PUBLICATIONS. Journal of Geophysical Research: Solid Earth. Shear wave automatic picking and splitting measurements at Ruapehu volcano, New Zealand

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1 PUBLICATIONS Journal of Geophysical Research: Solid Earth RESEARCH ARTICLE Special Section: Stress, Strain and Mass Changes at Volcanoes Key Points: Automatic shear wave picking program is adapted to New Zealand local earthquakes Fully automatic shear wave splitting performed around Ruapehu volcano Nine year data show correlation between delay time and initial polarization Supporting Information: Texts S1 S3, Figures S1 S4, and Tables S1 S3 Correspondence to: M. K. Savage, Citation: Castellazzi, C., M. K. Savage, E. Walsh, and R. Arnold (2015), Shear wave automatic picking and splitting measurements at Ruapehu volcano, New Zealand, J. Geophys. Res. Solid Earth, 120, , doi: / 2014JB Received 1 SEP 2014 Accepted 29 JAN 2015 Accepted article online 11 APR 2015 Published online 19 MAY American Geophysical Union. All Rights Reserved. Shear wave automatic picking and splitting measurements at Ruapehu volcano, New Zealand Claire Castellazzi 1, Martha K. Savage 1, Ernestynne Walsh 2, and Richard Arnold 2 1 Institute of Geophysics, School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington, New Zealand, 2 Institute of Geophysics and School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand Abstract Automatic shear wave picking and shear wave splitting measurement tools (Multiple Filter Automatic Splitting Technique (MFAST)) are combined to build a near-real time application for monitoring local stress around volcanoes. We use an adapted version of Diehl et al. (2009) on seismograms provided by the New Zealand GeoNet network and having an origin time and location based only on P picks. The best automatic picks are processed by MFAST, which computes the corresponding shear wave fast direction ϕ, and splitting delay time δt, interpreted, respectively, as the principal direction of stress underneath the station and the amount of anisotropy integrated along the wave raypath. We applied our system to 9 years of local earthquakes recorded at seven stations around Ruapehu volcano, New Zealand. Results are compared against MFAST measurements from manual S picks when available and show less than 10 difference for 90% of ϕ measurements and less than 0.05 s difference for 95% of δt measurements. Shear wave splitting from automatic S arrival times are slightly more consistent than those from manual arrival times. At some stations, two populations of delay times occur, which depend upon computed initial polarization. This may be caused in part by cycle skipping, an artifact usually associated with monochromatic signals. However, spatial consistency in the behavior suggests a physical cause as well, such as focal mechanisms varying with earthquake source location or a spatially varying near-source anisotropic region. The numbers of events in each population group vary over time, possibly related to activity at Ruapehu volcano. 1. Introduction Changes in stress and strain at volcanoes may cause cracks to open or close, locally changing the anisotropy in the area, which can potentially be monitored by shear wave splitting [e.g., Miller and Savage, 2001; Gerst and Savage, 2004;Bianco et al., 2006]. Such monitoring would be complementary to the more common GPS and seismicity studies, and hence, near-real time techniques could help to improve eruption predictions. To this end, we developed an automatic shear wave splitting code, MFAST [Savage et al., 2010], but it still relied on manual S arrivals. Traditionally, seismic networks hired workers to manually determine both P and S arrival times to determine earthquake locations. However, with increasingly larger amounts of data and the demand for real-time earthquake monitoring, hand timing becomes prohibitively expensive and automatic arrival time pickers are necessary. P arrivals are relatively easy to identify because they are the first arrivals on the seismograms, and automatic P pickers have been used for many applications [e.g., Allen, 1978, 1982; Baer and Kradolfer, 1987]. S arrival times aremoredifficult to pick as they are surrounded by other phases. Yet precise knowledge of S arrival times is also vital to determine accurate hypocenter locations (particularly depths) and to calculate models of shear wave velocities and attenuation, one of the main components of seismic hazard modeling. Therefore, automatic S arrival time pickers are being developed. Here we incorporate the Diehl et al. [2009] automatic S arrival picker with the MFAST algorithm to determine consistent measurements of shear wave splitting for 9 years in the Ruapehu area to test how well such automatic S arrival time picks can be used for anisotropy analysis. Aligned cracks cause anisotropy and reflect the change of stress conditions in the ground, which can be determined by shear wave splitting measurements. Anisotropic stress closes cracks perpendicular to the maximum horizontal stress direction, leaving only those open that are parallel to the maximum horizontal stress [Nur and Simmons, 1969]. Shear waves travel faster when polarized in the direction parallel to the crack planes and slower in the perpendicular direction, causing the wave to separate [Nur and Simmons, 1969]. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3363

2 Figure 1. Study region and spatial distribution of measurements. Each panel has a map of the Ruapehu region with the best MFAST measurements (AB, F2, and F3) at the station identified in black. Earthquakes are colored by initial polarization according to the scale. (left) Small δt and (right) large δt for measurements at (top) station WTVZ and (bottom) station FWVZ. Other stations are the white triangles. The white boxes in each panel show the regions considered in this study. Figure 1 (top left) contains an inset with the location of the box on a map of New Zealand, the names of all stations and regions, and regions defined in other studies. The boxes with red dashed lines are the Erua and Waiouru regions considered by Godfrey et al. [2014], and the black dashed lines are those considered by Keats et al. [2011] and Johnson and Savage [2012]. The delay time, δt, between the fast and slow arrivals is proportional to the integrated amount of anisotropy along the wave raypath. The polarization of the fast arrival, ϕ, reflects the crack orientation and hence the maximum horizontal stress direction of the last layer traveled by the wave Previous Work on Mount Ruapehu Mount Ruapehu is an active stratovolcano in the south end of the Taupo Volcanic Zone (TVZ), the back-arc system of the Hikurangui subduction zone on the east side of the New Zealand North Island (Figure 1). It is the highest mountain in the North Island, and it is a popular tourist area with three ski fields on its flanks. Eruptions often lead to lahars due to collapse of the crater lake wall, threatening roads, trains, and villages. Historically, major eruptions have been on average 50 years apart, with the most recent large eruptions in and small phreatic eruptions in 2006 and 2007 [Scott, 2013]. Standing about 15 km northeast of Mount Ruapehu is Mount Tongariro, a complex of multiple volcanic cones including different active vents. It hosted a large swarm of earthquakes in 2009 [Jacobs, 2013], and its Te Maari crater erupted twice in 2012, on 6 August and 21 November. Precursors to eruptions in the region are rare [Sherburn et al., 1999], so different approaches have been examined. Seismic velocity changes measured from ambient noise cross correlation [Mordret et al., 2010], temporal variation of far-field seismicity [Hurst and McGinty, 1999], temporal variation in seismic attenuation [Titzschkau et al., 2010], changes in anisotropy, and b values of nearby swarm areas [Keats CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3364

3 et al., 2011; Miller and Savage, 2001; Gerst and Savage, 2004; Johnson and Savage, 2012] have all been suggested as being related to changes in stress or fluid content and have all been correlated with activity retrospectively. However, none of those techniques are implemented for standard monitoring yet in New Zealand. Previous studies using automatic splitting measurements were limited in time and space by the need to provide a shear wave (S) arrival time, which was done by manually picking each record. The most comprehensive spatial analysis of anisotropy in the region was carried out by Johnson et al. [2011]. They used the MFAST program [Savage et al., 2010] to perform shear wave splitting measurements for local events recorded at 34 seismometers deployed around Mount Ruapehu in 2008 to provide background values against which changes could be measured. The ϕ measurement can be interpreted either as structural anisotropy, when it is aligned with faults or other structural features, or as stress-induced anisotropy, when it agrees with stress estimation from focal mechanism inversion. Johnson et al. [2011] suggest that the regions of stress-induced anisotropy highlighted by their measurements are affected by loading of the volcano and a lithospheric discontinuity. Keats et al. [2011] evaluate shear wave splitting on earthquakes from a small swarm of events in the Erua region recorded at four stations from 2004 to They detect a change in the delay time δt as well as in the fast direction ϕ and in earthquake b values prior to the small phreatic eruptions for station FWVZ. Shear wave splitting analysis was carried out by Johnson and Savage [2012] for temporary deployments around Mount Ruapehu in 1994, , 2001, and 2002 and for permanent stations recording events in the Waiouru cluster of events. They highlighted a region of strong anisotropy centered on the volcano at the time of the 1995 major magmatic eruption, which is interpreted to be due to an increase in fluid-filled fractures during the eruption. They also observe strong anisotropy and a change in fast direction at Mount Tongariro in 2008, which is attributed to a change in the geothermal system. Godfrey et al. [2014] examine shear wave splitting and V P /V S ratios for seismograms recorded at four stations for a 16 month period before the Te Maari eruption at Mount Tongariro in August Although they do not find any change prior to the eruption for the studied stations, they observe a distribution of ϕ tangent to the Tongariro massif that could come from gravitational loading of the volcano Previous Work on Automatic S Arrival Time Picking The most common traditional S pickers are the short-term average to long-term average (STA/LTA) ratio developed by Allen [1978, 1982] and a later adaptation of it [Earle and Shearer, 1994], algorithms based on the wavelet transform [Anant and Dowla, 1997], algorithms based on wave polarization analysis [Cichowicz, 1993], autoregressive methods applied on predicted waveforms [Takanami and Kitagawa, 1991, 1993; Leonard and Kennett, 1999; Küperkoch et al., 2012], or a combination of those methods [Bai and Kennett, 2000; Sleeman and van Eck, 2003; Diehl et al., 2009]. Ross and Ben-Zion [2014] used STA/LTA and kurtosis detectors after polarization filtering to pick P and S waves and fault zone-guided waves. The increase of computational power and speed has also seen the rise of new methodology involving artificial intelligence. Dai and MacBeth [1995, 1997] developed a picker based on artificial neural network (ANN) that used the recorded seismogram directly as input. Other methods based on ANN usually use features of the signal such as the STA/LTA ratio and the autoregressive coefficients [Wang and Teng, 1997] or the variance, skewness, and kurtosis of the waveform [Gentili and Michelini, 2006]. All these methods require a predetection of the event; even the STA/LTA algorithm needs a targeted window when it comes to identifying an S arrival in the middle of an earthquake signal. Recently, more advanced methods inspired by speech recognition software are trying to combine detection and classification in one single step by using a hidden Markov model [Beyreuther and Wassermann, 2008] with extracted features of the signal. The New Zealand GeoNet project ( last accessed January 2014) is currently run with the SeisComP3 ( last access July 2014) processing system and uses a STA/LTA algorithm on band-pass-filtered traces followed by a postpicker with an autoregressive algorithm to pick the P arrival and uses a three-dimensional velocity model to carry out routine locations. SeisComP3 has recently implemented a version of the Diehl et al. [2009] S picker (hereafter Diehl S picker), which may be used for GeoNet in the future. Diehl S picker combines an STA/LTA algorithm, an adaptation of Cichowicz [1993] wave polarization analysis and an autoregressive method based on the Akaike information criterion (AR- AIC) [Takanami and Kitagawa, 1991; Leonard and Kennett, 1999] to provide an S pick with an error CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3365

4 estimation and a derived quality classification. We chose Diehl S picker because of its quality assessment and to be consistent with the New Zealand network processing. If artificial neural network or hidden Markov models are employed in the future, results provided by Diehl S picker will be ready to use as input features for this new layer of processing. The Diehl S picker was developed and tested on data from regional networks in Europe, where local earthquakes are rare. The first goal of this study was to assess its performance on local events in New Zealand. The processing of the events recorded during the month following the 4 September 2010 magnitude 7.1 earthquake in the Darfield region was our first trial because we wanted a data set with a high density and high quality of manual S pick to use as reference for Diehl S picker results. This allowed us to analyze the different algorithms involved, then correct and adapt Diehl S picker to our needs. Once we were confident in the method, we used it to process 9 years of data, between January 2004 and December 2012, at seven stations around Mount Ruapehu. We compared the automatic S picks with the manual S picks from the GeoNet analyst when they were available. Then we investigated how well MFAST shear wave splitting measurements from the automatic S picks compare to those from the manual S picks. Finally, we examined time variations in ϕ and δt measurements to test for correlation with volcanic processes. 2. Method 2.1. S Picker Methodology Here we summarize the parts of the methodology of Diehl S picker [Diehl et al., 2009] version that are relevant to this article. Text S1 in the supporting information discusses details of the method and the changes that were made in it for this study. A list of parameters and their definitions is available in Table S1 in the supporting information. Diehl S picker combines STA/LTA, a polarization filter, and an autoregressive method (AR-AIC) to achieve a stable S pick with an associated error. It requires input seismograms with a P phase arrival time pick and a first approximation of the location and event origin time. A weighting scheme of the times picked by the three methods provides a final S arrival time and its error interval estimate. The error interval estimate is combined with the amplitude signal-to-noise ratio around the pick to assign a discrete quality classification to the final pick. The STA/LTA method compares the amplitude of the horizontal components within a small time window (sta) to the amplitude within a longer time window (lta)[allen, 1978]. It is the same method used in many seismic processing software P pickers such as SAC [Goldstein and Snoke, 2005; Goldstein et al., 2003]. It is a strong, stable, and reliable method that will pick any abrupt change of amplitude, with only a few parameters to configure. When it comes to picking S arrivals, the problem is to define an application window that does not include any prior arrival as the STA/LTA is not able to discriminate between P and S waves. The polarization filter is designed to discriminate between P and S wave arrivals by computing a characteristic function whose value should be close to zero when the maximum energy is in the wave propagation direction and close to one when the maximum energy is in a perpendicular direction. The polarization filter is, by design, a perfect S wave detector. However, the detection rate is much lower than for the STA/LTA, especially when an S to P converted phase blurs the arrival of the targeted S wave. The autoregressive AR-AIC method computes a model of the recorded signal in a small time window, the model window, to predict the value of the following, or preceding, sample belonging to the search window. The prediction error between the predicted signal and the real signal reflects any changes in amplitude, frequency, phase, or polarization of the recorded signal [Küperkoch et al., 2012]. The AR-AIC implemented in Diehl S picker works both forward, modeling the noise before the targeted arrival and backward, modeling the coda of the targeted signal. It is a powerful picker but needs a precise configuration with a small search window around the targeted S arrival as it will pick any new phase arrival, including P to S conversions. Because of its sensitivity, the AR-AIC picker needs a very small investigation window set up around the expected S arrival. Therefore, the polarization filter pick, if it exists, is used to define the center of the AR- AIC searching window. If there is no polarization filter pick, the STA/LTA pick is used instead. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3366

5 Finally, a quality assessment classification, ranging from class0 (best) to class2 (worst), is attributed to the final pick, depending on its associated error and on the noise level around the pick, compared to user-defined levels (see Text S1 in the supporting information). Since the error is a measure of the differences of the picks from the different methods, it is a strong estimate of the reliability and quality of the final pick Corrections and Modifications to the Original Code We started to work with Diehl S picker version We first fixed an error in the STA/LTA function computation and in its picking subroutine window. This corrected version of the program, spicker1.4.0.a, (Text S1 in the supporting information) was tested with two different reference data sets that had been manually analyzed and picked for S wave arrivals, one from the Darfield region [Syracuse et al., 2013] and one from the Taupo Volcanic Zone [Johnson, 2011]. These tests led us to modify the code slightly to better fit our circumstances, such as a closer distance between stations and earthquakes and the presence of S to P converted waves arriving just before the S arrivals at some stations. We changed the coarse window, the searching window, and the clipped amplitude check as described in Text S1 in the supporting information. We also added a check to avoid picking converted P arrivals close to the targeted S arrival. To allow comparison with the previous version spicker1.4.0.a, this latest version will hereafter be referred to as the spickerc. For convenience, the Diehl S picker version a will be referred as the spicker. Overall, the spickerc was able to successfully process 67 events with a strong converted P arrival at station MCHD, either by rejecting them (65%) or by properly repicking them (25%). When applied to stations with no obvious converted P arrival, it gives comparable results (Text S1 and Figures S2 and S3 in the supporting information). Therefore, we recommend its use, although the parameters setting the time intervals before an S arrival might need to be changed in the case where conversions occur closer to the S arrival, such as in shallower sedimentary basins. We checked the results against the manual picks for each station. We also checked the improvement of the results against the processing parameters used by Diehl et al. [2009] for their European Alpine region tomography application. The number of class0 final S picks increased from 10% of the input data for the Alpine parameters to 32% for the Darfield parameters, with 94% of the class0 automatic final picks less than 0.2 s from the manual picks for all three stations together (Figure S3 in the supporting information). The class1 (error estimate no bigger than 0.2 s) has 62% of its picks in the 0.2 s vicinity of the manual picks. The most important outcome of this first trial are the following. (1) For a group of stations within the same region, the same set of processing parameters gives satisfactory results for all stations (Figure S3 in the supporting information). This means that the processing parameters strongly depend upon the input data set characteristic and less on station-specific factors. (2) The quality classification of the automatic final picks reflects the reliability of the picks. Consequently, we can confidently use the highest class, the class0, for further processing. Furthermore, for the sets of data we examined, tuning the processing parameters by aiming to increase the number of class0 final picks led to high consistency with manual picks. Only tuning the parameters related to the added code for rejecting possible converted P picks (H2Zratio and checkwlen) will not necessarily increase the number of class0 events. Those will require a visual check of the output automatic final S pick Shear Wave Splitting/MFAST Methodology Once S wave arrivals are picked, the Multiple Filter Automatic Splitting Technique (MFAST) program developed by Savage et al. [2010] performs shear wave splitting measurements, measuring the polarization of maximum energy of the fast component of the split wave when reaching the recording station, ϕ, and the time difference between the fast wave and its orthogonal component, δt. MFAST also provides a measure of the incoming polarization (ϕ in ) by computing the eigenvalues of the corrected components after shear wave splitting is removed. The ϕ in corresponds to the polarization of the wave before it entered the last anisotropic layer responsible for the measured ϕ value. A brief description of the method is given in the next paragraph; for details, see Savage et al. [2010]. A series of band-pass filters are first applied to the waveforms. The product of the bandwidth and the signal-to-noise ratio after filtering determines the best three filters. Splitting measurements using Silver and Chan s [1991] method with the corrected version of the error calculation [Walsh et al., 2013] are determined on all three filtered records. Rotation of ϕ values between 90 and 90, with a 1 increment CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3367

6 and delay time correction of δt values from 0 to 0.8 s, with a 0.01 s increment, are applied to a selected time window of the east and north components of the filtered record. The (ϕ, δt) pair that best removes the splitting, as measured by the smallest eigenvalue of the corrected covariance matrix, is the measurement for the given time window. This procedure is then repeated for 75 windows covering slightly different time spans. Cluster analysis over the 75 windows measurements is used to pick the final measurement for the considered filter and calculate the associated uncertainty [Teanby et al., 2004]. The measurement is given a grade ranging from A to D depending on the consistency between the (ϕ, δt) measurements from the different windows and their uncertainty. Events for which no measurable splitting occurs (or for which ϕ is within 20 of parallel or perpendicular to ϕ in ) are given a null grade. Finally, a selection of the best measurement for each record can be performed between the final measurements from the three filters: if estimates for different filters are too dissimilar, all results for this record are rejected. If two or three filters give similar results, the measurement with the smallest error is kept, and the record is given a filter grade of F2 or F3. If only one filter passed the AB criteria, then the record is kept and graded F1. 3. Application of the System to the Mount Ruapehu Events (Taupo Volcanic Zone) 3.1. Data We wanted to investigate the possible change of stress in the Ruapheu region during a time period covering the two recent phreatic events on 4 October 2006 and the 25 September Since 2004, Mount Ruapehu has become well instrumented, with 10 permanent seismometers currently installed directly on its flank and 7 monitoring its neighbor Mount Tongariro (GeoNet, last accessed July 2014). We processed the records between January 2004 and December 2012 from four of the Ruapehu stations (FWVZ, TRVZ, TUVZ, and WNVZ) and three of the Tongariro stations (KRVZ, OTVZ, and WTVZ). Stations KRVZ, TUVZ, WNVZ, and WTVZ are equipped with short-period seismometers (Sercel L4C-3D, 1 s natural period), while FWVZ has a broadband seismometer (Guralp CMG-40T, 60 s natural period). Stations OTVZ and TRVZ were upgraded from short-period to broadband, respectively, on 10 November 2011 and 14 April We use clusters of local events and look at the time variation of the anisotropic measurements between one station and one cluster of events to minimize path differences. We process events with their epicenters bounded by longitudes E to E and latitudes 38.9 S to 39.6 S, an area of approximately 100 km 80 km around Mount Ruapehu (Figure 1). Inside this boundary, we define three smaller boxes covering the main clusters of earthquakes around Mount Ruapehu: the Tongariro box (38.9 S, 39.1 S, E, E), NE of Ruapehu, corresponds to the Tongariro Volcanic complex and was mainly active between June 2008 and June 2010 [Jacobs, 2013], the Waiouru box (39.4 S, 39.6 S, E, E) named after Waiouru village and the Erua box (39.1 S, 39.4 S, E, E) named after Erua village west of Ruapehu have both been active for our 9 year period (Figure 1). There are multiple earthquake clusters in the Erua region. Including the small clusters between E and E in the Erua box, as was done in previous studies, yields raypaths coming from an angle larger than 90 at station FWVZ, increasing the weight of the spatial variation compared to the temporal variation of the splitting measurements. We therefore decided to leave them out. The Tongariro, Waiouru, and Erua boxes are only used to look at the results for specific station-cluster pairs, while the splitting measurements are performed for all the events with their origin in the km box. The waveforms and metadata of all events with a magnitude 1.5 and with at least a manual or an automatic P pick in the catalog were loaded from GeoNet. This represents 65,350 records for the seven stations for the time period between 2004 and During this period, S waves were usually picked by GeoNet analysts on seismograms with the clearest records. Those with the most picks were stations FWVZ and WNVZ. Considering all seven stations together, 30% of the records have manual picks for the 9 year period, with 43% on station FWVZ and 37% on station WNVZ (Table 1 and Figure 2) Results SpickerC processing parameters are the same for all seven stations (Table S1 in the supporting information). To set them up, we used a subset of our final data set: 761 events with magnitude 2.0 recorded during the year 2008 at the three stations FWVZ, OTVZ, and WTVZ and with their origin in the Ruapehu box defined above. In total, this represents 1742 event-station records. This subset was chosen because it was manually picked by Johnson et al. [2011]. As a blind test, we tuned the processing parameters by trying to increase the number of class0 picks without checking the intermediate results against the manual picks. Only the CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3368

7 Table 1. Summary of the Number of Records Through the Different Processing Steps Station Original No. of Events SpickerC Class0 S Picks GeoNet Manual S Picks MFAST SpickerC Class0 Best Measurements AB MFAST GeoNet Manual S Best Measurements AB Total 65,350 18,517 19,783 11,171 F1: 3,963 11,139 F1: 4,190 60% input F2: 3,122 56% input F2: 3,099 28% 30% 17% F3: 4,086 17% F3: 3,850 Tongariro stations KRVZ 8,878 2,387 2,268 1,338 F1: 576 1,392 F1: % input F2: % input F2: % 25% 15% F3: % F3: 472 OTVZ 9,302 2,985 2,300 1,978 F1: 595 1,333 F1: % input F2: % input F2: % 25% 21% F3: % F3: 609 WTVZ 10,258 2,912 3,182 1,988 F1: 581 2,041 F1: % input F2: % input F2: % 31% 19% F3: % F3: 836 Ruapehu stations FWVZ 10,228 3,858 4,408 2,368 F1: 854 2,475 F1: 1,056 61% input F2: % input F2: % 43% 23% F3: % F3: 750 TRVZ 8,835 1,979 1,944 1,089 F1: 421 1,065 F1: % input F2: % input F2: % 22% 12% F3: % F3: 329 TUVZ 10,043 2,404 2,738 1,435 F1: 504 1,443 F1: % input F2: % input F2: % 27% 14% F3: % F3: 471 WNVZ 7,806 1,992 2, F1: 432 1,390 F1: % input F2: % input F2: % 37% 13% F3: % F3: 383 Figure 2. Flowchart of the processing for measurements at Mount Ruapehu. The numbers of measurements include only the highest quality output. Numbers next to F1, F2, and F3 refer to the numbers of measurements of that quality from the filter criterion. final picks corresponding to the best set of parameters were checked against the manual picks from Johnson et al. [2011]. Table S1 in the supporting information compares the parameters used for the Darfield test to those used for these data. They differ by the length of the STA/LTA and the AR-AIC windows, which is an adaptation to the longer epicentral distances. We also expanded the class0 definition to the final picks with an error estimate less than or equal to 200 ms. This new definition of the class0 picks was used because the corresponding accuracy of the S automatic picks was expected to be good enough to pursue with MFAST processing. Using this definition, when processing the same data as Johnson et al. [2011], the number of spickerc class0 picks represented 35% of the input records with 89% of the class0 final picks having a time difference with the S wave manual pick lower than 0.2 s range. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3369

8 We used the same parameters for the four other stations. The results of spickerc may be improved by tuning the processing parameters for each station separately, but here we demonstrate that a common set of parameters is enough to get a similar number of S picks to the ones provided by GeoNet (Table 1) Comparisons of Splitting Measurements From Manual and Automatic S Arrivals Figure 2 is a flowchart of the processing scheme, which explains the different numbers of successful events in each stage. For all stations together, the number of class0 picks generated with the chosen set of processing parameters corresponds to 28% of the input records. Station FWVZ has the highest rate with 38% of the input records having a class0 final S pick, while station TRVZ has the lowest rate with only 22% of the input records in the class0 picks. These percentages are close to the percentages achieved by manual picking. Even though the number of manual picks provided by GeoNet is not exhaustive, it appears to reflect the quality of the records at the station. The differences of success rates of spickerc between different stations may be explained by seismogram quality and therefore may not necessarily be linked to the processing parameters. SpickerC provides 18,517 class0 S arrival picks from the 65,350 input records (Figure 2). Of these class0 automatic picks, 53% are records with a GeoNet manual pick as well. This allowed for comparison between the automatic picks and the manual ones. Of these 9808 common records, 73% have a time difference of less than 0.2 s between the two picks (Figure 3). We next run MFAST for the records with a spickerc class0 pick. The time of the S arrival is usually not the same on both horizontal components because of the splitting phenomena. Human analysts pick the first arrival between the east and north components, and it is the one expected by MFAST. The final S pick provided by spickerc is an average of the different picks from the different methods, so it does not necessarily satisfy this definition. Using the first pick between the east and north components of the STA/LTA pick or the AR-AIC will be more accurate. Figure S2 in the supporting information shows that the polarization filter picks tend to be more scattered and would not be a good choice. Also, it is possible to get a class0 final pick when the STA/LTA pick or the polarization filter pick is missing but not if the AR-AIC method is unsuccessful. Figure 3 shows that for all stations except OTVZ, the percentages of class0 automatic picks within 0.2 s of the manual pick are approximately the same for the final pick and for the AR-AIC east and north earliest picks but with a greater number of picks within the 0.1 s range for the AR-AIC picks. Therefore, we use the AR-AIC earliest pick between the east and north components as input for MFAST for all the events having a spickerc class0 final pick. We run MFAST separately for the spickerc class0 AR-AIC automatic picks and for the GeoNet manual S picks but with the same parameters. The percentage of best measurements (grade A or B, F1, F2, and F3) given by MFAST is 4% higher for the automatic picks than for the manual picks (Figure 2). The difference could be explained by the MFAST signal-to-noise ratio (SNR) criteria: only the band-pass filters with a SNR 3are considered for further analysis. At the same time, only the records with a SNR 3aroundthespickerC final automatic pick can be part of the class0. Automatic class0 picks are therefore more likely to pass the MFAST criteria than are manual picks. This compensates for the smaller number of automatic picks put into MFAST compared to manual picks. At the end, nearly the same percentage (9.6 to 9.7%) of the 65,350 initial records remains for both the automatic and manual S pick processing scheme, of which 4750 are common to both (Figure 2). We compare MFAST grade AB measurements of these common records. Figure 4 shows that most of the records giving different ϕ or δt values are those graded F1 for the manual or the automatic pick (red crosses). If we compare MFAST measurements graded F2 or F3 for both automatic and manual picks (blue crosses), 90% of the ϕ values have less than 10 difference, and 94% of the δt values have less than 0.05 s difference. Keeping only F3 measurements, those numbers increase to 94% for ϕ and 98% for δt. We therefore decided to consider only MFAST grade AB measurements F2 or F3 as reliable Results for Time Variation Finally, we analyze the results of the S wave splitting time δt and the fast orientation ϕ in terms of potential variation in stress conditions around Mount Ruapehu, perhaps as eruption precursors. For this, we want as many high-quality measurements as possible; therefore, we combine MFAST measurements from both automatic and manual picks (Figure 2). For the 4750 common records, it is necessary to select only one measurement, which is done for 1701 of them by keeping the record with the S pick that gives the best CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3370

9 Figure 3. SpickerC automatic class0 final pick (grey) and corresponding AR-AIC east and north component earliest picks (black) compared to GeoNet manual pick for the 9808 common records. On top of the grey arrow and on top of the black arrow are, respectively, the percent of the class0 final picks and the percent of the class0 AR-AIC east and north earliest picks that were less than 200 ms away from the manual pick. The vertical axis scale is different for (top left) all the stations together and for (top right) station FWVZ. Next to each name is the total number of events in the plot. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3371

10 Journal of Geophysical Research: Solid Earth Figure 4. Comparison of MFAST grade AB best measurements ϕ and δt values when results are common to GeoNet manual S picks (horizontal axis) and the spickerc automatic S pick using AR-AIC horizontal component earliest pick (vertical axis). The grade AB best measurements that come from filter class F1, for both the GeoNet results and the spickerc results are in red. The grade AB best measurements with filter grade F2 or F3 are in blue. The percentages of the data falling into ±10 around the 1-1 line for ϕ and the ±0.05 s around the 1-1 line for δt when considering only the F2 or F3 best measurements are in the right upper corner of each graph. Wrap-around effects make the measurements at (80, 85) appear to be far apart, while in fact they differ by less than 15. For the all stations graph, there are no error bars for clarity of the graph. N reports the number of measurements used. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3372

11 consistency over its filters results. For the 3049 records with the same grade, F1, F2, or F3, the measurement with the smallest error on the ϕ value is selected. Finally, keeping only F2 and F3 measurements from the automatic picks, the manual picks and the common records, we have 11,367 splitting measurement for our time period. Previous studies [Johnson et al., 2011; Johnson and Savage, 2012; Keats et al., 2011; Godfrey et al., 2014] demonstrated that there are differences between measurements made at stations depending upon the earthquake source region as well as the station location and possibly the origin time. Therefore, the evolution of δt and ϕ between 2004 and 2012 is analyzed for temporal variations by displaying the results for one station and one cluster, or box, of events at a time. For local events, the delay times are generally expected to be between 0.1 and 0.6 s [Savage et al., 2010], and there are only a few marginal measurements outside this range, so we limit our analysis to δt 0.6 s. Figures 5 and 6 present four stationbox pairings. They all show a minimum of two populations of delay time measurements, while there is only one population of fast orientations. For example, for Erua events recorded at station TRVZ (Figure 5, top), a population of small δt values lies between 0.03 and 0.13 s and a population of large δt values lies between 0.13 and 0.24 s, while the majority of ϕ values clusters between 20 to 40 (with respect to north). A similar behavior is visible for the Erua box events for station WTVZ (Figure 5), as well as for station FWVZ (not shown) despite more scattered measurements. Stations TRVZ and WTVZ have δt population of similar values, while FWVZ large δt values are slightly smaller with an average of 0.17 s. However, the fast orientation is specific to each station (40 to 70 for WTVZ, 20 to +30 for FWVZ). The low density of measurements for Erua events recorded at the four other stations does not allow a clear interpretation. The Waiouru box events display a similar pattern for station OTVZ (Table 2), but with a small population of large δt, and for station FWVZ (Figure 6) with a possible extra population of larger δt between 0.24 and 0.3 s. For station TUVZ (Figure 6), we have a slightly different pattern with a high-density central population of large δt between 0.15 and 0.25 s framed by a very low density population of small δt between 0.05 and 0.15 s and a population of larger δt between 0.25 and 0.4 s. Station WTVZ (Table 2), which is the farthest station from the Waiouru box with enough measurements to display a pattern, shows a completely different behavior with only one population of δt around 0.1 s and two possible populations of ϕ measurements between 0 and 40 and between 50 and 90. In contrast, station WNVZ gives a wide range of δt and ϕ values, probably because it is the closest to the Waiouru box and the S waves raypaths are too different. The low density of measurements at station KRVZ does not allow any interpretation, while measurements at station TRVZ do not present any clear pattern. The Tongariro cluster has a peak of activity in 2009 [Jacobs, 2013] that is primarily recorded at stations KRVZ, OTVZ, and WTVZ with δt and ϕ measurements widely scattered for this period, while there are not enough measurements outside this period to show any pattern Discussion Automatic Versus Manual Picks The patterns described for the Erua and Waiouru clusters are independent of the use of manual or automatic picks as MFAST inputs. Figure 4 shows the concordance of the splitting measurements between automatic and manual picks for the common records for all stations, and we also checked that the same patterns in time as seen in Figures 5 and 6 emerge when displaying only the manual or the automatic picks (shown here only for ϕ in ). The existence of different populations of δt is not a consequence of the implementation of an automatic picking system. We have a high level of confidence in the measurements graded F3 because they give consistent results for different filters and because they give consistent results between manual and automatic pick processing (98% of δt measurements with less than 0.05 s difference). Figures 5 and 6 both show that F3 (circles) and F2 (stars) measurements exhibit the same pattern Comparison to Other Studies As noted in the Introduction, our group has carried out a number of studies in the region already and has found variations in shear wave splitting both in space and time, always using hand-picked S arrival times [Miller and Savage, 2001; Gerst and Savage, 2004; Savage et al., 2010; Johnson et al., 2011; Keats et al., 2011; Johnson and Savage, 2012; Godfrey et al., 2014]. The most robust time variations observed in the previous studies were for changes related to the large phreatic eruption of Miller and Savage s [2001] results showing changes in ϕ between 1994 and 1998 were verified by new measurements of the same events, with new changes between 1998 and 2002 observed [Gerst and Savage, 2004]. These results were CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3373

12 Figure 5. Measurements from the Erua events at (top) TRVZ and (bottom) WTVZ. (a) The δt and (b) ϕ colored by ϕ in by the scale at the top. The vertical lines mark the time of the Ruapehu eruptive events on 04 October 2006 and 25 September 2007; the horizontal lines mark the populations limits used in Table 2. (c) The δt as a function of T/2. When cycle skipping occurs, δt values should increase by an integer number of the wave s half period. (d) Comparison of ϕ in (degrees from north) computed by MFAST for records common to the spickerc automatic and the GeoNet manual S picks. The red color highlights records giving significantly different δt values between manual and automatic input S picks. Records having the same δt for automatic and manual S picks (blue circles and stars) are mostly along the 1-1 line, with the same initial polarization measurement as well. Note the magenta circles (ϕ in between 120 and 150 ) between September 2011 and June 2012 for the WTVZ station. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3374

13 Figure 6. Same as Figure 5 but for Waiouru events recorded at (top) FWVZ and (bottom) TUVZ. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3375

14 Table 2. Average Value With Standard Deviation of δt, ϕ, and ϕ in of (Station-Box) Pair for Each Population a Erua Box Waiouru Box δt (s) ϕ ( ) ϕ in ( ) δt (s) ϕ ( ) ϕ in ( ) KRVZ Not enough measurements Not enough measurements FWVZ (0.03, 0.13) 3 ± 37 57±28 (0.03, 0.13) 12 ± ± ± ± 0.02 (0.13, 0.24) 54 ± ± 30 (0.13, 0.24) 6 ± ± ± ± 0.02 (0.24, 0.3)? 80 ± ± ± 0.01 OTVZ Not enough measurements (0.05, 0.16) 75 ± ± ± 0.02 (0.16, 0.28) 88 ± ± ± 0.02 TRVZ (0.03, 0.13)] 5 ± ± 27 No clear pattern 0.09 ± 0.02 (0.13, 0.24) 11 ± ± ± 0.02 TUVZ Not enough measurements (0.05, 0.15) 27 ± ± ± 0.03 (0.15, 0.25) 30 ± ± ± 0.02 (0.25, 0.4) 39 ± ± ± 0.03 WNVZ Not enough measurements Scattered measurements WTVZ (0.03, 0.13) 53 ± 21 3 ± ± 0.09 (0, 40) 139 ± ± ± 10 (0.13, 0.24) 54 ± ± ± 0.03 (50, 90) 31 ± ± ± 10 a Population definitions are given between brackets (for example, station FWVZ records coming from Erua box events have two populations of δt, one with values between 0.03 and 0.13 s, and one with values between 0.13 and 0.24 s) with the average value given below. Merged cells show common values, when standard deviations are overlapping. verified by Savage et al. [2010] during testing of the MFAST method. All were considered to be caused by variations in a dike or dike system that locally reversed the orientation of the near-isotropic stress field as a function of time. Johnson et al. [2011] found strong spatial variations in anisotropy that were not evident in the earlier studies. Johnson and Savage [2012] further verified the changes in fast direction associated with the eruptions and saw changes in delay time for a newly available data set from 1995 to 1996 using tomographic and spatial averaging. The eruptions were smaller and had less clear relations. Mordret et al. [2010] found changes in isotropic noise velocity before the 2006 eruption but only for two station paths. Keats et al. [2011] found changes in splitting near the time of the eruptions that were strong only at two stations, and they also found the changes to correlate with changes in b values, with more small earthquakes compared to large ones during the time of changed b values. Godfrey et al. [2014] found that apparent changes in splitting associated with the 2012 Tongariro eruptions could be explained by variations in properties with region and changing numbers of earthquakes in each region over time. Johnson and Savage [2012] examined changes at stations near Ruapehu over a 12 year period for events coming from the Waiouru cluster but did not examine other clusters because of the time-consuming nature of picking the S arrivals for more events. While testing our method, we compared the results directly with those of Johnson et al. [2011] and found good agreement. Although we use slightly different boxes to delineate the different regions, our fast directions are consistent with the previous studies, and delay times are consistent if the same cutoff time was used in the delay time analysis. For example, Johnson and Savage [2012] show moving averages of fast directions and delay times at several stations for Waiouru events from 1999 to Johnson handpicked more S arrivals than were provided by the GeoNet team, but for example, at FWVZ (Figure 6 of CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3376

15 Johnson and Savage [2012]), they find the same average ϕ of 0 to 30 and delay times between 2006 and 2008 that are smaller than those at other times, and with smaller error bars, probably due to fewer high delay time population (Figure 6). Johnson et al. [2011, their Figure 8] also show the variation of δt and ϕ for stations TUVZ and TRVZ during 2008 but without distinguishing source locations. Their averages of between 30 and 60 and s at TUVZ and 0 and 0.2 s for TRVZ are consistent with our δt and ϕ for station TUVZ for Waiouru (Figure 6) and ϕ at TRVZ for Erua, but the δt in this paper for TRVZ is somewhat lower, caused by somewhat fewer high delay time events (Figure 5). Godfrey et al. [2014] examined splitting between 9 August 2011 and 8 January 2013 for stations near the Tongariro eruption. Station WTVZ (Figure 6 of Godfrey et al. [2014]) exhibits mainly delay times close to 0.2 s for Erua events and 0.1 s for Waiouru events. Parameter ϕ is between about 45 and 90 for most of the Erua and Waiouru events. Thus, Godfrey et al. [2014] mainly found population 2 for Erua. Comparing the regions used in the two studies, the region Godfrey et al. [2014, Figure 8] considered as the Erua swarm contains the northeast corner of the region that we considered as Erua and includes more data to the east. Figure 1 confirms that the region considered in Godfrey et al. [2014] indeed contains mostly high delay time, population 2, events. The Waiouru events at WTVZ predominantly have low delay times (Figure 1), consistent again with Godfrey et al. [2014]. In most of these previous studies, we did not report on ϕ in, because we did not notice strong patterns in them. Only the supporting information of Gerst and Savage [2004] included a discussion of ϕ in ; then, we found consistent NNE/SSW ϕ in for both shallow and deep earthquakes in 1994 and for shallow earthquakes in The deep earthquakes in the downgoing slab (deeper than 50 km) in 2002 yielded predominately ENE/WSW ϕ in. In 1998, ϕ in, like ϕ, were scattered. We attributed the well-aligned ϕ in to splitting in a lower layer of anisotropy, which was later resplit in an anomalous region near the volcano to yield the observed fast directions. We also considered the more random ϕ in in 1998 to indicate a lack of the anomalous region The Possibility of Cycle Skipping As far as we are aware, the first understanding that cycle skipping could affect the incoming polarizations as well as the delay times and fast directions was by our group [Walsh, 2013; Walsh et al., 2013]. To try to find the cause of the bimodal pattern in δt, we looked at the correlation between the δt values and hypocenter depth, back azimuth, and magnitude of the events as well as the dominant period, incidence angle, ϕ, and ϕ in of the S wave as computed by MFAST. We found that ϕ in is the only parameter that varies strongly with δt. This relationship is clearly shown by the color scale used for the circles (F3 measurements) and stars (F2 measurements) in the δt display in Figures 5a and 6a. We looked at the consistency of ϕ in by analyzing its value coming from MFAST processing of S manual picks versus S automatic picks for the same record. Again, we limited our analysis to grade AB best measurements, F2 and F3. For the Erua events recorded at station TRVZ (Figure 5), the population of small δt values (~0.1 s) corresponds to events with ϕ in between 0 and 60 (cyan and royal blue circles/stars), while the population of large δt values (~0.2 s) corresponds to events with ϕ in between 120 and 180 (light and dark pink circles/stars). Looking at the comparison of ϕ in for the common measurements (Figure 5d), 93% of the records give similar initial polarization (difference less than 20 ) for manual and automatic S picks, while both ranges of ϕ in, (0 60 ) and ( ), are present. The lack of measurements around 0, 90, and 180 corresponds to the rejected measurement due to ϕ in being within 20 of either parallel or perpendicular to the ϕ measurement, which is around 0 in this case. For Waiouru events recorded at station FWVZ and TUVZ, 20% of the common records give significantly different ϕ in (difference larger than 20 ) for different S picks (Figure 6d). The majority of these records are also the ones giving significantly different δt values (difference larger than 0.5 s) for different S picks (red stars/circles). We conclude that using two different S arrival time picking methods, MFAST returns either the same δt and the same ϕ in or significantly different values for both δt and ϕ in. This demonstrates that these two parameters are strongly dependent, either by construction of the MFAST algorithm or by some unknown physical relationship. Similar behavior was found when examining records from earthquake multiplets at Piton de la Fournaise volcano, and there the numbers of events in each group of polarizations/delay times varied in time [Walsh, 2013; Savage et al., 2015]. Similar jumps in delay times were also observed for an earthquake multiplet at Okmok volcano in Alaska, which did not correspond to obvious cycle skipping in ϕ [Johnson et al., 2010]. CASTELLAZZI ET AL. AUTOMATIC SHEAR WAVE ANALYSIS 3377