On 16 September 2015, an Mw 8.3 earthquake struck middle Chile due to the subduction of the Nazca plate beneath the South America plate. This earthquake is the consequence of 72 years of strain accumulation in the region since the 1943 M 8.3 event. In this study, we apply the compressive sensing method (CS) to invert for the spatiotemporal distribution of the coseismic radiation at different frequencies of this event. The results show clear frequency‐dependent feature of earthquake rupture with low‐frequency (LF) radiation located in the updip region while high‐frequency (HF) radiation concentrated in the downdip region of the megathrust. We also compare the CS results with three coseismic slip models as well as the stress drop distributions inferred from these slip models. The comparison confirms our understanding of coseismic radiation that energy sources are mostly located in the margin of large coseismic slip regions. Furthermore, we find that the LF radiation sources are mainly within the stress‐decreasing (releasing) regions while the HF radiation sources are mainly located in the stress‐increasing (loading) regions due to rupturing of relatively large asperities nearby (stress decreasing and releasing). These results help to better understand the physics of the rupture process during megathrust earthquakes. Moreover, our results do not show radiation sources south of the epicenter, suggesting that the subducting Juan Fernandez Ridge probably stopped the rupture of this earthquake toward the south.

Earthquake source time functions carry information about the complexity of seismic rupture.We explore databases of earthquake source time functions and find that they are composed of distinct peaks that we call subevents. We observe that earthquake complexity, as represented by the number of subevents, grows with earthquake magnitude. Patterns in rupture complexity arise from a scaling between subevent moment and main event moment. These results can be explained by simple 2-D dynamic rupture simulations with self-affine heterogeneity in fault prestress. Applying this to early magnitude estimates, we show that the main event magnitude can be estimated after observing only the first few subevents.Plain Language Summary Seismograms are measurements of waves from earthquakes.They give us information about what happened on the fault at the place where the earthquake occurred. Seismograms can be difficult to interpret because they are often very complicated. Why? One reason is that the waves change when they travel long distances between the fault and a seismometer. Seismologists correct for this effect, however, by constructing something called a source time function. Source time functions are much easier to understand than raw seismograms. We examine a catalog of source time functions from around the world. We find that large earthquakes are composed of many smaller events that we call subevents. The size of a subevent is related to the size of the main earthquake. One important outcome is that we can predict the final size of an earthquake after observing only the first few subevents.

Earthquake source time functions carry information about the complexity of seismic rupture. We explore databases of source time functions of earthquakes and find that source time functions are composed of distinct peaks that we call subevents. We observe that earthquake complexity, as represented by the number of subevents, grows with earthquake magnitude. We find that subevent magnitudes are nearly proportional to their corresponding main event magnitude. We show that the main event magnitude can be estimated after observing only the first few subevents.

We cluster a global database of 3529 Mw>5.5 earthquakes in 1995–2018 based on a dynamic time warping distance between earthquake source time functions (STFs). The clustering exhibits different degrees of complexity of the STF shapes and suggests an association between STF complexity and earthquake source parameters. Most of the thrust events have simple STF shapes across all depths. In contrast, earthquakes with complex STF shapes tend to be located at shallow depths in complicated tectonic regions, exhibit long source duration compared with others of similar magnitude, and tend to have strike-slip mechanisms. With 2D dynamic modeling of dynamic ruptures on heterogeneous fault properties, we find a systematic variation of the simulated STF complexity with frictional properties. Comparison between the observed and synthetic clustering distributions provides useful constraints on frictional properties. In particular, the characteristic slip-weakening distance could be constrained to be short (<0.1  m) and depth dependent if stress drop is in general constant.

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We develop a methodology that combines compressive sensing backprojection (CS‐BP) and source spectral analysis of teleseismic P waves to provide metrics relevant to earthquake dynamics of large events. We improve the CS‐BP method by an autoadaptive source grid refinement as well as a reference source adjustment technique to gain better spatial and temporal resolution of the locations of the radiated bursts. We also use a two‐step source spectral analysis based on (i) simple theoretical Green's functions that include depth phases and water reverberations and on (ii) empirical P wave Green's functions. Furthermore, we propose a source spectrogram methodology that provides the temporal evolution of dynamic parameters such as radiated energy and falloff rates. Bridging backprojection and spectrogram analysis provides a spatial and temporal evolution of these dynamic source parameters. We apply our technique to the recent 2015 Mw 8.3 megathrust Illapel earthquake (Chile). The results from both techniques are consistent and reveal a depth‐varying seismic radiation that is also found in other megathrust earthquakes. The low‐frequency content of the seismic radiation is located in the shallow part of the megathrust, propagating unilaterally from the hypocenter toward the trench while most of the high‐frequency content comes from the downdip part of the fault. Interpretation of multiple rupture stages in the radiation is also supported by the temporal variations of radiated energy and falloff rates. Finally, we discuss the possible mechanisms, either from prestress, fault geometry, and/or frictional properties to explain our observables. Our methodology is an attempt to bridge kinematic observations with earthquake dynamics.

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 multi-task 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 (EV) 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 broadband 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 (STA/LTA) 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.

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