163 results about "Geologic map" patented technology

The invention discloses a geological map-oriented breakpoint repairing and closed curve reconstruction method and system and a medium. The method comprises the following steps of: preprocessing an original geological map to obtain a grayscale image; carrying out binarization and morphological closed operation processing to obtain a grayscale image of the edge of the smooth contour curve; performing pixel skeletonization to obtain a pixel-level skeleton diagram; performing secondary edge extraction, connected domain marking and endpoint statistics on the pixel-level skeleton diagram, and screening out a non-closed curve and an endpoint set; carrying out nearest neighbor retrieval by adopting KD-Tree to obtain a candidate pairing set of each end point; performing priority pairing in combination with the spatial distance and the direction smoothness; and generating a transition line segment according to a pairing result to supplement the fracture part of the non-closed curve. According to the method, non-closed breakpoints generated by scanning or drawing errors in the geological map can be automatically restored, and a closed curve with topological integrity and a smooth boundary is generated.

The invention relates to a geological analysis technology, and discloses a multi-head attention model training method fused with geological rules, which is used for improving the scientificity and accuracy of geological body model prediction and improving the interpretability of a geological analysis process. According to the scheme, firstly, feature vectors of geological attribute data and geological coordinate data are spliced to obtain a fusion feature vector, then spatial locality and directivity are considered at the same time, geomorphic multi-head attention calculation is conducted on the fusion feature vector, and an attention feature sequence is obtained; then extracting multi-scale features from the input geological map, constructing a cross-fault weight mask matrix based on fault constraints, and obtaining comprehensive features through fusion; calculating a loss value by adopting a joint loss function containing prediction loss and fault non-penetrability constraint terms, and updating model parameters through a back propagation algorithm to complete model training; and finally, drawing an attention thermodynamic diagram for visual display, and superposing the attention thermodynamic diagram with the three-dimensional geologic model.

The invention discloses a method for constructing a large mineralization potential evaluation model based on LLM, and relates to the field of models.The method for constructing the large mineralization potential evaluation model based on LLM comprises the following steps that 1, a mineral geological map file is converted into a structured JSON format data set; step 2, constructing cue words, wherein the cue words at least comprise geological information extraction cue words, function task cue words and entity, relation and attribute extraction cue words; compared with the prior art, the method has the beneficial effects that the large mineralization potential evaluation model constructed by the method realizes text and map multi-mode mineral product geological map information identification and analysis, automatic mineralization geological feature analysis, mineralization feature information extraction and mineralization potential evaluation; the problems that in the prior art, man-made subjective influence exists, professional knowledge dependence is high, the geological information recognition accuracy rate in the recognition process is low, and information is lost during knowledge retrieval are solved.

The invention relates to the technical field of geological image processing, and particularly discloses a three-dimensional modeling method for a geological section, which is used for solving the problem of how to accurately identify and hide irrelevant geological areas and improve the modeling efficiency in geological three-dimensional modeling. Comprising the steps of data acquisition and preprocessing, geologic model constraint, implicit modeling and machine learning optimization, three-dimensional modeling and graphic rendering optimization, model correction and optimization and dynamic interaction and analysis, and four analysis formulas based on hidden requirements are provided; according to the method, by setting the hiding demand judgment formula for the coal seam, the rock stratum, the mined area and the inactive fault, the geological area irrelevant to target research is screened, the geological three-dimensional modeling process is optimized, the accuracy and efficiency of the model are improved, interference of irrelevant data is reduced, and the method has good application prospects. It is ensured that the mineral resource evaluation result better conforms to the actual geological condition, and the precision and credibility of mineral resource prediction are effectively improved.

The invention discloses an intelligent mineral resource reserve management method and system based on big data, and the method comprises the steps: obtaining a mineral resource data set, mining the potential correlation between different data modes in the mineral resource data set through a self-attention mechanism, and visualizing key features through an attention weight; based on the cross-modal mineral feature data, introducing a GNN graph neural network to be combined with an InSAR technology, constructing a dynamic three-dimensional geological map, and modeling an ore body, a fault and a rock stratum into graph nodes and edges to represent a spatial topological relation; and generating a reserve report and a mining plan according to the ore body space structure map, encrypting the reserve report and the mining plan by using a Hash algorithm, and uploading the encrypted reserve report and mining plan to a block chain network for storage. The actual situation of mineral resources can be reflected more accurately and comprehensively, and the resource mining efficiency is improved.

The invention discloses a rock core identification method and system based on regional geological data, and the method comprises the steps: S1, collecting a rock core sample image, and constructing a rock core image data set; s2, dividing the core image data set into a training set and a test set, and training and testing the constructed classification model based on artificial intelligence; and S3, extracting formation lithology information in the regional geological map, and adjusting and optimizing the trained classification model by using the extracted formation lithology information. When the rock core recognition model is constructed, the regional geological data is introduced to optimize the model, the initial weight assignment of the model is optimized by introducing the regional geological data, the training efficiency, the accuracy and the generalization ability of the model are improved, and the model can be applied to different geological conditions and different regions.

The invention relates to the technical field of geological disaster early warning, and discloses a geological deformation early warning method and system based on AI vision, and the method comprises the steps: obtaining surface micro-deformation features through an AI vision system, and constructing a geological map structure network; fusing a nonlinear geomechanical equation in a graph neural network to realize physical constraint driven feature extraction; creating a multi-dimensional graph structure model representing different depth layers, and realizing cross-layer information transmission through a physical information guide type graph attention mechanism; deploying a graph neural network of a Bayesian probability framework to carry out geological stress field distribution modeling, and quantizing prediction uncertainty; and finally, generating intelligent early warning decision information with risk grade division. According to the method, the AI vision and the physical constraint graph neural network are combined, accurate inference of the underground stress field is realized, the uncertainty can be quantitatively predicted, and the reliability and timeliness of geological disaster early warning are improved.

The invention belongs to the technical field of geological prospecting evaluation, and discloses an ore body positioning method and a rapid sampling device for niobium-tantalum ore target region prediction and evaluation. According to the method, based on existing geological data, various geologic bodies in a 1: 250,000-1: 10,000 geological map are analyzed, distribution, form, invasion contact relation, formation times and other characteristics of alkaline rock masses are found out, and a favorable metallogenic area is delineated; a linear structure, an annular structure and alteration information are recognized through satellites and aerial images, and target area positioning is assisted; the method comprises the following steps: exploring and detecting ore-bearing rock mass and fracture structures through a magnetic method, delineating potential positions of ore bodies, and inferring geological attributes of abnormal sources in combination with geological data; measuring the total amount of gamma at different positions in the niobium-tantalum prediction target area by using radioactive detection equipment, and preliminarily judging the position of the niobium-tantalum ore according to a gamma abnormal result; and carrying out systematic sampling and assay analysis on the target area by using the rapid sampling device. By means of the method and device, the metallogenic geological condition and the prospecting prospect can be evaluated, and the exploration network degree is optimized.

A software toolbox installing and using method comprises the following steps of S1, installing a main program, S2, opening a main program interface to select a module needing to be used, S3, achieving intelligent layer management and control through a layer management module, S4, achieving intelligent processing of geological elements through a geological map management module, and S5, achieving intelligent processing of the geological elements through a geological map management module. The method comprises the following steps: S1, carrying out intelligent construction of a two-dimensional profile map through a profile map generation module, S6, carrying out batch processing of reserve elements through a reserve map, S7, carrying out lithological layering, sampling position and geological structure discrete data of a drill hole through a drill hole dynamic block, and S8, providing a high-frequency tool rapid channel through a common tool box module. Compared with the prior art, the tool box has the advantages that the tool box is seamlessly integrated with the CAD environment, so that a user can quickly access and utilize efficient working tools on the basis of keeping the existing CAD skills; meanwhile, the section map generation module and the drilling dynamic block display module further simplify the visualization process of two-dimensional and three-dimensional data.

The invention discloses a rock identification method and equipment based on geological semantic guidance, and a medium, belongs to the technical field of geological rock detection and identification, and is used for solving the problems that blindness and redundancy exist in a feature fusion process, and the efficiency is high due to lack of effective modeling of geoscience features in existing rock target detection. And the identification requirements of complex geological images are difficult to adapt. The method comprises the following steps: performing semantic analysis on effective data in multi-source geological original data under related rock characteristics to generate a geological semantic map; performing feature fusion processing under related feature mapping between the geological semantic map and the feature pyramid to obtain a target fusion feature under the current rock image; performing collaborative recognition based on visual features on rock physical attributes in the current multi-source geological data, and outputting target detection features under the current rock image; outputting a geological rock classification result of the current rock image; and carrying out visualization processing on the geological rock classification result under the integrated platform.

The invention relates to the technical field of geophysical exploration, in particular to an optimal selection method for an oil-gas structure favorable area of a low-exploration-degree area of a mountain-front thrust zone. The method comprises the following steps: selecting an oil and gas prospective area, carrying out surface geological survey based on a regional geological map, compiling a surface fracture structure outline map and determining a main structure trend; performing wavelet decomposition on the gravity data to determine a substrate fracture adaptation result; deploying a magnetotelluric sounding profile in a direction perpendicular to the structure to obtain an electrical model, and compiling a substrate fracture diagram; deploying a sounding profile and a wide-line two-dimensional seismic profile fixed low-resistance strip in a parallel structure trend; a base depth map is calibrated, and a hidden strike-slip fracture layout is tracked; performing mesh generation to calculate indexes, performing standardized assignment, and performing mean value gridding; and preferably selecting an oil-gas structure favorable area by combining a drilling gas content fixed boundary. By adopting the ground measurement and multi-geophysical technology, the method is low in cost, simple to operate, high in efficiency and adaptive to a low exploration area, the optimization accuracy is improved, and powerful technical support is provided for optimization of an oil-gas exploration target area.

The invention discloses a multi-source three-dimensional geological data fusion and management method and related equipment, and relates to the technical field of geological information, and the method comprises the steps: obtaining standardized geological data; fusing the standardized geological data based on a Bayesian fusion framework; constructing a multi-scale three-dimensional geologic model by adopting a potential field interpolation method based on probability constraints provided by posterior probability distribution; performing uncertainty quantification on the multi-scale three-dimensional geologic model by adopting a Monte Carlo simulation method; and storing the multi-scale three-dimensional geologic model and the uncertainty quantification result to a data management platform conforming to an OGC standard, and providing visualization, query and access of a geologic map layer through a Web service interface. According to the method, the sharing efficiency, the reuse value and the application convenience of the geological result data are greatly improved, and the intelligent level and the decision support capability of geological data analysis are enhanced on the whole.

The invention provides a geological map three-dimensional visualization method and system based on a database technology, and belongs to the field of geological map visualization. The method focuses on geological map resources, deeply fuses spatial information and attribute information of the geological map, designs a geological map three-dimensional coordinate system based on geological age strata, designs a geological map three-dimensional space data and attribute data storage structure, and provides a relatively effective solution for multi-modal geological resource association. The geological map resources and associated data thereof are systematically stored and managed through a database, the geological map resources are standardized and efficiently integrated, and the innovative purpose of effectively supporting true three-dimensional visualization expression and analysis of geological elements is achieved by relying on a three-dimensional visualization platform. The geological map three-dimensional visualization method based on the database technology comprises the following steps: step (1), data and association thereof; step (2), data processing; step (3), database design; step (4), application; multi-modal geological data is stored based on a database, and a geological map three-dimensional visualization platform is built in combination with a three-dimensional visualization engine.

The present invention relates to the field of slope monitoring technology, and more specifically to a radar monitoring and early warning method and system based on the instability trend of mine slopes. The method comprises the following steps: using a pre-trained Fast R-CNN model to identify slope areas at risk of instability on a geological image as instability risk areas; using a slope radar to scan the instability risk areas in real time to obtain a radar image sequence of the instability risk areas; obtaining a geological image sequence of the instability risk areas that is co-sequential with the radar image sequence, and determining the instability trend of the instability risk areas based on the radar image sequence and the geological image sequence, and then assigning an early warning level based on the instability trend. The present invention uses the Fast R-CNN model to achieve early locking of instability factors, and based on the LSTM network, quickly and in real time analyzes the sliding trajectory trends of falling rocks and loose soil based on the actual trajectory of falling rocks and loose soil obtained by solving multimodal data, thereby achieving integrated display of risk points in the mining area, focused monitoring, and graded early warning.

The present invention discloses a mine disaster tracing method based on a knowledge graph, applied in coal mine safety. First, a mine disaster-related knowledge graph is built, involving three main steps: constructing a conceptual model, extracting entities from relational databases and geological maps, and establishing entity relationships based on location and process logic. Next, characteristic indexes and transmission rules for entity objects are set, categorizing entities into four types: discrete reporting, continuous monitoring, geological structure, and geological continuity. When an early warning occurs, disaster tracing is performed using the entity transmission rules and a graph traversal algorithm, identifying direct and root causes. This invention offers systematic and timely disaster tracing by utilizing a specialized knowledge graph and graph algorithms.

The invention relates to the technical field of three-dimensional geologic modeling, in particular to a fine three-dimensional modeling method for a thin-vein ore body. According to the method, an ore body contour line sequence and an ore body outer boundary line are generated through vectorization of geological maps such as a given exploration line profile map, line segment nodes of the ore body outer boundary line are encrypted through natural cubic spline function interpolation, and then corresponding biharmonic spline functions are fitted respectively; and adaptive dynamic interpolation is carried out on scanning lines in the sequence based on typical features of the thin-vein ore body. The curved surface meeting the specific smoothness condition is generated, it is ensured that the morphological change of the model is more reasonable, meanwhile, the interpenetration phenomenon is avoided, detailed features of areas such as faults and boundaries are effectively recognized, the accuracy of the model and the authenticity of geological expression are improved, and the method is suitable for popularization and application. Therefore, a more reliable thin-vein ore body refined three-dimensional modeling method is provided for geological exploration and resource evaluation.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.

The invention discloses a neural network-based water drive oil field dominant channel grade identification method, which comprises the following steps of: acquiring multi-source data of an oil field injection and production well, including multi-dimensional information such as liquid production capacity, oil production capacity, water content, working fluid level, perforation information, production and absorption profile, tracer response and the like; longitudinal multiple parameters, time evolution characteristics and spatial distribution relations are fused to construct a multi-modal data set; a multi-input neural network structure is designed, a multi-layer perceptron is adopted to extract time sequence features of structured data, a graph convolutional network is adopted to extract geological map features, and cross-modal fusion is realized in combination with a feature splicing module; a causal inference mechanism is introduced, the causal effect between injection and production wells is estimated through attention weight and anti-factual inference, and the dominant channel level is quantified. The method can effectively improve the recognition capability of dominant channels under a complex injection-production relation and a heterogeneous geologic structure, has high practical value and popularization potential, and is suitable for profile control optimization and injection-production structure adjustment tasks of various water drive oilfields.

A method for evaluating geological hydrogen source rock in a region is provided. The method includes: collecting magnetic susceptibility data from a plurality of subsurface locations in the region; receiving geophysical survey data of the region, wherein the geophysical survey data comprises geophysical data collected from above ground; tying the magnetic susceptibility data of the plurality of subsurface locations to the geophysical survey data; generating a geologic map of igneous rock, metamorphic rock, or mafic mineral- or iron-rich sedimentary rock within the region based on the magnetic susceptibility data using a model; and determining, based on the geologic map, a target zone of the geological hydrogen source rock with a potential for hydrogen production or with the potential for hydrogen accumulation.

The invention discloses a stratigraphic range constrained sequence modeling method, which relates to the technical field of three-dimensional geologic modeling, and comprises the following steps: preparing data including drilling data, regional geologic map data and a regional DEM, standardizing the drilling data including a standard stratigraphic table, a drilling basic information table and a drilling layered information table, the format of the form is unified as. Xlsx and a standard stratum table, the standard stratum table records the lithology of the whole stratum, and arrangement is carried out from top to bottom according to the new-to-old relationship; according to the sequence modeling method based on the stratum range constraint, the modeling boundary of each stratum is subjected to forced constraint, sequence modeling is carried out by using the stratum range constraint, the distribution condition of each stratum in a modeling area can be subjected to forced constraint, and meanwhile, modeling is carried out by combining drilling data and area elevation data, so that the model is finer, and the modeling efficiency is improved. And the error between a section cut through three-dimensional analysis of a sectioning model and the like and an original section is small.

The invention relates to the technical field of remote sensing prospecting, and discloses a uranium-copper multi-metal remote sensing prospecting method which comprises the following steps: S1, data acquisition and preprocessing; s2, alteration information is extracted; s3, multi-source information comprehensive analysis and prospecting prediction; s4, alteration abnormity verification is carried out; and S5, delineating the prospecting target area. According to the method, vegetation spectrum interference is stripped by carrying out principal component analysis on visible light-short wave infrared of a remote sensing image, weak iron staining and hydroxyl alteration signals are extracted, a geological and remote sensing coupling technology is carried out, alteration anomaly is superposed with a geological map, geochemical data and a construction interpretation result, a comprehensive remote sensing anomaly map is obtained, and the comprehensive remote sensing anomaly map is obtained. According to the method, the multi-parameter grading model GGS is established, and the target region is graded based on the multi-parameter grading model GGS, so that the probability of missed judgment of the target region is reduced.

The invention discloses a ground fracture detection method based on remote sensing image visual analysis, which comprises the following steps of: sending a remote sensing image to a trained ground fracture dynamic monitoring model, outputting a fracture path by the ground fracture dynamic monitoring model, outputting a vector element graph layer containing a detected fracture position, and identifying a section of path identified as a fracture corresponding to each element, displaying through a GIS platform; the construction and training process of the ground fracture dynamic monitoring model comprises the following steps: S1, constructing the ground fracture dynamic monitoring model, and manually marking the accuracy of a detection result of a DOM image sample; s2, improving the recognition precision of the fine-line object target in the model structure; s3, optimizing the extraction efficiency of the interference features; s4, obtaining landform parameters of a to-be-monitored area, and introducing a high-speed interpretation image sample; and S5, obtaining a trained ground fracture dynamic monitoring model. The invention belongs to the technical field of geological image processing, and significantly improves the efficiency and accuracy of ground fracture recognition.

The invention is suitable for the technical field of multi-modal data fusion, and provides a multi-modal data fusion deep learning geological map intelligent prediction method, which comprises the following steps: acquiring multi-modal geological data, constructing a heterogeneous multi-modal feature extraction network, and respectively extracting high-dimensional feature representation of each modal data by adopting a special encoder; designing a multi-modal dynamic fusion module based on a multi-head attention mechanism; establishing a deep learning architecture of a geological knowledge guiding mechanism, and embedding geological rules and priori knowledge into a model training process through a constraint loss function; implementing a multi-task collaborative learning framework, and synchronously completing lithology classification, structure identification and mineralization potential prediction tasks; outputting reliability evaluation of a prediction result by adopting an uncertainty quantification system of a Bayesian deep learning framework; deploying an intelligent active learning system; according to the multi-modal dynamic fusion method based on the multi-head attention mechanism, the problem of depth feature interaction and complementation among heterogeneous geological data is effectively solved.

The invention discloses a coastal zone ecological safety early warning method and system based on big data, and relates to the technical field of big data analysis, and the method comprises the steps: determining a coastal type through remote sensing and geological map spots, carrying out the ecological multi-source data collection according to the coastal type, and carrying out the data preprocessing; based on preprocessed data, a sparse attention mechanism is combined with an LSTM network, key ecological factors are dynamically recognized, and ecological index trends are predicted, the time causal relationship between ecological variables is recognized through a Granger causal test method, the root cause traceability of early warning is improved, information granulation modeling is combined, and the early warning efficiency is improved. According to the method, the processing capacity of the system on nonlinear and fuzzy information is enhanced, the stability and adaptability of an early warning model are improved, through conditional transfer entropy analysis, intermediary interference is effectively eliminated, the accuracy of causal identification is ensured, and finally accurate identification, interpretable early warning and scientific intervention on coastal zone ecological safety problems are achieved.

The invention discloses a method for assisting resistivity inversion based on a geological map spatial database, which comprises the following steps of: S1, selecting a geological map with a specified proportional scale, constructing a geological map database, and extracting specified parameters of resistivity inversion; s2, constructing a gridding geological surface body, and obtaining a lithology distribution condition and a physical property parameter in each grid unit; s3, constructing an underground physical property distribution model by using the obtained parameter combination; s4, on the basis of a preset inversion optimization algorithm, geological constraints are introduced, and spatial variation regional regularization parameters are obtained through calculation according to the underground physical property distribution model and used for executing resistivity inversion calculation; s5, based on the resistivity obtained through inversion calculation, information data superposition and comparison are carried out in GIS software; and S6, defining a consistency scoring function, and quantifying an inversion result, a geological boundary line and a tectonic zone matching degree. According to the method, the existing geological information is utilized, the accuracy and efficiency of inversion modeling can be improved, and the geological consistency of inversion results is enhanced.

The application relates to the technical field of surveying and mapping, and discloses a mine geological surveying and mapping system and method based on artificial intelligence, which comprises the following steps: substituting a sub-mine geological map of each to-be-surveyed mine sub-region into a twin neural network model to determine the geological type of each to-be-surveyed mine sub-region, comparing the geological type with a historical geological type, judging whether the geological type is changed, establishing a geological mark for the corresponding to-be-surveyed mine sub-region according to the geological condition of the target geological type, determining a radar scanning angle based on the sequence of the geological mark, determining whether to adjust the radar scanning angle based on a historical scanning set, when radar scanning is carried out on a changed geological mark or a multi-source geological mark, determining a compensation scheme of a radar reflection signal according to a signal-to-noise ratio, and completing the surveying and mapping of each to-be-surveyed mine sub-region based on the target radar scanning angle or the compensation scheme of the radar reflection signal, so that the stability and reliability of mine geological surveying and mapping are ensured.

The invention relates to the field of image recognition, in particular to a geological structure recognition method based on deep learning. The method comprises the following steps: firstly, carrying out image preprocessing on an acquired original geological image, and carrying out feature extraction based on the preprocessed geological image to generate a feature map; calculating gradient vectors and gradient norms of the pixel points based on the feature map; calculating a local connectivity factor based on the gradient norm of the pixel point; then, on the basis of the gradient norm and the local connectivity factor of the pixel points, a dynamic boundary feature reconstruction algorithm is used for processing to obtain a reconstructed feature map; and finally, based on the reconstructed feature map, classifying the geological structure through a geological structure probability classification algorithm, and generating a classification probability vector. The technical problems that the precision of automatic geological structure recognition is insufficient, boundary feature extraction is limited, feature enhancement and noise suppression are unbalanced, and the generalization ability of geological structure classification is insufficient are solved.

The invention provides a geological map understanding method combining knowledge guidance and few-sample learning, and relates to the field of geological maps, comprising the following steps: constructing a data set composed of geological maps containing legend; constructing an expert knowledge base composed of geological experts and a geological legend sample set as a domain knowledge guide large model; adding a local visual enhancement prompt to the legend of each geological map in the data set; constructing prompts for a geological map understanding task, wherein the prompts comprise few-sample learning example guidance, result output specifications and the like; and constructing a feedback mechanism, feeding back the map with low accuracy of the output result of the large model, and returning to the prompt design step to redesign prompt. Through a method from local understanding to global understanding, domain knowledge is provided for a large model as assistance, understanding of legend in the map is enhanced, the problems of insufficient universality of the large model and dependence on large-scale annotation data are effectively relieved, and the efficiency and accuracy of geological map understanding are improved.

The invention discloses a geological map scene implementation method and system based on measure potential tapping, relates to the technical field of oil and gas field development, and is used for solving the problems that conflicts among a caliber layer, a method layer and a data layer are accurately positioned based on the same reference system, and a minimum recalculation set is difficult to stably solve according to the conflicts. Establishing a strong primary key index of a well ID, a layer ID, a grid unit and a time step, and registering and generating a caliber; a bidirectional Hausdorff distance is minimized through a multi-resolution pyramid and elastic registration, isolines are extracted and vectorized into a boundary set, scale factors and registration evidences are recorded, and co-located grids are generated; calculating four components including space overlapping, boundary consistency, trend correlation and abnormal co-location of CI in co-location grids, and performing structured output; and forming a conflict graph, locally replaying and generating a factor importance sequence by using a single factor, and solving a minimum recalculation set.

Detection technology to monitor a flare stack during drilling operations and determine information based on the imaging. Various actions may be taken based on the determined information. For example, subterranean geology may be mapped. This may include mapping formation properties, fractures, faults or reservoir zones. Additionally, drilling operational parameters may be adjusted. This may include adjusting rate of direction, penetration, weight on bit, rotary speed, mud flow rate, mud weight, actuating a valve, or changing the pump speed.