# Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science

## TL;DR

This paper demonstrates how Locality Sensitive Hashing can be optimized and scaled for earthquake detection in large seismic datasets, enabling the discovery of thousands of new earthquakes across multiple stations.

## Contribution

The authors introduce end-to-end optimizations of LSH-based seismic analysis, significantly improving scalability and enabling long-term, multi-station earthquake detection workflows.

## Key findings

- Over 100× speedup in analysis pipeline
- Detection of 6123 new earthquakes in New Zealand
- Discovery of 597 new earthquakes near Diablo Canyon

## Abstract

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.