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Climate Sciences Sciences Dynamics Chemistry of the Past Solid Earth Techniques Geoscientific Methods and and Physics Atmospheric Atmospheric Data Systems Geoscientific Earth System Earth System Measurement Instrumentation Hydrology and Ocean Science Annales Annales Biogeosciences The Cryosphere Natural Hazards Hazards Natural , E. Priolo 2 and Earth System System and Earth Model Development Geophysicae cients have been estimated as ffi value, possibly suggesting that the b , L. Peruzza 2

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2354 2353 2 , M. Garbin 3 Climate , 1 Sciences Sciences Dynamics Chemistry of the Past Solid Earth Techniques Geoscientific Methods and and Physics Advances in Advances Atmospheric Atmospheric Data Systems Geoscientific Geosciences Earth System Earth System Measurement Instrumentation Hydrology and 6.3, Chiaraluce et al., 2011; Lavecchia et al., 2012) with the Ocean Science Biogeosciences The Cryosphere Natural Hazards Hazards Natural = EGU Journal Logos (RGB) Logos Journal EGU and Earth System System and Earth w Model Development M , R. de Nardis 2 , 1 , and M. Romanelli 1 1.5 and 3.7 and the completeness magnitude for the Sulmona area during − This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. GeosisLab, DiSPUTer, Università G. d’Annunzio, Campus Universitario di Madonna delle Centro Ricerche Sismologiche, Istituto Nazionale di Oceanografia e Geofisica Sperimentale, Dipartimento della Protezione Civile, Via Vitorchiano, 2 – 00189 Roma, Italy earthquake (6 April, Sulmona area is currently undergoingies. high stress, The in time-space agreement distribution with of otherfaults, the as recent seismic stud- well activity the with seismogenic respect layer to thickness, the are known preliminarily active investigated. 1 Introduction A small, temporary seismometricItaly, network Fig. was 1), during deployed the in seismic the sequence Sulmona which followed area the (central devastating L’Aquila 2009 as online Supplement. Localbetween magnitude values ofthe the study newly period is detected about eventswell, 1.1. for ranges Duration comparison/integration magnitude purposes. coe Localmated Gutenberg–Richter from relationship, the esti- microseismic data, features low a total period ofmore acquisition than of 800 local aboutseismicity earthquakes 30 previously has months. unknown. been Using About detected,ing a which 70 a % semi-automatic highlight 1-D of procedure, the velocity these background quality model events estimated is have specifically checked been for and relocatedtion the discussed, Sulmona algorithms, us- area. too. with The Phase respectavailable integration readings in to of the temporary weighting region network schemes enablequake data used us locations. with to Both by all obtain the loca- the a final other statistically hypocentral data more solutions robust and dataset phase of pickings earth- are released Thanks to the installation ofbeen a conducted temporary seismic in network, theknowledge a of Sulmona microseismicity seismogenic study area potential has of (Abruzzo,months existing Italy) (from active 27 faults. with May In to the 31 this December work aim 2009) the of of first recorded data increasing seven have been the analysed, over Abstract 1 Piane – 660132 Chieti Scalo (CH), Italy Via Treviso, 55 – 33100 Udine,Italy Italy and3 Borgo Grotta Gigante 42/C – 34010 Sgonico (TS), Received: 22 April 2013 – Accepted: 11Correspondence May to: 2013 M. – A. Published: Romano 29 ([email protected]) May 2013 Published by Copernicus Publications on behalf of the European Geosciences Union. Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/1/2353/2013/ Discuss., 1, 2353–2395, 2013 doi:10.5194/nhessd-1-2353-2013 © Author(s) 2013. CC Attribution 3.0 License. Temporary seismic monitoring of the Sulmona area (Abruzzo, Italy): quality study of microearthquake locations M. A. Romano G. Lavecchia 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3.7 cient = ffi M ). Low seis- 3.8 in March = M ), a final dataset of nearly 2 readings are necessary but not su S orts: good stations coordinates, reasonable ff 2356 2355 and P 6.3 L’Aquila earthquake (see for example Margheriti = cient to constrain the problem (Husen and Hardebeck, w http://iside.rm.ingv.it/iside/standard/index.jsp ffi M -line semi-automatic procedure. In addition, they required ff -line by an o ff After presenting the temporary network in the tectonic framework of the Sulmona This paper has two main goals: (1) to quantify the precision of phase readings and In this study, we analyse the first seven months of our seismic recordings (e.g. from Reliability and accuracy in earthquake location are topics often neglected by earth- In the study area, some active faults are deemed capable of generating impend- temporary stations, after the et al., 2011). Finally, we discuss our results (Sect. 7). 2 The Sulmona temporary seismic surveyOn in 27 the May seismotectonic 2009 OGS context (Istitutoand Nazionale GeosisLab di Oceanografia (Laboratorio e di disity) Geofisica Geodinamica installed Sperimentale) a e temporary Sismogenesi,This seismometric Chieti-Pescara sector network Univer- of around the the Central Sulmona Apennines basin is (Fig. adjacent 1). to the area extensively covered by basin and surrounding areas (Sect. 2),and we remind assessed how its we built reliabilityprocedure our arrival adopted in times for dataset computing terms the oflocating local the velocity uncertainty recorded model microseismicity (Sect. (Sect. (Sect. 5). 4),together 3). Next, afterwards we used with Then, focus on in completeness magnitude we estimates, describe threshold and the Gutenberg–Richter parameters (Sect. 6). the accuracy of therithms, locations, in by order exploring to crustalSulmona release area velocity an (provided models original as and dataset Supplement);threshold location of (2) and to algo- small estimate magnitude a the earthquakes reliable completenessity for magnitude Gutenberg–Richter of the the characterization study ofon area, background the useful seismic- seismogenic for layerpattern, seismic thickness based hazard on and purposes. the on Preliminary space-time the distribution considerations of geometric microseismicity, links are also with advanced. the active faults processing are given in detemporary Nardis network et with al. those (2011). retrieved By from(globally, integrating national 76 the and stations data regional spread permanent recorded over an networks by7000 area our of phase about readings 54 and 000 km of about 800 located earthquakes was obtained. was high in some cases. Details on the detection/recognition procedures and data pre- intense activity of the L’Aquila seismic sequence, and the noise level at temporary sites events accurately requires considerable e crustal structure models, and reliable conditions, as earthquake location isexists a if nonlinear input problem, data and2010). are no not “fool-proof” su method 27 May to 31 Decemberprocessed 2009). o Data were acquired inad continuous hoc recording manual mode and elaborations, as small local earthquakes were blurred in the ongoing evolution of brittle deformations on the major faults of thequake area. catalogues; they aretemporary even monitoring less and properly earthquake addressedstructural when distribution interpretations. datasets is As come used from discussed to support by geological Lee and and Stewart (1981), locating local with only very minor andin sporadic October events (in 1992, a2009, 20 from km from distance CSI ISIDE from database, database, Sulmona, micity Castello rates have et also al.,seismic been network found 2006, by by Bagh and the etvey al. experiment was (2007). performed to So through the highlight a mainNational the Seismic goal temporary occurrence Network of of (RSNC) our and microseismicity temporarypost-seismic the seismic not Abruzzo phase sur- located Seismic of Network by (RSA) the the during 2009 Centralized the earthquake, and to recognize, if any, the space-time This network, started on 27 May 2009 and it has beening operating till strong 22 November earthquakes 2011. et by al., seismotectonic 2004; andertheless, Pace seismic et during hazard al., the studies 2006; last (e.g. Peruzza decades, Boncio et the al., area 2011; has De been Natale almost et completely al., aseismic, 2011). Nev- aim of increasing the knowledge of the seismogenic potential of existing active faults. 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ; ), 6.6, = w M http://oasis. STATIONS = 6.8) and in Septem- 1&page = = 5.4, Pace et al., 2002). w = M w M 6.3, Lavecchia et al., 2012) and = X/XI MCS, w = M max I 7, Castenetto and Galadini, 1999), the Pa- ective strategy in order to distinguish weak ff http://iside.rm.ingv.it/iside/standard/index.jsp 2358 2357 = w M 5.97) (Rovida et al., 2011; Guidoboni et al., 2007). = w M ). However, the advantage of having a complete dataset col- 1 − iside.rm.ingv.it/iside/standard/result.jsp?rst IX MCS, = max ). These stations are integrated by 2 permanent ones (INTR and LPEL, I The OGS-GeosisLab temporary network, hereinafter referred as STN (Sulmona Since early times in instrumental seismometry, the Fucino fault has been activated Other active extensional structures outcrop on the outskirts of the temporary network The Sulmona plain is one of the intermountain basins of the Abruzzo Apennines, it allowed the integration of our data with those from other existing networks, thanks to The STN seismological survey provideding a data huge amount (190 MBday oflides continuous with seismic record- the drawbackseismic of signals needing from an noise.seven e Here months, as we they analysesequence, required only including peculiar manual the operations data datarefer hereinafter treatment acquired to described. due during a In to preliminary the this location the first section which ongoing we represents L’Aquila also an essential step of our work. Indeed were moved during the monitoring,their for performance. logistical A reasons full as description wellavailable of as at the in the sites, order OASIS the website. to equipment improve and their functioning is 3 Waveform data processing, dataset of arrival times and uncertainty analysis belonging to the RSNC managedgia), by INGV for (Istituto which Nazionale continuous di recordingsstations Geofisica were e were given Vulcanolo- located as on data bothMt. exchange the (see Porrara hangingwall Table and faults, 1). the The withwas footwall an set of the in inter-station Mt. continuous distance Morrone modeAntelope and of and system (BRTT, the about 2004). collected The 10 data network kmremoval of had were (Fig. all been managed the operating 1). at mobile for the Acquisition stations, 30 OGS occurred months, on by till 22 the the November 2011. Some STN stations ber 1933 ( Temporary Network), consists ofOGS 6 Archive mobile System stations ofcrs.inogs.it (SU Instrumental network Seismology, in code the in section OASIS, “Sites”, the Ceccaroni et al., 2009), in November 1706 ( IV network code in by the 1915 Avezzanoganica earthquake fault ( by thethe 2009 Barrea L’Aquila fault earthquake byNo ( the relevant 1984 instrumental Val earthquake diwhich is Sangro associated up earthquake to to ( the(Castello now Morrone-Porrara et has alignment, al., been 2006;Bagh ISIDE only et database characterized al., – 2007). by Conversely, inof a historical three very times, destructive the earthquakes, minor Sulmona which instrumental plain occurred has in activity been the the second site century AD ( its southward continuation intorespectively). the Eastward Marsicano of the and Morrone-Porrara system, Barreamal an faults fault impressive outcrops, (n. SW-dipping nor- known 6 as andactivity “Caramanico of 7 Valley such fault” in structure, (n. that Fig. 8 boundsin in the 1, Maiella the Fig. Massif literature 1). to (Ghisetti The the and Quaternary west, is Vezzani, still 2002; controversial Galadini and Messina, 2004). It dislocates Late Pleistoceneslope (related deposits to and the thereforethe is Last Morrone considered Glacial active fault Maximum) (Gori systemruns alluvial et about continues fan 18 al., km and in 2011). in the Southeastward, NNW–SSE direction. SSW-dipping Porrara normal(Boncio fault, et al., which 2004; Gallithe et Middle al., Aterno 2008; Valley and Lavecchiain the et Conca al., Fig. Subaequana 2012). 1), faults They (n. the are 1, Cinque the 2 Miglia Paganica, and fault 3, respectively (n. 4 in Fig. 1), the Fucino fault (n. 5 in Fig. 1) and east of the best-knownposits Fucino of Pleistocene-Holocene basin age (Fig. andfault it 1). is system. It bounded This eastward is system by filledextending the is for Morrone by characterized nearly normal lacustrine by 20 km two continental alonga SW-dipping strike de- huge sub-parallel (Gori fault et segments, scarp al., at 2011); the the contact westernmost between one the shows carbonate bedrocks and slope deposits. 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | http://iside.rm.ingv.it/iside/standard/index. er zone corresponding to a 20 km distance 6.3, Chiaraluce et al., 2011) and by the high ff 2360 2359 = w M distance only; most of them were recorded by SL06 P arrivals of less than about 3 s) were analysed. About – S S and P erence between ) are 35 % of the ones localized by STN network (104 earthquakes versus 293), The detection capabilities of the STN network obviously decrease by increasing the Applied to the whole 7-months dataset, the automatic procedure extracted about Earthquake recognition is performed by a sequence of two operations, i.e. trigger de- ff tively the data, using otherand networks’ their data, overall and reliability. assessed the quality of phase readings for the Abruzzo. Baghnumber of et events al. we did (2007) in detected 7 months, in but3.2 referred 18 to months a wider approximately Improvement area. of the phase same readings The parameters commonly used(number of to phases, evaluate GAP, RMS, the etc.)dataset, projected quality clearly in indicate of the that map the theanalysis. of quality earthquake With Fig. the of 2, locations aim is our of not preliminary strengthening enough the for the earthquake purpose catalogue, of we seismotectonic integrated selec- 25 km. Several singlemay station be events located were in detectedstation, terms at as of the well southern (e.g.automatic tip in event of recognition Fig. Mt. and Porrara 3),vides of fault. a but In a much they conclusion, more detailed as dataset dense a for temporary the result network, Sulmona of this area the than study semi- any pro- other currently available croearthquakes. About 70 % of theseily events localized with by more using than Hypo71 3for ISIDE phases code were locations (Lee preliminar- (Fig. and 2). Lahr, 1975) and the velocityevent-to-station model distance. used In fact,from in each a STN bu station,jsp the RSNC eventswhile ( they rise to 73 % (264 by RSNC versus 359 by STN) if the distance is set to 16 000 windows ofample signal which the included L’Aquila aftershocks), teleseismicthe local events, windows regional were earthquakes visually events (our inspected, (fordi target) but ex- only and those false containing4700 events. local phases events All were (with recognised time and manually re-picked, identifying more than 800 mi- number of minimum stations, aimed atrecognition. increasing They the overall can sensitivity be of identified the automatic and removed by human visual inspection. tection and trigger association, performed byfunctions, Antelope through respectively. dbdetect The and first dbgrassoc oneAverage while (STA/LTA), the uses second a one declares classical anis event Short-Time when found Average/Long-Time a to group of be detections are compatible several with parameters the controlling theoreticalselection these travel algorithms of times (e.g. their for pass-band best asites) filtering) combination unique increase and is source. with the There not the trivial. choice False of events decreasing (very the frequent STA/LTA threshold in and noisy using a low and true event identification bysystem visual implemented inspection. at CRS This (Centro procedure dilogical is Ricerche data. Sismologiche) part for It of processing uses: the seismo- (1)earthquakes general Antelope automatically, (BRTT, and 2004) for extractingfor acquiring/storing earthquake data, phase waveforms; recognizing picking (2) andand a Hypo71 location, “pick-server” (Lee which and Lahr, are 1975), performed respectively. by Seisgram2K (Lomax, 2008) events, from the STNmicroseismicity waveform detection data. was In hamperedwhich the started by period some the months analysed before ongoing the inevents STN L’Aquila of deployment this seismic and 6 culminated experiment, sequence, April with the the 2009,level deadly at noise of 03:32 LT some ( stationsorder due to to gain the the temporarysimilar installation maximum to of sensitivity that sensors. a Therefore, used semi-automatic in in by procedure Garbin the has and Trento been Province, Priolo applied, which (2013) combines for detecting an small automatic magnitude detection events of all possible events velocity model. 3.1 Earthquake detection and preliminary location The first step of this study is to recognize all the local seismicity, down to the weakest the origin times, and the selection of good quality events through which we refine the 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | arrival times the detected S ff phases), not repre- S and 984 P ’s). Three stations (i.e., SL01, P phases, 1 for P arrival times were merged with ours with S 2362 2361 and P original phases have a reading error less than or equal S and phases (black sector in Fig. 4a) were manually picked. P S phases and 64 P The permanent INTR station (see its location in Fig. 1) is the site with the greatest The quality of pickings is statistically very good and similar for all the sites (Fig. 5c). Some histograms of the phase reading errors are given in Fig. 5. The dataset con- Uncertainty in phase readings is rarely declared in earthquake locations and cata- Similarly, the integration of our dataset with other RSNC stations (INTR and LPEL The complete dataset contains 6889 phases. It refers to 817 earthquakes, of which: In particular, we included the data of 6 stationsThe of considered the RSA Abruzzo stations regional (AIE1, network ORT1, PSC1, PTS1, SBP1 and SEM1) are and SL06 are well represented (aboutSL04 or and more LPEL) than do 300 have less(for data, because details, of see some instrumental de acquisitionthe Nardis problems RSA et stations al., is 2011).earthquakes due Conversely, at the to a small the higher number threshold. triggered-mode of acquisition, readings which on cutFor o 11 over 16median stations (i.e. and except 75th for percentilethough SLA3, is SL04, it within SLA5, never 0.1 INTR reaches s; and 0.3 the s. LPEL) 95th This both percentile the is is due more to scattered, either al- the low sampling rate of the two to 0.2 s (seesented Fig. in 5a), Fig. while 5a, few are4. outliers in (7 the range for 0.4–0.54 s. Thus, none ofnumber our of picks readings is (Fig. 5b) given related weight to local events. Also sites SL03/SLA3, SL05/SLA5 rors into weights may bedeclared. controlled For by our the case, seismologist, the evenwith setting though Hypo71 of this is weighting is shown scheme not in tuned always Table for 2. performing locations tains readings fromwhereas the we STN cannot retrieve andthe the RSNC RSA reading network, networks uncertainties forMore obtained for than which all 90 for % only the of this the the phases study weight provided code by manually, and polarities, if any, are given. operator and depends onarriving the phase signal-to-noise (Husen ratio and Hardebeck, andinto 2010). the Phase categories dominant reading errors which frequency are of correspondalgorithms. then the classified The to larger weight the codesand reading the that uncertainty less are is, this directly the reading higher used influences the by earthquake Hypo71 location location. weight The code mapping of reading er- signal and are usually represented by a time window whose width is estimated by the are detected by a change in the amplitude and in the frequency content of the seismic logues, but this isin an their important weighting element because schemes.evaluated from location Formally, a codes the probabilistic use estimatephase point this should of be of information measurement described view. by error According aat probabilistic to has the function this, that to arrival reaches the time its be deviation onset maximum of exactly of of this the a phase, population. seismic rors and In could the this be standard way retrieved. error informationis More corresponds on available, often, and to statistical the only the properties operator’s a standard of choice qualitative the cannot evaluation be er- of evaluated rigorously. the Reading reading errors error from 60 RSNC stations (Fig. 1, blue triangles) were collected382 (yellow sector are in identified Fig. by 4a). readings; 1 225 or are 2 located exclusively STNtions with stations, of STN and STN, stations; located 210 RSA are and only located RSNC if with stations having observa- (Fig. at 4a least and b). 4 phase and automatic starttriggered time events of correspond RSA to115 recordings earthquakes have recognized by been our searched. network. In From 7 them, are months, already 37 partcorrespondence of of the the networkdatabase origin experiment), in times the has same recognized been period.no by Those further carried STN revision on out to phase by picks. those As verifying a reported result, the a by total of ISIDE 1251 (RSA, yellow triangles in Fig.blue 1) triangles and in of several the stations same of figure), the not national acquired network in (RSNC, the real time nearest during to our STN. experiment. (De Their Luca, recordings 2011). are Time coincidences discontinuous, between as the stations origin work time on of triggers our local earthquakes 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | S k (2) (3) (1) /V P V phases. S cients and line, ffi phases recorded by S and P the hypocentral distances of the erent structures were obtained and j ff x ected by errors on both the variables, ff erence of ff and i equal to 1.85, which is slightly higher than x 2364 2363 S phases by using the modified Wadati method /V S P V wave velocity values, respectively. Figure 6a plots and S P ected by systematic or random errors, however most of ects strongly the final solution. Therefore, we compiled ff ff and S P P ), the following equations hold: /V /V j ) ) , j j wave velocity models with di i x x P − − i i calculated for the original data and for all available pairs of STN sta- x x are the ( ( P S = = V j s between earthquake locations and crustal model in terms of travel time j ff S P − − S P and i i V V P S estimated by ordinary least squares regression (equation coe P = versus DT V = = S S P P S S /V Phase readings may be a As previously said, for the other RSNC stations we have only their weight code, with P DT DT on the crustal stratigraphya in result, terms twelve 1-D of layers thickness and seismic wave velocity. As inversion approach using the well-known Velest code by Kissling4.1 et al. (1994). Starting velocity models and selectedIn dataset non-linear inversions whichguess linearize accurately, since the it problem,a a it collection of is possible crucialtion velocity and to integrating models the define across available seismological, the the geophysical and initial study geological area, information taking into considera- theless, it is possible tobest estimate tradeo an optimized localresiduals. velocity No model specific which velocity ensuresSulmona model the basin to and be surrounding usedtoo areas. for As small event the for location number feeding is of a available earthquakes 3-D for of the tomographic this inversion, study we is adopt a 1-D velocity structure estimated by Chiarabba et al.exceeds (2010) 1.83. by local earthquake tomography, for which 4 Estimate of local velocity model The systematic errors associatedsince to earthquake the hypocentres, velocity earthquake model(never origin cannot exactly times be known) and properly are seismic quantified, intrinsically velocity coupled structure (Husen and Hardebeck, 2010). Never- regression (in blue inthogonal Fig. regression 6) is more results adequate inwe for more data choose a stable as values, final allthose value obtained around by the 1.85. other Since ratio studiesDe or- in Luca the same et region al., (1.83 2000; by Bagh 1.77 et by al., Boncio 2007; et 1.80 by al., 2009), but in agreement with the values with standard deviation, in red in Fig. 6) changes from 1.78 to 1.82. The orthogonal tal of 2414 additionalV phases. By refining and integrating the dataset of phases, the with DT tions, for a total of 4716by phases. correcting At or this step, erasing outlierscounts the are 241 reading. picks identified removed and Figure from removed, 6b the either integrated original is with dataset. thus those Then, STN obtained, read phase and from picks have it RSA been takes stations into or ac- provided by RSNC stations, for a to- couples of corresponding stations. Letevent be at two stations ( DT DT VCEL, VVLD – theFig. location 1) of are well the represented, stations withOnly weight nearest few equal to observations to the are 0 (best retrieved STNrefer quality) for to network assigned the events is to already remaining, reported listed more in in distant ISIDE. stations, and they them can be identified andreliability fixed through and some consistency conventional analyses. of We(Chatelain, checked 1978), the that compares the time di high-sampling channels – or the high noise level of theunknown temporary weighting stations. scheme. Therefore, wefor all represent the the 60 stations, histograms someof of of which them weight sporadically codes (CERA, enter in CERT, FAGN, this GIUL, dataset (Fig. GUAR, 5d). MIDA, Only POFI, 13 PTQR, RNI2, SDI, VAGA, permanent stations – INTR and LPEL are often sampled at 20 Hz due to the lack of 5 5 25 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff wave 0.5 s, ≤ S and arrivals). As erent authors P ff S ective unknowns) of ff arrivals) and a less restric- S erent starting velocity models (Fig. 7). ff 2366 2365 6 phase readings with at least 2 clear ≥ ratio of 1.85 was imposed, as retrieved by the modified N S wave pickings). The table in Fig. 8 reports the start and final , ◦ /V S P V 250 and ≤ wave arrival times of local earthquakes recorded during a specific P 10 phase readings with at least 4 clear S ≥ N arrival times (Fig. 2) and 12 di , and ◦ S P 0.5 s, GAP 180 wave velocity values have been attributed to the same layer by di and ≤ ≤ P P The obtained results are summarized in the panels and in the table of Fig. 8. Specifi- The best 1-D velocity model was estimated by a trial and error process. First, we With the purpose to estimate the optimum 1-D velocity model, we selected the Five models (Fig. 7a–e) were derived from seismological data. Models a, b and c wave readings were not inverted, but only included to better constrain the earthquake uncertainty of the travel-time residual for each of the 12 used models. The final velocity model (red line in performing trial inversions. Moreover, for eachhypocenter model we locations, performed as ten initial runsRMS using parameters final (root for mean the square) nextbest misfit one. model trend Finally, (the we versus oneeach analysed the corresponding guess the to number velocity the structure. of minimum iterations misfit and of chose travel-time the residuals)cally, Fig. for 8b shows the range ofmodels variability with (grey envelope) best of the misfit calculated less 1-D velocity than or equal to 0.1 s (value which is compatible with the the inverse problem was at leastnoted greater that than the 1.5. output Analysing the modelsof preliminary were the results, quite we starting similar, in ones spiteout (Fig. of further 8a), the tests wide implying varying range astructures of the including stable variability control phantom solution. layers parameters. Notwithstanding, in Afterwards,not we each we automatically initial carried adjust created model. layer new In thicknesses velocity fact, and the the Velest code appropriate does layering must be found of S locations. A constant Wadati diagram discussed in Sect. 3.2. performed several inversions consideringtical the input collected anddetermination control velocity factor models, parameters (total and using number systematically iden- of verifying observations/number that of the e formal over- pickings having a mean reading uncertainty of 0.07 and4.2 0.09 s respectively. Minimum 1-D velocity model fromFor travel the times identification inversion of the best 1-D velocity model, we considered a selected dataset a result, a subset of 231 events (red dots in Fig. 2) was obtained, with RMS emphasizes their great variability. best constrained earthquakes,Since based the on study areatively the significant is quality seismic mainly of sequences characterizedFig. localized their by 2), to sparse preliminary we NW seismicityfor locations. and adopted (except the SW two two events of selection rela- locatedGAP the criteria. at Sulmona the basin, A edgetive more one of restrictive for weaker the criterion earthquakes study located was within area the applied (107 local STN events network with (124 events RMS with rived from the interpretation ofacross the geological the structure Sulmona-Maiella at depth areaent along (Lavecchia a and crustal section de(Patacca Nardis, et al., 2009). 2008; Note Diand that Luzio that di et the al., 2009; models Barchi often et feature al., 2003; velocity Trippetta inversions et with al., 2013), depth (Fig. 7). Figure 8a sector of Fig. 1) (Chiarabbavelocity et model al., valid 2009; for the Chiarabba wholelogue et CSI Italian al., (1981–2001) territory 2010). (Chiarabba and Model et used al., dmodels 2005) for is and the f–h a model Italian were regional e Seismic was resulted Cata- deriveddata from from (DSS it. geophysical Three 11 investigations, by asreflection Scarascia deep profile et seismic (CROP al., sounding 11 1994; bygrated see Patacca with the et results trace al., obtained in 2008; fromModels Fig. see teleseismic 2), Fig. i–l receiver 2), near functions were also vertical (Di opportunely built Luzio seismic inte- et by al., integrating 2009). and correlating the stratigraphic layering as de- therein). are local velocity models optimizedinverting for the intra-Apennine area. Theycampaign were (Bagh obtained et by al., 2007) or during L’Aquila 2009 seismic sequence (north-western taken as a starting point to calculate a reliable velocity model (Fig. 7 and references 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) may 1 − 24 km (see ∼ characterises 1 − is identified at depths 1 are distinguished from − 1 − ) may correspond to the upper 1 residuals returned by Hypoellipse − S and P 2368 2367 in Patacca et al., 2008; Trippetta et al., 2013), do 1 − . 1 − Very important parameters in Hypoellipse are the so-called WEIGHT OPTIONS that Furthermore, time delays were associated to seismic stations, in order to con- It is important to specify that above optimized 1-D model is suitable for the most The sharp increase in the velocity gradient observed at 13 km might represent an Based on speculative correlations between the obtained velocity model and a suit- The thick layer identified at depths between 6 and 13 km (average 5.8 kms Three layers with velocity increasing from 5.1 to 5.7 kms sider both their elevation and possible local anomaly of velocity under them. These rule the influence of the uncertaintyIn associated order to to each define reading on theweighting the best location values scheme process. to (RESET assign TEST toa the 29 4 procedure and parameters based WEIGHT involved inspace OPTIONS on Hypoellipse is parameters) genetic driven we algorithms so adopt of (Bondar, as the 1994). to averages minimize and Exploration standard theover of deviations the target of parameter whole function earthquake defined dataset. The as parameterization a obtained is linear shown combination in Table 4. (Lahr, 1980, 1984, 1999). Itall provides events. We estimates recall of that absolutelocations this position in program Fig. and is 2): origin the it timecept evolution uses of of of a Hypo71 error weighted regression (used ellipsoid, technique representing forwith and preliminary Gaussian latitude, introduces error longitude a distribution and new not depth con- necessarily axes, as aligned Hypo71 does. they are slightly higher NNE-ward ofthe Sulmona positive and eastward corrections of reach thedeposits Maiella 1 of ridge, s, where the consistently Plio-Pleistocene with Adriatic the foredeep. presence of thick terrigenous 5 Final earthquake locations After the minimization ofthe reading study errors area and we went the to optimization the of final earthquake the locations velocity by model using for the code Hypoellipse depth histogram in Fig. 8c). of the intra-mountain zoneincluded of when the used Abruzzo onstation region, more corrections extended but are areas. station very In corrections low fact, need (less in to than Fig. be 0.1 8d, s) it over is the evident most that of the the study area, but value is not well resolved, due to the lack of seismic activity beneath which would characterize suchslow depth Verrucano interval, formation mainly (4.5 kms duenot to result the in presence of thestarting the final models very velocity in Fig. model, 7. although they were introducedincrease in in metamorphic several grade, of asterpreted the well as as the a top of decrease therather in crystalline sharp silica Palaeozoic increase content, basement in of and velocity the can isCROP middle be observed 11 crust. in- at Another results, a we depth might of interpret 27 km. it In as agreement the with top the of the lower crust, but evidently such dolomites) overlain by open-ramp carbonates and locally by Miocene turbidites. represent the oversimplification of a complex(Anidriti thrust di zone where Burano late Triassic formation)quarzites evaporites and are phyllites tectonically (Verrucano formation). interbedded We with observe late that Permian-Triassic the velocity inversions surface to a depth of 6 km,between a 6 fourth and layer with 13 km velocity of andthe 5.8 a interval kms fifth between 13 layer with andan average 27 km velocity average (Table of value 3); 6.8 of at kms 7.1 higher kms depths, the velocity increases to able geological compositional layeringthat for the the study uppermost area,sedimentary we three advance crust layers the made (average hypothesis of 5.6 kms Jurassic-Paleocene carbonate sequence (limestone and of the model with respectreferences to in local Fig. a 7) prioriand and geological velocity taking and structure into geophysical account of informationMooney, 1995). (see worldwide the compilations crust of in the analogous thickness tectonic provinces (Christensen and Fig. 8b) was chosen on the basis of goodness of the fit (0.110 s) and the consistency 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | L M 5.35 km ≤ against 3.2843; (32) − < γ ). D D wave velocity of M ) is respectively: M γ P of this study is higher L M 1.5 to 3.7 (Fig. 11a). The − (Fig. 9f), i.e. their location is ◦ ) and duration ( 0.9 and 3.7. L 0.2, except for stations SLA3 and cients of the orthogonal regression − M 0.4 s (Fig. 9a, b), horizontal/vertical ± ffi ± between 2 and 3 and only the 1 % has L M is calculated as the mean of the magnitude 2.67 km for quality B, 2.67 km L 2370 2369 ≤ M < γ cients have been estimated and applied, too. They 0.1 and 2, while the remaining 7 % is distributed as ffi − 0.30), which were repositioned after logistic problems values range from + D has not been estimated for 207 of 535 events. For the residuals less than M D 0.1, the 4 % has − S M estimates to those derived from ISIDE for about 200 events < between ) is obtained by applying Antelope’s dbampmag (BRTT, 2004) L cients have been estimated by calibrating 5.35 for quality D (Lahr, 1999). Figure 9 shows the histograms L L L M and 0.42 and ffi M M M + P γ > ) (4) τ log( 2 a is the signal duration in seconds; the distance term is not considered as it in the sector of crust above the sea level, the latter setting the RELOCATE + 1 1 τ − a 3. Furthermore eight of 535 earthquakes lack amplitude data, for noise problems. ), as well. We used the following simplified formula: 1.34 km for quality A, 1.34 km = > D Mean station coe Local magnitudes, estimated in this study, range from By comparing our As a shortcut to local magnitude estimation, we calculate the duration magnitude Local magnitude ( Final earthquake locations (Fig. 10a), obtained after building a good quality dataset Finally, we located 535 earthquakes occurred from 27 May to 31 December 2009, D L ≤ M into the pertinent RESET TESTequal values to of 3.3129; Hypoellipse (33), (i.e. (40), (31) and equal (43) to set to zero). It can be seen from Fig. 11c that the M where turned out to bebeen negligible. read Due to and the therefore remaining presence 328 of earthquakes noise, the signal duration has(Fig. not 11c) bymagnitudes applying are ordinary then obtained least by entering squares the and coe orthogonal regressions. Duration common to both networksthan (Fig. that 11b), estimated by it ISIDE comes for out a that value of the about( 0.15. exhibit randomly distributed valuesSLA5 smaller (respectively than to the original sites. 93 % of events has follows: the 2 % has M and using the Hutton andibility Boore’s attenuation with law older (1987). magnitude Inestimated estimates, order for to each preserve station, compat- wheremean the of magnitude the at waveform each amplitudeSingle station of station is the calculated compensation from two coe the horizontal components (Bormann, 2002). Magnitudes are computed for theestimated final them hypocentral in locations terms previously of described. both We signal amplitude ( distance. and estimating the localbackground velocity seismicity, model, initially improve widespread the over preliminary theknown area, tectonic ones now structures (Fig. tends or, 2). in to Indeed any cluster case, close to to focus in restricted areas. 6 Magnitude, completeness and local G–R relationship by using data fromGAP. other Moreover, thanks networks to (RSA the andare enrichment RSNC), estimated of with using our the more dataset, than advantagedistance more 16 of between phases than reducing the (Fig. 30 hypocentre % 9g). andand of In the in 45 locations closest % about station 30 of % is the it cases less is the than less minimum or than equal or equal to to 5 km, 10 km (Fig. 9h), i.e. to the inter-STN station γ for quality C, and which describe the locations ofsolutions quality features A events. Moreerrors than less 90 % than of 1 km these60 % (Fig. hypocentral 9c, of d), the andreliable. events This RMS has result less GAP is than satisfying less or ifrecorded than we equal within consider or to that the equal 0.3 relatively s STN, few to (Fig. and and 180 small 9e). among events Only were those outside only the strongest were located 4 kms option and running through a number of iterations. 352 of which are ofmeans quality that A, 32 their of horizontal quality B, and 16 vertical of quality 68 % C and confidence 135 interval of quality ( D. This corrections were automatically calculated, the former assuming a 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | value b confirms that c M ective, even though quite ff value are nearly constant, if a 3), while only the 1 % is repre- ratio of 1.85 was obtained, which < L S fairly well, even if the data are quite M /V L P ≤ M V ) of our relocated dataset (27 May to 31 De- c cients of the Gutenberg–Richter relationship, 2372 2371 M ffi 3 (Hagiwara, 1964) (Fig. 11a). The completeness value (0.85), are not representative of the Sulmona to 0.72. ≥ b L c M M ective for investigating the microseismicity. ff 1) and micro-earthquakes (1 er zone of 20 km around the STN stations, thus selecting earthquakes value (3.49) and ff a , based on local magnitude estimates, is well constrained and reaches c M < M 1 recorded just by one or two stations. The integration of the STN recordings − time delays). This approach has proved to be very e < P A well constrained G–R slope was estimated from the microseimic data (Fig. 11d). In this paper, an on line catalogue of the analysed earthquakes is compiled and re- Finally, a statistical analysis of magnitude versus event frequency relationship and an L – decreases from 0.85 to 0.71. Following results from recent studies that consider the sented by small earthquakes magnitude the value of 1.1 forstrictly the pertaining whole to dataset the of STN locatedthe stations events. adopted It is lowers considered. semi-automatic to This procedure 0.7manual low if value picking based of is only on very the area e automatic detection of eventsWe and observe that thenormalized productivity to the rates area shown shaped by on the temporary stations coverage, whereas the about 60 % more thanset the of ones 282 reported not in located theanalyses. earthquakes national is The ISIDE given quality database. by location An phasetheir additional readings of only, nearly very for possible 66 small % further magnitude. ofultramicro- the Indeed ( located the events 99 is % A, of nevertheless located seismicity is represented by guarantees accurate earthquake locationsin and the may area. be useful for forthcoming studies leased as Supplement togetherorigin with times the and the phase hypocentral coordinates pickings.useful of The located to earthquakes; catalogue establish all contains: the the parameters the minimum quality distance, of dimension their and locationsmate, orientation both (RMS, of local GAP, error number and ellipsoids); of duration the phases if magnitude possible. used, esti- The catalogue includes 535 events, that are M with the data gathered byenriched regional and and strengthened national the permanent location networks qualityare of (RSA homogeneously the and strongest read, RSNC) earthquakes. and As accuracy phases ity is clearly model stated, of a the well-constrained Sulmona 1-D veloc- area and a reliable time consuming, in identifying even very small earthquakes, such as local events with tion of windows, and aS fully manual readings of waveforms on local events (chosen on times (Bagh et al., 2007; Boncioseismic et network al., 2009). and Thanks to toerly the the processed, deployment we analysis of have a of obtained temporary auntil the detailed now first picture by of seven the either month microseismicityother existing of not permanent similar revealed recorded networks experiments data, (ISIDE performedgathered prop- database; during in De this the period Luca, were past 2011)cerns somehow or (Bagh peculiar signal and et treatment, time-demanding al., for dueprocessing what 2007). to combines con- an The the automatic ongoing meta-data detection seismic procedure activity with in operator-assisted the selec- L’Aquila area. The 7 Discussion and conclusions The present paper aimsity at in improving the the Sulmona knowledge of basin,strong seismic an the hazard extensional background (Pace active seismic et al., area activ- 2006), of but Central substantially aseismic Apennines since ascribed instrumental of cember 2009) is 1.1.i.e. Note the that annual the coe basin only, as part ofSW Sora the region L’Aquila seismic (see sequence Fig.spatially and in 10a) a a fall bu bulk inside of thewhere relocated earthquakes the events. in detection If the we capabilitiesa select of equal the to the events 3.11, temporary b network to 0.71 are and at the best, we obtain dispersed. estimation of the completenessmodel magnitude is inferred carried on out theFig. by 11d. Gutenberg–Richter The using (1956) completeness Zmap magnitude ( software (Wiemer, 2001). The result is shown in orthogonal regression represents the highest 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | up L M up to 2.5, up to 1.2, L L M M ected by a little ff 6.3, Lavecchia et al., 2012); = w M up to 1.7, and C1, with L M 2374 2373 up to 1.6 in Fig. 10a) and at the hangingwall of the up to 2.9 in Fig. 10a). L 6.3). The remaining portion of the cumulative slopes 3.5 and 3.6 (Fig. 10a). Two other swarms occurred at L M = = M w L M M up to 0.9 in Fig. 10a), near the southern and the northern tip ends of L M up to 1.7; cluster E in Fig. 10a) was located at the hangingwall of Marsicano fault value as a stress indicator (Gulia and Wiemer, 2010), we advance the hypothesis To conclude, we point out that the detailed analysis and quality study performed in We also performed a preliminarily evaluation on the Sulmona seismogenic layer, de- On 4 August the area near Roccasicura (Molise Region) sited along the SSE-ward The temporal evolution of the recorded seismic activity, schematized as cumulative A seismotectonic analysis of the geometric and kinematic relationship between the L of the seismogenic layer which17 releases km. A 95 % thickness of of 12 thesidered km seismicity as (from the 5 is layer to located within 17 at which km)in nearly a may agreement 90 be depth % assumed with of of for other the the seismicitybased independent brittle occurs. on layer, estimates, These con- rheological values done are evaluations in (Boncio this et sector al., 2009). of thethis Apennines, paper, to obtain a low-magnitude completeand catalogue further for the highlight Sulmona area, the confirm important low implications activity in seismicity seismic rate hazard characterizing evaluation. the study area with of the seismicityfrequency-depth histogram – of Fig. D95, 9i whichquakes, was Williams, only shows built 1996; on that the Fernandez-Ibañez basisdepths 7 of % between quality and A 5 of earth- and Soto, 9 the km,tween 2008). 42 12 events % and concentrated The were 17 in km the and shallower 9–12 the km than remaining depth 5 interval, 5 % km, 22 between % 17 24 be- and % 21 km. occurred Therefore, the at base at depths of 6–12 km; theto second 3.6 one as (80 in events betweenof ISIDE; 6 6 cluster and to F 8 14 in October, km. with distribution Fig. Finally, on 10a) similar 19–20 nucleated to November, near that anotherMorrone Sora of increase (Lazio fault the of (cluster Region) late seismicity, G1 at with JunePorrara depth with spatial activity, fault was (cluster recorded G2 with at the footwall of the fined as the depth layer that releases the largest number of earthquakes (i.e. 95 % prosecution of theswarm Morrone-Porrara of extensional ten earthquakes alignment,with (cluster two was D, larger in a events Fig.the of beginning 10a), of at October 2009. depthsM The between first 13 one (12 and events 17 between km, 4 and 5 October, with the Morrone-Porrara extensional alignment, respectively. number of events versusthe time end (Fig. of 10b), June, shows6 e.g. a April after sharp near 2009 decrease one earthquakeshows in month ( other seismic of jumps, registration rate corresponding andactivity. at to nearly Three local two swarms and months short werehangingwall the of lasting recorded the increases Porrara from fault in (cluster 2in the A, Fig. seismic with to 10a), 22 andand June. within C2 the They with footwall occurred of within the Morrone the fault (cluster B, with active faults (Fig. 10a).of the A study prevailing area, which activitysource coincides responsible is with for the the observed 2009 southern L’Aquila at earthquakeconversely end the ( the of area the northwestern of Paganica the seismogenic cornerall Sulmona the plain observation remained time. almost completely aseismic during by the 1984 Valet di al. (2011) Sangro based earthquake on the in results the from coseismic south,Sulmona Coulomb were microearthquake stress pointed change activity studies. and out theof by active this faults De paper. in Nevertheless Natale the some areation preliminary are of observations beyond identified the on clusters aim thecan of space-time be seismic distribu- activity advanced. andtributed We on in observe the the study that overall area, seismogenic the but thickness rather background clustered in seismicity specific is zones, mainly not close to uniformly known dis- that the Sulmona areaobtained G–R might values be can be moreditions considered or stressed representative might than of have been stationary the influencedmain background by surroundings. 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C.: HYP071 (Revised): A Computer Program for Determining 5 5 30 10 10 25 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Data Logger Lennartz3Dlite/ RefTek RT 130 CMG-40, FBA ES-T/ RefTek RT 130 Lennartz -3Dlite/ RefTek RT 130 Lennartz -3Dlite/ RefTek RT 130 Lennartz -3Dlite/ RefTek RT 130 Lennartz -3Dlite/ RefTek RT 130 Lennartz -3Dlite/ RefTek RT 130 CMG-40, FBA ES-T/ RefTek RT 130 Trillium 40S/ Trident-FS-16-VPP SG-1 Trillium 40S/ GAIA2-FS-16-VPP Date ON/OFF Sensor/ 24 Mar 2010 22 Nov 2011 1 Oct 2009 22 Nov 2011 22 Nov 2011 1 Oct 2009 22 Nov 2011 24 Mar 2010 – – Elevation (m a.s.l.) 0.01 s 1.00 s 2382 2381 Reading Error < ≥ Lat (DD) Weight Code 0 1234 [0.01–0.04 s) [0.04–0.20 s) [0.20–1.00 s) Lon (DD) 13.7827 42.0835 769 27 May 2009/ 13.8539 42.174513.9336 684 42.089013.9342 27 484 May 2009/ 42.089514.0296 27 523 May 2009/ 42.073013.9787 1 1281 Oct 2009/ 41.937113.9773 26 May 2009/ 1067 41.932514.1127 26 May 2009/ 1108 41.908313.9046 1 Oct 2009/ 1279 42.011514.1832 26 May 2009/ 924 42.0468 9 760 Mar 2003/ 11 Apr 2008/ Municipality (PROVINCE) (L’AQUILA) (PESCARA) (L’AQUILA) (L’AQUILA) (L’AQUILA) (L’AQUILA) (L’AQUILA) (CHIETI) (L’AQUILA) (CHIETI) Weighting scheme adopted in the preliminary locations performed by using Hypo71 Main characteristics of the Sulmona Temporary Network: data taken from OGS and Station Code/ Network SL01/SU Goriano Sicoli SL02/SU Popoli SL03/SU Sulmona SLA3/SU Sulmona SL04/SU Pacentro SL05/SU Rocca Pia SLA5/SU Rocca Pia SL06/SU Palena INTR/IV Introdacqua LPEL/IV Lama dei Peligni Table 2. (Lee and Lahr, 1975). Table 1. INGV sites archives. SLA is the identification code of relocated SL0 stations. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 1 − represent WEIGHT Velocity (km s 5.1 5.4 5.7 b Computed weight 1/4 1/1.69 1/156.25 38 8.0 (km) 0–2 2–4 4–6 6–13 5.8 > 2384 2383 b b b Standard error relative to readings with weightzero code 1 1 LithostratigraphyJurassic-Paleocene carbonates Triassic evaporites and Late Permian-Triassic quarzites and phyllites Depth interval a represents RESET TEST 29. Parameters labelled with a Standard error UPPER CRUST Miocene turbidites and MIDDLE CRUSTLOWER CRUST CrystallineMANTLE Paleozoic basement Mafic granulite 13–27 Peridotite 6.8 27–38 7.1 Weighting scheme adopted in the locations performed by using Hypoellipse. Parame- The best 1-D velocity model estimated for the Sulmona area and its inferred composi- 3 0.4375 s 12.50 Weight code 02 0.0350 s 4 0.0700 s INFINITE 2.0 INFINITE 0 1 0.0455 s 1.3 OPTIONS. Refer to Hypoellipse User’s Guide (Lahr, 1999) for details. Table 4. ter labelled with Table 3. tion layering. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) in the Sulmona -line as data exchange. Selected data of other ff 2386 2385 http://iside.rm.ingv.it/iside/standard/index.jsp Preliminary locations of earthquakes from 27 May to 31 December 2009, obtained with Station locations and epicentral distribution of seismic events recorded by the INGV Na- Fig. 2. Hypo71 (Lee and Lahr, 1975) and the ISIDE velocity model. Fig. 1. tional Seismic Network (RSNC, area (Abruzzo region) duringthe the epicentres period of 27 the May–31 strongestin December events the 2009. located legend. The in SU this stations blackthe are study: stars RSNC labelled from indicate acquired by north in plusses, to continuous crossespermanent and south, show treated stations they the o are of two permanent listed the stationsLuca, RNSC of 2011) and were of used the inThe Abruzzi this red Regional study numbers Seismic and correspond Network are to marked (RSA, fault by systems see blue cited De and in yellow the triangles, text. respectively. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | time of 1 s. P – S Total number of seis- (a) 2388 2387 Total number of earthquakes representing our com- (b) Meta-data obtained by the STN network in the first 7 months. Seismograms and amplitude spectra of a typical single station event recorded by three- plete dataset. Fig. 4. mic phases obtainednational after (RSNC) the permanent networks. integration of STN data with those from regional (RSA) and Fig. 3. component station SL06. Datethis and event, time a above source-station refer distance to of about starting 8 point km of can be the estimated seismic from trace. For Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | for S Initial /V P (a) V Number of (a) phases identified for each erent locations of the same S readings respectively. ff S cients are given in the formula. Final diagram obtained after in- and ffi P and (c) , as well as the changes of P (c) to Number of (a) 1.85 is the final value chosen. (b) 2390 2389 S from /V R P V readings error distribution for stations of STN and RSA S cient ffi and P (c) weight code histograms for all the 60 RSNC stations used in this study. Red and erent regressions. The ratio S ff phases (in red and blue respectively) versus reading errors. More than 90 % of pickings Left: accuracy of pickings for all the phases read in this study. Period from 27 May to Modified Wadati plot (Chatelain, 1978) of the arrival time dataset of this study. S and P As a, but after reading refinement and outlier removal. and (b) tegration of other station’s phaseincrease readings of (either linear re-picked correlation or coe as given by bulletins). Note the blue colour scales represent weight codes associated to dataset of STN stations only. Reddard and deviation) blue and lines represent orthogonal ordinary regression, least squares respectively. Coe (with its stan- the two di station at nearby sites. networks. P5 and P95by indicate thin, 5th grey bars; and P25 95th andcoloured percentile P75 bars; indicate of P50 25th the is and distribution the 75th(d) percentiles median and and and are are it represented represented is by represented thick, by the white thin line inside the thick bar. station of STN and RSA networks.SL03/SLA3 The dashed and columns SL05/SLA5, show the respectively, sum which of correspond the phases to for di stations Fig. 6. P has a reading error less than or equal to 0.2 s. Fig. 5. 31 December 2009. Right: weight code distribution for the RSNC stations used. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (f) from (i–l) geostructural (i–l) Chiarabba et al. (2009); (b) Envelope (e.g. range of vari- (b) Chiarabba et al. (2005) modified; (e) Di Luzio et al. (2009); from many sources (Patacca et al., 2008; Envelope (e.g. range of variability) of the P V (h) (a) Bagh et al. (2007); from geophysical investigations; and (a) 2392 2391 ) models from literature for the Sulmona area. Models (f–h) P V Chiarabba et al. (2005); Patacca et al. (2008); (d) wave velocity ( (g) P Depth distribution of the selected events (Fig. 2) used to compute the velocity from seismological data; (c) Location map of the seismic network with station corrections related to the best wave velocity models individually plotted in Fig. 7. P (a–e) (d) 1-D velocity model in the Sulmona area. Compilation of Chiarabba et al. (2010); ability) of the best 1-Dor velocity equal models computed to with 0.1ity the s. models. Velest code The having stripped a misfit area less represents than the unconstraint depth interval of the veloc- Fig. 8. starting model. model; positive values correspond tosynthesized velocity the slower start with and respect final travel-time to residual the for model. each of In the the 12 table used are models (a–l in Fig. 7). derive: geological interpretation. Key references are: (c) Scarascia et al. (1994); layering from Lavecchia and de NardisDi (2009) Luzio and et al., 2009; Barchi et al., 2003; Trippetta et al., 2013). Fig. 7. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) S TOT and P Root mean (e, f) Number of used phases and Depth distribution of the hypocen- (g, h) Histograms of residuals of (i) , on the upper-right corners mean (M) (h) (a, b) to (a) 2394 2393 Horizontal and vertical formal errors (SEH, SEZ). Epicentral distribution of the 535 events localized by using Hypoel- (c, d) (a) Number and cumulative number of earthquakes versus time; location quality is given Seismicity recorded by STN in the Sulmona area (Abruzzo region) from 27 May to Features of quality A earthquake locations. (b) Fig. 10. 31 December 2009. lipse (Lahr, 1980, 1984, 1999)colours and refer the to best the 1-D qualitylocal velocity of magnitude. model Coloured estimated earthquake areas for locations; this (A–G) symbolmic area. point shape swarms Symbol out as and the described dimension seismicity in referFig. clusters the to 1. corresponding text. their to For seis- location map of the represented area and legend see with respect to relocated events, described in Sect. 5. of quality A events is indicated. Fig. 9. minimum hypocenter to station distance.and From standard deviation (SD) ofters. distributions The are black arrow reported. identifies thetral seismogenic layer distribution. corresponding to On the the 95depth % right intervals of the 0–5 side km, hypocen- are 5–9 km, reported 9–12 km, the 12–17 km percentages and of deeper. Also the the events total occurred number (NE in the phases (ResP, ResS). square of travel time residuals (RMS) and GAP distribution. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | L M magnitude, through D M . c M Calibration of (c) ). In red and blue ordinary least squares and τ 2395 Gutenberg–Richter slope evaluated with 527 events for which estimates of earthquakes localized in this study. On the top of the L (d) M against event duration ( L M Histogram of Histogram of the residuals between local magnitude estimated in this study and that (b) Fig. 11. (a) histogram are reported thenitude. percentages of the eventsreported within the on corresponding ISIDE range database, of for mag- coincident events. linear regression of orthogonal regressions, respectively. Dashed lines representleast the squares standard regression. deviation of ordinary has been estimated. In blue, the magnitude of completeness