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Predicting the recurrence times of earthquakes and understanding the physical processes that immediately precede them are two outstanding problems in seismology. Although geodetic measurements record elastic strain accumulation, most faults have recurrence intervals longer than available measurements. Foreshocks provide the principal observations of processes before mainshocks, but variability between sequences limits generalizations of pre-failure behaviour. Here we analyse seismicity and deformation data for highly characteristic caldera collapse earthquakes from 2018 Kīlauea Volcano (Hawaii, USA), with a mean recurrence interval of 1.4 days. These events provide a unique test of stress-induced earthquake recurrence and document processes preceding mainshocks with magnitude greater than five. We show that recurrence intervals are well predicted by stress histories inferred from near-field deformation measurements and that cycle-averaged seismicity reveals a critical phase, minutes before mainshocks, where earthquakes grew larger and seismic moment rate surged dramatically. The average moment rate in the final 15 minutes (0.7% of the mean cycle duration) was 4.75 times the background, a highly significant change. We infer that as the average stress increased, ruptures were more likely to overcome geometric barriers and grow larger, leading to characteristic, whole-fault ruptures. These findings imply that stress heterogeneity influences both earthquake nucleation and growth, including on potentially hazardous tectonic faults. 

Accurate estimates of earthquake magnitude are necessary to improve our understanding of seismic hazard. Unbiased magnitudes for small earthquakes are especially important because magnitude exceedance probabilities for large earthquakes are derived from the behavior of small earthquakes. Also, accurate characterization of small events is becoming increasingly important for ground motion models. However, catalog magnitudes may vary for the same event depending on network procedures and capabilities. In addition, different magnitude scales are often used for events of varying sizes. For example, moment magnitude (Mw) is the widely preferred estimate for earthquake size but it is often not available for small earthquakes (M < 3.5). As a result, statistical measures such as magnitude frequency distribution (MFD) and b-value can be biased depending on magnitude type and uncertainties that arise during the measurement process. In this research we demonstrate the capability of the relative magnitude method to provide a uniform and accurate estimate of earthquake magnitude in a variety of regions, while only requiring the use of waveform data. The study regions include the Permian Basin in Texas, central Oklahoma, and southern California. We present results in which only relative magnitudes are used to estimate MFD and b-value as well as relative magnitudes that are benchmarked to an absolute scale using a coda-envelope derived Mw calibration for small events. We also discuss potential sources of uncertainty in the relative magnitude method such as acceptable signal-to-noise ratios, cross-correlation thresholds, and choice of scaling constant, as well as our attempts to mitigate those uncertainties. 

Abstract Low‐frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low‐frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017),https://doi.org/10.1002/2017jb014047as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low‐frequency earthquake (LFE) S‐wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017),https://doi.org/10.1002/2017jb014047catalog. Overall, machine‐learning approaches show great promise for identifying additional low‐frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.