Water source identification in mines combining LIF technology and ResNet

The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

Distribution characteristics and source identification of polychlorinated dibenzo-p-dioxin and dibenzofurans,and dioxin-like polychlorinated biphenyls in the waters from River Kanzaki,running through Osaka urban area,Japan

This is the first to elucidate the distribution and sources of polychlorinated dibenzo-p-dioxins and dibenzofurans （PCDD/PCDFs）,and dioxin-like polychlorinated biphenyls in the waters from Kanzaki River,which is one of the most heavily polluted rivers in Japan.The World Health Organization （WHO）-toxic equivalent quantities （TEQs） in waters from the mainstream exceeded the Japanese environmental standard （1.0 pg-TEQ/L）.The PCDD/PCDFs were dominated by highly chlorinated DFs,which predominantly contributed to the WHO-TEQs,suggesting that the main causes would be the incineration-related wastes.To find the sources,the dioxin congener concentrations in water and sediment samples from its tributary small waterways were determined.Abnormally high WHO-TEQs were detected in a water （50 pg-TEQ/L） and a sediment sample （41,000 ng-TEQ/kg dry weight） near the industrial solid waste incinerators （ISWIs）.The PCDD/PCDF characteristics agreed well with those of the incinerator-related wastes as seen in the mainstream.These facts indicate that the dioxin pollution in the mainstream could be largely related to the industrial wastes from the ISWIs.Here,a TEQ apportionment method was used to understand the contribution of the pyrogenic sources to the WHO-TEQs.The average contribution ratios of the pyrogenic sources to WHO-TEQs were more than 80% for river waters from the mainstream,indicating that the elevated WHO-TEQs in the mainstream had been largely caused by the ISWIs.