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We analyze active and passive seismic data recorded by the Stanford distributed acoustic sensing array (SDASA) located in conduits under the Stanford University campus. For the active data we used low-energy sources (betsy gun and sledge hammer) and recorded data using both the DAS array and 98 three-component nodes deployed along a 2D line. The joint analysis of shot profiles extracted from the two data sets shows that some surface waves and refracted events are consistently recorded by the DAS array. In areas where geophone coupling was suboptimal because of surface obstructions, DAS recordings are more coherent. In contrast, surface waves are more reliably recorded by the geophones than the DAS array. Because of the noisy environment and weak sources, neither data set shows clear reflections. We demonstrate the repeatability of DAS recordings of local earthquakes by comparing two weak events (magnitude 0.95 and 1.34) with epicenters 100 m apart that occurred only one minute from each other. Analyzing another local, and slightly stronger, earthquake (magnitude 2.0) we show how the kinematics of both the P-arrival and S-arrival can be measured from the DAS data. Interferometric analysis of passive data shows that reliable virtual-source responses can be extracted from the DAS data. We observe Rayleigh waves when correlating aligned receivers, and Love waves when correlating receivers belonging to segments of the array parallel to each other. Dispersion analysis of the virtual sources shows the expected decrease in surface-wave velocity with increasing frequency.

Due to the broadband nature of distributed acoustic sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, it measures the earth's quasistatic deformation caused by the weight of cars (less than 0.8 Hz) as well as Rayleigh waves induced by earthquakes (less than 3 Hz) and by dynamic car-road interactions (3–20 Hz). We directly utilize the excited surface waves for shallow shear-wave velocity inversion. Rayleigh waves induced by passing cars have a consistent fundamental mode and a noisier first mode. By stacking dispersion images of 33 passing cars, we obtain stable dispersion images. The frequency range of the fundamental mode can be extended by adding the low-frequency earthquake-induced Rayleigh waves. Due to the extended frequency range, we can achieve better depth coverage and resolution for shear-wave velocity inversion. To assure clear separation from Love waves and to align apparent and true phase velocities, we choose an earthquake that is approximately in line with the array. The inverted models match those obtained by a conventional geophone survey, performed using active sources by a geotechnical service company contracted by Stanford University, from the surface to about 50 m. To automate the VS inversion process, we introduce a new objective function that avoids manual dispersion curve picking. We construct a 2D VS profile by performing independent 1D inversions at multiple locations along the fiber. From the low-frequency quasistatic deformation recordings, we also invert for a single Poisson's ratio at each location along the fiber. We observe spatial heterogeneity of both VS and Poisson's ratio profiles. Our approach is less expensive than ambient field interferometry, and reliable estimates can be obtained more frequently because no lengthy crosscorrelations are required.

Over the last couple of years, it has become more difficult to assess which hardware can deliver the most cost-optimal solution for demanding imaging tasks. The days of faster and faster CPUs are over. A clear choice of hardware has been replaced with many core technologies, and a proliferation of alternatives. In particular, accelerators like GPGPU and field programmable gate arrays (FPGAs) have emerged as strong contenders for the title of hardware platform of choice. With the radical differences in hardware architectures, it has also become more and more difficult to evaluate which platform is optimal for the application in question. An apples-to-apples comparison is no longer possible. Through the example of reverse time migration (RTM), we demonstrate that only through a careful optimization for each platform, with the involvement of hardware, computer-science and algorithmic scientists, can we come up with a reasonable assessment of the alternatives available today.

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