In the process of mining Carboniferous coal resources in China’s coal mines, catastrophic water inrush from the floor often occurs. The water inrush source is mainly the fifth limestone aquifer of Carboniferous or Ordovician limestone aquifers. Conventional elements cannot effectively identify the source of water inrush as limestone aquifers of different geological ages. Against the background of floor water inrush in Baizhuang Coal Mine in Feicheng Coalfield, water samples of the fifth-layer limestone aquifer, Ordovician limestone aquifer and water inrush point water samples of Feicheng Coalfield were collected. Trace components F⁻, Br⁻, I⁻, H₃BO₃ and Rn were selected for compositional analysis. The minimum deviation method was used to combine and weight the weights obtained by the entropy weight method, principal component analysis method and analytic hierarchy method. An improved grey correlation model was established for water inrush source identification. The model discrimination result shows that the water inrush source comes from the Ordovician limestone aquifer, and the discrimination accuracy is high.

Underwater image processing technology has become increasingly crucial in aquatic environmental monitoring and underwater equipment inspection, where edge-side algorithmic implementation forms the cornerstone for practical deployment. The underwater image algorithm suitable for the above purposes faces two problems: firstly, uneven distribution of underwater media can easily cause insufficient brightness and distorted underwater images, and secondly, optimization of algorithm deployment on the end side. Therefore, this article proposes an FPGA-based algorithm for underwater image enhancement - SCRE(Self-adaptive Correction and Retinex Enhancement). The algorithm integrates adaptive Gamma correction and Multi-Scale Retinex with Color Restoration (MSRCR). It establishes a coordinated “luminance optimization–color correction” enhancement framework in the HSV color space. Adaptive Gamma correction is applied to the Value (V) channel to enhance luminance effectively. Concurrently, during the color correction stage, a modified MSRCR method incorporating a compensation offset mechanism targeting underwater spectral attenuation is employed to perform color restoration and enhancement on the entire image.Furthermore, hardware optimization strategies achieved hardware deployment and algorithm acceleration based on the FPGA platform, pipeline architecture, and data parallelization techniques. Experimental verification shows that the SCRE algorithm improves UIQM and UCIQE metrics by 70.1%and 92.5%, respectively, compared to the original images, and deploying on FPGA hardware platforms, the processing speed of 1280×720 and 640×480 pixel images reaches 40fps/s and 70fps/s, respectively. Its processing speed is 9 to 10 times faster than a PC (Intel Core i7-14650H). Meeting real-time processing needs.