A document by Yiyu Ni. Click on the document to view its contents.

The Antarctic ice sheet is buttressed by floating ice shelves that calve icebergs along large fractures called rifts. We report the first-ever seismic recording of a multiple-kilometer rift propagation event located in Pine Island Glacier Ice Shelf. The rift grew 10.5 km at a speed of 34.8 m/s, the fastest known ice fracture at this scale. We simulate ocean-coupled rift propagation and find that hydrodynamics control rupture velocities. During rift propagation, ocean water flows into the rift at a rate of at least 2300 m3/s and causes mixing in the subshelf cavity. Our observations support the hypotheses that large ice shelf rift propagation events are brittle, hydrodynamically limited, and exhibit sensitive coupling with the surrounding ocean.

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 \cite{yin_multitask_2022} 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 $M_0\simeq \tau^{4.16}$, and a resulting strong scaling of stress drop and radiated energy with magnitude ($\sigma\simeq M_0^{0.2}$ and $E_R \simeq M_0^{1.23}$). The median radiation efficiency is 5\%, a low value compared to shallow 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 \citeA{poli_global_2016} with a doubled amount of earthquakes investigated and with earthquakes of lower magnitudes.

We monitored the time history of the velocity change (dv/v) from 2002 to 2022 to investigate temporal changes in the physical state near the Parkfield Region of the San Andreas Fault throughout the interseismic period. Following the coseismic decrease in dv/v caused due to the 2003 San Simeon and the 2004 Parkfield earthquakes, the dv/v heals logarithmically and shows a net long-term increase in which the current dv/v level is equivalent to, or exceeding, the value before the 2003 San Simeon earthquake. We investigated this long-term trend by fitting the model accounting for the environmental and coseismic effects to the channel-weighted dv/v time series. We confirmed with the metrics of AIC and BIC that the additional term of either a linear trend term, or a residual healing term for the case where the healing had not been completed before the San Simeon earthquake occurred, robustly improved the fit to the data. We eventually evaluated the sensitivity of the dv/v time history to the GNSS-derived strain field around the fault. The cumulative dilatational strain spatially averaged around the seismic stations shows a slight extension, which is opposite to what would be expected for an increase in dv/v. However, the cumulative rotated axial strain shows compression in a range near the maximum contractional horizontal strain (azimuth of N35°W to N45°E), suggesting that the closing of pre-existing microcracks aligned perpendicular to the axial contractional strains would be a candidate to cause the long-term increase observed in the multiple station pairs.

Ice shelves regulate the stability of marine ice sheets. We track fractures on Pine Island Glacier , a quickly-accelerating glacier in West Antarctica that contributes more to sea level rise than any other glacier. Using an on-ice seismic network deployed from 2012 to 2014, we catalog icequakes that dominantly consist of flexural gravity waves. Icequakes occur near the rift tip and in two distinct areas of the shear margin, and TerraSAR-X imagery shows significant fracture in each source region. Rift-tip icequakes increase with ice speed, linking rift fracture to glaciological stresses and/or localized thinning. Using a simple flexural gravity wave model, we deconvolve wave propagation effects to estimate icequake source durations of 19.5 to 50.0 s, and transient loads of 3.8 to 14.0 kPa corresponding to 4.3 to 15.9 m of crevasse growth per icequake. These long source durations suggest that water flow may limit the rate of crevasse opening.

Cross-correlations of ambient seismic noise are widely used for seismic velocity imaging, monitoring, and ground motion analyses. A typical step in analyzing Noise Cross-correlation Functions (NCFs) is stacking short-term NCFs over longer time periods to increase the signal quality. Spurious NCFs could contaminate the stack, degrade its quality, and limit its use. Many methods have been developed to improve the stacking of coherent waveforms, including earthquake waveforms, receiver functions, and NCFs. This study systematically evaluates and compares the performance of eight stacking methods, including arithmetic mean or linear stacking, robust stacking, selective stacking, cluster stacking, phase-weighted stacking, time-frequency phase-weighted stacking, $N^{th}$-root stacking, and averaging after applying an adaptive covariance filter. Our results demonstrate that, in most cases, all methods can retrieve clear ballistic or first arrivals. However, they yield significant differences in preserving the phase and amplitude information. This study provides a practical guide for choosing the optimal stacking method for specific research applications in ambient noise seismology. We evaluate the performance using multiple onshore and offshore seismic arrays in the Pacific Northwest region. We compare these stacking methods for NCFs calculated from raw ambient noise (referred to as Raw NCFs) and from ambient noise normalized using a one-bit clipping time normalization method (referred to as One-bit NCFs). We evaluate six metrics, including signal-to-noise ratios, phase dispersion images, convergence rate, temporal changes in the ballistic and coda waves, relative amplitude decays with distance, and computational time. We show that robust stacking is the best choice for all applications (velocity tomography, monitoring, and attenuation studies) using Raw NCFs. For applications using One-bit NCFs, all methods but phase-weighted, time-frequency phase-weighted, and $N^{th}$-root stacking are good choices for seismic velocity tomography. Linear, robust, and selective stacking methods are all equally appropriate choices when using One-bit NCFs for monitoring applications. For applications relying on accurate relative amplitudes, both the robust and cluster stacking methods perform well with One-bit NCFs. The evaluations in this study can be generalized to a broad range of time-series analysis that utilizes data coherence to perform ensemble stacking. Another contribution of this study is the accompanying open-source software, which can be used for general purposes in time-series stacking.

Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to 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 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 seismogram as input and independently predicts the 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 a seismic station. We estimate the network’s performance using the Explained Variance (EV) metric on both earthquake and noise waveforms. We explore different network architectures and find that the long-short-term-memory bottleneck performs best over other structures, which we refer to as the WaveDecompNet. Overall we find that WaveDecompNet provides satisfactory performance down to 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 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 detection 27 times. We also measure single-station cross-correlation and autocorrelations 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 range of seismological research.

Earthquake signals in seismic data are inevitably contaminated with signals from unwanted sources. Separating noise from earthquake signals can greatly improve the analysis of the seismic data, such as earthquake characterization and ambient noise analysis. In this work, we develop a new auto-encoder to extract transient signals from ambient signals directly in the time domain for 3-component seismograms. We benchmark our architecture development and performance against a time-frequency counterpart (similar to the DeepDenoisier). We explore the generalization of our time-domain denoiser by training on various scales of seismic data. First, we train purely on observed seismograms of local ( < 350 km) events using the STandford EArthquake Dataset (STEAD) data set. Second, we generate a data set of observed seismograms from regional earthquakes (350 km-2000 km), which we complement with seismograms generated by hybrid low-frequency deterministic, high-frequency stochastic synthetic waveforms. We explore the robustness of the denoiser on various noise structures. Finally, we explore the quality of the extracted signals, for earthquake characterization and for ambient noise seismology.