Building 1D reference velocity model of the Irpinia region (Southern Apennines): microearthquakes locations and focal mechanism

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1 Building 1D reference velocity model of the Irpinia region (Southern Apennines): microearthquakes locations and focal mechanism Tutor Prof. Raffaella De Matteis PhD student Emanuela Matrullo Geophisics XXIV cycle Alma Mater Studiorum Bologna

2 The aim of the the work To obtain information about the stress field acting in the Irpinia region (Southern Appennines) using low magnitude earthquake. In the first part of the work we studied the area from seismological and geological point of view and we analized the influence of the velocity model on the earthquake locations and on the determination of focal mechanisms. We analyzed the instrumental seismicity of the Irpinia region recorded by the ISNet (Irpinia SeismicNetwork, AMRA) and the nearby INGV (Istituto Nazionale di Geofisica e Vulcanologia) stations, during the period August 2005 October The denser station coverage, yields a significant improvement in the hypocentral locations. We used standard seismological methods to compute Vp/Vs ratio, onedimensional velocity model, and station corrections for earthquake relocations. Focal mechanisms were computed using first motion polarities (Reasemberg et al., 1985). 2/15

3 Investigated area: geologic and tectonic setting 3/15

4 Recent seismicity ( ) and station configuration 4/15

5 Construction of the data-set Longitude Latitude Weights Picking accuracy (s) <= > 0.5 We re-picked manually P- and S- first wave arrival times for a total of 5685 P- and 3118 S- phases of about 600 earthquakes, in a range of local magnitudes between 0.2 and 3.2 inside the seismic network. 5/15

6 Evaluation of picking consistency The picking quality has been assessed by performing a preliminary location and looking, for each station, for outliers on the histograms of residuals. Modified Wadati diagram (Chatelain, 1978) : we plot the difference of Ts vs difference of Tp for each couple of stations. R 2 =0.97 6/15

7 Building 1D velocity model: data selection and inizial model We performed an analysis to find the best P-wave one-dimensional velocity model for the crustal structure of the study area, using the VELEST algorithm (Kissling et al., 1994). This approach incorporates iterative simultaneous inversion of hypocenters and 1-D velocity model. For this purpose we select 230 events which have been preliminary located with different velocity models available from the literature, showing no significant variation in hypocentral coordinates. 7/15

8 Building 1D velocity model: results (a) (b) After inversion the two model (a layer and a gradient-like model) showing the same final RMS of 0.13s, but described by a different number of parameters. The linear model is preferred, based on the principle of parsimony on model building, for which the model with the smaller number of parameters is chosen among a set of models giving the same fit of the data. Note that model (a) introduce a gap in the distribution of number of eqk vs depth at about 10 km depth which disappear when using the gradient model. 8/15

9 Station corrections Station corrections are part of the 1D velocity model since they should partly account for the three-dimensionality of the velocity field that cannot be adequately rapresented by the 1-D model (Kissiling et al., 1995). Station corrections are strongly coupled to the velocity structure right beneath the station. We rapresent the station corrections calculated by VELEST code relatively to CSG3 station. The distribution shows a strong lateral variation in a direction orthogonal to the Apenninic chain, which is consistent with the transition between the carbonatic platform outcrops at South-West and the Miocene sedimentary basins at North-East. 9/15

10 Earthquake locations NLLoc code (Lomax et al., 2000) 10/15

11 Focal mechanisms We estimated the focal mechanisms of the earthquakes from the P-wave firstmotion polarities using the FPFIT algorithm (Reasemberg and Oppenheimer 1985). Rose diagram for the take-off angles has been computed using the (a) 1D layered and (b) 1D gradient P-wave velocity models. The length of the radius represents the 13% of data. Note the effect on the take-off angle distrubution when using a layered velocity model. (a) 0 (b) /15

12 Focal mechanisms Best-selected fault plane solutions show a normal component faulting (pure normal fault and normal fault with a strike-slip component) according to the NE-SW extensional tectonics of the Southern Apennines. This pattern is very consistent with stress orientations inferred by past moderate to large magnitude earthquakes occurred in the area during last few decades. This evidence support the hypothesis that background microearthquake seismicity mainly occur along preexisting fault system which are causative of large earthquakes in southern Apennines. 12/15

13 Summary 1 We analized instrumental seismicity recorded using a dataset of waveforms collected from a dense monitoring of the Irpinia region in the last 3 years. We computed the Vp/Vs ratio using a modified Wadati method, obtaining a value of An analysis for the one- dimensional (1D) velocity model that approximates the seismic structure of the study area is carried out obtaining a gradient like model The distribution of stations correction shows a strong lateral variation in a direction orthogonal to the Apenninic chain, which is consistent with the transition between the carbonatic platform outcrops at South- West and the Miocene sedimentary basins at North- East. 13/15

14 Summary 2 We ri- locate all the events with the NLLoc code (Lomax et al., 2000) with the gradient velocity model and station corrections, using the Vp/Vs ratio previously estimated. Relocations are well constrained and significantly improved with respect by other previous studies The spatial earthquake distribution is better concentrates along the preexisting fault structures. We estimated the focal mechanisms for the selected earthquakes from the P- wave first-motion polarities using the FPFIT algorithm The selected focal mechanisms show mostly normal and strike-slip solutions. The tensional axes (T-axes) display a generalized NE-SW orientation 14/15

15 Future perspective To obtain information about the stress field acting in the Irpinia region (Southern Appennines) using low magnitude earthquake. The stress orientations can be determined directly from earthquake focal mechanisms through the use of inversion techniques (Gephart and Forsyth 1984; Michael 1984; Angelier 1990). The accuracy of these inversion techniques depends on the uncertainty of the focal mechanisms and the fault/auxiliary plane ambiguity. We will try to reduce the uncertainty on the focal mechanisms with introduction in the calculation of other osservable like S-wave polarization and S/P amplitude ratio to introduce a method (Rivera et al., 1990) whose major advance is to use not previously determined focal mechanisms but the original data that is the polarities of the P arrival and take-off angles for source-station pairs. This method could be useful when the number of the polarities is scarce to determine reliable focal mechanisms. 15/15

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17 Earthquake locations B E Depth (km) C A B C D F E F

18 Coupled Hypocenter Velocity Model Problem The seismic wave traveltime is a non-linear function of the hypocentral parameters and seismic velocities sampled along the ray path between hypocentre and station. It can be linearized and written in matrix notation as (Kissiling at al., 1994): t=hh+mm+e= Ad+e t: vector of travel time residuals (t obs -t calc ) H: matrix of partial derivatives of travel time with respect to hypocentral parameters h: vector of hypocentral parameter adjustments M: matrix of partial derivatives of travel time with respect to model parameters m: vector of velocity parameter adjustments e: vector of travel time error A: matrix of all partial derivatives d: vector of hypocentral and model parameter adjustments

19 Coupled Hypocenter Velocity Model Problem In standard location procedure, the velocity parameters are maintained fixed to a-priori values and the observed travel times are minimized by perturbating the four hypocentral parameters. Precise hypocenter locations and error estimate demand the simultaneous solution of the both velocity and hypocentral parameters. The correct hypocentral coordinates are most reliably achieved by solving the coupled hypocenter-velocity model problem, rather than alternating independet hypocenter and velocity adjustment steps (Kissiling et al., 1994)

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