Search

To identify water inrush sources quickly and effectively, six hydrated ions, namely, Na++ K+, Ca2+, Mg2+, Cl−, SO42−, and HCO3−, were selected as discriminant indices. Considering the unobvious and fuzzy boundaries of water quality between aquifers, Elman neural network (ENN) was chosen as the basis of the discrimination model; in order to overcome the error backpropagation algorithm of ENN, genetic algorithm (GA) was selected to optimize ENN through initialization, fitness evaluation, selection, crossover, and mutation, creating an improved discrimination model called GA-ENN. On the basis of giving consideration to the principle of fairness, GA-ENN was compared with ENN, backpropagation neural network (BPNN), and the BPNN optimized by GA (GA-BPNN). The results shows that GA-ENN outperformed the other three models in convergence speed and output accuracy and GA-ENN can reflect the dynamic changes of the water quality in the aquifer through years of mining. Apart from the hydrogeological conditions of the coalmine, representative and accurate hydration data should be selected to improve the accuracy and validity of source discrimination of mine water inrush.

Previous studies on graptolites have mainly focused on stratigraphic division and sedimentary environment analysis. On the basis of previous studies of Ordovician and Silurian graptolites in the Middle–Upper Yangtze region, this paper makes full use of core analysis, drilling geological, experimental analysis, and logging data from wells and combines graptolite types in different sedimentary environments to identify 4 graptolite zones, WF1–WF4, and 9 graptolite zones, LM1–LM9, in the Silurian Longmaxi Formation in the Middle–Upper Yangtze region, among which WF2–LM4 present high total organic carbon (TOC), organic pore development, and shale gas enrichment and are favorable development zones. This study reveals that enrichment in graptolites has positive correlations with TOC, organic porosity, and total hydrocarbon content. The evolution of graptolites has contributed to the enrichment in organic matter and the formation of organic pores in shales and affects shale gas–bearing properties to a certain extent, which is an important indicator for shale gas reservoir evaluation.