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The 2009 Mw6.1 L'Aquila normal fault system imaged by 64,051 high-precision foreshock and aftershock locations.

<p>The earthquake catalogue is composed by 64,051 high-precision foreshock and aftershock recorded during&nbsp;the Mw6.1 2009 L&#39;Aquila (Central Italy) normal faulting seismic sequence.&nbsp;The catalog includes events occurred between 1<sup>st</sup> of January and 31<sup>st</sup> December 2009. The completeness magnitude is&nbsp;0.7. Earthquake locations were obtained by combining an automatic picking procedure for P and S&nbsp;waves, together with cross-correlation and double-difference location methods.&nbsp;</p> <p>Seismic data were recorded at a&nbsp;very dense local network composed of 67 three-component&nbsp;seismic stations (20 permanent stations of the Italian National Network&nbsp;located within 80 km from the epicentral area and&nbsp;47 temporary stations&nbsp;installed soon after the occurrence&nbsp;of the main shock&nbsp;[Margheriti et al., 2011]).&nbsp;</p> <p>Earthquakes were extracted by the continuous recordings by&nbsp;applying a detection algorithm to all stations, based&nbsp;on the classical STA/LTA coincidence-sum algorithm&nbsp;applied to the trace of the 3C covariance matrix.&nbsp;To these events, we applied&nbsp;an automatic picking&nbsp;algorithm (Manneken Pix)&nbsp;[Di Stefano et al., 2006] able to provide about 1.9 million P-wave and 503,000 S-wave accurate readings, with an estimation of the measurement errors.&nbsp;</p> <p>We applied a time domain cross-correlation method (Schaff and Waldhauser, 2005)&nbsp;to all&nbsp;event pairs with separation distances &le; 5 km at all stations&nbsp;that recorded the pair.&nbsp;Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter.&nbsp;We selected&nbsp;measurements with correlation coefficients greater than 0.85, resulting in a total of ~190&nbsp;million P and ~85&nbsp;million S-wave delay times.&nbsp;</p> <p>Earthquakes&nbsp;were located following a two steps procedure. Initial locations for&nbsp;133,236 events were&nbsp;computed with the Hypoellipse&nbsp;code [Lahr , 1989] using a 1D&nbsp;P-wave gradient velocity model optimized for the area [Chiaraluce et al., 2011]. In the second step, we computed relative locations by applying the&nbsp;large scale double-difference method described in Waldhauser and Schaff, (2008) to the catalog picks and phase delay times measured from waveform cross correlation.&nbsp;The entire dataset was sub-divided in 84 rectangular overlapping boxes, containing a maximum of 3000 earthquakes, orthogonal to the mean strike of the seismic sequence. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008).&nbsp;</p> <p>The final double-difference catalog includes 64,051 events. A subset made of 51,271 earthquakes (i.e., 80% of the whole dataset) indicates highly correlated earthquakes, having at least 10 P-waves and 5 S-waves correlated phases with at least one other event. Highly correlated events (flag=1 in the attached file) mostly occur on the major fault segments, while poorly correlated earthquakes (flag=0 in the attached file) mostly occur in the volume around the major faults.</p> <p>The attached file is a plain text with &quot;;&quot; separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>id_dd:&nbsp;</strong>the hypoDD unique event identifier</p> <p><strong>origin_time: </strong>date of the origin time in the format&nbsp;YYYY-MM-DD[T]hh:mm:ss.msec</p> <p><strong>lat</strong>: hypocenter latitude expressed in degrees&nbsp;</p> <p><strong>lon</strong>: hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep</strong>: hypocenter depth expressed in km&nbsp;</p> <p><strong>mag</strong>: magnitude (pure number)</p> <p><strong>flag</strong>:<strong>&nbsp;</strong>1 for highly correlated earthquakes; 0 for poorly correlated earthquakes.&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
8
Access
16
Reuse readiness
0
Engagement
4

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