An in-dePTh AnAlysis of seismic ePisodes: The exAmPle of The 2010 sAmPeyre swArm And The 2013 luniGiAnA sequences. barani, r. de ferrari, G. ferretti, m. Pasta, d. scafidi, d. spallarossa, c. TurinoDISTAV, Università degli Studi di Genova, Italy

Foreword and scope of work. The analysis of earthquake sequences and swarms provides useful hints to identify and characterize the seismogenic structures of an area, to investigate earthquake source properties, to study the propagation of seismic waves, and to investigate the recurrence of earthquakes. Accurate location methods, such as those based on the “master event” (e.g., Peppin et al., 1989; Joswig and Schulte-Theis, 1993; Cattaneo et al., 1999) or “double difference” (e.g., Waldhauser and Ellsworth, 2000; Zhixian et al., 2003; Yang et al., 2005) techniques, and seismogram cross-correlation are usual tools in these fields of research. For instance, seismogram cross-correlation has often been applied to identify distinct lineaments belonging to a fault system and to define clusters of dependent events (e.g., Shearer, 1998; Astiz et al., 2000). Similarly, the double-difference technique has been widely used to study fault structures and to investigate the spatial and temporal evolution of seismic sequences or swarms (e.g., Prejean et al., 2002; Schaff et al., 2002; Waldhauser and Ellsworth, 2002; Fukuyama et al., 2003). Note that these methods have been generally applied to analyze low-to-moderate magnitude seismicity episodes, including micro-seismicity events and aftershocks. Indeed, location of small earthquakes is one of the primary tools used by seismologists in order to constrain fault locations and orientations and to study the seismogenic process.

In this paper, we apply a comprehensive approach, including major techniques used to analyze sequences and swarm (i.e., waveform cross-correlation, double-difference location, and micro-event detection), to exhaustively investigate the Sampeyre swarm (left panel of Fig. 1), which occurred between October and November 2010 in the Southwestern Alps, and the recent Lunigiana sequence which started on June 21, 2013 in the Northern Apennines (Fig. 2).

Regarding the Sampeyre swarm, 550 earthquakes (the strongest one had a magnitude of 3.2) were recorded and localized by the Regional Seismic network of Northwestern Italy – RSNI

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(http://www.distav.unige.it/geofisica/) using standard monitoring procedures. Approximately 2800 micro-earthquakes were subsequently identified using an automatic detection algorithm based on the STA/LTA (short-term average/long-term average) triggering method (e.g., Withers et al., 1998; Sharma et al., 2010).

The Lunigiana sequence shocked an area comprised between the provinces of Lucca and Massa Carrara. The main shock, with local magnitude of 5.2, occurred near the municipalities of Minucciano (Lucca) and Casola in Lunigiana (Massa Carrara). More than 1800 aftershocks, with magnitude up to 4.4, were recorded by the RSNI network following the main shock.

The analysis of such seismic episodes is performed both to study the causative sources and to investigate possible changes in the scaling exponent (b-value) of the Gutenberg and Richter (1944) relationship within the rock volume involved during the events.

Fig. 1 – Left panel: distribution of the instrumental seismicity (Ml ≥ 2.0) recorded by the RSNI network in the area surrounding the Sampeyre swarm; the events belonging to the investigated seismic crisis are displayed by red points. Right panels: seismicity distribution (top panel) and longitudinal cross section of hypocenters (bottom panel) after double-difference location. Light- and dark-gray circles indicate the events belonging to Family 1 and Family 2, respectively.

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Area seismotectonics. Concerning seismotectonics, the Sampeyre area is located in the inner part of the Dora Maira crystalline massif, which corresponds to the northern Tethyan margin (part of the stretched European continental crust) exhumed during the collision of the Eurasia and Africa plates. The Dora-Maira massif is a large tectonic window that crops out as a broad half-dome. On the eastern side, it is delimited by steep faults and it is directly onlapped by Tertiary deposits of the Po basin (Wheeler, 1991; Michard et al., 1993; Avigad et al., 2003). On the western side, it structurally underlies the oceanic Penninic nappes, under which it subducted during the early Alpine events (Eoalpine stage). The Dora Maira massif is located near the border between the internal (Penninic zone, nearly corresponding with the axial sector of the Alpine chain) and external (eastern sector at the Po Plain border) sectors of the Western Alps where the tectonic regime changes from (prevalent) extension to compression.

Fig. 2 – Top panels: seismicity distribution (left panel) and seismic cross-section (right panel) after double-difference location. Bottom panels: seismic families identified by EQUI (left panel) and FIVI (right panel) stations, respectively.

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In this area, two distinct seismicity trends, one following the Penninic front to the west and one following the Austro-Alpine front to the east, converge delimiting an almost aseismic corridor (Giglia et al., 1996). Following the large-scale zonation of Delacou et al. (2004) and Barani et al. (2010), the Dora Maira massif can be ascribed to the internal sector of the Western Alps, a continuous zone of extension characterized by low-to-moderate but relatively frequent earthquakes with hypocenters down to approximately 15-20km depth. Analyzing focal mechanism solutions and Global Positioning System (GPS) data shows that extension (i.e., T-axes) is virtually perpendicular to the structural trend of the Alps (e.g., Eva and Solarino, 1998; Calais et al., 2002; Nocquet and Calais, 2003, 2004), following a radial pattern (e.g., Frechet, 1978; Nicolas et al., 1990; Champagnac et al., 2004; Delacou et al., 2004). The focal mechanisms of the two major events of the swarm, which were computed by applying the first onset methodology through the FPFIT program (Reasenberg and Oppenheimer, 1985), agree with the regional seismotectonics, which, as stated above, is characterized by a prevalent extensional-transtensional regime. In particular, the orientation of the T-axis is in agreement with the direction of extension (approximately E-W) in this sector of the Alps.

Regarding the second seismic episode considered in this study, the Lunigiana-Garfagnana area represents the western border of the northern Apennines. This area is characterized by a complex structural setting which is related to the evolution of the northern Apennines, including three major episodes: 1) consumption of oceanic crust driven by a west-dipping slab, 2) post-collisional evolution, and 3) rotation of the Corsica-Sardinia block with opening of the Tyrrhenian sea (Cattaneo et al., 1983; Ponziani et al., 1995). The structures derived from these main events are related both to compressive and extensive forces. Compression, which is associated to the collision between Africa and Europe, is responsible for the emplacement of different tectonic units belonging to different paleogeographic domanis (the Liguride allochton formed by oceanic crust, the Tuscan unit, and the Umbria-Marche-Romagna unit) (Castaldini et al., 1998). Extensional stresses are due to the roll-back of the subducting Adria-Ionian lithosphere (Negredo et al., 1999), which created tectonic depressions such as the Lunigiana and Garfagnana basins (Bartolini and Bortolotti, 1971). The Lunigiana and Garfagnana basins are two NW-SE-striking asymmetric grabens, originated in the hanging wall of regional low-angle detachment faults, sloping gradually beneath the Apennine chain (Brozzetti et al., 2007). The Lunigiana sequence took place between the Lunigiana and the Garfagnana basins, activating a fault system with anti-apenninic direction. The focal mechanism of the major event (Ml = 5.2), as derived from the INGV moment tensor solution database (http://ingvterremoti.wordpress.com/), shows a strike-slip rupture process confirming that the fault responsible of the main shock is NE-SW oriented.

Methodology. The waveform similarity analysis is used in this work to identify and characterize the seismogenic structures activated during the two seismic episodes. This technique is used to identify groups of events (i.e., earthquake families) characterized by similar locations, source mechanisms, and by similar propagation patterns. The waveform similarity analysis is performed on signals recorded along the vertical component by the seismic stations nearest to the area affected by the seismic events. More precisely, DOI and PZZ stations (Fig. 1) are considered for the Sampeyre swarm whereas EQUI and FIVI stations (Fig. 2) for the Lunigiana sequence. The cross-correlation analysis is performed on selected subsets of events with magnitude greater than or equal to 1.0, gap in azimuth coverage (i.e., maximum azimuthal distance between two nearby-distance seismic stations) less than or equal to 200°, horizontal and vertical location error less than 5 km. First, all waveforms are filtered by a band-pass filter to reduce the bias of noise and high frequency wiggles as well as minor waveform dissimilarities due to differences in magnitude, focal mechanisms, and small-scale heterogeneities. Then, seismogram cross-correlation is performed on pairs of signals including both the P-wave and S-wave onsets by using the normalized cross-correlation function (e.g., Augliera et al., 1995; Cattaneo et al., 1997, 1999). Multiplets are defined as groups of events

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with cross-correlation coefficient greater than a minimum threshold, defined by trial and error in order to recognize the minimum number of families that allows us to correctly reproduce the spatial distribution of earthquakes. In order to overcome possible dissimilarities between events differing from each other by more than one order of magnitude, the bridging technique, which is based on the Equivalence Class approach (Press et al., 1988), is applied. Finally, differential times derived from earthquake cross-correlation (computed for all available station within 50km from the epicenters) are used in conjunction with travel-time differences from manually picked P- and S-phases to relocate the events using the double-difference (DD) algorithm “HypoDD” (Waldhauser and Ellsworth, 2000; Waldhauser, 2001). Results are discussed in the next section.

Results and discussion. The right panel of Fig. 1 shows the hypocenter distribution obtained after the relocation of the earthquakes belonging to the Sampeyre swarm. The figure indicates the existence of two nearby sources, recognized as two distinct families by the waveform similarity analysis; one (Family 1) is shallower and collects 12 events characterized by impulsive P-wave arrivals and one (Family 2) is slightly deeper and collects a larger number of earthquakes (72). As evident from the seismic cross section in the bottom panel of Fig. 1, both sources would present high-angle dipping planes directed towards S-SE, compatible with the geological setting of the area where a SE-dipping shear zone occurs in the lower part of the Maira Valley and along the southern side of the Varaita Valley (Balestro et al., 1995).

In order to prove the hypothesis about the activation of two distinct sources, we compare the b-value of the Gutenberg and Richter (1944) relationship for the two earthquake families. To improve the completeness of the two data sets at lower magnitudes, an automatic procedure for detecting micro-seismicity is applied to the stream of waveforms recorded by DOI and PZZ stations. The procedure uses the STA/LTA triggering method with parameters (e.g., filter band, length of the STA and LTA windows, STA/LTA threshold) calibrated for the two stations on the grounds of the ambient noise and micro-earthquake duration. Specifically, the algorithm implements a coincidence system which detects a potential earthquake whether both DOI and PZZ signals exceed the STA/LTA threshold (= 3) within a common 10s window. Approximately 2800 micro-earthquakes are identified and then separated into distinct clusters via cross-correlation analysis. In particular, 40% of micro-earthquakes are joined into families: 280 of them (those characterized by a very impulsive P-wave arrival) are associated to Family 1 while 592 to Family 2. b-values are calculated by applying the maximum likelihood method proposed by Weichert (1980). A b-value equal to 0.98 (+/- 0.05) is obtained for Family 1 while a b = 0.81 (+/- 0.03) for Family 2. The two magnitude-frequency distributions are shown in Fig. 3. Comparing the b-values indicates that Family 1 and Family 2 are characterized by a different proportion of small and larger earthquakes, thus suggesting that they do not come from the same population. To verify this, the Utsu’s p-test (Utsu, 1992) is applied. The test confirms our hypothesis that the 2010 Sampeyre swarm is the consequence of the activation of two distinct interacting fractures having different seismic productivity. Given the small difference

Fig. 3 – Cumulative magnitude-frequency distributions for Family 1 and Family 2.

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in depth between the two earthquake families, fault heterogeneity, variation of rheological properties, and pore pressure variation appear reasonable causes for b-value variation observed in this application.

Fig. 2 (top panel) shows the preliminary results obtained for the Lunigiana sequence. The locations of the events obtained by using HypoDD indicate the existence of three different clusters. In particular, two of them could be associated to an anti-Apenninc system of faults oriented NE-SW, compatibly with the trend of the main shallow strike-slip faults linking the graben basins in the northern Apennines The third group of events, which is located near the FIVI station, seems to follow a NW-SE tectonic lineament (Apenninic direction). Looking at the NE-SW seismic cross-sections in Fig. 2, the anti-Apenninic sources present low-angle dipping planes, in accordance with the tectonic setting of the area. These observations are confirmed by the results of the waveform similarity analysis (Fig. 2, bottom panel), again showing the existence of different clusters of seismicity oriented both NE-SW and NW-SE. However, different families of earthquakes are identified along the same lineament. For instance, the southern NE-SW cluster appears to be characterized by three distinct families, indicating a very complex fault system.

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