Synthetic well logs generation via Recurrent Neural Networks

To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.

Deep learning based on connected vehicles for icing pavement detection

Slippery road conditions,such as snowy,icy or slushy pavements,are one of the major threats to road safety in winter.The U.S.Department of Transportation(USDOT)spends over 20%of its maintenance budget on pavement maintenance in winter.However,despite extensive research,it remains a challenging task to monitor pavement conditions and detect slippery roadways in real time.Most existing studies have mainly explored indirect estimates based on pavement images and weather forecasts.The emerging connected vehicle(CV)technology offers the opportunity to map slippery road conditions in real time.This study proposes a CV-based slippery detection system that uses vehicles to acquire data and implements deep learning algorithms to predict pavements’slippery conditions.The system classifies pavement conditions into three major categories:dry,snowy and icy.Different pavement conditions reflect different levels of slipperiness:dry surface corresponds to the least slippery condition,and icy surface to the most slippery condition.In practice,more attention should be paid to the detected icy and snowy pavements when driving or implementing pavement maintenance and road operation in winter.The classification algorithm adopted in this study is Long Short-Term Memory(LSTM),which is an artificial Recurrent Neural Network(RNN).The LSTM model is trained with simulated CV data in VISSIM and optimized with a Bayesian algorithm.The system can achieve 100%,99.06%and 98.02%prediction accuracy for dry pavement,snowy pavement and icy pavement,respectively.In addition,it is observed that potential accidents can be reduced by more than 90%if CVs can adjust their driving speed and maintain a greater distance from the leading vehicle after receiving a warning signal.Simulation results indicate that the proposed slippery detection system and the information sharing function based on the CV technology and deep learning algorithm(i.e.,the LSTM network implemented in this study)are expected to deliver real-time detec-tion of slippery pavement conditions,thus significantly eliminating the potential risk of accidents.

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

Tropical cyclone(TC)annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province.Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature(SST)V5 data in winter,the TC frequency climatic features and prediction models have been studied.During 1951-2019,353 TCs directly affected Guangdong with an annual average of about 5.1.TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution.338 primary precursors are obtained from statistically significant correlation regions of SST,sea level pressure,1000hPa air temperature,850hPa specific humidity,500hPa geopotential height and zonal wind shear in winter.Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis(PCA).Furthermore,the Multiple Linear Regression(MLR),the Gaussian Process Regression(GPR)and the Long Short-term Memory Networks and Fully Connected Layers(LSTM-FC)models are constructed relying on the above 19 factors.For three different kinds of test sets from 2010 to 2019,2011 to 2019 and 2010 to 2019,the root mean square errors(RMSEs)of MLR,GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45,1.00-1.93 and 0.71-0.95 as well as the average absolute errors(AAEs)0.88-1.0,0.75-1.36 and 0.50-0.70,respectively.As for the 2010-2019 experiment,the mean deviations of the three model outputs from the observation are 0.89,0.78 and 0.56,together with the average evaluation scores 82.22,84.44 and 88.89,separately.The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR.In conclusion,the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency.The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province.