The fields of flatfile are grouped as: (1) Event metadata; (2) Site metadata; (3) Metrics of event-to-site distances; (4) Intensity measures.

The 667 events are defined by their location (Latitude, Longitude, depth), origin time (date), and moment magnitude (M). Magnitudes were taken from the Integrated Seismic Catalog for Colombia (Montejo et al., 2023). This catalog was reviewed for locations and homogenised in M. For the events not included in the dataset of Montejo et al. (2023), we followed the same criteria, prioritizing locations made by ISC-EHB (Weston et al., 2018; International Seismological Centre, 2026) and SGC. M was compiled from the Global CMT catalog (Ekström et al., 2012), USGS-NEIC, ISC, and SGC. Focal mechanisms were taken from GCMT and SGC. Geometry of the rupture plane is a simple description from the nodal plane preferred from the focal mechanism selected by the authors of this dataset from the geological context, with strike, dip, and rake for slip kinematics.

M is the preferred magnitude in GMPMs and PSHA. Magnitude range is 3.5 to 7.2. When M was not available, mb or ML values were taken depending on availability. To homogenise these magnitudes into M, we estimated corner frequencies (fc) for each station-event recording. A single-corner ω-2 Brune (1970) source model was then fit to each corrected spectrum via a coarse grid search followed by non-linear least-squares refinement in log-log space, minimizing the residual between the observed and predicted spectral shape. From the resulting fc, the source radius r was computed following Brune (1970), r=2.34β/(2πfc), using a shear-wave velocity β= 3.5 km s⁻¹ representative of active continental crust. The seismic moment M0 was then obtained from the Eshelby circular-crack relation, M0=(16/7)(Δσ)r3, under an assumed constant stress drop (Δσ), and converted to M following Hanks and Kanamori (1979), M=(2/3)log₁₀(M0) - 10.7. Values of Δσ=1,3, and 5 MPa were tested and compared against independently reported M (GCMT and other M solutions) to select the value that best reproduced the catalog magnitudes. Ultimately, a stress drop of 5 MPa was chosen as it yielded the best agreement. This calculated magnitude was used when a reliable agency-reported M was unavailable.

The 227 sites featured in this dataset have been installed, maintained, and administered by SGC (Fig. 1). Of these, 154 sites are also present in the Mercado et al. (2024) database for north-western South America, where the predominant site period (Tn) and the H/V ratios in the Fourier (HVFSR) and pseudo-spectral acceleration (HVRSR) domains were derived from seismograms, they also reported the amplitude of the HVSR at the peak corresponding to Tn (P*). Within this subset, 28 sites additionally have in-situ, microtremor-based measured VS30, available from Mercado et al. (2024). For the remaining sites without in-situ measurements, we report an inferred VS30 taken from the topographic-slope-based VS30 map for Colombia (Eraso and Montejo, 2019).

Epicentral (Repi) and hypocentral (Rhyp) distances and Azimuth are reported for the entire dataset. Additionally, finite fault distances were computed from the geometry of the rupture plane derived from the preferred nodal plane for events with M> 5.5. Rupture dimensions were computed following Strasser et al. (2010) for subduction interface-related earthquakes and following Wells and Coppersmith (1994) for continental events. The hypocenter was placed at the midpoint of the fault plane, but for earthquakes with hypocentral depths shallower than 10 km, the midpoint of the rupture plane was placed at 12.5 km depth as the average thickness of the seismogenic depth. Joyner-Boore distance (RJB) was computed as the closest horizontal distance from the site to the surface projection of the rupture plane. Rx represents the horizontal distance from the top edge of the rupture measured perpendicular to the fault strike, with positive and negative values indicating sites on the hanging-wall (Rx>0) and footwall (Rx<0) sides, respectively. Ry0 is the horizontal distance measured parallel to the strike from the fault midpoint.

For the waveform processing, we followed the steps presented by Paolucci et al. (2011) and Lanzano et al. (2019):

Baseline correction;

For the bandpass filter limits, we used median values from Lanzano et al. (2019). As they pointed out, the low-pass filter frequency (flp) is magnitude independent, we took the mean value of 32.26 Hz, while for the high pass (fhp) filter we used a magnitude-dependent function as: 1fhp=0.15HzforM<4fhp=-0.05M+0.35Hzfor6≥M≥4fhp=0.05HzforM>6.

We test the sensitivity of the parameter RotD50 peak ground acceleration (PGA) with the fixed flp of the bandpass filter at 32.26 Hz, taking the records and changing flp in a range of 27 to 37 Hz. The sensitivity analysis shows that most records are weakly affected by the choice of flp, as the average variation is 2.31 % and the 90th quantile is 7.0 %. However, 6.4 % of the recordings exhibit variation larger than 10 % in RotD50 PGA. These records are concentrated around M 4.5 and Repi> 250 km (Fig. A1). These differences are likely related to high-frequency signal content or noise close to the selected filter corner frequency, which could affect the peak acceleration amplitude.

Fourier Amplitude Spectrum (FAS) from the acceleration records in the three components are computed in the frequency range 0.04 to 50.00 Hz, applying Konno and Ohmachi (1998) function with smoothing (Lanzano et al., 2019). An example of FAS computed for an M 6.1 earthquake recorded at 13 km is shown in Fig. 2.

Usable frequencies are limited to those between the high-pass and low-pass by a safety factor of 1.25 to ensure that the filters do not have a significant effect on the response spectral values (Abrahamson and Silva, 1997). Hence, the usable bandwidth decreases at low frequencies for small magnitudes, and the upper limit for our dataset is 25.808 Hz. Lowest usable frequencies (LUF) are: 2LUF=0.1875HzforM<4,LUF=-0.0625M+0.4375Hzfor6≥M≥4,LUF=0.0625HzforM>6.

To facilitate direct use of the usable frequency range by the community, we added two columns to the FAS dataset – LUF_Hz and UF_Hz – reporting the lowest and highest usable frequency for each record, respectively.

A compilation of Effective Amplitude Spectrum (EAS) (Kottke et al., 2018), defined in Eq. (3), binned by magnitude, is shown in Fig. 3.

The dataset includes IMs that are independent of sensor orientation, calculated using the median and maximum spectral ordinates over all non-redundant horizontal orientations, RotD50 and RotD100 (Boore, 2010), respectively. All spectral values were computed from the corrected accelerograms using standard response-spectrum analysis. Furthermore, the EAS is computed from the two horizontal components of FAS. The following IMs are provided:

RotD50 PGA from the horizontal components.

For validation purposes (Bindi et al., 2019), we predicted PGA, SA (RotD50), and EAS IMs of the continental earthquakes in our dataset (488 events) – to compute residuals (Eq. 4) – using the input parameters in the GMPM.

The GMPMs used here are the global development of Abrahamson et al. (2014) – made with the NGA-West 2 dataset – and the one of Arteta et al. (2023), which is a regional development for Northern South America of Abrahamson et al. (2014).

The comparison between observed and predicted IMs provides a consistency check for the data quality and processing methodology as shown in Fig. 6, where the total residual (δtotal) is plotted against Rhyp and M, without significant biases.

The δtotal can be decomposed into between-event (δBe), between-site (δS2Ss), and leftover residuals (ϵ) (Al Atik et al., 2010) as: 4yes(T)=ues(M,Res,VS30,T)+δBe+δS2Ss+ϵ where y is the natural logarithm of the observed IMs from an individual earthquake, e, with magnitude M observed at site, s, with VS30 located at a distance Res. ues is the predicted median IMs from the GMPM.

The total standard deviation of the GMPM (σ) can be written as: 5σ=τ2+ϕS2S2+ϕ02 where τ, ϕS2S, ϕ0 are the standard deviation of δBe, δS2S_s and ϵ respectively.

Following residual decomposition of Eq. (4), we get δBe and δS2S_s to show their distribution, results are shown in Figs. 7 and 8, respectively, and compiled in Table 1.

Trends of δBe observed in Fig. 7 support the consistency of this dataset, where both GMPMs show low variability. As expected, the regional GMPM (Arteta et al., 2023) shows lower overall variability than the global one (Abrahamson et al., 2014). Within the M [4.2, 4.7) range, around M 4.5, δBe in Arteta et al. (2023) shows a negative trend, suggesting that further magnitude calibration at these levels could help reconcile this discrepancy.

Given that only 28 of the 227 sites in this dataset have in-situ, microtremor-based measured VS30 (Mercado et al., 2024), the remaining sites rely on inferred VS30 from a topographic-slope-based proxy (Eraso and Montejo, 2019). We argue that this proxy-based approach could introduce additional epistemic uncertainty into the site parameter, potentially increasing the ϕS2S reported in this study. Some authors, such as Mercado et al. (2024), have proposed site classification schemes based on H/V spectral ratio characteristics (HVRSR and HVFSR) as a robust alternative/complement to VS30 based proxies for constraining site response in future applications of this dataset.

For Fourier IMs, we follow the same procedure with the EAS (5 Hz), comparing it with the GMPM of Bayless and Abrahamson (2019). Results are shown in Fig. 9. Statistical values computed are shown in Fig. 9 and Table 1.

We check the mean, standard deviation, and Gaussian-like shape of ϵ, and the Shapiro-Wilk test for normality. Residuals are Gaussian-like distributed for this dataset with respect to both GMPMs, the global and the regional developed one (Table 1 and Figs. A2 and A3). The same result is determined for the Fourier IMs EAS (Table 1 and Fig. A4).