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Template matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the 'Matrix Profile' (MP), a "correlate everything with everything" calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research. Plain Language Summary: Detecting and cataloging earthquakes through analysis of seismic data—the shapes of seismic waves, recorded by seismometers—is foundational to our understanding of Earth's interior structure and processes, as well as geological hazards such as earthquakes. Methods to improve the efficiency and sensitivity of earthquake detection while maintaining accuracy are critical, as seismic data volumes have grown exponentially in recent decades. Recently, methods that use recorded earthquakes as template patterns to identify in seismic data, have proven capable of detecting several times more earthquakes than traditional methods. However, such methods require knowledge of the template earthquakes ahead of time, and are best at identifying earthquakes whose waveforms are similar to the templates. We present here a new method, called the 'Matrix Profile' (MP), that takes short windows of data from a seismic data stream, and compares them to all other parts of that data stream, identifying parts of the data that are highly similar. The MP effectively identifies earthquakes, even those which are hidden within noise, does not require any templates to be provided upfront, and can be efficiently calculated. We demonstrate its success at detecting earthquakes in different types of seismic data, and provide practical guidelines for applying the method and interpreting the results. Key Points: The matrix profile finds the maximum autocorrelation of every subsequence in a time seriesPeaks of high similarity in the matrix profile can be used to identify seismic events and similar event pairsThe matrix profile method has similar sensitivity to template matching but does not require a priori templates [ABSTRACT FROM AUTHOR]

We use a dense seismic array of 1,108 vertical‐component geophones within a 600‐m footprint to detect thousands of small earthquakes near an active strand of the San Jacinto fault zone in southern California during a 26‐day period. We first correct site effects using multichannel cross‐correlations of the P‐waves of 256 cataloged earthquakes, and then perform beamforming analysis on the continuous waveforms in a slowness range from −0.4 to 0.4 s/km in both the east and north directions. At each time step, we identify the beam slowness with maximum amplitude and apply a picking algorithm to identify 13,408 events. These detections include over 55.6% of the events in the Quake Template Matching (QTM) catalog for all of southern California during the same time period and 70% of those within 100 km of the array. In addition, we detect over 10,000 new events, not in the QTM catalog. Many of these events can also be seen in records from nearby borehole seismic stations. Measured slownesses for the catalog and newly‐discovered events group into clusters that can be associated with QTM earthquake locations, but with slowness values considerably distorted from predictions based on a 1‐D velocity model, presumably owing to strong velocity heterogeneity near the San Jacinto Fault. Amplitudes of the detected events obey a Gutenberg‐Richter distribution with a b‐value close to one. Foreshocks are common among these detected events, increasing in rate before mainshocks following an inverse Omori's law. Plain Language Summary: We show that an array of over 1,000 seismometers located on a small patch of the San Jacinto Fault in southern California can detect many times more earthquakes than those in standard earthquake catalogs. Although accurate locations cannot be obtained for most of these newly identified events, their amplitudes indicate they obey similar scaling with size as larger magnitude earthquakes. Many of the larger events are preceded by foreshocks and the foreshock rate increases in the time leading up to the mainshocks. Our results suggest that future deployments of small‐aperture seismometer arrays could be used to complement existing seismic networks to probe earthquake activity in great detail. Key Points: We detect over 13,000 seismic events during 26 days in 2014 using a dense nodal array on the San Jacinto fault zone in southern CaliforniaMost of the detections are newly discovered earthquakes not in existing catalogsForeshocks are common among these earthquakes, increasing in the rate before their mainshocks following an inverse Omori's law [ABSTRACT FROM AUTHOR]