# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
## 4 Discussion and Conclusions  
This work shows that the spectral entropy method for extracting features from speech signals can be effectively adapted for real-time analyses of seismic time series. The dataset covers the period from 2023 to 2025. The spectral entropy was computed using a 38 s window, corresponding to 3800 samples at a sampling rate of 100 Hz, for 240 s segments of the HHZ component. [PERSON]'s spectral entropy shows a sharp transition into the informative region of normalized values (entropy exceeding 0.42 with an increase of more than 0.2 units) at the onset of a seismic event and the detection of primary seismic waves. The growth in spectral entropy under these conditions may serve as an additional indicator of event initiation. The characteristic value of the spectral entropy at the time of detection is defined as 0.53 \(\pm\) 0.02, with a typical increase of at least 0.2 units from a minimum average baseline of approximately 0.42. However, higher initial entropy values before event onset generally lead to smaller relative increases, often falling below the proposed threshold, while still conforming to the empirical curve defined by the equation \(dH=x-0.457\). This leads to a decrease in the reliability of detection. Therefore, multiple seismic events overlapping in the analysis window lead to a reduction in legibility. However, in the presence of high noise levels, long-term trends, or dominant low-frequency components, the technique becomes inappropriate. These findings are based on the analysis of 1260 seismic events recorded by seven stations. The proposed threshold yields successful detections in up to 65% of cases involving isolated events and up to 87% in noisy datasets. To improve these detection rates, preprocessing is necessary. It could be in the form of a trend removal procedure or low-frequency filtering. The current implementation of the spectral entropy method assumes the presence of isolated P-wave arrivals. In cases of overlapping seismic events or foreshocks, the distinction between noise and the signal becomes less pronounced, which may lead to underestimations of the entropy change \(\Delta H\) or ambiguous detection timing. While this study focuses on isolated waveforms to establish a performance baseline, future work should explore adaptive time-windowing or the integration of spectral entropy with traditional STA/LTA-based features to address the limitations under complex signal conditions. This point can also be a point of realization in NN algorithms. To evaluate the practical feasibility of the proposed method, we measured the computational cost under typical operating conditions. For a 40 s window sampled at 100 Hz, the average processing time on a typical desktop machine (Intel Core i7, 16 GB RAM) was approximately 40 ms, including both spectrogram generation and entropy calculation. This result suggests that the spectral entropy method is suitable for real-time monitoring and can be accelerated further using compiled implementations. We intend to establish a framework where entropy-based features are integrated as a pre-attentive layer within deep learning models for continuous seismic monitoring. Recent studies have demonstrated the effectiveness of such hybrid approaches [6,7]--for example,hybrid neural network architectures that incorporate spectral entropy as an informative input feature for event detection and classification tasks. Specifically, time series of entropy values can be used as sequential inputs to recurrent models such as long- and short-term memory (LSTM) networks, enabling temporal context learning. Alternatively, spectral entropy can be included as an additional feature map in convolutional neural networks (CNNs) to enhance the spatial pattern recognition. This method constitutes an independent and complementary tool for detecting primary P-waves. In subsequent findings, this approach can be integrated as an input feature for neural network models within a comprehensive framework to detect and analyze seismic events.