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Abstract 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 beM₀ ≃ τ⁴, and a resulting strong scaling of stress drop and radiated energy with magnitude ( and ). 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. 

SUMMARY Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient vibrations contaminate the earthquake signals, while the earthquake signals pollute the ambient noise’s statistical properties necessary for ambient-noise seismology analysis. Separating ambient noise from earthquake signals would thus benefit multiple seismological analyses. This work develops a multitask encoder–decoder network named WaveDecompNet to separate transient signals from ambient signals directly in the time domain for 3-component seismograms. We choose the active-volcanic Big Island in Hawai’i as a natural laboratory given its richness in transients (tectonic and volcanic earthquakes) and diffuse ambient noise (strong microseism). The approach takes a noisy 3-component seismogram as input and independently predicts the 3-component earthquake and noise waveforms. The model is trained on earthquake and noise waveforms from the STandford EArthquake Dataset (STEAD) and on the local noise of seismic station IU.POHA. We estimate the network’s performance by using the explained variance metric on both earthquake and noise waveforms. We explore different neural network designs for WaveDecompNet and find that the model with long-short-term memory (LSTM) performs best over other structures. Overall, we find that WaveDecompNet provides satisfactory performance down to a signal-to-noise ratio (SNR) of 0.1. The potential of the method is (1) to improve broad-band SNR of transient (earthquake) waveforms and (2) to improve local ambient noise to monitor the Earth’s structure using ambient noise signals. To test this, we apply a short-time average to a long-time average filter and improve the number of detected events. We also measure single-station cross-correlation functions of the recovered ambient noise and establish their improved coherence through time and over different frequency bands. We conclude that WaveDecompNet is a promising tool for a broad range of seismological research. 

Abstract Megathrust earthquakes exhibit a ubiquitous seismic radiation style: low‐frequency (LF) seismic energy is efficiently emitted from the shallowest portion of the fault, whereas high‐frequency (HF) seismic energy is efficiently emitted from the deepest part of the fault. Although this is observed in many case‐specific studies, we show that it is ubiquitous in global megathrust earthquakes between 1995 and 2021. Previous studies have interpreted this as an effect of systematic depth variation in either the plate interface frictional properties (Lay et al., 2012) or the P wavespeeds (Sallarès & Ranero, 2019). This work suggests an alternative hypothesis: the interaction between waves and ruptures due to the Earth's free surface is the leading mechanism that generates this behavior. Two‐dimensional dynamic rupture simulations of subduction zone earthquakes support this hypothesis. Our simulations show that the interaction between the seismic waves reflected at the Earth's free surface and the updip propagating rupture results in LF radiation at the source. In contrast, the downdip propagation of rupture is less affected by the free surface and is thus dominated by HF radiation typical of buried faults. To a second degree, the presence of a realistic Earth structure derived from P‐wave velocity (V_P) tomographic images and realistic V_P/V_Sratio estimated in boreholes further enhances the contrast in source radiation. We conclude that the Earth's free surface is necessary to explain the observed megathrust earthquake radiation style, and the realistic structure of subduction zone is necessary to better predict earthquake ground motion and tsunami potential.