# Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)  
[PERSON]  
1 National Institute for Earth Physics, Calugareni 12, 077125 Magurele, Romania; [EMAIL_ADDRESS] (C.I. ); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 National Institute for Earth Physics, Calugareni 12, 077125 Magurele, Romania; [EMAIL_ADDRESS] (C.I. ); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 National Institute for Earth Physics, Calugareni 12, 077125 Magurele, Romania; [EMAIL_ADDRESS] (C.I. ); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
1 National Institute for Earth Physics, Calugareni 12, 077125 Magurele, Romania; [EMAIL_ADDRESS] (C.I. ); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.M.)  
[PERSON]  
2 Sch.C. Elect  
introducing a complementary service. This new system aims to inform authorities of the possible occurrence of seismic events with a lead time of several hours, while ensuring an optimal cost-benefit ratio. The Vrancea region, which has the highest seismic potential in Romania, is the focus of this effort, primarily to protect Bucharest--the country's largest and most vulnerable city. This article outlines the evolution of the OEF system developed by the National Institute for Earth Physics (NIEP), with an emphasis on its decision-making methodology, which is based on the weighted analysis of anomalies (events) detected during earthquake forecasting. Although the theoretical foundations of the system were established several years ago, its development has largely depended on the availability of funding. The most significant recent project supporting this development was AFROS (Analysis and Forecasting of Romanian Seismicity, 2021-2023), which focused on fundamental research and improving seismicity assessment methods for the Vrancea region ([[http://afros.infp.ro/project_en.php](http://afros.infp.ro/project_en.php)]([http://afros.infp.ro/project_en.php](http://afros.infp.ro/project_en.php)), accessed on 2 March 2025). Previous studies have detailed the network's structure, instrumentation, and evolution [1; 2; 3; 4]. In the AFROS project, the monitoring network was expanded with a broader range of instruments and included applications related to climatic influences. Many existing OEF systems rely solely on current earthquake activity and are based on statistical models such as ETAS (Epidemic Type Aftershock Sequence), ETES (Epidemic-Type Earthquake Sequence), EEPAS (Every Earthquake a Precursor According to Scale), STEP (Short-Term Earthquake Probability), Omori-Utsu, and Gutenberg-Richter laws [5; 6; 7; 8]. However, these models have not been validated for the Vrancea region, which is characterized by both crustal and intermediate-depth earthquakes that do not necessarily produce aftershocks. For example, [PERSON] et al. [5] described a real-time aftershock forecasting system in Japan using only seismicity data, despite the availability of radon monitoring for earthquake prediction [9]. In that study, event detection was based on singular spectrum transformation (SST), applied directly to raw observational data without removing seasonal variations. The same study also explored the use of Random Forest (RF) algorithms, a form of ensemble machine learning. Although NIEP's OEF does not currently employ machine learning techniques, it collects the necessary data and does incorporate seasonal variation analysis in anomaly detection. A key advantage of the NIEP monitoring network is its integration of both seismic and geophysical data, including various precursors such as radon, CO2, telluric fields, very low frequency (VLF) radio signals, and others. The development of New Zealand's OEF system, as described by [PERSON] et al. [6], involves hybrid models combining short-term and medium-term, time-invariant forecasts based on seismic catalog data. These methods rely on the accuracy and real-time updating of catalogs, as well as the conversion between different magnitude scales.