Mapping micro-seismicity around a nuclear power station in stable South Africa through machine learning

Ground motion caused by near-source seismic waves from shallow earthquakes can be dangerous to vital infrastructure such as nuclear power plants. South Africa is a stable continental region (SCR), however significant seismicity is known to occur. Nearby Cape Town, and the Koeberg Nuclear Power Station, historical sources record an earthquake with a potential magnitude of 6.5 in 1809. On September 29th, 1969 the magnitude 6.3 Ceres-Tulbagh earthquake affected an area less than 100 kilometers of the Koeberg Nuclear Power Station. These events emphasize the need to take the potential seismic hazard in this area seriously. Previous research has shown that the source zones of historic and even prehistoric SCR earthquakes are frequently related with enhanced microseismicity over hundreds or even thousands of years. This study seeks to investigate possible source zones for the 1809 event, and possible sources of future damaging earthquakes, by establishing whether earthquakes can be detected on regional structures. To accomplish these goals, we deployed 18 3-component seismographs over a 40-by-35-kilometer area near the Koeberg Nuclear Power Station. The network, which covered the Colenso fault zone, was also near the postulated Milnerton fault, the Ceres-Tulbagh region, and the Cape Town area. The network recorded for three months between August and October 2021. We looked for seismicity around known structures, like the Colenso fault, using supervised machine learning algorithms like PhaseNET, traditional STA/LTA algorithms, and manual inspection in addition to unsupervised machine learning algorithms such as Density-based spatial clustering of applications with noise (DBSCAN) and Bayesian Gaussian Mixture Models (BGMMs). We found 35 occurrences dispersed throughout our research area. These events appear to be organized into three broad groups, the first being an offshore cluster outside of the study region, and the second being a scattered cluster between the Colenso fault system and the postulated Milnerton Fault. The third concentrates on the Colenso Fault system, implying that it may be active. Additional results from our research show that traditional methods like STA/LTA are far less accurate at detecting micro-seismic events than manual inspection of waveform data and machine learning (i.e., where the unsupervised and supervised machine learning algorithms get combined to form an earthquake identification tool).