A method and system for intelligent analysis of geological exploration drilling data

Abstract

The present invention discloses an intelligent analysis method and system for geological exploration drilling data, which relates to the technical field of geological exploration. The key points of the technical solution include the following steps: obtaining original data collected by geological exploration drilling, and identifying and analyzing the original data to determine the geological information contained in the original data; classifying and dividing the original data according to the geological information to obtain performance characteristic data with different characteristics; extracting key geological characteristic information stored in a database, and marking the performance characteristic data containing the key geological characteristic information as target data; performing geological structure analysis on the target data to obtain geological structure analysis results; performing geological element correlation analysis on the target data to obtain element correlation analysis results; judging the geological structure analysis results and the element correlation analysis results according to the target result indicators and outputting the geological exploration drilling data analysis results, so that the potential geological structure information can be deeply mined even in the face of incomplete geological data.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and more particularly, to a method and system for intelligent analysis of geological exploration drilling data. Background Art

[0002] Existing analytical techniques face numerous challenges when processing geological exploration drilling data. Raw drilling data comes from a wide range of sources, encompassing measurements from a variety of specialized equipment, such as rock samples obtained from core drilling and formation physical parameters derived from geophysical logging. These data vary in format and are complex in structure. Traditional methods lack efficient and unified data analysis tools, making it difficult to quickly and accurately extract key geological information such as rock type, formation depth, and mineral composition from the raw data. For example, in areas with complex stratigraphic structures, where different rock layers intersect, traditional data recognition methods may be unable to accurately distinguish rock types, resulting in inaccurate data for subsequent analysis.

[0003] Even when geological information is available, traditional methods for classifying and categorizing raw data based on this information are inefficient. It's difficult to quickly and appropriately categorize massive amounts of data based on multi-dimensional information such as rock type, stratigraphic depth, and mineral composition, resulting in a data set with distinct characteristics. This makes it difficult to quickly locate and process specific data types in subsequent analysis, impacting the overall efficiency of geological analysis.

[0004] When geological structures are determined to be incomplete, traditional methods struggle to systematically determine the correlation between target data and other characteristic data. This makes it difficult to effectively explore potential connections between missing geological structure data and known data, and to distinguish between correlated and independent data. Consequently, when processing incomplete geological structure data, existing data resources cannot be fully utilized to restore the true geological structure, reducing the reliability of geological structure analysis results. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method and system for intelligent analysis of geological exploration drilling data.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for intelligent analysis of geological exploration drilling data, the method comprising the following steps:

[0008] Obtaining raw data collected by geological exploration drilling, and identifying and analyzing the raw data to determine the geological information contained in the raw data; wherein the geological information includes rock type information, stratum depth information and mineral composition information;

[0009] Classify and divide the original data according to geological information to obtain performance characteristic data with different characteristics (for example, different performance characteristic data are divided according to different rock types, or performance characteristic data are divided according to stratum depth intervals);

[0010] Extracting key geological feature information stored in a database and marking the performance feature data containing the key geological feature information as target data; wherein the key geological feature information is key information required for the target set by geological exploration; performing geological structure analysis on the target data to obtain geological structure analysis results; performing geological element correlation analysis on the target data to obtain element correlation analysis results;

[0011] According to the target result indicators, the geological structure analysis results and element correlation analysis results are judged and output as geological exploration drilling data analysis results.

[0012] Preferably, performing geological structure analysis on the target data to obtain geological structure analysis results specifically includes the following steps:

[0013] Match the target data with the preset normal geological structure data to determine whether the geological structure reflected by the target data is complete;

[0014] If the geological structure of the target data is determined to be complete, the corresponding geological structure analysis results are directly output based on the target data;

[0015] If the geological structure of the target data is determined to be incomplete, the target data is judged to be associated with the performance characteristic data that does not contain key geological feature information to determine whether the target data has a portion associated with other performance characteristic data; and the portion of the target data associated with the other performance characteristic data is marked as associated data, and the portion of the target data not associated with the other performance characteristic data is marked as independent data;

[0016] The corresponding geological structure analysis results are obtained by analyzing whether the target data is associated with other characteristic data.

[0017] Preferably, the analysis is performed based on whether there is a correlation between the target data and other characteristic data to obtain the corresponding geological structure analysis results, which specifically includes the following steps:

[0018] If the target data is not associated with other characteristic data, the geological structure data contained in the independent data is determined based on the preset normal geological structure data, and the geological structure data contained in the independent data is analyzed to obtain the corresponding geological structure analysis results;

[0019] If the target data is associated with other characteristic data, geological information recognition is performed on the associated data to obtain associated geological information, and geological information reading is performed on the target data corresponding to the associated data to obtain target geological information;

[0020] The associated geological information is matched with the target geological information respectively. If the match is successful, the structure of the associated data and the corresponding independent data is judged based on the preset normal geological structure data. If the combination of the associated data and the corresponding independent data can make the geological structure of the target data complete, the two are combined to obtain the corresponding geological structure analysis results; if there is structural overlap between the associated data and the corresponding independent data, the associated data is determined to be a non-associated part, and the corresponding geological structure analysis results are obtained based on the corresponding independent data.

[0021] Preferably, the target data is subjected to correlation judgment with the performance characteristic data that does not contain key geological characteristic information, specifically comprising the following steps:

[0022] Based on the preset normal geological structure data, the target data is matched with the structure data to obtain the known geological structure data and the missing geological structure data;

[0023] Matching the missing geological structure data with the known geological structure data to determine whether the known geological structure data contains the missing geological structure data;

[0024] The correlation is analyzed and judged based on whether there is missing geological structure data in the known geological structure data.

[0025] Preferably, the analysis and judgment of the correlation is performed based on whether there is missing geological structure data in the known geological structure data, specifically including the following steps:

[0026] If it is determined that there is missing geological structure data in the known geological structure data, characteristic data (such as rock color, texture characteristics, etc.) of the known geological structure data that matches the missing geological structure data is collected to obtain matching characteristic data;

[0027] Collect features of other data in the original data excluding the target data to obtain overall feature data;

[0028] Matching the matching feature data with the overall feature data to filter out feature data identical to the matching feature data, and marking the position of the filtered feature data to obtain the overall position data;

[0029] Obtaining the location information of the missing geological structure data, and matching the location information of the missing geological structure data with the overall location data; if the match is successful, it is determined that there is a correlation; if the match fails, it is determined that there is no correlation;

[0030] If it is determined that there is no missing geological structure data in the known geological structure data, the known geological structure data is screened according to the preset normal geological structure data to obtain the known geological structure data connected to the missing geological structure data and record it as connected structure data;

[0031] The connected structure data is subjected to feature collection, and the collected features are matched with the overall feature data. If the match is successful, it is determined that there is a correlation; if the match fails, it is determined that there is no correlation.

[0032] Preferably, the geological structure data included in the independent data is determined according to the preset normal geological structure data, and the geological structure data included in the independent data is analyzed to obtain the corresponding geological structure analysis results, which specifically includes the following steps:

[0033] Obtain known and missing geological structure data from independent data;

[0034] Based on the known geological structure data and the missing geological structure data, the correlation between the two is determined and recorded as a structural relationship;

[0035] Determine location information of known geological structure data based on independent data;

[0036] Based on the structural relationship and the location information of known geological structure data, the position of the missing geological structure data is simulated to obtain the location information of the missing geological structure data and output the corresponding geological structure analysis results.

[0037] Preferably, performing geological element correlation analysis on target data to obtain element correlation analysis results specifically includes the following steps:

[0038] Judging the target data based on the original data to determine the performance feature data that is element-related to the target data and recording it as the associated performance feature data;

[0039] Based on the original data, the element association point between the associated performance feature data and the target data is judged, and the action point of the associated performance feature data on the target data is determined and then the action point data is output;

[0040] Based on the action point data, the geological element information of the associated performance characteristic data is judged to determine whether the associated performance characteristic data can produce a geological effect on the target data;

[0041] If it is determined that there is no geological effect on the target data, the geological structure analysis results of the target data are balanced based on the built-in geological element balance system. The geological element effects when the equilibrium state is reached through the self-regulation of the geological environment are obtained and recorded as the element correlation analysis results;

[0042] If it is determined that a geological effect is produced on the target data, a balance judgment is performed on the geological structure analysis results of the target data to obtain the geological element effect when the associated characteristic data has an effect on the target data, and record it as the element correlation analysis result.

[0043] Preferably, judging the geological structure analysis results and the element correlation analysis results according to the target result indicators and outputting the geological exploration drilling data analysis results specifically includes the following steps:

[0044] Match the geological structure data contained in the geological structure analysis results based on the built-in geological exploration standard system to determine the reasonable variation range of the geological structure data;

[0045] The data of geological structure analysis results in which all geological structure data are within a reasonable range of variation are recorded as the first screening results;

[0046] Based on the element correlation analysis results, the conflict of the first screening results is judged, and the geological structure data that can be established under the interaction of geological elements is marked as the second screening result;

[0047] Obtain target result indicators, evaluate the geological structure data in the second screening results according to the target result indicators to obtain corresponding evaluation results, and output geological exploration drilling data analysis results.

[0048] Preferably, conflict judgment is performed on the first screening results based on the element correlation analysis results, and geological structure data that can be established under the interaction of geological elements is marked as the second screening results, which specifically includes the following steps:

[0049] Based on the interaction force between the known geological elements and the missing geological elements in the element correlation analysis results, the conflict judgment is performed on each element of the geological structure data in the first screening result;

[0050] If the relevant data (such as content, distribution location, etc.) of the missing geological elements in the first screening result changes under the influence of the interaction force of the known geological elements on the missing geological elements, it is determined that there is a conflict between the missing geological elements and the known geological elements;

[0051] If the relevant data of the missing geological elements in the first screening result does not change under the influence of the interaction force of the known geological elements on the missing geological elements, it is determined that there is no conflict between the missing geological elements and the known geological elements, and the geological structure data corresponding to the missing geological elements without conflict are output to obtain the geological exploration drilling data analysis results.

[0052] A geological exploration drilling data intelligent analysis system, comprising:

[0053] Acquisition and identification module: acquires the raw data collected by geological exploration drilling, and identifies and analyzes the raw data to determine the geological information contained in the raw data; wherein the geological information includes rock type information, stratum depth information and mineral composition information;

[0054] Division module: classify and divide the original data according to geological information to obtain performance characteristic data with different characteristics;

[0055] Extraction and analysis module: extract key geological feature information stored in the database, mark the performance feature data containing key geological feature information as target data; perform geological structure analysis on the target data to obtain geological structure analysis results; perform geological element correlation analysis on the target data to obtain element correlation analysis results;

[0056] Output module: judge the geological structure analysis results and element correlation analysis results according to the target result indicators and output the geological exploration drilling data analysis results.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] This technical solution can accurately obtain the original data collected by geological exploration drilling, and determine geological information such as rock type, stratum depth, and mineral composition from it through identification and analysis. On this basis, the original data is classified and divided according to this geological information, making data processing more targeted and organized. When performing geological structure analysis on the target data, the integrity of the geological structure is fully considered. When the geological structure of the target data is determined to be complete, the result can be directly output; if it is incomplete, a series of complex and sophisticated correlation judgment operations are performed, such as comparing with characteristic data that does not contain key geological feature information to determine related data and independent data. This comprehensive analysis method allows deep mining of potential geological structure information even in the face of incomplete geological data, thereby restoring the true geological structure to the greatest extent. In practical applications, the geological structure can be accurately analyzed in areas damaged by geological movements and where data collection is incomplete. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The present invention proposes a flow chart of an intelligent analysis method for geological exploration drilling data;

[0060] Figure 2 The present invention proposes a module schematic diagram of a geological exploration drilling data intelligent analysis system. DETAILED DESCRIPTION

[0061] Reference Figure 1 As shown, Example 1 further illustrates the intelligent analysis method for geological exploration drilling data proposed by the present invention.

[0062] A method for intelligent analysis of geological exploration drilling data, the method comprising the following steps:

[0063] Obtaining raw data collected by geological exploration drilling, and identifying and analyzing the raw data to determine the geological information contained in the raw data; wherein, geological information includes rock type information, stratum depth information and mineral composition information;

[0064] Classify and divide the original data according to geological information to obtain performance characteristic data with different characteristics (for example, different performance characteristic data are divided according to different rock types, or performance characteristic data are divided according to stratum depth intervals);

[0065] Extract key geological feature information stored in the database and mark the performance feature data containing the key geological feature information as target data; wherein the key geological feature information is the key information required for the target set by geological exploration; perform geological structure analysis on the target data to obtain geological structure analysis results; perform geological element correlation analysis on the target data to obtain element correlation analysis results;

[0066] According to the target result indicators, the geological structure analysis results and element correlation analysis results are judged and output as geological exploration drilling data analysis results.

[0067] This application first obtains raw data collected from geological exploration drilling, identifies and analyzes the raw data to extract geological information such as rock type, formation depth, and mineral composition. Then, based on this geological information, the raw data is classified and divided to obtain characteristic data with different characteristics, achieving preliminary organization and classification of the raw data for subsequent screening of key data.

[0068] Raw data is a collection of unprocessed information collected during geological exploration drilling. It contains a variety of complex and unordered information, which is then analyzed to identify and determine the geological information contained therein. During geological exploration drilling, various specialized equipment is used to collect data. For example, coring drills obtain core samples, which contain information such as rock physical properties; logging instruments can measure parameters such as the electrical and radioactive properties of the formation. The raw data collected by these devices includes physical rock signals (such as waveform data corresponding to the propagation speed of sound waves in rock), chemical signals (such as the spectral response data of elements in the rock), and spatial location information (such as drilling depth and coordinates).

[0069] Raw data often contains noise interference, so preprocessing operations such as filtering are required. Taking acoustic wave data as an example, digital filtering technology is used to remove clutter caused by environmental noise, retaining the effective waveform signal generated by the acoustic characteristics of the rock itself, improving data quality and laying the foundation for subsequent accurate analysis.

[0070] Rock type identification utilizes physical properties such as density and hardness. Pressure sensors and density meters are used to obtain the compressive strength and density of rock samples during drilling. For example, granite typically has a density of 2.6-2.8 g / cm³ and a relatively high hardness, significantly different from lower-density, lower-hardness rocks like shale. These properties allow for a preliminary identification of rock types.

[0071] Different rocks are composed of specific mineral combinations. Spectral characteristics of minerals in rocks can be determined using equipment such as spectrometers. For example, quartz has a specific absorption peak in the infrared spectrum. If this absorption peak is detected and the concentration is high, combined with other characteristics, it can be determined that the rock is likely a quartz sandstone or other rock type containing quartz minerals.

[0072] Drilling equipment typically uses a depth measurement device installed on the drill pipe. This device measures the length of the drill pipe lowered to determine the borehole depth, thereby determining the formation depth corresponding to the acquired data. Using geophysical logging methods (such as resistivity logging and gamma logging), differences in the physical properties of different formations will be reflected in different characteristics on the logging curve. Comparing and correcting the depth of the logging curve with the mechanically measured depth eliminates depth errors caused by factors such as formation compaction and drill string stretching, thereby accurately determining formation depth information.

[0073] Chemical analysis involves placing core samples in an X-ray fluorescence spectrometer. By measuring the intensity of the fluorescence produced by the excited elements, the concentrations of various elements in the rock are quantitatively analyzed, and the mineral composition can be inferred. For example, high concentrations of calcium, carbon, and oxygen may indicate the presence of carbonate minerals such as calcite.

[0074] Microscopic observation of rock slices allows identification of mineral species based on their optical properties (such as color, transparency, and crystal morphology). Combined with chemical analysis, this allows for a more accurate determination of mineral composition and proportions.

[0075] After obtaining the geological information (rock type, stratigraphic depth, and mineral composition) from the raw data, the data is classified and divided based on this information to produce characteristic data with different characteristics. Based on the previously identified and analyzed rock physical properties (such as density, hardness, elastic modulus, etc.) and mineral composition, rocks are preliminarily divided into three categories: igneous rocks, sedimentary rocks, and metamorphic rocks. For example, rocks with a crystalline structure formed by the cooling and solidification of magma are classified as igneous rocks; rocks formed by sediment compaction and cementation are sedimentary rocks; and rocks formed under high temperature and high pressure are metamorphic rocks.

[0076] Igneous rocks are further subdivided based on their silica content and mineral composition into ultramafic rocks (such as peridotite, rich in minerals such as olivine), basic rocks (such as basalt, primarily composed of pyroxene and plagioclase), intermediate rocks (such as andesite), and acidic rocks (such as granite). Sedimentary rocks are divided into clastic rocks (such as sandstone and conglomerate), clay rocks (such as shale), and chemical rocks (such as limestone) based on the sediment source and depositional environment. Metamorphic rocks are divided into regional metamorphic rocks (such as gneiss) and contact metamorphic rocks (such as marble) based on the type of metamorphism and mineral composition. Each rock has unique petrological characteristics. By categorizing the raw data according to these subcategories, the resulting performance data is characterized by rock type subcategories.

[0077] Different stratigraphic depth intervals are set based on the exploration objectives and the geological characteristics of the study area. For example, for shallow surface exploration projects, intervals such as 0-50 meters, 50-100 meters, and 100-200 meters might be used; for deep geological studies, deeper intervals such as 1000-1500 meters and 1500-2000 meters might be used. These intervals can be equally spaced or unequally spaced depending on the complexity of stratigraphic variations.

[0078] Raw data containing stratigraphic depth information is grouped into intervals based on their corresponding depth values. For example, if a core sample was collected at a depth of 80 meters, all raw data related to that core sample, including rock type and mineral composition, would be grouped into a characteristic data set for the 50-100 meter depth interval. This method generates characteristic data based on stratigraphic depth intervals, facilitating the study of geological differences at different depths, such as comparing rock type variations and mineral enrichment patterns in shallow and deep strata.

[0079] First, determine the primary mineral composition of the rock in the raw data. For example, if the quartz content is high, the rock will be classified as primarily quartz; if feldspar is the primary mineral, it will be classified as primarily feldspar. For polymetallic mineral regions, if the ore contains a high copper content, raw data primarily composed of copper minerals (such as chalcopyrite) will be classified into one category; if the zinc content is high, raw data primarily composed of zinc minerals (such as sphalerite) will be classified into another category. This creates a data classification based on the primary mineral composition.

[0080] For example, according to the rock type, it can be divided into granite performance characteristic data and shale performance characteristic data.

[0081] A core sample collected during a geological survey in a mountainous area was identified as granite. A series of raw data surrounding this core was compiled into characteristic data for granite. For example, its density was measured to be approximately 2.7 g / cm³, and a hardness test revealed a Mohs hardness of 6-7. Spectral analysis confirmed that its primary mineral components are quartz, feldspar, and mica, with a ratio of approximately 25%, 50%, and 25%. Furthermore, the rock's color is flesh-red or grayish-white, and its texture is equigranular. This collection of data on granite's physical properties, mineral composition, and appearance constitutes the characteristic data for granite as a rock type.

[0082] A shale sample was obtained during geological drilling in a certain basin. Its characteristic data include a density of approximately 2.0-2.4 g / cm³ and a relatively low hardness, with a Mohs hardness of approximately 1-2. Microscopic observations reveal that it is primarily composed of clay minerals such as kaolinite and illite, with minor amounts of quartz and feldspar fragments. The shale exhibits foliation, is primarily black and gray in color, and has a fine texture. These data, including density, hardness, mineral composition, texture, and appearance, constitute the shale characteristic data.

[0083] According to the mineral composition, it is divided into the performance characteristic data mainly composed of copper minerals and the performance characteristic data mainly composed of quartz minerals.

[0084] A series of data related to copper minerals was generated during geological exploration of a copper mine. Chemical analysis determined that the copper content of the ore averaged 2%-5%. The primary copper mineral is chalcopyrite, which is golden yellow, often with mottled blue and purple-brown tints on the surface. X-ray diffraction analysis determined its crystal structure parameters, and associated minerals such as pyrite and sphalerite were also detected, with concentrations of approximately 10% and 5%, respectively. Furthermore, data on the geological environment in which the ore was found were recorded, including the specific age of the sedimentary formation and the predominant alteration of the surrounding rock, such as silicification and sericitization. These data on copper mineral content, properties, associated minerals, and the environment in which it was found comprise the characteristic data primarily focused on copper minerals.

[0085] Data obtained during the exploration of quartz veins. Quartz crystals were tested to have a purity of over 98% SiO2 content. The crystals are mostly hexagonal prisms, with a hardness of 7, a conchoidal fracture, and a typical vitreous luster. Inclusions within the quartz crystals were observed using an optical microscope, and electron microprobe analysis determined the presence of trace elements such as Al and Fe. These data, including quartz mineral purity, crystal morphology, optical and chemical properties, and vein occurrence, constitute the characteristic data primarily focused on quartz minerals.

[0086] In addition to the primary mineral composition, the assemblage relationships between minerals are also considered. For example, in some hydrothermal deposits, quartz and sulfide (such as pyrite and galena) assemblages are present, and the raw data with this specific mineral assemblage are grouped together. In other areas, however, rocks with feldspar, mica, and quartz assemblages may be found, and the corresponding raw data are grouped together in different categories. Mineral assemblage classification allows for the study of mineral paragenesis and their formation mechanisms in geological processes, providing valuable information for mineral exploration and geological research.

[0087] Extract pre-set key geological feature information from the database and mark the characteristic data containing this key information as target data. The key geological feature information is determined based on the geological survey objectives.

[0088] Extract key geological characteristics stored in the database. The data in the database comes from a wide range of sources, including the accumulated results of long-term geological exploration projects. This includes information on stratigraphic distribution, rock outcrops, and geological structures obtained from field geological mapping; data related to core samples collected from drilling projects, such as rock type, mineral composition, and stratigraphic depth; physical properties of underground geological bodies obtained from geophysical exploration (such as gravity, magnetic, electrical, and seismic exploration) to infer geological structure and lithologic distribution; and elemental content data in soil, rock, and stream sediment samples analyzed through geochemical exploration to identify possible mineralized areas.

[0089] This multi-source data is standardized, with unified data formats and encoding rules to eliminate inconsistencies and redundancies. This data is stored using a database management system (such as MySQL or PostgreSQL). Tables are created based on different geological elements (such as strata, rocks, minerals, and structures), establishing relationships between the data to facilitate subsequent queries and retrieval. For example, a stratum table records information such as stratum age, thickness, and lithologic composition, while a rock table records rock type, physical properties, and chemical composition. The two are linked using a stratum-rock association field.

[0090] Key geological characteristic information is selected based on specific geological exploration objectives. In mineral exploration, if the goal is to find copper deposits, then information related to copper mineralization is key geological characteristic information. This includes characteristics of copper-bearing minerals (such as chalcopyrite and bornite), such as the mineral's crystal morphology, optical properties, and chemical composition; abnormal copper content and distribution patterns in rocks, soils, and stream sediments; geological structures related to copper mineralization (such as faults and folds), as these structures may control the migration and enrichment of ore fluids; and surrounding rock alteration phenomena (such as silicification, sericitization, and chloritization), which are often indicators of mineralization.

[0091] Key information is determined based on relevant geological theories. When studying the sedimentary environment of a stratum, according to sedimentological theory, the lithologic composition of the strata (e.g., sandstone-shale-limestone interbeds), sedimentary structures (e.g., cross-bedding, ripples), and biofossil assemblage can reflect the hydrodynamic conditions, paleoclimate, and paleogeographic environment at the time of deposition. This information is crucial for reconstructing the paleogeological environment and is extracted as key geological characteristics. In structural geology, characteristics such as the occurrence (strike, dip, inclination), fault throw, rock fragmentation within the fault zone, and friction mirrors are of great significance for analyzing the regional tectonic stress field and geological evolution history, and are also key information.

[0092] Leverage the query capabilities of a database management system to filter data based on set criteria by writing SQL statements or using visual query tools. For example, to extract key geological characteristics related to gold mineralization in a particular region, search the database for records containing gold anomalies and quartz veins (a common host rock for gold deposits). Simultaneously, query the geological structure data for the region to obtain information on faults, folds, and other structures potentially related to gold mineralization.

[0093] The retrieved data is screened to remove erroneous, duplicate, or irrelevant data. Data mining techniques, such as cluster analysis and association rule mining, are used to further extract key information. For example, cluster analysis can group similar gold element anomaly data together to identify the concentrated areas and patterns of outlier values. Association rule mining can also be used to identify correlations between gold content and the contents of other elements (such as arsenic and antimony), thereby extracting key geological characteristics that are instructive for gold mine exploration, such as specific element ratio ranges and element symbiotic combination patterns.

[0094] The extracted key geological feature information is used to mark the characteristic data containing this information as target data, providing core data for subsequent geological structure analysis and geological element correlation analysis. In geological structure analysis, the integrity and uniqueness of the geological structure reflected by the target data are judged based on the key geological feature information. In geological element correlation analysis, key element information is used as a starting point to study the impact of element interactions on geological processes such as geological structure and mineralization.

[0095] As geological exploration continues and research deepens, new data and research results are constantly generated. This requires regular database updates to reassess and screen key geological features. For example, when a new mineral species or new mineralization type is discovered, relevant information is promptly added to the database, and the extraction rules for key geological features are adjusted to ensure that the extracted information always meets the latest geological exploration needs and research directions.

[0096] The target data is matched with pre-set normal geological structure data to determine the integrity of the geological structure. If complete, the geological structure analysis results are directly output; if incomplete, the correlation between the target data and the characteristic data that does not contain key geological characteristics is determined by correlation, and the associated data and independent data are determined. Based on the correlation between the target data and other characteristic data, the geological structure data is further analyzed and determined, such as by analyzing the geological structure data in the independent data or determining the combination of the associated data and the independent data, to obtain the geological structure analysis results.

[0097] Based on the original data, the correlation performance characteristic data that has elemental correlation with the target data is determined to find the element correlation point and action point. It is judged whether the correlation performance characteristic data can have a geological effect on the target data. If not, the geological structure analysis results are balanced based on the geological element balance system to obtain the element correlation analysis results. If so, the geological structure analysis results are balanced based on the geological element balance system to obtain the element correlation analysis results.

[0098] Based on the built-in geological exploration standard system, the reasonable variation range of geological structural data in the geological structural analysis results is determined, and data within this range is screened (the first screening result). Based on the results of element correlation analysis, the first screening results are judged for conflicts, and geological structural data that can be established under the interaction of geological elements is screened (the second screening result). Finally, the geological structural data in the second screening results are evaluated according to the target result indicators and the final geological exploration drilling data analysis results are output.

[0099] Performing geological structure analysis on target data to obtain geological structure analysis results specifically includes the following steps:

[0100] Match the target data with the preset normal geological structure data to determine whether the geological structure reflected by the target data is complete;

[0101] If the geological structure of the target data is determined to be complete, the corresponding geological structure analysis results are directly output based on the target data;

[0102] If the geological structure of the target data is determined to be incomplete, the target data is judged to be associated with the performance characteristic data that does not contain key geological feature information to determine whether the target data has a portion associated with other performance characteristic data; and the portion of the target data associated with the other performance characteristic data is marked as associated data, and the portion of the target data not associated with the other performance characteristic data is marked as independent data;

[0103] The corresponding geological structure analysis results are obtained by analyzing whether the target data is associated with other characteristic data.

[0104] The normal geological structure data preset in this application are model data established based on a large number of geological exploration cases, long-term geological research and industry standards, covering the characteristic parameters of various common geological structures, such as the normal stratigraphic sequence of strata, rock combination rules, etc. When matching the target data with it, compare the correspondence between the rock type, stratigraphic depth, mineral composition and other information in the target data and the normal geological structure data. For example, a certain depth segment in the normal geological structure data should be sandstone-shale interlayers. If the target data only shows sandstone in this depth segment and there is no other reasonable geological explanation, it can be determined that the geological structure reflected by the target data is incomplete; if all the information can be well matched with the normal geological structure data, the geological structure is determined to be complete, and the corresponding geological structure analysis results, such as stratigraphic stratification, rock characteristics, etc., are directly output.

[0105] If the geological structure of the target data is determined to be incomplete, it is necessary to find data that may supplement the missing information. The performance characteristic data that does not contain key geological characteristic information is classified and divided from the original data. Although this data does not contain the key information of the exploration target, it may contain geological information related to the target data. The target data is matched with the preset normal geological structure data to determine the known geological structure data and the missing geological structure data. The missing geological structure data is then compared with the known geological structure data, and the degree of feature matching between the two in terms of rock type, mineral composition, stratum depth, etc. is analyzed to determine whether there is a correlation. For example, if the target data lacks rock type information for a certain section of stratum, but rocks with similar mineral composition are found in the same depth section in other performance characteristic data and conform to geological laws, it can be determined that there is a correlation, and the relevant part is then marked as associated data, and the unrelated part is marked as independent data.

[0106] If the target data is not correlated with other characteristic data, the system uses geostatistics, geological modeling, and other methods to infer the possible characteristics of the missing geological structure data based on the pre-set normal geological structure data and the known geological structure data in the independent data. For example, the rock types of the missing part can be inferred based on the rock types of adjacent strata. The geological structure data contained in the independent data is then determined and the geological structure analysis results are output. If a correlation exists, the associated data is subjected to geological information identification to obtain the associated geological information. The target geological information in the target data corresponding to the associated data is also read. The two are then matched. If a match is successful, the pre-set normal geological structure data is used to determine whether the combination of the associated data and the independent data can complete the geological structure of the target data. If so, the combined data is combined to obtain the geological structure analysis results. If there is structural overlap between the two, the associated data is determined to be non-correlated, and the geological structure analysis results are obtained based solely on the independent data.

[0107] The corresponding geological structure analysis results are obtained by analyzing whether the target data is associated with other characteristic data, specifically including the following steps:

[0108] If the target data is not associated with other characteristic data, the geological structure data contained in the independent data is determined based on the preset normal geological structure data, and the geological structure data contained in the independent data is analyzed to obtain the corresponding geological structure analysis results;

[0109] If the target data is associated with other characteristic data, geological information recognition is performed on the associated data to obtain associated geological information, and geological information reading is performed on the target data corresponding to the associated data to obtain target geological information;

[0110] The associated geological information is matched with the target geological information respectively. If the match is successful, the structure of the associated data and the corresponding independent data is judged based on the preset normal geological structure data. If the combination of the associated data and the corresponding independent data can make the geological structure of the target data complete, the two are combined to obtain the corresponding geological structure analysis results; if there is structural overlap between the associated data and the corresponding independent data, the associated data is determined to be a non-associated part, and the corresponding geological structure analysis results are obtained based on the corresponding independent data.

[0111] When the target data is not correlated with other characteristic data, the pre-set normal geological structure data is derived from a large amount of actual exploration data and geological theory research, representing common and standard geological structure patterns. The starting point is the known information in the independent data. For example, the rock type and mineral composition of some strata in the independent data of a certain region are known, but the stratigraphic relationship of the strata is missing. Referring to the stratigraphic patterns of the same rock type and mineral composition in similar geological environments in the normal geological structure data, geological modeling is used to infer the missing stratigraphic information, and then the complete geological structure data in the independent data is determined. On this basis, by analyzing these data, such as the sedimentary environment of the strata and the formation process of the rocks, the corresponding geological structure analysis results are finally obtained.

[0112] If the target data is associated with other characteristic data, the associated data is first subjected to geological information identification. Using a variety of professional geological detection methods, such as using X-ray diffraction analysis to determine the mineral crystal structure, and analyzing the rock microstructure through rock thin section identification, etc., detailed geological information such as rock type, mineral composition, structural structure, and stratigraphic age contained in the associated data is obtained, which is the associated geological information. At the same time, existing geological information including the determined stratigraphic depth and rock physical properties is extracted from the target data corresponding to the associated data to form the target geological information. For example, the associated data is the core data obtained from adjacent boreholes. Through analysis, it is found that the rock is limestone and contains a specific fossil combination to form the associated geological information; the target data has clearly defined the depth range of some stratigraphic layers in the area and the hardness of the rock, which is the target geological information.

[0113] The associated geological information is matched with the target geological information to compare their consistency in key geological features. For example, the associated data and the target data are compared to determine whether they share the same rock type, mineral composition, or similar stratigraphic age. If a match is successful, the associated data and the independent data are then combined to determine whether the target data's geological structure is complete, based on the pre-set normal geological structure data. For example, if the associated data supplements stratigraphic contact information missing in the target data and, when integrated with the independent data, conforms to the logic and patterns of normal geological structure, the two are combined and analyzed to yield a complete and accurate geological structure analysis. If there is structural overlap between the associated and independent data, such as a conflict between the stratigraphic sequence in the associated data and the independent data and normal geological structure data, this indicates that the associated data contains unreasonable or interfering elements. This portion of the associated data is then deemed non-associated, discarded, and the analysis is performed solely based on the independent data, combined with the normal geological structure data, to produce a reliable geological structure analysis.

[0114] Then, the target data is judged for correlation with the characteristic data that does not contain key geological characteristic information, which specifically includes the following steps:

[0115] Based on the preset normal geological structure data, the target data is matched with the structure data to obtain the known geological structure data and the missing geological structure data;

[0116] Matching the missing geological structure data with the known geological structure data to determine whether the known geological structure data contains the missing geological structure data;

[0117] The correlation is analyzed and judged based on whether there is missing geological structure data in the known geological structure data.

[0118] The preset normal geological structure data is a standard data model constructed based on extensive geological exploration experience, theoretical research, and a large number of actual cases, covering the parameters and characteristics of various typical geological structures. When matching the target data with this model, the rock type, stratigraphic depth, mineral composition, and other information in the target data are compared with the corresponding elements in the normal geological structure data. For example, the normal geological structure data shows that a certain depth section should be a sandstone-shale-limestone sequence combination, while the target data only detects sandstone and limestone in this depth section. Through comparison, it can be determined that the known sandstone and limestone information is known geological structure data, and the missing shale information is missing geological structure data, thereby achieving a preliminary division of the structural data of the target data.

[0119] After obtaining the missing and known geological structural data, the missing data is further compared with the known data. For example, the missing shale information may have similar mineral composition to certain clay minerals in the known geological structural data, or may have related rock structural characteristics. Through this meticulous matching process, it is determined whether the known geological structural data contains relevant parts that are relevant to the missing data. This step utilizes the inherent connections between geological data in terms of mineral composition, rock structure, and stratigraphic characteristics, searching the known data for clues that may provide missing information.

[0120] If there are relevant parts, it means that the target data may be correlated with other performance characteristic data. Subsequently, the characteristics of these relevant parts can be further analyzed, such as the physical and chemical properties of rocks, the spatial distribution of strata, etc., to determine the degree of correlation and the specific correlation method; if there are no relevant parts, it is necessary to judge whether there is a potential correlation from other perspectives, such as the similarity of geological environment, regional geological evolution laws, etc., or to determine whether the target data has no direct correlation with the performance characteristic data currently being analyzed, so as to provide a basis for the selection of subsequent geological structure analysis strategies.

[0121] Analyze and judge the relevance based on whether there is missing geological structure data in the known geological structure data, specifically including the following steps:

[0122] If it is determined that there is missing geological structure data in the known geological structure data, characteristic data (such as rock color, texture characteristics, etc.) of the known geological structure data that matches the missing geological structure data is collected to obtain matching characteristic data;

[0123] Collect features of other data in the original data excluding the target data to obtain overall feature data;

[0124] Matching the matching feature data with the overall feature data to filter out feature data identical to the matching feature data, and marking the position of the filtered feature data to obtain the overall position data;

[0125] Obtaining the location information of the missing geological structure data, and matching the location information of the missing geological structure data with the overall location data; if the match is successful, it is determined that there is a correlation; if the match fails, it is determined that there is no correlation;

[0126] If it is determined that there is no missing geological structure data in the known geological structure data, the known geological structure data is screened according to the preset normal geological structure data to obtain the known geological structure data connected to the missing geological structure data and record it as connected structure data;

[0127] The connected structure data is subjected to feature collection, and the collected features are matched with the overall feature data. If the match is successful, it is determined that there is a correlation; if the match fails, it is determined that there is no correlation.

[0128] When missing geological structure data is determined within known geological structure data, feature data is first collected from the known geological structure data that matches the missing data. For example, if data on a specific rock layer is missing, and rocks with similar mineral composition are present in the known data, feature data such as the rock's color, texture, and mineral grain size are collected to form matching feature data. This approach leverages the principle that rock features under similar geological conditions share a certain degree of similarity to identify clues that may be related to the missing data.

[0129] Feature collection is performed on all data in the original data, excluding the target data. This data contains extensive geological information within the study area. Through geological mapping and geophysical exploration, various characteristics of rocks and soils, such as magnetism, conductivity, and color, are collected to obtain overall feature data, thereby constructing a comprehensive picture of the regional geological characteristics.

[0130] Match the matching feature data with the overall feature data to filter out identical feature data. For example, find the parts of the overall feature data that match the matching feature data in color or texture. Then, position-mark the filtered feature data to determine its spatial location within the study area, obtain overall location data, and clarify the distribution of these similar feature data.

[0131] Obtain the location information of the missing geological structure data in the target data, such as its depth, stratigraphic interval, etc. Compare it with the overall location data. If a corresponding location can be found in the overall location data, it indicates that there is a correlation, that is, a geological structure similar to the missing data may be found at that location; if no match is found, it is determined that there is no correlation.

[0132] Based on the pre-set normal geological structure data, the known geological structure data is screened. The connected structure data is identified by searching for areas that may be connected to the missing geological structure data in terms of spatial location or geological characteristics. For example, based on the sedimentary patterns of the strata, rock layer data at adjacent depths to the missing data that may have a transitional relationship with the missing data is screened.

[0133] The connected structural data is characterized by collecting features such as rock physical properties and mineral composition. These features are then matched with the overall feature data. If matching features can be found in the overall feature data, it indicates that there is a correlation, which means that the connected structural data is related to other geological data in the study area. If the match fails, it is determined that there is no correlation.

[0134] The geological structure data contained in the independent data is determined based on the preset normal geological structure data, and the geological structure data contained in the independent data is analyzed to obtain the corresponding geological structure analysis results, which specifically includes the following steps:

[0135] Obtain known and missing geological structure data from independent data;

[0136] Based on known geological structure data and missing geological structure data, the correlation between the two is determined and recorded as a structural relationship;

[0137] Determine location information of known geological structure data based on independent data;

[0138] Based on the structural relationship and the location information of known geological structure data, the position of the missing geological structure data is simulated to obtain the location information of the missing geological structure data and output the corresponding geological structure analysis results.

[0139] The pre-set normal geological structure data is a standard reference model formed through long-term geological exploration practice and research. It covers the characteristic parameters, stratigraphic sequence, rock assemblage, and other information of various typical geological structures. Based on this standard model and existing geological exploration results, known geological structure data and missing geological structure data are distinguished from independent data. For example, in a geological exploration of a certain region, information such as the rock type and mineral composition of some strata is considered known geological structure data, while information such as the thickness and interlayer contact relationships of the strata that are missing is considered missing geological structure data.

[0140] Determine the geological relationship between known and missing geological structural data, such as the gradual relationship between rock types and the symbiotic relationship between mineral components, to determine the correlation between the two, or the structural relationship. For example, if known data indicates that a stratum consists of sandstone at the top and shale at the bottom, and the missing data is information about the intermediate transition layer, by analyzing the mineral composition and sedimentary environment of the sandstone and shale, the possible rock type and characteristics of the transition layer can be inferred, clarifying the structural relationship between them.

[0141] Based on the depth measurement, coordinate positioning and other information in the independent data, combined with the results of geological mapping and geophysical exploration, the location information of the known geological structure data in the actual geological space is determined, including the stratigraphic depth, horizontal coordinates, etc.

[0142] Based on the previously determined structural relationships and the location information of known geological structural data, geological modeling methods are used to simulate the location of missing geological structural data. For example, based on the known inclination angles of the strata, the trend of sediment thickness changes, and structural relationships, the possible location of the missing geological structural data in geological space is inferred, and the location information of the missing geological structural data is then obtained. Finally, this information is combined to output the corresponding geological structural analysis results, presenting a complete geological structure of the area.

[0143] Performing geological element correlation analysis on target data to obtain element correlation analysis results specifically includes the following steps:

[0144] Judging the target data based on the original data to determine the performance feature data that is element-related to the target data and recording it as the associated performance feature data;

[0145] Based on the original data, the element association point between the associated performance feature data and the target data is judged, and the action point of the associated performance feature data on the target data is determined and then the action point data is output;

[0146] Based on the action point data, the geological element information of the associated performance characteristic data is judged to determine whether the associated performance characteristic data can produce a geological effect on the target data;

[0147] If it is determined that there is no geological effect on the target data, the geological structure analysis results of the target data are balanced based on the built-in geological element balance system. The geological element effects when the equilibrium state is reached through the self-regulation of the geological environment are obtained and recorded as the element correlation analysis results;

[0148] If it is determined that a geological effect is produced on the target data, a balance judgment is performed on the geological structure analysis results of the target data to obtain the geological element effect when the associated characteristic data has an effect on the target data, and record it as the element correlation analysis result.

[0149] Raw data contains comprehensive information about rocks, minerals, strata, and other aspects acquired during geological exploration. Based on this raw data, geostatistical methods are used to determine which characteristic data are elementally correlated with the target data, based on characteristics such as element type, content, and distribution. For example, if an abnormal content of a specific metal element is detected in the target data, characteristic data containing the same element and with similar content trends can be identified by comparing other samples in the raw data and marking them as correlated characteristic data.

[0150] Based on the original data, further analysis is performed to obtain the associated characteristic data and target data. Using mineralogy and petrology expertise and analytical techniques such as electron microprobe analysis and isotope analysis, the specific points of elemental correlation between the two are determined, known as elemental correlation points. The specific location and method of the effect of the associated characteristic data on the target data at these correlation points are further determined to determine the effect points and output the effect point data. For example, if analysis reveals that a certain mineral in rocks at a specific depth exchanges elements with minerals in the target data, this depth location is the elemental correlation point and effect point.

[0151] Based on the action point data, combined with geological theory and relevant experimental research results, it is determined whether the associated performance characteristic data can produce a geological impact on the target data. For example, the migration, enrichment, chemical reaction, etc. of the elements at the action point are analyzed. If the behavior of the elements at the action point does not cause significant changes in the geological structure and mineral composition of the target data, then it is determined that there is no geological impact. If it causes changes in the rock properties of the target data, or the formation or decomposition of minerals, then it is determined that there is a geological impact.

[0152] If it is determined that there is no geological influence on the target data, the built-in geological element balance system is a model based on the natural distribution and migration patterns of elements in the geological environment, as well as the principles of conservation of matter. Based on this system, the geological structure analysis results of the target data are judged to be balanced. By simulating the natural adjustment process of elements in the geological environment, the content and distribution of each element are calculated when the geological environment reaches equilibrium, and the element correlation analysis results are obtained.

[0153] If it is determined that a geological effect can affect the target data, the specific mode and intensity of the effect of the associated characteristic data on the target data are considered, and a balance judgment is made on the geological structure analysis results of the target data. The migration, transformation, enrichment, and other changes of geological elements under this effect are analyzed to determine the new distribution and effect of each element in the geological structure, and the corresponding element correlation analysis results are obtained.

[0154] The geological structure analysis results and element correlation analysis results are judged and outputted according to the target result indicators, specifically including the following steps:

[0155] Match the geological structure data contained in the geological structure analysis results based on the built-in geological exploration standard system to determine the reasonable variation range of the geological structure data;

[0156] The data of geological structure analysis results in which all geological structure data are within a reasonable range of variation are recorded as the first screening results;

[0157] Based on the element correlation analysis results, the conflict of the first screening results is judged, and the geological structure data that can be established under the interaction of geological elements is marked as the second screening result;

[0158] Obtain target result indicators, evaluate the geological structure data in the second screening results according to the target result indicators to obtain corresponding evaluation results, and output geological exploration drilling data analysis results.

[0159] The geological exploration standard system built into this application is a comprehensive standard that brings together industry norms, research results, and a large number of actual exploration cases. Compare various types of data in the geological structure analysis results, such as rock type, stratum thickness, mineral composition content, etc., with the corresponding standard values ​​in the standard system. For example, the standard system stipulates that the rock type of a specific stratum in a certain geological area should be sandstone, and its thickness range is within a certain interval, and the quartz content in the mineral composition should be within a certain proportion range. The reasonable variation range of each geological structure data is determined by matching, and the data within this range is screened out to obtain the first screening result.

[0160] Element correlation analysis results reflect the interactions between geological elements, such as their migration, enrichment, and chemical reactions. Based on these results, the geological structure data in the first screening results are judged for conflicts. For example, if element correlation analysis indicates that certain elements react chemically to form new minerals under specific geological conditions, but the data in the first screening results do not show the conditions or results for the formation of such new minerals, a conflict is determined. Data that can be supported by geological element interactions and does not generate conflicts is selected and marked as the second screening result.

[0161] Target result indicators are set based on the specific geological exploration objectives. For example, when searching for specific mineral resources, relevant indicators such as mineral reserves and grade are used; when assessing geological stability, indicators such as formation integrity and rock strength are used. The geological structure data in the second screening results are evaluated based on these target result indicators. For example, in mineral exploration, the geological structure data related to mineral reserve calculations in the second screening results is evaluated to see if they meet the target reserve requirements. A comprehensive score or judgment is then performed on the data to ultimately generate an evaluation result and output the geological exploration drilling data analysis results, providing a basis for subsequent geological decision-making.

[0162] Based on the element correlation analysis results, the conflict judgment of the first screening results is performed, and the geological structure data that can be established under the interaction of geological elements is marked as the second screening results, which specifically includes the following steps:

[0163] Based on the interaction force between the known geological elements and the missing geological elements in the element correlation analysis results, the conflict judgment is performed on each element of the geological structure data in the first screening result;

[0164] If the relevant data (such as content, distribution location, etc.) of the missing geological elements in the first screening result changes under the influence of the interaction force of the known geological elements on the missing geological elements, it is determined that there is a conflict between the missing geological elements and the known geological elements;

[0165] If the relevant data of the missing geological elements in the first screening result does not change under the influence of the interaction force of the known geological elements on the missing geological elements, it is determined that there is no conflict between the missing geological elements and the known geological elements, and the geological structure data corresponding to the missing geological elements without conflict are output to obtain the geological exploration drilling data analysis results.

[0166] The results of elemental correlation analysis reveal the interactions between known and missing geological elements, such as element migration, chemical reactions, adsorption and desorption, and other interactions. The geological structure data from the first screening results were analyzed. This data contains information on rock type, mineral composition, element content, and distribution. Conflict determination was performed using each element within this data as a specific research point.

[0167] The data related to the missing geological elements in the first screening results is determined based on the interaction forces between known geological elements and the missing geological elements. For example, in a geological region where iron is known to exist, the missing copper element may react chemically with the iron. If this interaction causes a significant change in the copper content in the first screening results, or a shift in its distribution that is inconsistent with geological principles, then a conflict is determined between the missing geological element (copper) and the known geological element (iron). This is because the change does not conform to the expected interaction patterns of geological elements.

[0168] If the data associated with a missing geological element remains unchanged under the influence of the interaction forces of known geological elements, it indicates that there is no conflict with the known geological elements and that it conforms to the natural laws of geological element interaction. In this case, the geological structure data corresponding to the non-conflicting missing geological element is output, and this data forms part of the geological exploration drilling data analysis results. This series of steps ensures that the final output data is reasonable and accurate in terms of geological element interaction.

[0169] Reference Figure 2 As shown, Example 2 further illustrates a geological exploration drilling data intelligent analysis system proposed by the present invention.

[0170] A geological exploration drilling data intelligent analysis system, comprising:

[0171] Acquisition and identification module: acquires the raw data collected by geological exploration drilling, and identifies and analyzes the raw data to determine the geological information contained in the raw data; the geological information includes rock type information, stratum depth information and mineral composition information;

[0172] Division module: classify and divide the original data according to geological information to obtain performance characteristic data with different characteristics;

[0173] Extraction and analysis module: extract key geological feature information stored in the database, mark the performance feature data containing key geological feature information as target data; perform geological structure analysis on the target data to obtain geological structure analysis results; perform geological element correlation analysis on the target data to obtain element correlation analysis results;

[0174] Output module: judge the geological structure analysis results and element correlation analysis results according to the target result indicators and output the geological exploration drilling data analysis results.

[0175] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for intelligent analysis of geological exploration drilling data, characterized in that: The method comprises the following steps: Obtaining the raw data collected by geological exploration drilling, and identifying and analyzing the raw data to determine the geological information contained in the raw data; Classify and divide the original data according to geological information to obtain performance characteristic data with different characteristics; Extracting key geological feature information stored in the database, and marking the performance feature data containing the key geological feature information as target data; Performing geological structure analysis on target data to obtain geological structure analysis results specifically includes the following steps: Match the target data with the preset normal geological structure data to determine whether the geological structure reflected by the target data is complete; If the geological structure of the target data is determined to be complete, the corresponding geological structure analysis results are directly output based on the target data; If the geological structure of the target data is determined to be incomplete, the target data is judged to be associated with the performance characteristic data that does not contain key geological feature information to determine whether the target data has a portion associated with other performance characteristic data; and the portion of the target data associated with the other performance characteristic data is marked as associated data, and the portion of the target data not associated with the other performance characteristic data is marked as independent data; The corresponding geological structure analysis results are obtained by analyzing whether the target data is associated with other characteristic data, specifically including the following steps: If the target data is not associated with other characteristic data, the geological structure data contained in the independent data is determined based on the preset normal geological structure data, and the geological structure data contained in the independent data is analyzed to obtain the corresponding geological structure analysis results; If the target data is associated with other characteristic data, geological information recognition is performed on the associated data to obtain associated geological information, and geological information reading is performed on the target data corresponding to the associated data to obtain target geological information; Match the associated geological information with the target geological information respectively. If the match is successful, the structure of the associated data and the corresponding independent data is judged based on the preset normal geological structure data. If the combination of the associated data and the corresponding independent data can make the geological structure of the target data complete, the two are combined to obtain the corresponding geological structure analysis results. If there is structural overlap between the associated data and the corresponding independent data, the associated data is determined to be a non-associated part, and the corresponding geological structure analysis results are obtained based on the corresponding independent data. Performing geological element correlation analysis on target data to obtain element correlation analysis results, which present the interaction relationship between known geological elements and missing geological elements; According to the target result indicators, the geological structure analysis results and element correlation analysis results are judged and output as geological exploration drilling data analysis results.

2. The method for intelligent analysis of geological exploration drilling data according to claim 1, characterized in that: Then, the target data is judged for correlation with the characteristic data that does not contain key geological characteristic information, which specifically includes the following steps: Performing structural data matching on the target data based on the preset normal geological structure data to obtain known geological structure data and missing geological structure data, and matching the missing geological structure data with the known geological structure data to determine whether there is missing geological structure data in the known geological structure data; The correlation is analyzed and judged based on whether there is missing geological structure data in the known geological structure data.

3. The method for intelligent analysis of geological exploration drilling data according to claim 2, characterized in that: Analyze and judge the relevance based on whether there is missing geological structure data in the known geological structure data, specifically including the following steps: If it is determined that there is missing geological structure data in the known geological structure data, characteristic data of the known geological structure data that matches the missing geological structure data is collected to obtain matching characteristic data; Collect features of other data in the original data excluding the target data to obtain overall feature data; Matching the matching feature data with the overall feature data to filter out feature data identical to the matching feature data, and marking the position of the filtered feature data to obtain the overall position data; Obtaining the location information of the missing geological structure data, and matching the location information of the missing geological structure data with the overall location data; if the match is successful, it is determined that there is a correlation; if the match fails, it is determined that there is no correlation; If it is determined that there is no missing geological structure data in the known geological structure data, the known geological structure data is screened according to the preset normal geological structure data to obtain the known geological structure data connected to the missing geological structure data and record it as connected structure data; The connected structure data is subjected to feature collection, and the collected features are matched with the overall feature data. If the match is successful, it is determined that there is a correlation; if the match fails, it is determined that there is no correlation.

4. The method for intelligent analysis of geological exploration drilling data according to claim 3, characterized in that: The geological structure data contained in the independent data is determined based on the preset normal geological structure data, and the geological structure data contained in the independent data is analyzed to obtain the corresponding geological structure analysis results, which specifically includes the following steps: Obtain known and missing geological structure data from independent data; Based on known geological structure data and missing geological structure data, the correlation between the two is determined and recorded as a structural relationship; Determine location information of known geological structure data based on independent data; Based on the structural relationship and the location information of known geological structure data, the position of the missing geological structure data is simulated to obtain the location information of the missing geological structure data and output the corresponding geological structure analysis results.

5. The method for intelligent analysis of geological exploration drilling data according to claim 4, characterized in that: Performing geological element correlation analysis on target data to obtain element correlation analysis results specifically includes the following steps: Determine the target data based on the original data to determine the performance feature data that is elementally associated with the target data and record it as the associated performance feature data; determine the elemental association point between the associated performance feature data and the target data based on the original data, determine the point of action of the associated performance feature data on the target data, and then output the action point data; Based on the action point data, the geological element information of the associated performance characteristic data is judged to determine whether the associated performance characteristic data can produce a geological effect on the target data; If it is determined that there is no geological effect on the target data, the geological structure analysis results of the target data are subjected to a balance judgment, and the geological element effects when the equilibrium state is reached through the self-regulation of the geological environment are recorded as the element correlation analysis results; If it is determined that a geological effect is produced on the target data, a balance judgment is made on the geological structure analysis results of the target data, and the geological element effect obtained when the associated characteristic data has an effect on the target data is recorded as the element correlation analysis result.

6. The method for intelligent analysis of geological exploration drilling data according to claim 5, characterized in that: The geological structure analysis results and element correlation analysis results are judged and outputted according to the target result indicators, specifically including the following steps: Match the geological structure data contained in the geological structure analysis results based on the built-in geological exploration standard system to determine the reasonable variation range of the geological structure data; The data of geological structure analysis results in which all geological structure data are within a reasonable range of variation are recorded as the first screening results; Based on the element correlation analysis results, the conflict of the first screening results is judged, and the geological structure data that can be established under the interaction of geological elements is marked as the second screening result; Obtain target result indicators, evaluate the geological structure data in the second screening results according to the target result indicators to obtain corresponding evaluation results, and output geological exploration drilling data analysis results.

7. The method for intelligent analysis of geological exploration drilling data according to claim 6, characterized in that: Based on the element correlation analysis results, the conflict judgment of the first screening results is performed, and the geological structure data that can be established under the interaction of geological elements is marked as the second screening results, which specifically includes the following steps: Based on the interaction force between the known geological elements and the missing geological elements in the element correlation analysis results, the conflict judgment is performed on each element of the geological structure data in the first screening result; If the relevant data of the missing geological elements in the first screening result changes under the influence of the interaction force of the known geological elements on the missing geological elements, it is determined that there is a conflict between the missing geological elements and the known geological elements; If the relevant data of the missing geological elements in the first screening result does not change under the influence of the interaction force of the known geological elements on the missing geological elements, it is determined that there is no conflict between the missing geological elements and the known geological elements, and the geological structure data corresponding to the missing geological elements without conflict are output to obtain the geological exploration drilling data analysis results.

8. A geological exploration drilling data intelligent analysis system, applied to a geological exploration drilling data intelligent analysis method according to any one of claims 1 to 7, characterized in that: include: Acquisition and identification module: acquires the raw data collected by geological exploration drilling, and identifies and analyzes the raw data to determine the geological information contained in the raw data; wherein the geological information includes rock type information, stratum depth information and mineral composition information; Division module: classify and divide the original data according to geological information to obtain performance characteristic data with different characteristics; Extraction and analysis module: extract key geological feature information stored in the database and mark the performance feature data containing key geological feature information as target data; Performing geological structure analysis on target data to obtain geological structure analysis results specifically includes the following steps: Match the target data with the preset normal geological structure data to determine whether the geological structure reflected by the target data is complete; If the geological structure of the target data is determined to be complete, the corresponding geological structure analysis results are directly output based on the target data; If the geological structure of the target data is determined to be incomplete, the target data is judged to be associated with the performance characteristic data that does not contain key geological feature information to determine whether the target data has a portion associated with other performance characteristic data; and the portion of the target data associated with the other performance characteristic data is marked as associated data, and the portion of the target data not associated with the other performance characteristic data is marked as independent data; The corresponding geological structure analysis results are obtained by analyzing whether the target data is associated with other characteristic data, specifically including the following steps: If the target data is not associated with other characteristic data, the geological structure data contained in the independent data is determined based on the preset normal geological structure data, and the geological structure data contained in the independent data is analyzed to obtain the corresponding geological structure analysis results; If the target data is associated with other characteristic data, geological information recognition is performed on the associated data to obtain associated geological information, and geological information reading is performed on the target data corresponding to the associated data to obtain target geological information; Match the associated geological information with the target geological information respectively. If the match is successful, the structure of the associated data and the corresponding independent data is judged based on the preset normal geological structure data. If the combination of the associated data and the corresponding independent data can make the geological structure of the target data complete, the two are combined to obtain the corresponding geological structure analysis results. If there is structural overlap between the associated data and the corresponding independent data, the associated data is determined to be a non-associated part, and the corresponding geological structure analysis results are obtained based on the corresponding independent data. Performing geological element correlation analysis on target data to obtain element correlation analysis results, which present the interaction relationship between known geological elements and missing geological elements; Output module: judge the geological structure analysis results and element correlation analysis results according to the target result indicators and output the geological exploration drilling data analysis results.

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