Isotropic scattering and seismic imaging of crustal fault zones using earthquakes

Summary

The dominance of P-wave reflectivity over S-wave reflectivity from crustal fault zones suggests that variations in Lamé parameter control fault zone reflections. Reflections due to variations radiate isotropically, unlike the backscattering from impedance contrasts or the forward scattering from velocity contrasts. We therefore suggest that crustal faults may be distinguishable from non-tectonic crustal structures on the basis of this isotropic P-wave scattering. We show crustal reflectivity images from the Los Angeles Basin of southern California that select for such isotropic reflectivity, preferentially showing crustal fault structure. Our depth sections sum together backscattered and forward scattered arrivals from subsurface depth points, effectively filtering out simple impedance or velocity variations. Imaging beneath the 1991 Sierra Madre aftershock zone from seismograms recorded on the southern Calif. Seismic Network shows the same "lower crustal reflective zone" below the San Gabriel Mts. imaged by the 1994 Los Angeles Region Seismic Experiment.

Introduction

Variations in P-velocity anisotropy dominate the reflectivity of exhumed mylonitic fault zones, producing stronger P-wave than S-wave reflectivities. On the other hand, overall variations in density or rigidity dominate most other types of geologic contrasts, such as pluton boundaries; resulting in stronger S-wave reflectivities. For instance, McCaffree and Christensen (1993) show that for a mylonite zone compressional reflectivity is stronger than shear reflectivity. Hence, mylonites have a wide V_P/V_S range and stronger P-wave reflection coefficients, making it possible to devise an effective imaging technique to image V_P and signatures that identify crustal fault zones.

However, earthquake seismologists may be reluctant to believe that one can successfully image crustal faults using only P-P scattering characteristics and earthquake sources, as we do here. For instance, Aki (1992) shows that P-S conversion for earthquake sources is much greater than S-P. Thus, the dominance of waves in the coda of seismograms raises concern about the validity of dealing with only P-P scattering in imaging crustal reflectors. In fact, the mode conversion problem is inherently troublesome in earthquake seismology, and this remains the case in exploration seismology. In spite of this, successful P-P scattering applications of this type abound in oil-related seismic imaging for fracture detection and reservoir characterization. We believe this is the first time this is done for crustal targets using earthquake recordings.

Crustal imaging technique

We are interested in acoustic images or -images. contributions are isotropic (Wu, 1989) and come from local P-wave energy, mostly P-P reflections and transmitted P-wave arrivals (Fig. 1). This is clearly not the case when we try to obtain density images or rigidity images (-images). For instance, variations lead to anisotropic scattering and one needs to take S-waves into account (Fig. 1) to correctly image such variations. Fig. 2 shows that in the presence of , back and forward scattered events constructively interfere while for a they tend to cancel out. Thus, the search for structures becomes practical with an acoustic processing scheme.

Fig. 1. Vertical-component elastic synthetics for a two-layer model showing the early P-wave coda for variations in Lamé parameters ( and ) during back- and forward- scattering.

Clipped and saturated records are quite common in short-period earthquake data. We regard them as sign-bit recordings (O'Brien et al., 1982) that will acquire dynamic range through Kirchhoff summation. Based on these working assumptions, the crustal imaging technique proceeds as follows: a) the record sections we use comprise 200 km in epicentral distance and 30 s duration to include wide-angle reflections between first compressional, P_g, and first shear, S_g, arrivals, and we mute outside the window between P_g and S_g traveltime branches to extract only compressional arrivals, mostly P_g, P_mP and S-P converted energy. b) Preprocessing includes trace equalization for receiver amplitude balancing, i.e., the amplitudes are normalized so that the mean-squared amplitude over the whole trace is the same for all traces; this is roughly equivalent to energy normalization for varying magnitudes. c) We obtain images of subsurface reflectors by summing data at traveltimes computed through one-dimensional velocity models, ignoring lateral velocity variations. Traveltime versus distance matrices are computed with Vidale's (1988) finite-difference solution to the eikonal equation. d) We obtain the final depth imaging result by Kirchhoff prestack summation of the migrated partial images for each event.

Fig. 2. Dynamic range enhancement for through Kirchhoff summation of reflected (P-P) and converted (P-S) arrivals. P-P arrivals, as well as P-S, tend to cancel out during the summation process for the case.

The Kirchhoff depth migration process we use is similar to that of Louie et al. (1988). The usefulness of using such an acoustic scheme to process vertical-component earthquake data is that it leads to practical images. This is relevant because even with the use of elastic iterative migration schemes (e.g., Mittet et al., 1997) images appear very similar after several iterations.

Fig. 3 (bottom) shows a depth section (from Azusa north to the margin of the Mojave desert) obtained by migration of records from five synthetic events with increasing epicentral depths and recorded on the locations (Fig. 3, top) of the southern California Seismic Network (SCSN; Wald, 1996). Earthquake sources, with an assumed Ricker wavelet of 2 Hz central frequency, are located at depths of 2.8, 10.8, 20, 28 and 37 km. The velocity model used to compute traveltimes is a modified version of the southern California velocity model (Hadley and Kanamori, 1977). Interfaces and property variations are located at 5.5 (), 16 ( or V_P), 24 (), and 32 km (). Note how, despite imaging artifacts in Fig. 3 (the elliptical trajectories) and inherent problems with sparse station coverage and spatial data aliasing, we are able to image the approximate locations (dotted lines) where (or ) occurs. Defocusing of P-S and S-P converted energy by the P-P migration also contributes to image degradation.

Fig. 3. View of SCSN station locations (above), with depth section from migration of records from five synthetic events having increasing epicentral depths. The image results from acoustic processing of vertical-component elastic seismograms (near offset = 15 km; far offset = 175 km). , and stand for rigidity, P-wave velocity, and density interface depths, respectively.

Migration of real earthquake data

The ideal sources for performing crustal imaging would be underground explosions, due to their simple focal mechanism. Successful Kirchhoff summation depends upon consistent focal mechanisms or a source correction. Here, we have made no correction for the source, but we use only data with high-quality impulsive P-wave picks to roughly correct for sign reversals (due to varying focal mechanisms and source directivity). Our data are short-period vertical-component seismograms from the SCSN. We use only well-located events.

Fig. 4. Sierra Madre event and aftershock imaging section (black line) and LARSE Line 1 stack of Fuis et al. (1996; dark on light line) located on Los Angeles region topography and 1990-1991 magnitude 2.0 and greater seismicity map.

By using the aforementioned imaging sequence, depth imaging (Figs. 4, 5) beneath the 1991 Sierra Madre aftershock zone (Hauksson, 1994) from seismograms recorded on the SCSN shows the same "lower crustal reflective zone" (Fig. 6) below the San Gabriel Mts. imaged by the 1994 Los Angeles Region Seismic Experiment (LARSE). The velocity model used to compute traveltimes is an updated version of the southern California velocity model. Interfaces and P-wave velocity variations are located at 5.5, 16 and 32 km (Moho) depth. We consider Fig. 5 is a rather good image despite the lack of lateral velocity variations, and the sharp, well-defined interfaces in the velocity model used to compute traveltimes. With a smoothed velocity model or velocity gradients one would probably obtain even better migration results.

Fig. 5. Sierra Madre depth section (from Azusa north to the margin of the Mojave desert; see Fig. 4) from migration of records from 18 earthquakes, including the mainshock. The image depicts (arrows) a strong north-dipping reflective zone (~15-20 km depth).

Fuis et al. (1996) propose that the top of this "lower crustal reflective zone" may be a décollement or an igneous contact. In fact, the depth to brittle-ductile transition seems to be prescribed by the existence of this reflective structure. This is relevant because lateral variations in lithology may control the depth extents of potential future earthquakes. These depths, which seem to correlate with the presence of schist basement rocks, can be determined from the depth of the current background seismicity (Magistrale and Zhou, 1996) and from seismic images like that of Fig. 5.

Fig. 6. LARSE Line 1 explosion stack of Fuis et al. (1996). Note the correspondence of the "lower crustal reflective zone" (~15-19 km depth) with the reflector of Fig. 5.

Conclusions

We image crustal fault zones by searching for isotropic scatterers with a Kirchhoff prestack migration scheme of earthquake sequences. Sierra Madre aftershock migrations reproduce the "lower crustal reflective zone" seen in the LARSE Line 1 explosion data in a practical, cost effective manner.

Acknowledgments

The first author acknowledges financial support by CONACYT, Mexico's National Council for Science and Technology. Aftershock data were recorded by the SCSN which is operated jointly by the Seismological Lab at Caltech and the US Geological Survey, Pasadena. Sathish K. Pullammanappallil provided useful suggestions.

References

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