  In this work, we report on a novel application of Locality Sensitive Hashing
(LSH) to seismic data at scale. Based on the high waveform similarity between
reoccurring earthquakes, our application identifies potential earthquakes by
searching for similar time series segments via LSH. However, a straightforward
implementation of this LSH-enabled application has difficulty scaling beyond 3
months of continuous time series data measured at a single seismic station. As
a case study of a data-driven science workflow, we illustrate how domain
knowledge can be incorporated into the workload to improve both the efficiency
and result quality. We describe several end-to-end optimizations of the
analysis pipeline from pre-processing to post-processing, which allow the
application to scale to time series data measured at multiple seismic stations.
Our optimizations enable an over 100$\times$ speedup in the end-to-end analysis
pipeline. This improved scalability enabled seismologists to perform seismic
analysis on more than ten years of continuous time series data from over ten
seismic stations, and has directly enabled the discovery of 597 new earthquakes
near the Diablo Canyon nuclear power plant in California and 6123 new
earthquakes in New Zealand.
