# Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)
## 4 Discussion
A moving time window is used to calculate the \"b\" value and \"Mc\" (magnitude of completeness) from the Gutenberg-Richter law. In practice, this window can vary and typically includes a predefined number of earthquakes to ensure accurate determination of these parameters. In Figure 9, a fixed time window of 7 days is used, based on the analysis of seismicity in the Vrancea Zone. Errors may occur when the number of earthquakes is too small or absent; such anomalies may indicate a deviation from the normal seismic rhythm and can act as precursors to changes in seismicity. Figure 9 highlights a distinctive feature of the Vrancea region: the clustering of earthquakes interspersed with periods of \"seismic silence\". When using a fixed time window, the focus shifts from absolute values to tracking the variations of \"b\" and \"Mc\" over time. Article [2] examines the seismicity in the Vrancea region and the frequency-magnitude distribution of earthquakes at different depths. The time windows used for analyzing  
temporal variations of the \"b\" value are defined by a fixed number of events to ensure stable calculations. Incorporating geophysical data (e.g., radon, CO\({}_{2}\)) into Operational Earthquake Forecasting (OEF) requires additional methods. However, this integration enhances the reliability of seismic forecasts and enables exploration of broader topics, such as the impacts of global warming, thereby justifying the added complexity. Table 3 in the article \"Monitoring of Gas Emissions in Light of an OEF Application\" [2] presents a comparative analysis between seismic parameters (magnitude, depth, epicentral distance) derived from the Gutenberg-Richter law (GR a-b) and various geophysical indicators (radon, CO\({}_{2}\), ions, etc.). Table 2 of the same article reports a 50% probability for GR a-b, comparable to other geophysical metrics. A key difference between an OEF based solely on seismicity and one that also integrates geophysical data lies in real-time anomaly detection. The latter allows for near-instantaneous forecasting, as seen with Rapid Earthquake Warning Systems (REWS). The proposed solution is to combine seismic and geophysical analyses for cross-validation, as demonstrated in the TURNkey project. Another limitation of relying exclusively on seismic data is the delay inherent to time-windowed analysis (7 days in this case), as well as the infrequency of significant local earthquakes (magnitude > 6). The case study presented--a magnitude 5.4 event on 16 September 2024--is the first such earthquake exceeding magnitude 5 since 2022. This underscores the need for a robust geophysical monitoring network, which also contributes to understanding climate change effects. In general, the statistical analyses discussed in the articles are retrospective and not conducted in real time. This limitation further motivates the inclusion of geophysical data in our analyses to enhance forecasting accuracy and relevance. An earthquake forecast must determine the location, magnitude, and time of an earthquake. In the case of NIEP OEF, the target area is Vrancea, where a single threshold value for the magnitude is used (>4.5 R). A more precise localization can be achieved based on the number of events per station (Table 3). A real-time seismic prediction system based on artificial intelligence and data from a network of electromagnetic and geoacoustic sensors was implemented in [31] for the seismogenic area in southwest China. The results provide predictions for the location and magnitude of potential earthquakes occurring within the following week, based on data from the current week. This approach uses historical data from 2016 to 2020 and assumes the existence of repeating patterns. However, this assumption is uncertain, as the effects of global warming may influence seismic activity. Moreover, in the case of [31], we did not find any web platform displaying real-time seismic forecasts.