102 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 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 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 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 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 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 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.

The invention relates to the technical field of soil detection, in particular to a geological high background area-based regional soil remediation amount determination method, which comprises the following steps: arranging and collecting a soil sample and a soil profile sample of a to-be-detected area, and obtaining soil heavy metal content data and physicochemical indexes thereof; acquiring a principal component analysis and geological map of the sample, identifying main source types of heavy metals in the soil, and drawing a spatial distribution map; establishing a vertical distribution model of the soil heavy metal content changing along with the depth, fitting the vertical distribution model by adopting a logarithmic function, obtaining a reserve calculation formula based on the vertical distribution model, and calculating the reserve of the soil heavy metal in the vertical direction; and dividing statistical units, and respectively calculating the potential to-be-repaired amount of the soil heavy metals in each statistical unit. According to the method, the vertical distribution model is constructed, and the regional soil remediation amount is determined by considering the geological background and the vertical distribution non-uniformity of the heavy metal at the same time according to the characteristic of the vertical distribution height non-uniformity of the heavy metal in the geological high background region.

The invention is suitable for the technical field of geological mapping, and provides a regional geological survey intelligent mapping method based on multi-source data fusion, which comprises the following steps: acquiring multi-source geological data of a to-be-mapped region, including remote sensing images, geophysical data, geochemical data, field measured data and historical geological maps; performing space-time registration, feature normalization and cross-modal alignment on the multi-source data through a multi-modal data intermediate layer fusion architecture; a two-stage collaborative interpretation mechanism is adopted; integrating results of the two stages to generate an intelligent geological sketch, and superposing an uncertainty quantization layer; fusing earth surface and deep part data based on a three-dimensional implicit neural radiation field algorithm, and constructing a dynamic three-dimensional geologic model; verifying and feeding back the correction model in real time through a field AR terminal, and outputting a final geological interpretation map; and the efficiency, the precision, the depth and the intelligent level of geological mapping are integrally and remarkably improved.

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 multi-objective collaborative decision optimization method for configuration of a cutter head and a scraper of a slurry shield tunneling machine. The method comprises the following steps: firstly, constructing a dynamic geological map through multi-source sensor data fusion, and predicting a scraper wear trend by using a depth time sequence convolutional network; then, a meta-evolution hierarchical optimization framework is adopted, self-adaptive population initialization is carried out on a macroscopic layer according to geological partitions, and co-evolution is carried out on a microcosmic layer based on a scraper mechanics interaction relation; and finally, algorithm parameters are optimized in real time through a dynamic multi-target weight adjustment mechanism and a reinforcement learning strategy adaptive module. According to the method, the limitation of traditional empirical layout is broken through, the problems of multi-target, strong constraint and nonlinear optimization are solved, the optimal scraper layout scheme considering the tunneling efficiency, the economic cost and the service life can be automatically generated on the premise that the stress balance of the cutter head is guaranteed, and the intelligent level and comprehensive benefits of shield construction are improved.

The application discloses a front land basin surface outcrop three-dimensional geological modeling method, system and device, which comprises the following steps: superimposing research area surface elevation data and a geological map, stereoscopically displaying the geological map and matching the actual surface outcrop stratum; establishing a research area surface outcrop stratum information comprehensive table by combining the geological map through field outcrop geological investigation; superimposing a two-dimensional seismic profile and a three-dimensional geological map, cutting to obtain a modeling range, performing surface layer interpretation according to the research area surface outcrop stratum information comprehensive table, and establishing a shallow geological structure model; calibrating a layer according to research area drilling data, establishing an underground deep geological structure model; and performing three-dimensional space structure modeling according to a geological map and field investigation understanding through computer three-dimensional reconstruction technology in a region without seismic data, and establishing a surface outcrop three-dimensional structure geological model. The application has low operation cost, controllable modeling time, high rationality and reliability of the model, and saves exploration cost.

The invention relates to the technical field of computer graphic processing and geology drawing making technologies, and discloses a lithologic symbol splicing method. The method comprises the steps of obtaining lithology naming data and industry template rules, and generating a splicing priority sequence through standardization processing and analysis binding; carrying out basic symbol selection and parameter instantiation based on the sequence to form an assembly candidate set; generating a pose solution set and a conflict list through adsorption matching, topological constraint solution and conflict detection; performing rule checking and conflict resolution to obtain a compliant splicing format and a quality mark; performing dependency analysis and increment regeneration on the compliant splicing format to obtain a regeneration result packet; and finally generating an export result package through pattern matching, parameter synthesis and cross-platform adaptation construction. According to the method, automation, standardization and cross-platform compatibility of lithologic symbol generation are realized, and the efficiency and consistency of geological map manufacturing are remarkably improved.

This invention belongs to the field of risk prediction technology for mountainous cities, and discloses a CNN-based method for quantifying and predicting landslide susceptibility in mountainous cities by integrating multi-source data. The specific steps are as follows: Step 1: Multi-source data collection and processing, collecting remote sensing image data, geological data, topographic data, and building data. This invention achieves deep fusion of multi-source data by integrating remote sensing images, geological maps, topographic data, building distribution, and other multi-dimensional data; it automatically extracts deep features from the data through the convolutional and pooling layers of CNN, eliminating the need for manual feature design and improving feature extraction capabilities; through the construction of a CNN model that integrates heterogeneous data fusion and multi-source data fusion, it can directly capture spatial neighborhood relationships, improving spatial correlation; compared with traditional techniques, this method significantly improves the accuracy and robustness of landslide susceptibility prediction in complex mountainous cities through multi-source data complementarity and deep feature extraction.

The present application belongs to the technical field of geological prospecting and evaluation, and discloses a kind of ore body positioning method and rapid sampling device for niobium-tantalum ore target area prediction and evaluation.The present application is based on existing geological data, through the analysis of various geological bodies in 1:250,000-1:10,000 geological map and the identification of the distribution, shape, intrusive contact relationship and formation age of alkaline rock mass, the favorable metallogenic area is delineated;Through satellite and aerial image identification of linear structure, ring structure and alteration information, target area positioning is assisted;Through magnetic exploration, ore-bearing rock mass and fault structure are detected, and potential position of ore body is delineated, combined with geological data, geological properties of anomaly source are inferred;Using radioactive detection equipment, the total amount of gamma at different positions in the niobium-tantalum prediction target area is measured, and the position of niobium-tantalum ore is preliminarily judged according to the gamma anomaly result;Using rapid sampling device, target area is systematically sampled and analyzed.Through the method and device, metallogenic geological conditions and prospecting potential can be evaluated, and exploration grid is optimized.

The invention relates to a landslide identification method and device. The method comprises the following steps: acquiring a remote sensing image and basic geographic data of an area after a landslide disaster occurs, and fusing the remote sensing image and the basic geographic data through spatial registration to obtain a fusion data set with unified spatial reference; the basic geographic data at least comprises a topographic map and a geological map; extracting a landslide influence factor from the fused data set, and constructing a landslide sample data set based on the landslide influence factor and the spectral band of the remote sensing image; constructing a network model fused with a feature extraction module, a gridding attention mechanism module, a feature fusion module and a prediction module; based on the landslide sample data set, training and optimizing the network model to obtain a trained network model; and inputting the test image into the trained network model, and outputting the contour of the area in which the landslide is estimated to occur in the test image. By adopting the method, high-precision identification and boundary segmentation of the landslide mass in a complex terrain environment can be realized.

The present application relates to the field of image recognition, and more particularly to a geological structure recognition method based on deep learning. First, the original geological image collected is preprocessed, and features are extracted based on the preprocessed geological image to generate a feature map; based on the feature map, the gradient vector and gradient norm of the pixel points are calculated; based on the gradient norm of the pixel points, the local connectivity factor is calculated; then, based on the gradient norm of the pixel points and the local connectivity factor, the reconstructed feature map is obtained by processing using a dynamic boundary feature reconstruction algorithm; finally, based on the reconstructed feature map, the geological structure is classified by a geological structure probability classification algorithm to generate a classification probability vector. The technical problems of insufficient precision of automatic geological structure recognition, limitations of boundary feature extraction, imbalance between feature enhancement and noise suppression, and insufficient generalization ability of geological structure classification are solved.

This invention provides a method and apparatus for analyzing oil and gas geological maps, belonging to the field of oil and gas exploration and development technology. The method includes: acquiring oil and gas geological map data; extracting features and their spatial location information from the oil and gas geological map data, wherein the features are in vector form and include text features and visual features; calculating the association weights between the features and their spatial location information, wherein the association weights between the text features and the visual features are calculated based on a cross-modal attention mechanism; constructing an association graph between the features based on the association weights; processing the association graph based on a graph neural network to obtain an updated association graph that incorporates global dependencies; and outputting the updated association graph to obtain a first analysis result of the oil and gas geological map. This method improves the comprehensive processing capability of graphic and textual information, thereby better meeting the accuracy requirements in practical applications.

The invention relates to the technical field of metallogenic prediction, in particular to a metallogenic prospective area prediction system and method based on embedded knowledge reasoning, and the system comprises a collection module which is used for collecting exploration data and geological map data; the data preparation module is used for cutting the exploration data into fishing net units; the knowledge graph construction and embedding module is used for constructing a knowledge graph database by utilizing the geological map data and embedding entities and relationships into a low-dimensional space; the knowledge-data fusion module is used for generating a knowledge-data fusion matrix of the corresponding fishing net unit for each fishing net unit; the model training and optimizing module is used for inputting the knowledge-data fusion matrix of the labeled fishing net unit into a deep learning network to construct a metallogenic prediction model; and the metallogenic prediction and result interpretation module is used for calculating the metallogenic probabilities of all the fishing net units in the research area by using the metallogenic prediction model. According to the method, knowledge graph embedding and exploration data are subjected to structured fusion in an input layer, so that the model not only learns a data mode, but also can understand a geological background.

The invention discloses a geological map point element recognition optimization method based on YOLOv7-SE, which effectively enhances the feature extraction and expression ability of a model to geological map point elements by introducing an SE attention mechanism into a YOLOv7 network structure, thereby improving the recognition precision of the point elements. The legend of the new-developed tectonic lithofacies paleo-geographic map in the Tibet Plateau and the adjacent region is used as an experimental object, a point element recognition data set is constructed, and the recognition performance of the YOLOv7 model and the recognition performance of the YOLOv7-SE model are compared and analyzed. Experimental results show that the recognition precision of the YOLOv7-SE model on a verification set is improved to 93.1% from 90.8%, the high-confidence point element recognition rate is remarkably improved to 86.1% from 61.2% and is remarkably superior to that of a traditional YOLOv7 model, and the effectiveness and the application prospect of the method in complex geological map point element recognition are verified.

The invention provides a construction method, device and equipment for a perforation level geological map of a horizontal well. Relates to the technical field of oil-gas well development. The method comprises the following steps: acquiring an original data table of fracturing perforation of any horizontal well; performing standardization processing on the original data table to obtain a standard data table; for any perforation level of the fracturing perforation, the center of the perforation level is determined as the circle center according to the standard data table; constructing a concentric circle distribution diagram by taking different distances as radiuses according to the circle center; taking a preset direction as a polar axis, and constructing a cross line distribution diagram according to a preset interval and a progressive increase direction; constructing a polar coordinate vector circle model according to the concentric circle distribution diagram and the cross line distribution diagram; and assigning the sensitive geological parameters to the polar coordinate vector circle model to obtain a polar coordinate vector circle plane graph of a perforation level. The specific position and direction of the geological parameters around the perforation level of the horizontal well can be accurately described, so that the rationality of the fracturing design of the horizontal well is improved, and the fracturing effect is ensured.

The invention discloses a heavy metal pollution accumulation prediction method and system fusing geological prior and a space-time memory mechanism, and belongs to the technical field of heavy metal pollution prediction. The method comprises the following steps: firstly, acquiring historical heavy metal concentration time sequence data, geological map vector data and environment covariable time sequence data of a target area; the method comprises the following steps: acquiring multi-source input data, converting geological map vector data into a geological prior probability grid field, and constructing a spatial weight matrix based on a GWR model; and constructing an improved LSTM containing an accumulative memory unit and a space-time dual-channel attention module, inputting time sequence data, static spatial characteristics and spatial prior guidance information to carry out space-time joint modeling, and outputting a future heavy metal accumulative concentration distribution diagram and an uncertainty interval. According to the invention, the prediction precision and the decision support capability are improved.

This invention discloses a method for accurately annotating multi-dimensional information on a 3D topographic and geological map, comprising the following steps: S1, obtaining a quadrilateral input by the user to determine the work area; S2, pasting a blank image texture onto the work area, naming the texture TEX1; S3, obtaining the color and stroke pixel width used for annotation input by the user; when the user holds down the mouse button and drags, drawing is performed on the texture TEX1 using the specified color and pixel width, then the drawn image texture is overlaid on a 3D digital globe, and finally displayed on the screen. The content drawn on the geological map by this invention accurately matches the map topography, is unaffected by map movement or scaling, and has excellent performance; even drawing massive amounts of content will not cause a performance degradation.