Weathering and hydrological processes in Earth's shallow subsurface are influenced by inherited bedrock structures, such as bedding planes, faults, joints, and fractures. However, these structures are difficult to observe in soil‐mantled landscapes. Steeply dipping structures with a dominant orientation are detectable by seismic anisotropy, with fast wave speeds along the strike of structures. We measured shallow (~2–4 m) seismic anisotropy using “circle shots,” geophones deployed in a circle around a central shot point, in a weathered granite terrain in the Laramie Range of Wyoming. The inferred remnant fracture orientations agree with brittle fracture orientations measured at tens of meters depth in boreholes, demonstrating that bedrock fractures persist through the weathering process into the shallow critical zone. Seismic anisotropy positively correlates with saprolite thickness, suggesting that inherited bedrock fractures may control saprolite thickness by providing preferential pathways for corrosive meteoric waters to access the deep critical zone.

Seismic interferometry is a technique for extracting deterministic signals (i.e., ambient-noise Green's functions) from recordings of ambient-noise wavefields through cross-correlation and other related signal processing techniques. The extracted ambient-noise Green's functions can be used in ambient-noise tomography for constructing seismic structure models of the Earth's interior. The amount of calculations involved in the seismic interferometry procedure can be significant, especially for ambient-noise datasets collected by large seismic sensor arrays (i.e., "large-N" data). We present an efficient parallel algorithm, named pSIN (Parallel Seismic INterferometry), for solving seismic interferometry problems on conventional distributedmemory computer clusters. The design of the algorithm is based on a two-dimensional partition of the ambient-noise data recorded by a seismic sensor array. We pay special attention to the balance of the computational load, inter-process communication overhead and memory usage across all MPI processes and we minimize the total number of I/O operations. We have tested the algorithm using a real ambient-noise dataset and obtained a significant amount of savings in processing time. Scaling tests have shown excellent strong scalability from 80 cores to over 2000 cores.