Improved Observations of Deep Earthquake Ruptures Using Machine Learning.

Elevated seismic noise for moderate‐size earthquakes recorded at teleseismic distances has limited our ability to see their complexity. We develop a machine‐learning‐based algorithm to separate noise and earthquake signals that overlap in frequency. The multi‐task encoder‐decoder model is built around a kernel pre‐trained on local (e.g., short distances) earthquake data (Yin et al., 2022, https://doi.org/10.1093/gji/ggac290) and is modified by continued learning with high‐quality teleseismic data. We denoise teleseismic P waves of deep Mw5.0+ earthquakes and use the clean P waves to estimate source characteristics with reduced uncertainties of these understudied earthquakes. We find a scaling of moment and duration to be M0 ≃ τ4, and a resulting strong scaling of stress drop and radiated energy with magnitude (Δσ≃M00.21 ${\Delta }\sigma \simeq {M}_{0}^{0.21}$ and ER≃M01.24 ${E}_{R}\simeq {M}_{0}^{1.24}$). The median radiation efficiency is 5%, a low value compared to crustal earthquakes. Overall, we show that deep earthquakes have weak rupture directivity and few subevents, suggesting a simple model of a circular crack with radial rupture propagation is appropriate. When accounting for their respective scaling with earthquake size, we find no systematic depth variations of duration, stress drop, or radiated energy within the 100–700 km depth range. Our study supports the findings of Poli and Prieto (2016, https://doi.org/10.1002/2016jb013521) with a doubled amount of earthquakes investigated and with earthquakes of lower magnitudes. Plain Language Summary: The vibration of the Earth's ground recorded at seismometers carries the seismic signatures of distant earthquakes superimposed to the Earth's natural or anthropogenic noise surrounding the seismic station. We use artificial intelligence technology to separate the weak signals of distant earthquakes from other sources of ground vibrations unrelated to the earthquakes. The separated signal provides new insights into earthquakes, especially those within the Earth's deep interior, most of which have not been investigated due to noise levels. In contrast with shallow earthquakes, deep earthquakes are less efficient at radiating energy, though they exhibit a higher rate of increase in both stress drop and radiated energy as they grow. This may suggest that deep earthquakes tend to be more confined fault surfaces. A dual mechanism between nucleation in the subduction‐zone core and propagation of larger events in the dry mantle explains our observations. Key Points: A neural network is used to double the number of earthquakes for source analyses compared to previous studies by improving the data qualityDenoising teleseismic waves improves the source signature in Mw5.0–6.0 events and reduces uncertainties over all magnitudesLarge deep earthquake ruptures are dissipative and compact relative to crustal earthquakes [ABSTRACT FROM AUTHOR]