are characterized by high density (Figure a), with density decreasing as acidity increases; acidic eruptive facies and volcanic sedimentary facies exhibit high sonic transit time (Figure b), reflecting their well-developed pores or loose structure; neutron porosity distribution further verifies the differences in hydrogen content among different facies (Figure c), with volcanic sedimentary facies showing higher CNL values; resistivity differences between different facies are significant (Figure d), with neutral eruptive facies exhibiting a high resistivity response, while volcanic sedimentary facies have the lowest resistivity. By combining the intersection characteristics of the above four types of parameters with GR, an identification template for different volcanic rock facies can be effectively constructed.

[0059] Figure 16 This invention provides a lithofacies-seismic correspondence and identification marker diagram according to embodiments of the present invention. Specifically, through well-seismic calibration, the lithofacies interpretation results of volcanic rocks in drilled wells can be transferred to seismic data. By analyzing the seismic response characteristics of different volcanic rock lithofacies, analysis can be performed from multiple perspectives, including reflection characteristics, amplitude intensity variations, frequency characteristics, and seismic phase axis continuity, and these characteristics can be transformed into different seismic attributes to highlight and enhance the seismic response characteristics of different volcanic rock lithofacies, ultimately achieving the prediction and analysis of volcanic rock lithofacies across the entire region.

[0060] Figure 17This is a regional volcanic lithofacies prediction map provided by an embodiment of the present invention. Specifically, it utilizes a multi-seismic attribute fusion analysis method to achieve spatial prediction of different volcanic lithofacies across the entire region. Specifically, volcanic conduit facies and eruptive facies are mainly developed near deep fault zones, exhibiting high variance and strong chaotic anomalies; while the peripheral areas mainly develop effusive facies and volcanic sedimentary facies, exhibiting continuous high-amplitude reflection structures. Combined with the regional tectonic evolution background, it can be clearly seen that the distribution of volcanic rocks is jointly controlled by the fault system and eruptive cycles. Early volcanic activity was mainly controlled by central eruptions, with limited lithofacies distribution; later volcanic activity gradually evolved towards fissure eruptions, with a significant expansion of the effusive facies distribution range.

[0061] Figure 18 This is a predicted typical volcanic structure depiction provided by an embodiment of the present invention. Specifically, based on lithofacies prediction results and multi-attribute seismic response characteristics, a detailed depiction of the typical volcanic structure is carried out. Among them, the central region of the volcanic structure mainly develops volcanic conduits and eruptive deposits, exhibiting a chaotic and weakly reflective structure; the outer region mainly develops effusive facies formed by the superposition of multiple lava flows, exhibiting continuous strong amplitude reflection characteristics; and the top and intermittent regions develop volcanic sedimentary facies.

[0062] Figure 19 A structural diagram of an electronic device provided in an embodiment of the present invention, such as... Figure 19 As shown, the device includes: a memory 21 for storing computer programs; and a processor 22 for executing the computer program to implement the steps of a method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas. The processor 22 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 22 may be implemented using at least one hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 22 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake-up state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 22 may integrate a graphics processing unit (GPU) for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 22 may also include an artificial intelligence (AI) processor for handling computational operations related to machine learning.

[0063] The memory 21 may include one or more computer-readable storage media, which may be non-transitory. The memory 21 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 21 is used to store at least the following computer program 211, which, after being loaded and executed by the processor 22, is capable of implementing the relevant steps of the method for identifying and predicting lithofacies spatial patterns of ultra-deep volcanoes in terrestrial areas disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 21 may also include an operating system 212 and data 213, etc., and the storage method may be temporary or permanent storage. The operating system 212 may include Windows, Unix, Linux, etc. The data 213 may include, but is not limited to, the data involved in the method for identifying and predicting lithofacies spatial patterns of ultra-deep volcanoes in terrestrial areas.

[0064] In some embodiments, the electronic device may further include a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27.

[0065] Those skilled in the art will understand that Figure 19 The structures shown do not constitute a limitation on electronic devices and may include more or fewer components than those shown.

[0066] The processor 22 implements the steps of the method for identifying and predicting lithological spatial structures of ultra-deep volcanoes on land provided in any of the above embodiments by calling instructions stored in the memory 21.

[0067] For an introduction to the electronic device provided by the present invention, please refer to the above method embodiments. The present invention will not be described in detail here. It has the same beneficial effects as the above-described method for identifying ultra-deep volcanic structures and predicting lithofacies space in terrestrial areas.

[0068] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by processor 22, implements the steps of the above-described method for identifying terrestrial ultra-deep volcanic structures and predicting lithofacies spatial patterns.

[0069] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] For an introduction to the computer-readable storage medium provided by the present invention, please refer to the above method embodiments. The present invention will not be described in detail here, but it has the same beneficial effects as the above-described method for identifying ultra-deep volcanic structures and predicting lithofacies space in terrestrial areas.

[0071] The foregoing has provided a detailed description of a method, apparatus, device, and medium for identifying structures and predicting lithofacies space in ultra-deep volcanoes on land, as provided by this invention. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of this invention.

[0072] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

Claims

1. A method for identifying structures and predicting lithofacies spatial patterns in ultra-deep volcanoes on land, characterized in that, The steps include the following: Step S1: Obtain core data for the study area. The core data includes thin section data, well logging data, and 3D seismic data. Perform well-seismic calibration on the well logging data and 3D seismic data, and compare them under a unified dimension to establish a unified well-seismic-geological integrated data system. The well logging data includes natural gamma logging (GR), density logging (DEN), sonic transit time (AC), neutron logging (CNL), and deep resistivity logging (RD). Step S2: Based on core observation, thin section identification, and well logging data identification, the volcanic rocks in the study area are classified into lithologies; based on the lithology classification, the lithofacies types of the volcanic rocks are further classified by combining rock structure, tectonic features, grain size variation, and eruption mode. Step S3: Based on the interpretation results of drilled lithofacies, statistically analyze the logging parameter characteristics corresponding to different volcanic rocks, and establish multi-parameter cross plots. The cross plots include: GR-DEN cross plot, AC-DEN cross plot, CNL-AC cross plot, and RD-GR cross plot. Step S4: Establish the correspondence between well-seismic data, extract the seismic response characteristics of different lithofacies, and further establish the correspondence between different volcanic lithofacies and seismic response based on the well-seismic calibration results; Step S5: Combine the seismic response characteristics of different volcanic rock facies, extract sensitive seismic attributes, and predict and analyze volcanic rocks in well-free areas based on seismic data; Step S6: Based on seismic multi-attribute fusion processing, and combined with horizontal slices, layer slices and vertical seismic profiles, a comprehensive identification of volcanic structures is carried out.

2. The method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas according to claim 1, characterized in that, Step S1 further includes the following steps: Step S11: Perform environmental correction and depth unification on the well logging data; Step S12: Perform seismic data denoising, amplitude preservation, and fine-scale stratigraphic calibration on the seismic data; Step S13: Perform well-to-seismic time-depth conversion; Step S14: Perform scale unification and normalization processing on the thin section data, well logging data, and 3D seismic data.

3. The method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas according to claim 2, characterized in that, Step S2 further includes the following steps: Step S21: Classify the volcanic rocks in the study area by lithology, including: volcanic lava, volcanic clastic rocks, volcanic sedimentary rocks, volcanic breccia, and tuff-like rocks; Step S22: Classify the volcanic rock facies types, including: effusive facies, eruptive facies, volcanic conduit facies, and volcanic sedimentary facies; The effusive facies mainly consist of massive lava, flow structures, and directional flow structures, reflecting a relatively stable lava flow environment. The eruptive phase mainly consists of volcanic breccia, tuff and clastic accumulation structures, reflecting a strong explosive eruption process; The volcanic sedimentary facies mainly develops layered sedimentary structures, reflecting the sedimentary environment during the inter-volcanic eruption period; The volcanic conduit facies mainly develop matrix-supported breccia and cryptovolcanic breccia, reflecting magma intrusion and migration conduits; Step S23: In conjunction with the regional tectonic evolution background, further clarify the spatial configuration relationship of different lithofacies in the volcanic eruption cycle. The spatial configuration relationship includes: the volcanic conduit facies and eruptive facies are mainly developed near the deep fault area, while the effusive facies and volcanic sedimentary facies are mainly developed far away from the conduit area, thus establishing the lithofacies combination model inside the volcanic structure.

4. The method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas according to claim 3, characterized in that, Step S3 further includes the following steps: Step S31: By analyzing the differences in logging responses of different volcanic rock facies, establish the identification threshold and quantitative discrimination criteria for volcanic rock facies. Basic effusive facies are characterized by low GR, high DEN, and low AC; acidic eruptive facies are characterized by high AC and medium-high GR; volcanic sedimentary facies are characterized by high CNL, low RD, and high continuity; and volcanic conduit facies are characterized by violent fluctuations in logging curves and high resistivity anomalies. Step S32: Combining the coupling relationship between different logging parameters, construct a volcanic rock facies logging identification template to achieve quantitative identification of different volcanic rock facies at the well point.

5. The method for identifying structures and predicting lithofacies spatial patterns of ultra-deep volcanoes in terrestrial areas according to claim 4, characterized in that, Step S4 further includes the following steps: Step S41: By extracting the seismic amplitude, frequency, continuity, and reflection structure characteristics of corresponding strata of different lithofacies, a lithofacies-seismic correspondence model is established; the lithofacies-seismic correspondence model includes: volcanic conduit facies exhibiting weak amplitude, low continuity, and chaotic reflection structure; eruptive facies exhibiting medium to weak amplitude, low frequency, and mound-like or lenticular reflection characteristics; effusive facies exhibiting strong amplitude and parallel continuous reflection characteristics; volcanic sedimentary facies exhibiting highly continuous layered strong reflection characteristics; Step S42: Geological constraints are imposed on the seismic response based on the regional volcanic activity evolution patterns; In step S42, the geological constraints include: Near the eruption center, there are disordered weak reflection structures and high-square anomalies, corresponding to the volcanic conduit phase or eruption phase; in areas far from the eruption center, there are more continuous parallel reflection structures, corresponding to lava flows or volcanic sedimentary bodies formed by stable eruptions.

6. The method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas according to claim 5, characterized in that, Step S5 further includes the following steps: Step S51: Extract sensitive seismic attributes to highlight the facies characteristics of different volcanic rocks. The sensitive seismic attributes include: variance attribute, root mean square amplitude attribute, and frequency attribute.

7. The method for identifying ultra-deep volcanic structures and predicting lithofacies spatial patterns in terrestrial areas according to claim 6, characterized in that, Step S6 further includes the following steps: Step S61: Conduct comprehensive identification, which includes: crater identification, volcanic conduit identification, lava flow distribution prediction, eruptive deposit identification, and volcanic sediment identification; Step S62: Combine the regional tectonic evolution background with the characteristics of volcanic eruption cycles to establish a spatial distribution model of volcanic structures; Step S62 further includes the following steps: establishing a spatial distribution model of the volcanic structure, including the following constraints: early volcanic activity is usually dominated by central eruptions, forming concentrated volcanic conduits and eruption deposits; late volcanic activity gradually evolves into fissure eruptions, forming large-area eruptive lava bodies; Step S63: By analyzing the characteristics of volcanic rock facies assemblage at different eruption stages, the longitudinal evolution and planar distribution patterns of the volcanic structure are restored, and finally a three-dimensional spatial model of the volcanic structure and a spatial prediction model of the facies are established for the study area.

8. A device for identifying structures and predicting lithofacies spatial patterns in ultra-deep volcanoes on land, characterized in that, Includes the following modules: Unit 1: Acquiring core data of the study area, including thin section data, well logging data, and 3D seismic data. Well logging data and 3D seismic data are calibrated using well-seismic methods and compared under a unified dimension to establish a unified well-seismic-geological integrated data system. The well logging data includes natural gamma-ray logging (GR), density logging (DEN), sonic transit time (AC), neutron logging (CNL), and deep resistivity logging (RD). The second unit: Based on core observation, thin section identification, and well logging data identification, the lithology of volcanic rocks in the study area is classified; on the basis of the lithology classification, the lithofacies types of volcanic rocks are further classified by combining rock structure, tectonic features, grain size variation and eruption mode. Unit 3: Based on the interpretation results of drilled lithofacies, statistically analyze the logging parameter characteristics corresponding to different volcanic rocks, and establish multi-parameter cross plots, including: GR-DEN cross plot, AC-DEN cross plot, CNL-AC cross plot, and RD-GR cross plot; Unit 4: Establish the correspondence between well-seismic data, extract the seismic response characteristics of different lithofacies, and further establish the correspondence between different volcanic lithofacies and seismic response based on the well-seismic calibration results; Unit 5: Combining the seismic response characteristics of different volcanic rock facies, extracting sensitive seismic attributes, and predicting and analyzing volcanic rocks in well-free areas based on seismic data; Unit 6: Based on seismic multi-attribute fusion processing, combined with horizontal slices, layer slices and vertical seismic profiles, a comprehensive identification of volcanic structures is carried out.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for identifying and predicting lithofacies spatial structures of ultra-deep volcanoes in terrestrial areas as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for identifying terrestrial ultra-deep volcanic structures and predicting lithofacies space as described in any one of claims 1-7.

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