Cluster analysis of the domain of microseismic event attributes for fl oor water inrush warning in the working face

Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working facefloor.Cluster analysis,which classifies samples according to data similarity,has remarkable advantages in nonlinear classification.A water inrush early warning method for coal minefloors is proposed in this paper.First,the short time average over long time average(STA/LTA)method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines.Then,ten attributes of microseismic events are extracted,and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events.Clustering results of synthetic andfield data demonstrate the effectiveness of the proposed method.The analysis offield data clustering results shows that thefirst kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working facefloor.

Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

Weeks-Ahead Epidemiological Predictions of Varicella Cases From Univariate Time Series Data Applying Artificial Intelligence

Background:"Chickenpox"is a highly infectious disease caused by the varicella-zoster virus,influenced by seasonal and spatial factors.Dealing with varicella-zoster epidemics can be a substantial drain on health-authority resources.Methods that improve the ability to locally predict case numbers from time-series data sets every week are therefore worth developing.Methods:Simple-to-extract trend attributes from published univariate weekly case-number univariate data sets were used to generate multivariate data for Hungary covering 10 years.That attribute-enhanced data set was assessed by machine learning(ML)and deep learning(DL)models to generate weekly case forecasts from next week(t0)to 12 weeks forward(t+12).The ML and DL predictions were compared with those generated by multilinear regression and univariate prediction methods.Results:Support vector regression generates the best predictions for weeks t0 and t+1,whereas extreme gradient boosting generates the best predictions for weeks t+3 to t+12.Long-short-term memory only provides comparable prediction accuracy to the ML models for week t+12.Multi-K-fold cross validation reveals that overall the lowest prediction uncertainty is associated with the tree-ensemble ML models.Conclusion:The novel trend-attribute method offers the potential to reduce prediction errors and improve transparency for chickenpox timeseries.

Seismic Attribute Analysis with Saliency Detection in Fractional Fourier Transform Domain

Most image saliency detection models are dependent on prior knowledge and demand high computational cost. However, spectral residual（SR） and phase spectrum of the Fourier transform（PFT） models are simple and fast saliency detection approaches based on two-dimensional Fourier transform without the prior knowledge. For seismic data, the geological structure of the underground rock formation changes more obviously in the time direction. Therefore, one-dimensional Fourier transform is more suitable for seismic saliency detection. Fractional Fourier transform（FrFT） is an improved algorithm for Fourier transform, therefore we propose the seismic SR and PFT models in one-dimensional FrF T domain to obtain more detailed saliency maps. These two models use the amplitude and phase information in FrFT domain to construct the corresponding saliency maps in spatial domain. By means of these two models, several saliency maps at different fractional orders can be obtained for seismic attribute analysis. These saliency maps can characterize the detailed features and highlight the object areas, which is more conducive to determine the location of reservoirs. The performance of the proposed method is assessed on both simulated and real seismic data. The results indicate that our method is effective and convenient for seismic attribute extraction with good noise immunity.

Entity attribute discovery and clustering from online reviews

The rapid increase of user-generated content （UGC） is a rich source for reputation management of enti- ties, products, and services. Looking at online product re- views as a concrete example, in reviews, customers usually give opinions on multiple attributes of products, therefore the challenge is to automatically extract and cluster attributes that are mentioned. In this paper, we investigate efficient at- tribute extraction models using a semi-supervised approach. Specifically, we formulate the attribute extraction issue as a sequence labeling task and design a bootstrapped schema to train the extraction models by leveraging a small quantity of labeled reviews and a larger number of unlabeled reviews. In addition, we propose a clustering By committee （CBC） ap- proach to cluster attributes according to their semantic simi- larity. Experimental results on real world datasets show that the proposed approach is effective.

Image Analytics:A consolidation of visual feature extraction methods

Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images,the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance.Consequently,this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques.We describe resources and techniques based on three visual feature extraction methods,namely calculation-,recognition-,and simulation-based.Additionally,we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.