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Due to the complex terrain and geological conditions in the blasting area, as well as errors in monitoring instruments, reflections of vibration propagation medium, and interference from magnetic fields, a significant amount of noise is often present in the original blasting vibration signals collected. To address this issue, a signal noise reduction smooth model based on complementary ensemble empirical mode decomposition (CEEMD) is proposed. Firstly, the measured blasting vibration signal is decomposed using CEEMD and an algorithm for low-pass filtering is established based on the obtained intrinsic mode function (IMF) component from the decomposition. Additionally, an objective function is constructed to calculate the optimal solution according to similarity and smoothness criteria for filtering algorithms. The resulting filtering algorithm model represents an optimal denoising smooth model for blasting vibration signals. To verify our noise reduction smooth model, a simulation signal is constructed and applied to actual open-pit deep-hole blasting vibration signal research. Finally, the noise reduction effects of empirical mode decomposition (EMD) method, wavelet threshold method, CEEMD-wavelet threshold method, and filter algorithm model BP3 are quantified and compared using two indexes: signal-to-noise ratio and root-mean-square error. It has been confirmed that the proposed noise reduction smooth model effectively reduces noise in open-pit blasting vibration signals. The findings demonstrate that our CEEMD-based noise reduction smooth model for open-pit deep-hole blasting vibrations possesses excellent denoising capabilities while preserving essential characteristic information from the original signals. Furthermore, the denoising effect of the proposed model surpasses that of EMD method, wavelet threshold method, and CEEMD-wavelet threshold method.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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