## 5 Results and Discussions
### Anomaly Pattern Detection  
Anomaly pattern detection is crucial for understanding seismic activity in the Red Sea region. By identifying irregular seismic patterns, unusual tectonic events could be detected. This proactive identification of anomalies aids in the assessment of seismic hazards in this dynamic area. Figure 7 delineates the variability and central tendencies of seismic parameters as recorded by the KSUSN, SANDSN, and SNSN seismic networks. The examination of Figure 7 reveals that the SNSN network tends to report a more constricted interquartile range of earthquake magnitudes compared to the KSUSN network, which documents a broader dispersion in magnitudes, with fewer atypical values. The SANDSN network demonstrates an interquartile range akin to that of SNSN but is distinguished by a significant number of outliers, suggestive of the intermittent recording of seismic events with considerably higher magnitudes. Regarding depth, both KSUSN and SNSN networks  
present a considerable number of outliers, implying the detection of events with depths markedly divergent from the majority. In contrast, the SANDSN network illustrates a more compact range and fewer outliers, reflecting a consistency in the recording of seismic depths. The distribution of latitude shows that KSUSN has outliers denoting seismic activities that occur substantially south of the primary cluster, whereas SNSN exhibits a wider range in the detection of latitudes. The SANDSN network, while encompassing a breadth of latitudes, records fewer outliers. For longitude, KSUSN's data indicate a narrower range but with conspicuous outliers, hinting at the occasional detection of seismic events at extreme eastern or western longitudes. Conversely, SANDSN indicates a wide spectrum of detection, and SNSN suggests a concentrated collection of events with a handful of outliers. When ordered by the visible frequency of outliers or deviations in their collected data, SANDSN appears most prominent in magnitude outliers, followed by SNSN, then KSUSN. For depth, KSUSN and SNSN portray a comparable prevalence of outliers, whereas SANDSN displays fewer. Regarding latitudinal and longitudinal distributions, SNSN exhibits the widest scope of outliers, with KSUSN in succession and SANDSN recording the fewest. The manifestation of outliers warrants meticulous investigation to affirm the integrity of the data. Figure 6: Multifaceted Analysis of Spatial Earthquake Distribution Characteristics in the Red Sea: (**a**,**c**) showing the distribution of earthquake magnitudes across latitudinal and longitudinal ranges. (**b**,**d**) depicting the depth distribution of earthquakes along latitudinal and longitudinal coordinates. This study introduces the application of the Isolation Forest algorithm to identify and eliminate anomalous patterns observed in the seismicity of the Red Sea region. Isolation Forest, an effective machine learning method for anomaly detection, operates by isolating outliers rather than profiling normal data points. This approach is particularly suited for seismic data, where anomalies can indicate either significant geological events or data irregularities. By applying this technique to the seismicity of the Red Sea, the study aims to enhance the accuracy and reliability of seismic data interpretation, crucial for understanding the complex tectonic dynamics of this geologically active area. In accordance with the methodology outlined in Algorithm 1, anomalous seismicity patterns were identified. The effectiveness of the Isolation Forest algorithm in refining seismic data for the Red Sea region is clearly demonstrated in Figure 8. This is evident from the general characteristics of the database, post-removal of outliers, which are the earthquakes that the isolation forest algorithm has identified as being the most different from the rest of the data. The algorithm proficiently differentiates between the prevailing seismic trends and atypical events, thereby purifying the dataset for enhanced analytical accuracy.