DATA REPOSITORY SUPPLEMENTARY MATERIAL. We analyzed focal mechanism solutions for aftershocks of the Loma Prieta

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1 GSA Data Repository item DATA REPOSITORY SUPPLEMENTARY MATERIAL DR.1 Selection of aftershock sets and subsets We analyzed focal mechanism solutions for aftershocks of the Loma Prieta earthquake that occurred between October of 1989 and December of 1990 (Figs. 1, 2, 5). Approximately 90% of these aftershocks have a magnitude less than 3, and almost all of those have magnitudes greater than 1.5. We visually subdivided the data into seventeen different sets on the basis of spatially distinct clusters of seismicity (distinguished by color in Figs. 1, 2, and 5) using the statistical analysis program JMP (SAS Institute Inc.), which includes features for rotating plots of three-dimensional data and for selecting out individual groups. We tested whether each data set records a homogeneous or a heterogeneous deformation, and if it was heterogeneous, we subdivided the set further into subsets each of which records a homogeneous deformation. We provide details of the protocol we used for assigning data to subsets in the following discussion. This process of subdivision resulted in a total of 33 separate subsets. Subsets within the sets (plotted with different symbols of the same color in Figs. 1, 2, and 5) were separated according to (1) the association of data with a prominent point maximum of P and/or T axes; and (2) the association of data with a prominent cluster of solutions, where the solutions are those for data sets chosen from all the data in a given subset by random selection with replacement. The protocol for assigning a datum to a particular solution is determined iteratively, as described below. (1) We make a Kamb-coutoured stereonet plot of the P and T axes for the focal mechanisms in each set, to reveal the preferred orientations of the axes (we used

2 GSA Data Repository item Allmendinger's StereonetPPC v.6.0). The P and T axis for the data almost always cluster into one or more groups for each axis. If the P and T axes plot shows multiple maxima for P and/or for T, the data set is probably heterogeneous and reflects a mixture of different deformations. We define initial data subsets by selecting the data that are associated either with individual P and T axis point maxima, or at worst with P or T axis girdles. In some cases, this separation may be done conveniently using histograms of the trends and/or plunges of the P and/or T axes (we used the statistical program JMP (SAS Institute Inc.) for this purpose). The inversion solutions for the subsets are generally in the vicinity of, but not identical to, the P and T axis maxima. (2) Alternatively, or in addition, we define 100 to 200 data sets of the same size as the original set, selecting members of each new set from the original data set by random selection with replacement, and we invert each new set for a best-fit solution. To avoid false minima and find the best-fit solution, we use 5 random-restarts for each inversion, each restart starting from a set of randomly selected values for the initial model parameters. We then plot the resulting best-fit solutions for the principal instantaneous deformations d 1 and d 3 on a stereonet. If the solutions for these data sets concentrate around a single model, then that is taken as evidence that the data reflect a homogeneous deformation. If the solutions for d 1 and d 3 show multiple maxima, or the solutions are widely dispersed, then, either the data are inhomogeneous, or the solution is poorly resolved by the data. The point maxima for the P and T axes, and the point maxima for the d 1 and d 3 axis solutions to the randomly selected data sets provide initial trial solutions for the different deformations in the heterogeneous data set.

3 GSA Data Repository item For this second procedure to identify the different solutions, an inhomogeneous data set should contain somewhat comparable numbers of events in each set, because the procedure relies on the random selection with replacement of the data to produce some data sets that will be best-fit by each of the possible solutions. The orientations of these solution-clusters on stereograms indicate the general location of the solutions for the subsets. These clusters usually are closely related to maxima in the P and T axis plots. We divide the data set into subsets, or refine the subsets defined in (1), such that each subset is most consistent with one of the solution clusters defined in (2), using the procedure described below. In a large data set that is overwhelmingly dominated by events associated with one solution but has a relatively small number of events that define a different solution, the probability is small of producing a data set by random selection with replacement that is dominated by the subordinate subset. Therefore, the solution to the subordinate subset is not likely to appear as a significant cluster in the inversion solutions for the randomly selected data sets. Similarly, if a data set contains more than two or three subsets with different solutions, the random selection of data might not reliably pick out the different solutions, because the probability of randomly selecting numerous data sets that are dominated by one of the homogeneous subsets may become too small. We have also found, especially for shear-plane/slickenline data gathered in the field, that orientation biases in the data set can lead to erroneous indications of solutions with this random selection method. For such data sets, criterion (1) provides a more reliable way of identifying subsets.

4 GSA Data Repository item In refining the definition of the subsets, we determine the consistency of each individual datum with a particular subset solution according to the following procedure: i. Identify K initial trial solutions for K different subsets by locating the approximate average d 1 and d 3 pairs of orientations that represent each cluster of solutions either from the P and T axis maxima described in (1) or from the inversions of randomly selected data sets as described in (2); ii. Determine the misfit e (k) i of every datum (i = 1:N) in the whole data set to each of the (k = 1:K) trial solutions, and assign each datum to a subset α based on the solution for which the misfit for the datum is a minimum. Thus for a given subset α, if we renumber all the data in that subset j = 1:n α, then each of those data must satisfy the relation (α e ) (k ) j < e j j =1:n α k =1K]α[KK where the notation implies that k takes on any value from 1 to K except for α. The same relation holds for any specific subset numbered as α = 1:K. iii. Calculate the inversion solutions for each subset using only those data assigned to each subset. (k iv. Calculate the new misfits e ) i for each datum in the whole data set (i = 1:N), for each of the new subset solutions (k = 1:K) (α ) v. Calculate the means e j (α ) and the standard deviations SD e j of the misfits for just the data (j = 1:n α ) that are assigned to each subset (α = 1:K). vi. Assign the datum to a subset according to the following criteria: If the P and T axes for a datum k (belonging to the set 1:N) appear to be a part of a particular maximum α in the plot of all P and T axes for the set (which we define qualitatively as described in

5 GSA Data Repository item (1) above), and if the misfit of that datum for the α th solution is within one standard deviation of the mean misfit for the α subset as determined in (iii), (α e ) (α ) k e i (α ) < SD e i i =1:n α where the angled brackets denote the mean and SD... denotes the standard deviation from the mean, then the datum is assigned to the α th subset, even if the misfit for a different solution β α is less than the misfit for the α th solution. If the P and T axes for a datum j are associated with a particular P or T axis maximum α, but its misfit for the α th solution exceeds the mean misfit for that subset by more than one standard deviation, (α e ) (α ) j e i (α > SD e ) i, i =1:n α and if the misfit of that datum for a different solution β α is within one standard deviation of the mean misfit for the β subset, i.e., (β e ) (β ) j e i (β < SD e ) i, i =1:n β then the datum is reassigned to the β subset; In all other cases, for example if a datum has no clear association with a particular P or T axis maximum, or if the misfits for the datum exceed the means for all solutions by more than one standard deviation, then it is assigned to the subset for which its misfit for the associated solution is a minimum. Steps (iii) through (vi) are iterated to a stable definition of the subsets. vii. Finally, the spatial clustering of events within individual hypocenter sets is in some cases a criterion used to test different possible subsets.

6 GSA Data Repository item The identification of subsets defined by this protocol resulted in generally stable subsets each of which records a homogeneous deformation. Some data were consistent with more than one solution, however, and other data did not fit any of the solutions well, so we cannot rule out the possibility that this protocol results in an imperfect separation. DR.2 The FLTSLP Program We used our program FLTSLP (Twiss and Guenther, 2002; see "Acknowledgements" for availability) to search for the best-fit deformation model that accounts for the shear-plane/slip-line data given by the aftershock focal mechanism solutions. For a given shear-plane-normal, the orientation of the slip-line is uniquely determined from a given set of model parameters. For a given trial deformation model, the program uses a conjugate gradient technique (Press et al. 1989) to find the orientation of the model shear-plane-normal that, with the associated calculated slip-line, optimizes the misfit to each datum shear-plane/slip-line pair. The measure of misfit to each datum is the mean of the cosines of two angles, the first measured between the datum shearplane-normal and the model shear-plane-normal, and the second measured between the datum slip-line and model slip-line. For small angles, this is approximately the same as the cosine of the unique rotation angle that takes the model shear-plane/slip-line pair into the observed datum pair (Unruh et al. 1996). For each datum, therefore, neither the shear-plane nor the slip-line is held fixed in finding the best-fit model pair. The modelmisfit assigned to a given model is then the average of the misfits for all the data shearplane/slip-line pairs. The best-fit model is the set of five model parameters that minimizes the model-misfit and we determine this set of parameters by using a downhill simplex routine ( amoeba, Press et al. 1989). The program avoids false minima in the

7 GSA Data Repository item five-dimensional model-misfit surface by restarting the search from at least 20 different randomly selected starting models. In the process of inverting seismic focal mechanisms, the inversion program always tests both possible nodal planes for the fit to the model, and it selects as the preferred nodal plane the one that yields a minimum misfit. Michael (1987) has done tests with his inversion algorithm, which does not include the relative instantaneous rotation and which uses different misfit criteria from ours, and has shown that the minimum misfit plane is very often not the correct choice. In such a case, the misfit will be smaller than the misfit to the correct shear plane. We have not run such tests with our program, and thus accept the resulting orientation maxima of the preferred nodal planes (see Section 3.2) as an indication, rather than a definitive determination, of the real dominant orientations of these planes. To determine the confidence limits on the model parameters, we invert 2000 bootstrap data sets, selecting each data set from the original data set by random selection with replacement. The inversion of each bootstrap data set uses the best-fit model as the starting model. To determine the 95% confidence limits we calculate the Mahalanobis distance of each bootstrap model from the mean bootstrap model. That distance is defined as the square root of the sum of the squares of the distance of each model parameter from same parameter of the mean model, where all parameters are normalized to values between 0 and 1. We ignore the 5% (100 models) with the largest distances, which leaves us with a cloud of models that defines the 95% confidence limits for each of the model parameters. We use the downhill simplex routine amoeba (Press et al. 1989) to find the mean bootstrap model by minimizing the L1 norm of the normalized

8 GSA Data Repository item parameter distances (sum of absolute values of the distances) between the mean model and those bootstrap models within the 95% confidence limits. The calculation for the mean model and for the confidence limits is iterated to a stable set of bootstrap models that define the 95% confidence limits. DR.3 Fitting Planes to Hypocenters Alignments A plane in space is characterized by the equation n x L = 0 (DR3.1) where n is the unit normal to the plane, x is the vector to any point in the plane, the dot indicates the scalar product, and L is the plane location constant, which is the orthogonal distance to the plane from the coordinate origin. We determined the best-fit plane to a set of hypocenters by minimizing the L1 norm, which is a measure of the misfit defined by the sum of the absolute values of the perpendicular distances of all hypocenters from the plane. Compared with the more usual L2 norm, or least squares misfit, the L1 norm gives less weight to the outliers on the solution. The misfit for each hypocenter is thus the left side of Equation (DR3.1) where x is the vector to the hypocenter and L is the orthogonal distance from the origin to the best-fit plane. Two coefficients of determination, c 13 and c 23, define the fit of the plane to the data (1) m i (3) m i c 13 i i i m i (1) (2) m i (3) m i c 23 i i i m i (2) (DR3.2) where m i (α) is the perpendicular distance of the i th datum to the α th plane, and where α = 3 for the best-fit plane, α = 1 for the worst-fit plane normal to the best-fit plane through the mean point of the data, and α = 2 for the plane normal to the other two planes through the

9 GSA Data Repository item mean point of the data. Thus the ranges of values for the two coefficients are 0 c 13 1; 0 c 23 c 13. The significance of different values of the coefficients is indicated in Table DR.3-1. The closer c 13 is to a value of 1, the closer the points are to lying in a plane, because then, in the first Equation (DR3.2), the length of the minimum misfit axis approaches zero, Σ m (3) i 0. The closer the ratio c 23 /c 13 is to 1, the closer the projection of hypocenters onto the best-fit plane approximates a circular distribution, because under those circumstances, the length of the intermediate misfit axis approaches the length of the maximum misfit axis Σ m i (2) Σ m i (1). The closer the ratio c 23 /c 13 is to 0, the closer the points are to defining a linear trend, because then length of the intermediate misfit axis approaches the length of the minimum misfit axis Σ m i (2) Σ m i (3). The local hypocenter alignment planes for the different sets were defined visually by rotating the data in a three-dimensional plot and noting orientations in which distinct planar alignments of the hypocenters occurred. The aligned hypocenters were then selected and the best-fit plane was calculated by minimizing the L1 norm of perpendicular distances from hypocenter to the plane. We used the three dimensional 'spinning plot' in the statistical program JMP (SAS Institute, Inc.) for this purpose. In a number of cases, the different alignment planes are defined predominantly by hypocenters from one particular subset, which are defined according to completely independent criteria. In these cases, we associate the different inversion solution for each subset with the planar structures defined predominantly by that subset. In other cases, there seems to be no preference of individual subsets to be associated with specific planar alignments, and in these cases, we infer that a partitioning into different deformations, as

10 GSA Data Repository item defined by the different subsets, has occurred within the volume containing the set of hypocenters. We discuss this partitioning in 3.8 and 4.2. We report the strike and dip of these best-fit planes in Table DR2, where we assume the convention of the right-hand rule: Facing in the direction of the strike azimuth, the plane dips down to the right. We also report the coefficients of determination c 13 and c 23 and the ratio c 23 /c 13. DR.4. Kinematics of Strain Partitioning The triaxial strain is calculated by summing two partial plane-strains that have different orientations, each of which is the sum of strains due to slip on a conjugate set of shear planes. We express the shear on each shear plane in terms of local orthogonal φ coordinate axes ξ ) α for the two partial strains (f = 1, 2), and for the two conjugate shear φ planes associated with each partial strain (φ = 1, 2) (Fig. DR1). ξ ) 1 is normal to the φ shear plane, ξ ) φ 2 is parallel to the intersection line of the conjugate planes, and ξ ) 3 is parallel to the slip direction. Each partial plane-strain has its separate principal axes x ) k, which are the symmetry axes for the conjugate shear planes (Fig. DR1). The shearing on φ each shear plane defined in the ξ ) α coordinates is transformed into the x ) k coordinates so that the strain from two conjugate shear planes can be summed to give a partial strain. Each of the two partial strains is then transformed into the X K coordinate system so the partial strains can be summed to give the total strain. X K are the principal axes of the total triaxial strain (Fig. DR1). The details of this derivation follow. Refer to Figure DR1 for the relations among the different axes and angles used in the derivation. The displacement gradient associated with shear on a shear plane is most easily φ described in terms of coordinates defined such that ξ ) 1 is normal to the shear plane,

11 GSA Data Repository item ξ 3 φ ) is parallel to the slip direction, and ξ 2 φ ) = ξ 3 φ ) ξ 1 φ ), where the right side of this equation is the vector product, or cross product. In such a coordinate system, the slip parallel to ξ 3 φ ) on a set of parallel shear planes produces a displacement gradient in the material given by φ ) ν α φ ) ξ = β S φ ) 0 0 (DR4.1) φ where ν ) α is the displacement field smoothed over the fault spacing. The strain from conjugate sets of shear planes (φ = 1, 2) with slip perpendicular to the intersection lines of the conjugate shears can be expressed in terms of the coordinates x ) k for each partial strain (f = 1, 2), defined so that x ) 1 bisects the acute angle between the normals to the two sets of shear planes (the obtuse angle between the planes themselves), x ) 2 is parallel to the intersection line of the planes, and x ) 3 bisects the obtuse angle between the normals to the conjugate sets of shear planes (i.e. the acute angle between the two planes themselves). For each of the partial strains (f = 1, 2), the components for the φ displacement gradient in the local coordinates ξ ) α for each of the conjugate shear planes ) (φ = 1, 2) given by Equation (DR4.1) are transformed into components in the x k coordinates and added together. The transformation is given by u k φ ) x l ) = ν φ ) α ξ Q φ ) φ ) φ ) αk Q βl β (DR4.2) where u k φ ) are the displacement components in the x k ) coordinates of the displacement on the φ th φ set of shear planes, and Q ) αk is the orthogonal transformation that relates

12 GSA Data Repository item vector components in the ξ β φ ) coordinates to components of the same vector in the x k ) coordinates. If λ β φ ) and i k ) are the unit base vectors respectively in the ξ β φ ) and x k ) coordinates, then, φ Q ) φ αk λ ) ) α i k (DR4.3) where the right hand side is the scalar product. We define θ φ ) to be the angle between the normal to the (fφ) th φ shear plane (i.e. ξ ) 1 ) and x ) 1, where for φ = 1, the angle is a positive (right-handed) rotation about the positive x ) 2 axis, and for φ = 2 the angle is a negative rotation. The orthogonal transformations in Equation (DR4.3) then become, Q 1) αk = cosθ 1) 0 sinθ 1) sinθ 1) 0 cosθ 1) Q 2) αk = cosθ 2) 0 sinθ 2) (DR4.4) sinθ 2) 0 cosθ 2) The total displacement gradient for the partial strain is the sum of the expressions like Equation (DR4.2) for both sets of shear planes, u k ) N φ ) u k x = ) (DR4.5) ) l x l φ=1 We assume for simplicity that θ 1) = θ 2) = θ for f =1, 2 (DR4.6) which defines conjugate shear planes that are symmetrical about the x ) 1 and x ) 3 axes, with a comparable geometry for both partial strains. In this case, performing the transformations indicated in Equation (DR4.2) with Equations (DR4.4), and adding as prescribed by Equation (DR4.5) gives

13 GSA Data Repository item Σ ) sinθ cosθ 0 Δ ) sin 2 θ ) u k ) x = l Δ ) cos 2 θ 0 Σ ) sinθ cosθ (DR4.7) where Σ ) S 1) + S 2) Δ ) S 1) S 2) (DR4.8) This is a plane-strain with no deformation parallel to the x ) 2 axis, as indicated by the zeros in the second row and column. If the magnitude of the shear on both shear planes for both partial strains is the same, S 1) = S 2) = S whereby Σ ) = 2S, Δ ) = 0 for f =1, 2 (DR4.9) Thus the off-diagonal terms in Equation (DR4.7) become zero, the deformation is irrotational, and the x ) k axes are the principal axes of the strain. The simplifying assumptions in Equations (DR4.6) and (DR4.9) maximize the symmetry of the system and ensure that the coordinate axes x ) k are the principal axes of the associated strains, and that the strains are irrotational. Our data indicate that a triaxial strain is accommodated by a pair of plane strains (f = 1, 2), and that each plane strain is commonly accommodated on a conjugate set of shear planes (Table 2; Fig. 14). Thus each plane strain (i.e. for f = 1, 2) is defined by Equation (DR4.7), and the principal axes x ) k of each of the plane strains are related by a rotation about the common axis. In our data, that common axis is usually the d 3 axis (Fig. 14A, B; the X 3 axis in Fig. DR1A), which means that d (1) 3 is parallel to d (2) 3 ( x (1) 3 is parallel to x (2) 3 in Fig. DR1A), but it can also be the d 1 axis (Fig. 14C, D; X 1, x (1) 1, and x (2) 1 are all

14 GSA Data Repository item parallel in Fig. DR1B). We can calculate the triaxial deformation by adopting a coordinate system X K defined such that the two sets of conjugate shear planes (four shear planes in all) are symmetrically arranged with respect to these axes. For the case in which d 3 is the common axis for the two sets of conjugate shear planes (Fig. 14A, B), we take X 3 to be parallel to d 3, and X 1 to bisect the angle between the maximum extension axes for the two plane-strains, i.e. d (1) 1 and d (2) 1 ( x (1) 1 and x (2) 1 in Fig. DR1A). Thus because each set of conjugate shear planes has its own local coordinate axes x ) k, we must transform the components of deformation given by Equation (DR4.7) in the each of the x ) k coordinates into the X K coordinate system, in order to add the two deformations. The components of the total displacement gradient tensor U K X L in the X K coordinates can therefore be calculated from P ) U K u = m ) X L x T ) mk T ) nl (DR4.10) n f =1 where we take P = 2, where u m ) x n ) is given by Equation (DR4.7) for each f = 1, 2, and where T ) mk is the orthogonal transformation that transforms components from the x k ) coordinates to the X K coordinates. If i m ) and I K are the unit base vectors in the x k ) and the X K coordinate systems, respectively, then the components of the two transformation tensors T ) mk are defined by the scalar product, T ) mk i ) m I K (DR4.11) We define α (f) to be the angle between X 1 and x 1 ), such that is a positive (right-hand) rotation by an angle α (1) about the X 3 axis carries X 1 to x 1 (1) (Fig. DR1A), and a negative

15 GSA Data Repository item rotation by an angle α (2) about X 3 carries X 1 to x 1 (2). The transformation matrices for f = 1 and 2, then, are T (1) mk = cosα (1) sinα (1) 0 sinα (1) cosα (1) T (2) mk = cosα (2) sinα (2) 0 sinα (2) cosα (2) 0 (DR4.12) We make the simplifying assumption that the angle between X 1 and x 1 ) for both values of f is the same, which means the conjugate planes are symmetrically arrayed about X 1 : α (1) = α (2) = α (DR4.13) Using the assumptions in Equations (DR4.6), (DR4.9), and (DR4.13) as well as Equations (DR4.7) and (DR4.12) in Equation (DR4.10) then gives for the triaxial displacement gradient tensor, U K X L cos 2 α 0 0 = 2Σsinθ cosθ 0 sin 2 α (DR4.14) Our simplifying assumptions in Equations (DR4.6), (DR4.9), and (DR4.13) have imposed symmetries on our model system that results in the triaxial instantaneous strain being irrotational and the X K axes being the principal axes of that strain. For the case in which d 1 is the common axis for the two partial plane-strains (Fig. 14C, D; Table 2), the derivation proceeds in a similar manner. Equations (DR4.10) and (DR4.11) remain the same, but we define the angle β (f) to be the angle between X 3 and x ) 3, where a positive (right-hand) rotation of angle β (1) (1) about the X 1 axis carries X 3 to x 3 (Fig. DR1B), and a negative rotation of angle β (2) about the X 1 axis carries X 3 to x (2) 3. Then for f = 1 and 2, the transformation matrices are

16 GSA Data Repository item T (1) mk = cosβ (1) sinβ (1) 0 sinβ (1) cosβ (1) T (2) mk = cosβ (2) sinβ (2) (DR4.15) 0 sinβ (2) cosβ (2) We make the same simplifying assumptions as in Equations (DR4.6) and (DR4.9), and we assume that the angle between X 3 and x 3 ) for both values of f is the same (Fig. DR1B), β (1) = β (2) = β (DR4.16) which means the two sets of conjugate planes are symmetrically arrayed about X 3. With these assumptions, we find the triaxial deformation gradient from Equations (DR4.7) and (DR4.10): U K X L = 2Σsinθ cosθ 0 sin 2 β cos 2 β (DR4.17) Equations (DR4.14) and (DR4.17) with Equation (DR4.9) give the Equations (6) and (7) presented in 4.2 of the main text.

17 GSA Data Repository item DATA REPOSITORY MATERIAL: REFERENCES CITED Michael, A.J., 1987, Use of focal mechanisms to determine stress: A control study: Journal of Geophysical Research, v. 92, p Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vettering, W.T., 1989, Numerical Recipes, the Art of Scientific Computing (FORTRAN Version): New York, Cambridge University Press, 702 p. Unruh, J.R., Twiss, R.J. and Hauksson, E., 1996, Seismogenic deformation field in the Mojave block and implications for the tectonics of the eastern California shear zone: Journal Geophysical Research, v. 101(B4), p

18 TABLE DR1. INVERSION RESULTS FOR LOMA PRIETA SUBSETS Data Set No. of data d 1 trend d 1 plung e d 1 C.I. angle from mean d 2 trend d 2 plung e d 2 C.I. angle from mean d 3 trend d 3 plung e d 3 C.I. angle from mean D D C.I. from mean 1 S.D. Max for 95% C.I. W # W C.I. from mean Best-fit Mean 1 S.D. Max for 95% C.I. Misfit Bestfit Bestfit Mean Mean 1 S.D. Max. for 95% C.I. Bestfit Bestfit Mean Mean 1 S.D. Max for 95% C.I. Bestfit Bestfit Mean Mean 1 S.D. Max for 95% C.I. Bestfit Mean Bestfit Mean V Vertical deformatio n ratio Best-fit @ ± ± / / &@ ± ± / / * &@ ± ± / / ± ± / / * 0.29&@ ± ± / / & ± ± / /

19 ± ± / / * 0.07@ ± ± / / @ ± ± / / &@ ± ± / / ± ± / / &@ ± ± / / * -0.56& ± ± / / & ± ± / / @ ± ± / /

20 ± ± / / ± ± / / & ± ± / / &@ ± ± / / &@ ± ± / / ± ± / / ± ± / / &@ ± ± / / &@ ± ± / /

21 & ± ± / / & ± ± / / @ ± ± / / * 0.23& ± ± / / & ± ± / / &@ ± ± / / &@ ± ± / / ± ± / / & ± ± / /

22 Notes: Solutions are listed as both the best-fit value to the data and the mean of the bootstrap solutions that lie within the 95% confidence limits. The two solutions usually differ by less than 1 in the orientations of the principal instantaneous strain axes, and less than 0.05 in D and W. Confidence Interval: listed first in terms of 1 S.D. (standard deviation) from the mean of the bootstrap solutions, and second in terms of the maximum angle from the mean of those bootstrap solutions defining the 95% confidence limits. An asterisk in this column indicates a value of D that is significantly different from 0.5 at the 95% confidence level. # An ampersand '&' in this column indicates a value of W that is significantly different from 0 at the 95% confidence level. An 'at' sign '@' in this column indicates that the negative of this value and orientation of W is plotted in Figures 11B and 8C The misfit is the angle given by the inverse cosine of the mean of the misfit cosines for each datum. The misfit cosine for each datum is the mean of the cosines of the angles between the model and the datum shear-plane-normals, and between the model and the datum slip-directions. 5

23 TABLE DR2: PLANAR BEST-FITS (L1 NORM) TO HYPOCENTER ALIGNMENTS* Plane Association Best-Fit Plane Unit Normal n to Best-Fit Plane All data but Sets 1 and 3 Sets 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 Plane Location Constant Strike Dip n 1 n 2 n 3 L [km] Coefficients of Determination Ratio Standard deviation [km] c 13 c 23 c 23 /c 13 strike of fault-zone segment Angle Between Strike of Best-Fit Plane and azimuth of plate motion Comment Southern Central Northern 2, 5, , 9, , 12.72, 13, 14, 15, 16, ne Shallow Zone Mostly Subset 1.32 data; 3 data from Subset. 1.42; vertical. plane on NE of set 1-sw1 Shallow Zone Subsets 1.32 & 1.42 data not in 1_ne; plane with steep dip SW 1-sw2 Shallow Zone Subsets 1.32 & 1.42 data not in 1_ne; plane with moderate dip SW 2-ne Southern Subsets 2.32 & 2.42 events in the NE planar alignment 2-sw Southern All events in SW alignment plane 3-nw Off-plane set All data from the NW scattered events not in the SE cluster 3-se Off-plane set Events in the SE cluster 4-62-ne Central Subset 4.62 data along NE plane 4-62-sw Central Subset 4.62 data for short SW planar alignment 5-pl1 (deep) Southern Events below 9 km except for Datum No pl2 (shallow) Southern Events above 9 km except for Data Nos. 72, 433, pl1 Shallow Zone Horizontal plane 6-pl2 Shallow Zone Shallowly-NE-dipping plane 6-pl3 Shallow Zone Steeply SW-dipping plane; N side of data set 6-pl4 Shallow Zone Steeply SW-dipping plane; S side of data set 1

24 7-02 Southern All subset data, refined data set Southern top subhorizontal alignment in [315, -01] view Southern nd from top subhorizontal alignment in [315, -01] view Southern rd from top subhorizontal alignment in [315, -01] view lp Southern th from top subhorizontal alignment in [315, -01] view lp Southern th from top subhorizontal alignment in [315, -01] view 8-51-pl1 Shallow Zone Shallow data defining NE-dipping plane 8-51-pl2 Shallow Zone Data defining vertical SW boundary to Subset pl3 Shallow Zone Data defining mod. SW dipping plane in Subset Central pl1 Shallow Zone Data selected to define vertical alignment plane 10-pl2 Shallow Zone Data selected to define SWdipping plane at top of hypocenter distribution 10-pl3 Off-plane set Data from NW cluster; steep offplane group Central Central Off-plane set pl2 12-pl1 Northern Northern 13 Northern pl1 Northern pl2 Northern 13-pl3 Northern 13-pl4 Northern 13-pl5 Northern 14 Northern Data below 12 km along planar alignment from and Data above 11 km along planar alignment from and Sets 13 (& 14) NS alignment on SE of the cluster Sets 13 (& 14) NS alignment on the SW of the cluster Set 13 NS alignment on the NE of the cluster Set 13 NS alignment on the NW of the cluster EW alignment on the N side of the cluster Set 14.0 data minus Data Nos. 103, 168, 388 2

25 15-pl1 Northern pl2 Northern pl3 Northern pl4 Northern pl5 Northern pl1b Northern pl2b Northern pl1 Northern Sub-horizontal plane at top of data 17-pl2 Northern Moderate W dip 17-pl3 Northern Moderate S dip * Refer to Figure 8; see discussion in Section DR.4 Azimuth of plate motion vector is

26 1 TABLE DR3: SLIP LINE ORIENTATIONS ON MAIN FAULT SEGMENTS FOR SUBSET SOLUTIONS Shear_Plane Slip_Line Slip_Line Dip Trend Plunge Subset Shear_Plane Strike Slip_Line Rake (from strike azimuth) Normalized Slip Magnitude Southern mean rake for planes 13.9 Plate slip vector (a) Plate slip vector (b) Central mean rake for planes 45.4 Plate slip vector (a) Plate slip vector (b) Northern mean rake for planes 62.2 Plate slip vector (a) Plate slip vector (b)

27 2 TABLE DR.3-1: INTERPRETATION OF COEFFICIENTS OF DETERMINATION FOR FITS OF PLANES TO HYPOCENTER DATA c 13 c 23 c 23 /c 13 Description All data lie exactly on the best-fit plane distributed in a circular cloud <1 = c 13 1 Data do not lie exactly on a plane, but they project onto the best-fit plane as a circular cloud Data lie exactly along a line perpendicular to the worst-fit plane Data are distributed in a spherical cloud 2

28 TABLE DR4: SLIP LINE ORIENTATIONS ON LOCAL HYPOCENTER ALIGNMENT PLANES FOR SUBSET SOLUTIONS Alignment Plane Subset Solution Slip Line Trend Slip Line Rake Comment Shear Plane Strike Shear Plane Dip Slip Line Plunge Normalized slip Magnitude Southern 2-ne & 2.42 events in the NE planar alignment 2-ne & 2.42 events in the NE planar alignment 2-sw All events in SW alignment plane 2-sw All events in SW alignment plane 5-pl Events above 9 km except for Data Nos. 72, 433, pl Events above 9 km except for Data Nos. 72, 433, pl Events below 9 km except for Data No pl Events below 9 km except for Data No Central 4-62-ne ne plane; 4.62 data: slip line for 4.52 solution 4-62-ne ne pl; 4.62 data: slip line for 4.62 solution 4-62-sw data for short sw planar alignment 4-62-sw data for short sw planar alignment Northern 12-pl Shallow data along planar alignment from and pl Shallow data along planar alignment from and pl Deep data along planar alignment from and pl Deep data along planar alignment from and pl Defined largely by Subset.732; partly by

29 15-pl pl Defined largely by Subset.732; partly by pl pl Defined mostly by subset pl Defined by subsets.832; partly by pl pl Defined by Subset.932, with.732 and pl pl pl Sets 13 (& 14) N S alignment on SE of cluster pl pl pl Sets 13 (& 14) N S alignment on SW of cluster pl pl pl Set 13 N S alignment on NE of cluster 13-pl pl pl Set 13 N S alignment on NW of cluster 13-pl pl pl E W alignment on the N side of cluster 13-pl pl Set 14 data minus Data Nos 103, 168, pl1b pl1b pl2b pl2b pl Data Nos. 458, 648, 805, 982 omitted 17-pl pl

30 3 Shallow Zone Off plane sets 1-ne ne sw sw sw Mostly 1.32 data; some 1.42 data 1-sw data that are not aligned with 1_ne plane 6-pl pl pl Data defining steep plane on NE of data set 6-pl Shallow data defining shallow NE dipping plane 8-51-pl Shallow data defining NE dipping plane 8-51-pl Data defining vertical SW boundary to 8_51 subset 8-61-pl Data defining moderately SW dipping plane in 8_61 subset 10-pl Data define SW dipping plane at top of hypocenter distribution pl Data selected to define vertical alignment plane pl _52_vert data 10-pl _sw_pl data 10-pl Data from NW cluster; steep off plane group 10-pl Data from NW cluster; steep off plane group 12_ _se Events in the SE cluster 3_se Events in the SE cluster 3_nw All data from the NW scattered events not in the SE cluster 3_nw All data from the NW scattered events not in the SE cluster 3