# Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)
## 3 Data Collection and Calculation Results, a Case Study  
The case study refers to the earthquake of 16 September 2024, with a local magnitude of 5.4 (Figure 1). This was the first earthquake with a local magnitude greater than 5 since 2022. In Figure 1, the faults, the position of the epicenter, and distances to the PLOR, PANC, BISRR, LOPR, and NEHR monitoring stations used in Table 1 are marked. The decision matrix is based on the number of detected events. In this example, the detection is based on exceeding predefined thresholds (trigger levels), and the method is analyzed and compared to the Allen-type STA/LTA (Short-Term Averages/Long-Term Averages) detection algorithm ([15; 16; 17]) applied to integrated signals. Figure 2 refers to the level-based detection (time series) compared to the STA/LTA method in Figure 3, which uses large time series processed on the NIEP servers. The first graphic in Figure 3, \"Integral Bisoca radon STA/LTA\", is the first time series of Figure 2, \"Bisoca radon Bq/m3\", after the integration and application of the STA/LTA method. The red dots were generated by STA/LTA and represent the detection events. The number of events was introduced in the decision matrix every day after a weighting (Table 1). The detection of events could be done on time series applying a trigger method (Figure 2) but the results were better using STA/LTA algorithm (Figure 3). The results of the decision matrix (Table 1), referred to as the OEF AFROS signal, indicate an increase 3.6 days before the earthquake, as shown in Figure 2. A similar analysis conducted over a longer time interval--two months prior to the earthquake with a local magnitude of 5.4--suggested that a one-month time window is sufficient for the decision matrix. This confirms that the method, which is based on threshold exceedance, is effective. For gas emissions (radon and CO\({}_{2}\)), a threshold-based trigger method was applied. This method involves detecting when the emission level exceeds a predefined threshold and then drops back below 99.96%. The outcome is a series of real-time detections transmitted as 'Event' files from the monitoring stations to the analysis server. Each station is assigned a weight in the logical decision tree, determined experimentally through an offline analysis. The maximum number of exceedances (events) per station was set at 4, a value established specifically for the Vrancea region. It was also experimentally determined that for earthquakes of local magnitude greater than 4 with epicenters in Vrancea, a four-day window captured the detection groupings from all stations. These findings are summarized in Table 1. Figure 4 illustrates the results for the earthquake of magnitude 5.4 that occurred on 16 September 2024, based on the monitoring stations listed in Table 2.  
In this example, the channel weights (Channel Weights) in the logical decision tree are equal. With eight stations, each was assigned a weight of 0.125, resulting in a total weight sum of 1 (8 \(\times\) 0.125). The algorithm allows for flexible adjustments, including:  
* Differentiating radon and CO\({}_{2}\) weights;
* Modifying weights based on a station's distance from the hypocenter;
* Changing the number of stations;
* Altering the maximum number of daily detections per station (set to 4 in this example);
* Adjusting the four-day grouping period for detections. Figure 1: Case study, location of the earthquake of 16 September 2024, with a local magnitude of 5.4. The crustal faults, distances from monitoring stations LOPR, BISRR, PANC, PLOR, Google Earth picture.