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We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950–2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open source together with the preprocessed data from the USGS. [ABSTRACT FROM AUTHOR]

Seismic bursts in Southern California are sequences of small earthquakes strongly clustered in space and time and include seismic swarms and aftershock sequences. A readily observable property of these events, the radius of gyration (RG), allows us to connect the bursts to the temporal occurrence of the largest M ≥ 7 earthquakes in California since 1984. In the Southern California earthquake catalog, we identify hundreds of these potentially coherent space‐time structures in a region defined by a circle of radius 600 km around Los Angeles. We compute RG for each cluster then filter them to identify those bursts with large numbers of events closely clustered in space, which we call "compact" bursts. Our basic assumption is that these compact bursts reflect the dynamics associated with large earthquakes. Once we have filtered the burst catalog, we apply an exponential moving average to construct a time series for the Southern California region. We observe that the RG of these bursts systematically decreases prior to large earthquakes, in a process that we might term "radial localization." The RG then rapidly increases during an aftershock sequence, and a new cycle of "radial localization" then begins. These time series display cycles of recharge and discharge reminiscent of seismic stress accumulation and release in the elastic rebound process. The complex burst dynamics we observe are evidently a property of the region as a whole, rather than being associated with individual faults. This new method allows us to improve earthquake nowcasting, which is a technique to evaluate the current state of hazard in a seismically active region. Plain Language Summary: Earthquake nowcasting is a method to evaluate the current state of seismic hazard from large earthquakes. In this paper, we connect the temporal occurrence of the largest and most potentially destructive earthquakes in California since 1984 with a readily observable property of small earthquake seismicity in the region. Our method involves the calculation of the time history of the average radius (horizontal size or extent) of "bursts" of small earthquakes, in the time leading up to and following major earthquakes in the region. We observe that the radius systematically and gradually decreases leading up to major earthquakes, increasing suddenly and discontinuously following the event. This observable pattern resembles the long‐hypothesized cycle of regional tectonic stress buildup and release, or elastic rebound, associated with large destructive earthquakes. We propose that the radius of these bursts might be considered to be a proxy variable for the changing state of regional stress in Southern California. Key Points: A new nowcasting method using earthquake swarms and aftershocks reveals the hazard from large M ≥ 7 earthquakes in Southern CaliforniaMachine learning defines a time series of bursts using an exponential moving average filtered and optimized to reveal temporal structureCycles of recharge and discharge associated with M ≥ 7 earthquakes are revealed, reminiscent of stress accumulation and release [ABSTRACT FROM AUTHOR]

We quantify the seismicity on the island of Taiwan using the frequency–magnitude statistics of earthquakes since 1900. A break in Gutenberg–Richter scaling for large earthquakes in global seismicity has been observed, this break is also observed in our Taiwan study. The seismic data from the Central Weather Bureau Seismic Network are in good agreement with the Gutenberg–Richter relation taking b ≈ 1 when M < 7. For large earthquakes, M ≥ 7, the seismic data fit Gutenberg–Richter scaling with b ≈ 1.5. If the Gutenberg–Richter scaling for M < 7 earthquakes is extrapolated to larger earthquakes, we would expect a M > 8 earthquake in the study region about every 25 yr. However, our analysis shows a lower frequency of occurrence of large earthquakes so that the expected frequency of M > 8 earthquakes is about 200 yr. The level of seismicity for smaller earthquakes on Taiwan is about 12 times greater than in Southern California and the possibility of a M ≈ 9 earthquake north or south of Taiwan cannot be ruled out. In light of the Fukushima, Japan nuclear disaster, we also discuss the implications of our study for the three operating nuclear power plants on the coast of Taiwan. [ABSTRACT FROM AUTHOR]