## 1 Introduction  
Understanding the discrepancies in reported parameters of earthquake sources is crucial for developing effective mitigation strategies to minimize potential seismic damage. Earthquake parameters, such as magnitude, depth, and origin-time, are pivotal in seismic hazard analysis and the design of earthquake-resistant structures [1]. Inconsistencies in these parameters, arising from varied measurement techniques and interpretational differences, can lead to significant variations in seismic hazard assessments [2]. These variations can impact the predicted ground motion intensities and, consequently, the design parameters for buildings and infrastructure. Accurate determination and consensus on these parameters are therefore imperative for the reliability of seismic risk assessments and the effectiveness of mitigation strategies [3]. This necessity is highlighted in regions with high seismicity, where discrepancies in earthquake parameters can lead to underestimation or overestimation of seismic hazards, significantly affecting public safety and economic losses [4]. Therefore, addressing these discrepancies is critical. It necessitates ongoing research to refine seismic measurement techniques and improve data interpretation. Seismic networks play a pivotal role in monitoring and reporting seismic activity in various regions, including the geologically significant Red Sea area. The Red Sea, a geologically dynamic region located between the Arabian and African Plates, has long been a subject of seismic study, given its complex tectonic interactions and the significant seismic activity they generate [5]. The Saudi seismic networks play an instrumental role in capturing this seismicity, providing crucial data that necessitates sophisticated spatial analysis for a comprehensive understanding [6]. These networks consist of an array of seismograph stations strategically positioned around the Red Sea. Furthermore, the seismic networks in the Red Sea region often collaborate with international seismic monitoring organizations. This collaboration enhances the overall understanding of seismicity patterns and contributes to the development of more comprehensive models for earthquake prediction and risk assessment [7]. The Saudi seismic networks, while collectively providing crucial insights into the seismicity of the Red Sea, are likely to report discrepancies in the parameters of earthquake sources, primarily due to variations in their operation times and spatial coverages. Each network, operating independently with distinct timelines and geographical focuses, may capture differing aspects of seismic events. These discrepancies can manifest in variations in reported earthquake magnitudes, depths, and epicentral locations, influenced by the specific technological capabilities and spatial distribution of the seismographs in each network [8]. Furthermore, the coverage area of each network, whether it's concentrated in specific regions or spread across a broader area, can lead to differences in the detection and characterization of seismic events, particularly in the spatially complex and tectonically active Red Sea region. These variations underscore the importance of integrating data from all networks for a more comprehensive understanding of the Red Sea's seismicity [9]. Traditionally, seismologists have relied on statistical methods to analyze seismic data, but these methods often struggle with the complex, multi-dimensional nature of the data generated by seismic networks [10]. Machine learning approaches, particularly the implementation of Isolation Forests [11], have opened new avenues for seismologists to identify discrepancies in seismic data, a crucial aspect in the study of earthquake phenomena. Isolation Forests are an ensemble-based machine learning algorithm, primarily designed for anomaly detection.