Microseismic monitoring data are characterized by strong background noise and low signal-to-noise ratio (SNR), posing challenges for effective signal identification and real-time processing. Existing methods generally suffer from poor adaptability, low processing accuracy, and insufficient computational efficiency. To address these issues, this study proposes an adaptive spectral segmentation method for microseismic signal extraction. First, the Ramanujan subspace method is employed to suppress periodic noise and eliminate spectral peak interference. Subsequently, an adaptive band number determination criterion based on the sampling rate is established, and adaptive empirical Fourier decomposition is adopted to achieve optimized spectral segmentation. Finally, the Gini coefficient is introduced to establish an adaptive threshold screening mechanism for automatic reconstruction of effective signals. For real-time processing of multi-channel data, a unified filtering strategy based on statistical overlapping bands is proposed, enabling rapid processing of subsequent data using common bands. Experiments on synthetic and field data demonstrate that the proposed adaptive spectral segmentation method yields significant advantages: on synthetic data, SNR improvements of up to 4.65 dB over modal decomposition methods (e.g., empirical mode decomposition, ensemble empirical mode decomposition, and variational mode decomposition) and wavelet packet decomposition, along with a 72% reduction in multi-channel processing time using the unified filtering strategy. These results highlight its high adaptability and practicality for low-SNR microseismic signal processing.