Aquifers are vital groundwater reservoirs for residential, agricultural, and industrial activities worldwide. Tracking their state with high temporal and spatial resolution is critical for water resource management at the regional scale yet is rarely achieved from a single type of groundwater data.Here we show that variations in groundwater levels can be mapped using perturbations in seismic velocity (dv/v). We measure dv/v in the San Gabriel Valley, California, from cross correlation of the ambient seismic field. dv/v reproduces the groundwater level changes that are marked by the multiyear depletions and rapid recharges, typical of California's cycles of droughts and floods. dv/v correlates spatially with vertical surface displacements and deformation measured with the Global Positioning System (GPS). Our results successfully predict the volume of water lost in the San Gabriel Valley during the 2012-2016 drought and thus provide a new, complementary approach to monitor groundwater storage. Plain Language SummaryThe largest reservoir of freshwater worldwide is stored beneath our feet in groundwater aquifers. Monitoring the availability of water stored in the ground is critical for communities that are susceptible to drought, as groundwater can supplement a lack of surface fresh water. We propose a new way to monitor groundwater levels in aquifers using weak seismic waves repeatedly generated by the ocean, wind, and human activities. We exploit the fact that the speed of seismic waves is sensitive to the amount of the water in the ground. Using a network of seismometers, we measure the change in seismic velocity throughout the San Gabriel Valley Basin, California. We find that with the change in seismic velocity we can estimate the flux of water in the San Gabriel Valley over the last three droughts and subsequent recoveries that have affected Southern California over the last two decades. These results provide a new approach to monitor groundwater storage.

Aquifers are vital groundwater reservoirs for residential, agricultural, and industrial activities worldwide. Tracking their state with high temporal and spatial resolution is critical for water resource management at the regional scale yet is rarely achieved from a single type of groundwater data. Here we show that variations in groundwater levels can be mapped using perturbations in seismic velocity (dv/v). We measure dv/v in the San Gabriel Valley, California, from cross correlation of the ambient seismic field. dv/v reproduces the groundwater level changes that are marked by the multiyear depletions and rapid recharges, typical of California's cycles of droughts and floods. dv/v correlates spatially with vertical surface displacements and deformation measured with the Global Positioning System (GPS). Our results successfully predict the volume of water lost in the San Gabriel Valley during the 2012–2016 drought and thus provide a new, complementary approach to monitor groundwater storage.

We introduce SeisNoise.jl, a library for high-performance ambient seismic noise cross correlation, written entirely in the computing language Julia. Julia is a new language, with syntax and a learning curve similar to MATLAB (see Data and Resources), R, or Python and performance close to Fortran or C. SeisNoise.jl is compatible with high-performance computing resources, using both the central processing unit and the graphic processing unit. SeisNoise.jl is a modular toolbox, giving researchers common tools and data structures to design custom ambient seismic cross-correlation workflows in Julia.

The state of California is subject to extreme natural events. It hosts infrequent, large magnitude (M w ≥ 7) earth-