Adaptive Processing of Multi-Source Drilling Sensor Signals for Coal–Rock Identification Under Limited Drilling Data: A Laboratory Case Study

Reliable coal–rock identification during drilling is challenged by limited labeled data and non-stationary, strongly coupled multi-source signals. This laboratory case study evaluates a recognition-oriented adaptive framework, IMGO–VMD–WT, that uses torque-guided improved moss growth optimization (IMGO) to select variational mode decomposition (VMD) parameters, grades intrinsic mode functions by information content, applies differentiated wavelet-threshold processing, and reconstructs the signal with residual preservation. A self-built laboratory drilling rig provided data from six material categories and eleven pressure, displacement, torque, and rotational-speed channels. Under five-fold contiguous-block validation, raw features, wavelet thresholding, fixed VMD–WT, PSO–VMD–WT, GWO–VMD–WT, and IMGO–VMD–WT achieved 90.26%, 91.94%, 92.17%, 92.13%, 92.13%, and 92.57% accuracy, respectively, with corresponding macro-F1 scores of 78.34%, 80.37%, 81.73%, 82.17%, 82.17%, and 82.22%. The exploratory paired comparison between IMGO–VMD–WT and fixed VMD–WT gave accuracy and macro-F1 differences of 0.40 and 0.49 percentage points, with exact sign-permutation p-values of 0.6875 and 0.5625 and bootstrap 95% intervals that included zero. In a controlled 10 dB noise-injection test on 12 measured torque windows, IMGO–VMD–WT reached 19.19 dB output SNR and an RMSE of 1.379. These results support decomposition-aware processing as a feasible offline strategy for limited-data laboratory drilling analysis.