## 2 Materials and Methods
### Dataset Collection from the IRIS  
This study uses a dataset from the IRIS database. The selected geographical area is in Almaty, Kazakhstan (43.22\({}^{\circ}\) N, 76.92\({}^{\circ}\) E), with a 3.0\({}^{\circ}\) (-333 km) coverage radius. Thisradius encompasses Almaty, the Almaty region, and parts of neighboring countries, such as Kyrgyzstan and China. The dataset consists of raw waveform data of three components sampled at 100 Hz, corresponding to the HHE, HHN, and HHZ components. Because the HHZ component allows for more efficient capture of P waves, this study uses mainly HHZ data for P-wave detection. According to IRIS data, 21 seismic stations are in the surrounding region. These stations are part of the following seismic networks: Kazakhstan (QZ) and Kyrgyzstan (KC, KN, KR). A geographic map was constructed based on coordinates, illustrating the distribution of seismic stations within a radius of 333 km from Almaty (Figure 1). The map also includes information on station elevations and operational start dates. Analysis of station elevations reveals that higher-altitude stations exhibit superior capability to record seismic events. This advantage arises from their greater distance from surface noise sources. In contrast, lowland stations are more susceptible to background noise, which may be due to human activities or meteorological conditions. As a result, high-altitude seismic stations provide cleaner recordings, notably for P waves, enhancing the reliability of seismic monitoring in the region.  
Seismic data in the IRIS database are categorized into the following accessibility classes: A, open access data; P, partially restricted access; R, restricted access; X, unavailable data--no information on availability. For this study, open-access class A data were collected from seven seismic stations (TARG, ARLS, BAET, JNK, BTLS, SHLS, and TLG).  
After analyzing the seismograms from seven stations, a slow drift was identified at some locations, such as BTLS and JNKS. To eliminate this drift and remove low-frequency noise, a 9 th-order Butterworth high-pass filter with a cutoff frequency of 2 Hz was applied. This filter effectively suppressed trends and microseismic noise while preserving the key high-frequency characteristics of P waves, which is crucial for their accurate detection.  
The ObsPy library retrieved and processed the seismic dataset [36]. The analysis covered two years, from 1 January 2023, to 31 December 2024. Seismic records were segmented into 240 s time windows, capturing P-wave arrivals for all available seismic events. Moreover, 219 seismic events were recorded at the seven selected stations during this period. Figure 2 illustrates the geographic distribution of these seismic stations and recorded events.  
Figure 1: A geographic coordinate grid displaying the distribution of seismic stations, their elevation (represented by color intensity), and the start date of operation (labeled next to each station). The higher the station, the lighter its color, ranging from dark blue (600 m) to yellow (3600 m).  
A diagram (Figure 3) was constructed to visualize the temporal distribution of seismic activity over two years. It illustrates the monthly recorded earthquakes from January 2023 to November 2024. This visualization enables the identification of periods of increased seismic activity. The highest number of seismic events was recorded in January 2024 (136 events) and February 2024 (28 events), suggesting increased tectonic activity. In contrast, the number of earthquakes in the remaining months is significantly lower, indicating the irregular nature of seismic activity in the region.