## 1 Introduction
This method is particularly adept at identifying data points that deviate significantly from the norm, which in the context of seismology, translates to discrepancies in earthquake parameters such as magnitude, depth, or epicentral coordinates reported by different seismic monitoring stations [12]. The strength of Isolation Forests lies in their ability to handle high-dimensional datasets efficiently, making them well-suited for seismic data which is typically multi-faceted and voluminous. This is especially valuable in regions like the Red Sea, where multiple seismic networks with varying operation times and spatial coverages operate [2]. The present study endeavors to leverage machine learning techniques, specifically Isolation Forests, to tackle the issue of identifying inconsistencies in seismic data collected by various Saudi seismic networks monitoring the Red Sea. The primary impetus for utilizing machine learning techniques in the study was to address the intrinsic complexities and challenges associated with seismic data analysis [13; 14]. Traditional methodologies frequently encounter difficulties due to the high dimensionality and non-linear characteristics of seismic data, often leading to inaccuracies in identifying and characterizing seismic events. Conversely, machine learning techniques are adept at managing such complexities through their capacity to learn patterns and relationships from extensive datasets. The Isolation Forest algorithm was selected for several reasons [11; 12]. Its efficiency in handling high-dimensional data makes it particularly suitable for analyzing intricate seismic datasets, ensuring effective anomaly identification without being overwhelmed by the data's volume and complexity. Unlike conventional anomaly detection methods that focus on modeling normal data points, the Isolation Forest algorithm isolates anomalies by constructing binary trees, facilitating more effective outlier identification, as anomaliestypically exhibit shorter path lengths in the trees. This feature is vital for detecting unusual seismic activity that may signify significant geological events. Additionally, the algorithm's scalability makes it appropriate for extensive seismic datasets, efficiently processing large volumes of data and proving ideal for regional seismic hazard assessments [2; 15; 16]. By isolating and removing outliers, the Isolation Forest algorithm also enhances the precision of seismicity parameter estimations, which is crucial for parameters such as the magnitude of completeness (\(M_{c}\)), \(a\)-value, and \(b\)-value, all of which are essential for seismic hazard assessments. The implementation of the Isolation Forest algorithm in the study conferred several notable advantages: it resulted in more precise estimations of seismicity parameters, improved the overall quality of the seismic dataset by filtering out anomalous data points, and enabled the prompt identification of unusual seismic patterns. This capability is crucial for timely interventions to mitigate seismic hazards [17; 18; 19]. The ultimate goal of this step is to enhance both the accuracy and comprehensiveness of seismic data interpretation, thereby achieving a deeper understanding of seismic activities in the Red Sea region. This research produces a harmonized seismicity catalog, which is subsequently used to compute the frequency-magnitude distribution, or the \(b\)-value, of seismic activity across multiple regions in the Red Sea. Additionally, the \(a\)-value and the magnitude of completeness (\(M_{c}\)) are assessed to refine seismic hazard mitigation strategies [1; 20]. The improved accuracy and reliability of the seismicity parameters--namely, \(M_{c}\), \(a\)-value, and \(b\)-value--are instrumental for vulnerability assessments. By providing more precise data on seismic activity, these refined parameters enable better-informed decisions regarding building codes, retrofitting strategies, and risk management practices.