Estimating normal moveout velocity using the recurrent neural network

2019 ◽  
Vol 7 (4) ◽  
pp. T819-T827
Author(s):  
Reetam Biswas ◽  
Anthony Vassiliou ◽  
Rodney Stromberg ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Mean Squared Error ◽  
Correction Term ◽  
Computation Time ◽  
Velocity Estimation ◽  
Data Set ◽  
Arrival Times ◽  
Nmo Correction ◽  
Novel Method

Machine learning (ML) has recently gained immense popularity because of its successful application in complex problems. It develops an abstract relation between the input and output. We have evaluated the application of ML to the most basic seismic processing of normal moveout (NMO) correction. The arrival times of reflection events in a common midpoint (CMP) gather follow a hyperbolic trajectory; thus, they require a correction term to flatten the CMP gather before stacking. This correction term depends on an rms velocity, also referred to as the NMO velocity. In general, NMO velocity is estimated using the semblance measures and picking the peaks in the velocity panel. This process requires a lot of human intervention and computation time. We have developed a novel method using one of the tools based on an ML- approach and applied to the NMO velocity estimation problem. We use the recurrent neural network (RNN) to estimate the NMO velocity directly from the seismic data. The input to the network is a seismic gather and corresponding precalculated NMO velocity (as prelabeled data set) to flatten the gather. We first train the network to develop a relationship between the input gathers (before NMO correction) and the corresponding NMO velocities for a few CMPs as a supervised learning process. Adam optimization algorithm is used to train the RNN. The output from the network is then compared against the correct NMO velocity. The error between the two velocities is then used to update the weight of the neurons and to minimize the mean-squared error between the two velocities. After the network is trained, it can be used to calculate the NMO velocity for the rest of the seismic gathers. We evaluate our method on a noisy data set from Poland. We used only 10% of the CMPs to train the network, and then we used the trained network to predict NMO velocity for the remaining CMP locations. The stack section obtained by using RNN-generated NMO velocities is nearly identical to that obtained by the conventional semblance method.

scholarly journals Multistep Prediction of Bus Arrival Time with the Recurrent Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhi-Ying Xie ◽  
Yuan-Rong He ◽  
Chih-Cheng Chen ◽  
Qing-Quan Li ◽  
Chia-Chun Wu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Traffic Congestion ◽  
Arrival Time ◽  
Short Term Memory ◽  
Model Simulation ◽  
Accurate Prediction ◽  
Data Set ◽  
Arrival Times ◽  
Public Transport System

Accurate predictions of bus arrival times help passengers arrange their trips easily and flexibly and improve travel efficiency. Thus, it is important to manage and schedule the arrival times of buses for the efficient deployment of buses and to ease traffic congestion, which improves the service quality of the public transport system. However, due to many variables disturbing the scheduled transportation, accurate prediction is challenging. For accurate prediction of the arrival time of a bus, this research adopted a recurrent neural network (RNN). For the prediction, the variables affecting the bus arrival time were investigated from the data set containing the route, a driver, weather, and the schedule. Then, a stacked multilayer RNN model was created with the variables that were categorized into four groups. The RNN model with a separate multi-input and spatiotemporal sequence model was applied to the data of the arrival and leaving times of a bus from all of a Shandong Linyi bus route. The result of the model simulation revealed that the convolutional long short-term memory (ConvLSTM) model showed the highest accuracy among the tested models. The propagation of error and the number of prediction steps influenced the prediction accuracy.

Automatic NMO correction and velocity estimation by a feedforward neural network

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1696-1707 ◽  
Author(s):  
Carlos Calderón‐Macas ◽  
Mrinal K. Sen ◽  
Paul L. Stoffa
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Spatial Data ◽  
Velocity Estimation ◽  
Feedforward Neural Network ◽  
Velocity Analysis ◽  
Seismic Velocities ◽  
Lateral Velocity ◽  
Data Set ◽  
Nmo Correction

We describe a new method of automatic normal moveout (NMO) correction and velocity analysis that combines a feedforward neural network (FNN) with a simulated annealing technique known as very fast simulated annealing (VFSA). The task of the FNN is to map common midpoint (CMP) gathers at control locations along a 2-D seismic line into seismic velocities within predefined velocity search limits. The network is trained while the velocity analysis is performed at the selected control locations. The method minimizes a cost function defined in terms of the NMO-corrected data. Network weights are updated at each iteration of the optimization process using VFSA. Once the control CMP gathers have been properly NMO corrected, the derived weights are used to interpolate results at the intermediate CMP locations. In practical situations in which lateral velocity variations are expected, the method is applied in spatial data windows, each window being defined by a separate FNN. The method is illustrated with synthetic data and a real marine data set from the Carolina Trough area.

Tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oluwafemi Ajayi ◽  
Reolyn Heymann
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Data Set ◽  
Content Type ◽  
Demand Pattern ◽  
The Neural Network ◽  
Input Parameters ◽  
Demand Profile

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern. Findings The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern. Research limitations/implications The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance. Practical implications Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost. Originality/value The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

scholarly journals Real-time event detection using recurrent neural network in social sensors

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985649 ◽  
Author(s):  
Van Quan Nguyen ◽  
Tien Nguyen Anh ◽  
Hyung-Jeong Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Word Embedding ◽  
Series Data ◽  
Accuracy Score ◽  
Data Set ◽  
Size Limitation

We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores.

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

Determination of Electron Optical Properties for Aperture Zoom Lenses Using an Artificial Neural Network Method

2016 ◽  
Vol 22 (2) ◽  
pp. 458-462 ◽  
Author(s):  
Nimet Isik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Charged Particle ◽  
Mean Squared Error ◽  
Classification Method ◽  
Image Point ◽  
Charged Particle Beam ◽  
Data Set ◽  
Artificial Neural ◽  
Ann Classification

AbstractMulti-element electrostatic aperture lens systems are widely used to control electron or charged particle beams in many scientific instruments. By means of applied voltages, these lens systems can be operated for different purposes. In this context, numerous methods have been performed to calculate focal properties of these lenses. In this study, an artificial neural network (ANN) classification method is utilized to determine the focused/unfocused charged particle beam in the image point as a function of lens voltages for multi-element electrostatic aperture lenses. A data set for training and testing of ANN is taken from the SIMION 8.1 simulation program, which is a well known and proven accuracy program in charged particle optics. Mean squared error results of this study indicate that the ANN classification method provides notable performance characteristics for electrostatic aperture zoom lenses.

Replicating Advanced Detection using Low Ping Frequency Probe Vehicle Trajectory Data to Optimize Signal Progression

2020 ◽  
Vol 2674 (7) ◽  
pp. 528-539
Author(s):  
Jonathan M. Waddell ◽  
Stephen M. Remias ◽  
Jenna N. Kirsch ◽  
Mohsen Kamyab
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Linear Interpolation ◽  
Traffic Signal ◽  
Trajectory Data ◽  
Arrival Times ◽  
Probe Vehicle ◽  
The Neural Network ◽  
Vehicle Trajectory

Probe vehicle trajectory data has the potential to transform the current practice of traffic signal optimization. Current scalable trajectory data is limited in both the penetration rate and the ping frequency, or the length of time between vehicle waypoints. This paper introduces a methodology to create binary vehicle trajectories which can be used in a neural network to predict when vehicles will arrive at a virtual detector. The methodology allows for vehicles with ping frequencies of up to 60 s to be utilized for the optimization of offsets at signalized intersections. A nine-signal corridor in west Michigan was used to test the proposed methodology. The neural network was compared to traditional linear interpolation strategies and found to improve the root mean squared error of the arrival times by up to 6.18 s. Using the virtual detector data stacked over time to optimize the offsets of the corridor resulted in 77% of the benefit of an offset optimization performed with continuously collected high resolution signal controller data. In the era of big data, this alternative approach can assist with the large-scale implementation of traffic signal performance measures for improved operations.

scholarly journals Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

2014 ◽  
Vol 7 (4) ◽  
pp. 132-143
Author(s):  
ABBAS M. ABD ◽  
SAAD SH. SAMMEN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Water Reservoir ◽  
Prediction Method ◽  
Training Data ◽  
Ann Model ◽  
Data Set ◽  
Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

Stacking velocity estimation using recurrent neural network

2018 ◽  
Author(s):  
Reetam Biswas ◽  
Anthony Vassiliou ◽  
Rodney Stromberg ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Velocity Estimation
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