57results about How to "Avoid underfitting" patented technology

The invention discloses an automatic driving system extreme environment decision-making method based on a neural evolutionary game. The method comprises the following steps: S1, collecting and constructing a real-time environment data set around a vehicle through an automatic driving system; s2, generating an environment feature vector; s3, based on the environment feature vector, analyzing the environment feature data through an extreme environment identification module; s4, activating a corresponding multi-target dynamic reward mechanism based on the extreme environment label, and setting a dynamic reward weight for the neural evolution process; s5, gradually evolving to generate an optimal neural network structure and parameter configuration suitable for different extreme environments; s6, generating a multi-target game strategy in combination with the environment feature vector; s7, generating an optimal driving path, vehicle speed adjustment, obstacle avoidance mode and direction control decision; and S8, inputting real-time feedback data into the neural evolution model, and further adjusting the structure and parameter configuration of the neural network. The automatic driving system can make a safer, more accurate and more efficient driving decision in an extreme environment.

The invention relates to the technical field of machine learning and software development, in particular to a software development system based on a machine learning algorithm, which comprises an acquisition processing unit, a model training unit, an interaction prediction unit and an optimization feedback unit, and is characterized in that the acquisition processing unit constructs a database, extracts key elements such as variable names and converts the key elements into vectors; the model training unit adopts recurrent neural network hierarchical training and gradually introduces intermediate and complex structure codes and learns logic relation and semantic relation from code snippets related to a basic grammar structure, the interaction prediction unit screens and extracts the code snippets according to code function module labels and historical use frequency, the code snippets are input into a model after vector conversion, and the model is used for prediction. A probability weighting algorithm is used for predicting subsequent fragments, an optimization feedback unit calculates the similarity between predicted fragments and actual fragments, an optimization mechanism is automatically triggered if a threshold value is not reached, parameters are adjusted by utilizing reinforcement learning, and prediction accuracy is used as a key part of a reward function so as to improve the system performance.

The invention discloses an unmanned aerial vehicle path optimization method, and belongs to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: S1, constructing an original data set based on the historical data of an unmanned aerial vehicle operation scene; s2, preprocessing the original data set based on a chaotic mapping strategy to obtain a training set and a verification set; s3, a sparrow search algorithm based on an improved individual updating mechanism is combined with the training set to construct and train a path optimization model; s4, inputting the verification set into the path optimization model for verification, and obtaining and outputting a verification result; s5, judging whether the path optimization model is qualified or not based on the verification result, if yes, outputting a qualified path optimization model, and if not, skipping to S1; s6, collecting the real-time data of the operation scene of the unmanned aerial vehicle as the input of the qualified path optimization model, and obtaining and outputting the optimal path of the unmanned aerial vehicle; according to the invention, the problem of poor accuracy in unmanned aerial vehicle path planning in the prior art is solved.

The invention belongs to the technical field of medical internet of things, and discloses a lung rehabilitation detection intelligent evaluation system, which comprises a database construction module used for constructing an evaluation reference database according to historical evaluation reference data, and the evaluation reference data comprises historical reference patient physiological parameter data and historical lung rehabilitation detection evaluation data; the data collection module is used for collecting physiological parameter data, chest image data and air quality data of the pre-evaluated patient; the data processing module is used for acquiring the physiological parameter data of the patient to be evaluated through the pre-evaluated physiological parameter data of the patient and the evaluation reference database; processing the acquired physiological parameter data, chest image data and air quality data of the to-be-evaluated patient to obtain a comprehensive feature data set of the to-be-evaluated patient; through systematic evaluation and dynamic adjustment, rehabilitation of the patient is more effectively promoted, an evaluation result provides a scientific basis for decision making of a doctor, and a treatment scheme can be more reasonably formulated.

The invention belongs to the technical field of emotion calculation, and particularly relates to a voice emotion recognition method based on diffusion enhancement and multi-granularity feature fusion, and the method comprises the steps: converting an audio into a Mel spectrogram; the Mel spectrogram is input into a diffusion model, forward diffusion noise injection and backward diffusion noise removal are carried out step by step, and clearer audio is recovered; extracting features of the enhanced audio, then extracting frame-level features, segment-level features and utterance-level features from the audio, then performing adaptive feature selection, and performing deep fusion through weight position dependence; and inputting the fused multi-granularity speech features into a dynamic graph convolution sentiment classification model to generate a sentiment state recognition result. According to the invention, services such as multi-classification speech emotion recognition and speech quality enhancement can be provided, so that a more humanized response is provided in interaction.

The invention relates to a river water quality inversion method and system, and specifically, the method comprises the steps: obtaining the hyperspectral data of a river region, carrying out the radiation correction of the hyperspectral data, constructing a plurality of indexes according to the wave band in the hyperspectrum, calculating the correlation between each index and a water quality parameter, and sorting the indexes according to the correlation. Determining an index set corresponding to the water quality parameters based on an index sorting result; clustering the water quality parameters based on the correlation of the water quality parameters and the correlation of the index set of the water quality parameters; in the training process, for each cluster, the importance of indexes is determined according to the correlation between the indexes in the index set of the cluster and water quality parameters in the cluster, the splitting index selection probability during splitting of each tree is determined based on the importance of the indexes, and splitting stop conditions are determined according to the contour coefficient of the cluster; and performing inversion on the new hyperspectral data by using the trained model to obtain water quality parameters.

The invention discloses an electric power daily load prediction method based on policy quantification and a long-short-term memory algorithm, which belongs to the technical field of electric power system load prediction, and comprises the following steps: converting policies into quantitative indexes according to qualitative description of the policies, generating numerical characteristics reflecting the influence of policy changes on electric power demands, and calculating the electric power daily load prediction results; performing data preprocessing through abnormal value cleaning, missing value filling and normalization operation to obtain input data of the model; based on a long short-term memory network, model training is carried out according to input data, and a prediction model is generated by initializing model parameters, setting training hyper-parameters and a self-adaptive training strategy and adjusting dynamic parameters and is used for predicting the daily power load. According to the method, the power load change can be quickly and accurately predicted, so that the uncertainty caused by policy adjustment is effectively dealt with, and the operation efficiency and safety of a power system are improved.

The invention discloses a bolt and pin defect detection method based on image features, and belongs to the technical field of image recognition, and the method comprises the steps: carrying out the preprocessing of a bolt and pin image photographed during the historical inspection of an unmanned plane through super-resolution reconstruction, and obtaining a training image set and a test image set; based on an SPD convolution module, improving YOLOV8 and combining with the training image set to construct and train a bolt pin detection model, and then inputting a test image set into the trained bolt pin detection model for testing to obtain a qualified bolt pin detection model; and finally, inputting a bolt and pin image shot during real-time inspection of the unmanned aerial vehicle into the qualified bolt and pin detection model for detection to obtain a detection result. The problem that in the prior art, the detection accuracy is poor in the bolt and pin image detection process is solved.

The invention relates to the technical field of vaccine dynamic evaluation and early warning, in particular to a method for constructing a vaccine dynamic evaluation and early warning model based on multi-source data. The method comprises the following steps: acquiring multi-source clinical medical data of a vaccinator through a medical management system, and carrying out time sequence sorting processing on the data to form a sorted data set; and then, in combination with vaccine adverse reaction prompt data, analyzing the delay influence of the vaccination abnormal reaction, evaluating the abnormal reaction delay difference of individuals, and identifying susceptible population. And based on a support vector machine algorithm, constructing a vaccine risk dynamic early warning model for identifying vaccination reaction risk factors, and feeding back a result to the terminal. Finally, vaccine risk dynamic early warning firmware is designed and embedded into a medical management system, and dynamic evaluation and early warning after vaccination are achieved. According to the method, the vaccine dynamic evaluation and early warning technology is optimized, so that the vaccine dynamic evaluation and early warning technology is more perfect.

The invention discloses an electric power market network attack identification method, system and device based on a gating circulation unit and a storage medium, and relates to the field of electric power systems and market operation thereof, and the method comprises the steps: obtaining data from an electric power spot market, and carrying out the preprocessing; important characteristic variables are screened out; dividing the data into a training set, a verification set and a test set according to a time sequence; constructing a plurality of GRU models based on different time periods, and fusing prediction results of the models; evaluating the precision and stability of the model through a test set, and deploying the fusion model to an actual system for real-time attack recognition; according to the invention, the complex rule of power market data can be captured more comprehensively, and the accuracy of network attack identification and the system stability are improved; the deviation of a single model is avoided, the risks of over-fitting and under-fitting are reduced, and the reliability of the model under different market conditions is ensured; the data quality is improved, and the adaptability and flexibility of the prediction system are enhanced.

The invention provides a stiffened wallboard life prediction method based on a data enhanced physical information neural network, which comprises the following steps: determining influence parameters of crack propagation fatigue life of a stiffened wallboard and a discourse domain of the influence parameters, obtaining sample points through sampling, calculating a stress intensity factor and fatigue life based on a finite element model, and constructing a training set; a double-layer physical information neural network is established, a first sub-model predicts a stress intensity factor by taking an influence parameter as input, a second sub-model combines the influence parameter and a stress intensity factor prediction value, a physical loss function based on a Paris crack propagation law is introduced, training is performed in combination with a data loss function, and fatigue life prediction is realized; and representing the influence parameters as fuzzy variables, obtaining a multi-level-cut set, and predicting the fuzzy response of the fatigue life by using the trained neural network. According to the method, a physical mechanism and data driving can be effectively fused, and efficient prediction of the crack propagation fatigue life of the stiffened wall plate under the uncertain condition is achieved.

The utility model relates to the technical field of paper box production gluing, and discloses a gluing machine for paper box production, which comprises a fixed seat and a conveying crawler belt, the outer surface of the fixed seat is fixedly provided with a connecting seat, and the outer surface of the connecting seat is rotatably connected with a fluted disc; the outer surface of the fluted disc is fixedly connected with a crank through the outer surface of the connecting base, the outer surface of the fluted disc is connected with a first rack plate and a second rack plate in a meshed mode, and a through opening is formed in the outer surface of the fixing base. A first rack plate and a second rack plate which are in meshed connection with a fluted disc are driven to move oppositely, so that a first conveying frame and a second conveying frame are close to each other, paper box raw materials placed on the outer surface of a fixed seat are clamped, and then two servo motors are matched to drive a conveying crawler belt to move; therefore, the effect that the paper box is prone to inclination during production and conveying, and poor fixing caused by inclination of the gluing position of the paper board is avoided.

The invention relates to the field of intelligent traffic, deep learning and artificial intelligence, in particular to a deep learning method for urban traffic node mode similarity cognition. Traffic speed data is organized into a graph data format consisting of a feature matrix and an adjacency matrix. The processed speed data are sent into a dynamic multi-graph constructor, an output dynamic adjacency matrix and a space adjacency matrix are sent into a multi-scale graph convolution module together, and the speed data are sent into the multi-scale graph convolution module after being subjected to expansion causal convolution processing in each space-time layer; and splicing from each space-time layer through jump connection to obtain a final result. And finally, analyzing the traffic mode similarity reflected by the dynamic multi-graph. According to the method, the space-time dependent features in the intelligent traffic scene can be extracted more efficiently, and the problem that the perceptual view in the graph convolutional network is limited and the problem that the long-term traffic mode is not considered sufficiently in traffic mode similarity analysis are solved.

The application provides a provincial meteorological and administrative collaborative wind speed prediction method based on dynamic federal learning, relates to the cross technical field of artificial intelligence and energy meteorology, S1, basic data of N meteorological monitoring stations in a province is acquired, is divided into M initial subgraphs according to the administrative boundary of a prefecture-level city and is allocated an edge server, each collaborative subgraph pair is determined according to the Pearson correlation coefficient of the wind speed sequence of the station, the time sequence data deviation of the synchronized subgraph is synchronized through a dynamic time warping algorithm after every 3 rounds of federal iteration, and the subgraph data after synchronization is output. Through an endogenous decision and optimization framework, three links of dynamic coordination of cross-domain collaboration, local learning and gradient transmission are coordinated, and finally a global optimal provincial meteorological and administrative collaborative wind speed prediction method based on dynamic federal learning is achieved among multiple targets such as prediction accuracy, convergence speed and communication cost.

The application relates to the computer technical field and discloses a modeling method of a statistical mixed model in a big data distributed scene. The method comprises the following steps: distributing and storing data shards; initializing model parameters; iteratively performing an expectation step and a maximization step; the expectation step is scheduled to a GPU node to perform parallel calculation on posterior probability; the maximization step is scheduled to a CPU node to aggregate statistics and update parameters, perform component merging / deletion, and perform convergence judgment; meanwhile, a memory reuse mechanism based on reference counting and scope analysis is adopted to reduce redundant data transmission. Through heterogeneous task scheduling and memory collaborative optimization, the training speed, resource utilization rate, and model self-adaptation capability are improved.

The application provides a ship type classification prediction method and system based on K-means and XG-Boost, acquires ship data and carries out pretreatment, then adopts a K-means clustering algorithm to respectively cluster each kind of data in the pretreated ship data to obtain multiple clusters, calculates error sum of squares of all data in each cluster, calculates the classification number of each kind of data according to the error sum of squares, selects a certain classification number by using an elbow method, marks the ship type of all clustered ships according to the classification number, then takes the ships with marked ship types as training set samples, adopts an XG-Boost classification algorithm to train the training set samples to obtain multiple classification prediction models, verifies the multiple classification prediction models to obtain an optimal classification prediction model, and predicts the ship types of all ships in the world according to the optimal classification prediction model and marks the ship types. The application can accurately classify all ships in the world, and can avoid overfitting and underfitting of the model while ensuring the accuracy.

The application relates to the technical field of microwave remote sensing, and proposes an adaptive wet troposphere path delay inversion method and system fusing wind speed overlapping partition modeling, multi-scale collaborative sample balancing and model soft fusion. The method comprises the following steps: acquiring multi-source data and preprocessing the multi-source data to obtain to-be-inverted samples; according to a preset threshold, the full-range wind speed of the to-be-inverted samples is divided into a plurality of wind speed subintervals with overlapping transition zones; the to-be-inverted samples are input into trained inversion submodels, the output results of the inversion submodels are weighted and summed according to final fusion weights, and finally, wet troposphere path delay inversion results are obtained; when abnormal wind speed or invalid submodel prediction output is detected, the output result of a pre-trained global unified model is taken as the final inversion result. While guaranteeing overall inversion precision and stability, the application can significantly suppress systematic deviation under sea conditions of extremely low and extremely high wind speed.

The invention provides a self-interference cancellation method based on a ResRNN network. The method comprises the following steps: determining input data based on a real part and an imaginary part of complex data; and processing the input data through the input layer to obtain a two-dimensional diagram of the input data. And performing feature extraction on the two-dimensional graph through the convolutional layer to obtain a feature extraction result, and processing the two-dimensional graph through the jump connection layer to obtain a first output result. And remodeling the feature extraction result through a data remodeling layer to obtain a remodeling result. And processing the remodeling result through a recursive network layer to obtain a recursive result. And processing the recursion result through an output layer to obtain a second output result. And outputting a self-interference cancellation prediction result based on the first output result and the second output result. According to the method provided by the invention, a relatively excellent self-interference cancellation effect is achieved while a relatively small network scale is ensured.

This invention discloses an intelligent detection method and system for bid format compliance of sealed bid documents, relating to the field of electronic bidding. The method includes: performing format detection based on the format rules of the bidding documents to obtain a set of violation records and a single format detection report; constructing a format error fingerprint vector, calculating cross-file error similarity, and generating a cross-file error similarity matrix; constructing an initial suspected association graph using each sealed bid document as a node and the similarity values ​​in the cross-file error similarity matrix as edge weights between nodes; dynamically adjusting the connectivity threshold, updating the node connection relationships in the weighted disjoint-set data structure, and defining the groups divided under the optimal connectivity threshold as abnormal similar file groups; calculating a bid-rigging suspicion score, generating a bid-rigging detection report, and outputting the bid compliance detection results. This solves the technical problem in existing technologies where isolated compliance judgments on individual bid documents fail to identify potential bid-rigging behavior.

The application discloses a multi-modal depth perception and grasping system based on transparent objects, and relates to the field of robot operation, comprising a multi-spectral perception module, a depth correction module, a grasping posture generation module and a control module. The application comprehensively acquires visual information and thermal radiation information of the transparent object by combining two perception methods of RGB-D image and thermal imaging (TIR) image, and analyzes systematic errors of the depth map to detect error sources of the RGB-D camera and the TIR camera. The system adopts an encoder-decoder model as a depth correction core, the model extracts complementary features of the RGB-D image and the TIR image through a modal exclusive encoder, and utilizes a feature fusion module to complete feature alignment and integration, thereby effectively improving the depth estimation accuracy on the transparent surface. Meanwhile, a Bayesian optimization method is adopted to optimize hyperparameters of the depth correction model, through hyperparameter optimization and model fitting processing, the system effectively avoids overfitting and underfitting problems, and further improves the robustness of the depth correction model and the grasping accuracy of the transparent object.

The present application relates to the field of new energy technology, and in particular to a training method, system, device and storage medium for a battery swap prediction model. The method includes obtaining environmental data and behavioral data, inputting into a feature extraction network, extracting feature vectors and fusing them to obtain user feature vectors; obtaining battery attribute data and inputting into a feature extraction network, extracting battery attribute feature vectors; inputting user feature vectors and battery attribute feature vectors into a battery matching network, calculating the degree of match between the battery attribute feature vector and the user feature vector, selecting several batteries of the same number and with the highest degree of match, and generating a target battery set with a matching order. Using environmental data and behavioral data as model inputs and a target battery set with a matching order as model outputs, the model is trained to obtain a battery swap prediction model. The present application can comprehensively consider battery characteristics and user behavior characteristics, recommend batteries more accurately, and thus enhance the user's battery swap experience.

The invention provides a medical insurance fraud cross identification method based on fusion of multiple machine learning algorithms, and belongs to the field of machine learning technology and insurance risk management cross technology in information technology. The technical problems that in medical insurance fraud recognition, the recognition precision is not high, characteristics are complex, behaviors are hidden, and distinguishing is difficult are solved. According to the technical scheme, the method comprises the following steps of S1, performing data preprocessing on an original data set D0; s2, finding out feature information with high correlation by using the heat map; s3, constructing a medical insurance fraud detection model based on multiple machine learning algorithms; s4, checking the model; and S5, extracting main medical features influencing fraud discrimination through an algorithm. The method has the advantages that the accuracy and efficiency of medical insurance fraud detection are improved, and a more effective risk management solution is provided for insurance companies.

The present invention relates to the technical field of destination recommendation, and discloses a next destination recommendation method, system and medium based on co-occurrence pattern mining, including obtaining a user's trajectory data set and preprocessing it; extracting user destination information and time information from the trajectory data set through a convolutional neural network to form a heterogeneous graph neural network; defining several meta-paths, respectively extracting path features of each meta-path; in the heterogeneous graph neural network, obtaining a target node and a source node, and calculating the first importance between each source node and the target node according to the target node, the source node and the path features; updating the nodes of the meta-path according to the first importance, and obtaining the updated embedding vectors of each node in the meta-path; obtaining the possibility scores of all destinations according to the embedding vectors, and obtaining the recommended destination according to the possibility scores. The advantage of the present invention is that it constructs rich feature information and connection relationships in human travel trajectories and mines human travel preferences.

The present invention discloses a ship motion response prediction method based on Gaussian interpolation, which relates to the technical field of ship motion response prediction, and includes the following steps: S1, data acquisition and preprocessing, including data cleaning, coordinate system conversion, data standardization and data set division; S2, constructing a Gaussian interpolation model, including setting an initial σ value and selecting interpolation points; S3, optimizing model parameters by a random search algorithm, including calculating the weight of each point in a training set, model verification, and model parameter optimization; S4, ship motion response prediction, substituting the ship to be predicted and sea state data into the Gaussian interpolation model to predict the ship motion response. The present invention uses a Gaussian interpolation model to accurately simulate the motion response of a ship under complex sea conditions. Compared with traditional methods, the Gaussian interpolation model can more accurately depict the complex relationship between sea state parameters and the ship motion state with its excellent fitting ability for multidimensional and nonlinear data.

The invention discloses an unmanned aerial vehicle path optimization method based on an improved fireworks algorithm, and belongs to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: S1, carrying out the preprocessing of unmanned aerial vehicle electric tower inspection historical data, and obtaining a training data set and a verification data set; s2, constructing a self-adaptive path planning model based on a fireworks algorithm improved by an optimized explosion strategy; s3, training the adaptive path planning model based on the training data set; s4, performing reliability verification on the trained adaptive path planning model based on the verification data set, if the reliability verification is qualified, executing S5, and if the reliability verification is not qualified, executing S3; s5, inputting the unmanned aerial vehicle electric tower inspection real-time data into the adaptive path planning model which is qualified in reliability verification for testing, and obtaining an unmanned aerial vehicle electric tower inspection optimal path; according to the invention, the problem of poor accuracy during unmanned aerial vehicle path planning in the prior art is solved.

A new category identification and dynamic imbalance processing method in an unsteady high-dimensional data stream based on deep learning comprises the following steps: preprocessing an input data stream, assuming that the data stream is a high-dimensional vector stream, and standardizing or normalizing each data sample; constructing a feature encoder based on a convolutional neural network, performing feature extraction on the high-dimensional sample of the data stream, and generating feature representation; a prototype ensemble classification framework is utilized, classification is carried out by maintaining prototypes of all categories, and category judgment is carried out according to the distance of newly input samples; the prototype set is dynamically updated through a feature correction mechanism, so that the prototype can better represent the current category distribution; in the classification process, new category detection is achieved through Mahalanobis distance calculation, if the distance between a sample and an existing category exceeds a set threshold value, the sample is judged as a new category, and a prototype set is dynamically expanded; a label distribution perception edge loss function is adopted, and the size of a loss edge is adaptively adjusted according to the number of category samples; a delay rebalance optimization strategy is utilized, a standard empirical risk minimization method is used during initial training, an LDAM loss function is switched to during later training, and the performance of the model is re-weighted and optimized for minority categories.

The application discloses a feature marker combination based on PET / CT molecular imaging and a human body aging clock evaluation method, and belongs to the technical field of medical data. The technical problem to be solved is to solve the defects of the existing human body aging evaluation method, such as insufficient comprehensiveness and limited accuracy. The technical solution points are to provide a feature marker combination composed of functional feature markers and volume feature markers; a support vector machine regression (SVR) algorithm is used to construct a model, the mean absolute error (MAE) in the independent verification set is 5.68 years, the determination coefficient R 2 is 0.85. The application can be used for human body aging clock evaluation, and provides a new perspective of aging evaluation based on multiple organ perspectives and verifiable marker support.

This application discloses a method, apparatus, equipment, and medium for assessing voltage sag fault risk, belonging to the field of voltage sag fault risk assessment. The method involves: collecting test points of the withstand characteristic curves of the electrical equipment under test under different voltage sag initiation angles and load conditions; each test point includes residual voltage and duration values; constructing multiple first Gaussian mixture models with different numbers of Gaussian components based on the test points; iteratively solving the optimization parameters of each model using the expectation-maximization algorithm until convergence, obtaining multiple second Gaussian mixture models; selecting a target Gaussian mixture model using the Bayesian information criterion; and finally, inputting the voltage sag test points to be assessed into the target Gaussian mixture model to obtain the assessment result characterizing the voltage sag fault risk of the electrical equipment under test. Therefore, by implementing this application, the problem of low accuracy in voltage sag fault risk assessment in the prior art can be solved.

This invention provides an anomaly early warning method for natural gas purification plants. The method includes: collecting historical and real-time data from various equipment within the purification plant based on the frequency of abnormal conditions and performing preprocessing; analyzing and selecting feature parameters from the preprocessed data and establishing a rule table; inputting the preprocessed real-time data into a data training model for classification and training to obtain classified abnormal operating condition data; establishing a multi-source data fusion algorithm model, inputting the classified abnormal operating condition data into the model for calculation to obtain the category and probability of the abnormal operating condition; and performing judgment and analysis on the obtained categories and probabilities of the abnormal operating conditions based on the rule table to achieve accurate monitoring and early warning of abnormal states of equipment within the purification plant. This invention introduces temporal analysis, using temporal convolutional networks and attention mechanisms to improve recognition accuracy; simultaneously, it proposes an imbalanced classification algorithm, improving the accuracy and efficiency of anomaly early warning for natural gas purification plants.

This invention provides a method for converting resistivity logging to sonic velocity logging. Based on a defined rock physics model, it utilizes the relationships and patterns between different physical parameters to establish an analytical expression between rock porosity, resistivity, and velocity, obtaining a discrete mapping relationship between resistivity and P-wave velocity. By continuously optimizing the rock physics parameters, it obtains the mapping curve between resistivity and P-wave velocity, avoiding underfitting caused by cross-sectional analysis and linear fitting of rock physics parameters alone. This reliably achieves an effective conversion from resistivity logging to sonic velocity logging. This invention can support pre-stack depth migration velocity updates based on logging information from drilled sections, predict seismic velocities ahead of the drill bit, and enable more accurate seismic imaging and drilling decisions.