## 2 Materials and Methods
### Location and Relocation of the Catalogue  
We employed a dataset of over 9000 events that occurred during 2013-2014 in the WGoC. Preliminary event detections and arrival-time data for these events were available from the websites of the Geodynamics Institute of the National Observatory of Athens (GI-NOA; [[https://bbnet.gein.noa.gr/HL/](https://bbnet.gein.noa.gr/HL/)]([https://bbnet.gein.noa.gr/HL/](https://bbnet.gein.noa.gr/HL/))) (accessed on 9 July 2021) and CRL [[http://ephesite.ens.fr/~eworm/](http://ephesite.ens.fr/~eworm/)]([http://ephesite.ens.fr/~eworm/](http://ephesite.ens.fr/~eworm/)) (accessed on 9 July 2021). Additional P- and S-wave arrival time picks at local stations of CRL and HUSN were manually added to enrich the dataset, which herein is referred to as \"Catalogue 1\", or briefly, Cat1. Initial locations were obtained with the HypoInverse code (HipoInverse, 2021), using the local velocity model of (HipoInverse, 2021) for the WGoC. The only exception is the seismic cluster of the 2013 Helike swarm, for which an optimal velocity model ([PERSON], 2016) was employed. We applied a first approximation of station corrections, by removing the average P- or S-wave travel-time residual on each station and recalculating locations. At this point, we separated the dataset into spatial groups based on the hypocentral distribution. Initial locations indicated that seismicity related to the shallow structures of the WGoC reaches a depth of -15 km. Seismicity at depths \(Z<15\) km was divided into clusters with the application of [PERSON]'s ([PERSON], 2021) linkage to the matrix of inter-event hypocentral distances. Smaller clusters were merged by visual inspection to produce a total number of 9 spatial groups (Figure 2). The seismicity around the margins of the study area or deeper than 15 km was placed in a 10 th \"miscellaneous\" group. All the following procedures were performed independently for each spatial group. At least one reference station, in close proximity to the respective epicenters, was selected to perform waveform cross-correlation. Each recording was band-pass-filtered in the range 2-15 Hz (e.g., ([PERSON], 2016; 2016) and then cropped in windows containing P, S and coda waves. A correlation matrix was constructed by registering the global maximum (\(X\)C\({}_{\text{max}}\)) of the cross-correlation function between all event-pairs with available recordings at the reference stations. Then, a combined correlation matrix was calculated, taking into account the maximum value of the individual matrices for each event-pair. Nearest-neighbor linkage was performed and each group was divided into multiplets by selecting an optimal threshold, \(C_{\text{th}}\). The latter is determined as the value that maximizes the difference between the size of the largest cluster and the sum of clustered events (HipoInverse, 2021), with a minimum of 0.60. Finally, for event-pairs within each multiplet, cross-correlation was performed on the recordings of all stations with available phase-pick data, in windows containing only P- or S-waves, after alignment of the waveforms on their respective P or S arrival-times. The \(X\)C\({}_{\text{max}}\) and its corresponding time-lag were registered on each case. These data, along with catalogue travel-times, were used as input for the double-difference relocation code, HypoDD (HipoDD, 2021). This algorithm performs iterative inversion to minimize the double-difference between observed and calculated travel-times in pairs of neighboring events. The HypoDD code relocates hypocenters by attributing their travel-time differences to their inter-event distance, reducing uncertainties caused by unmodelled velocity structure and arrival-time reading errors. As a result, spatially correlated events become more concentrated and aligned, increasing resolution and highlighting the activated structures. Location statistics for Catalogue 1 are presented in the Supplementary Section S1.