Ambient noise differential adjoint tomography reveals fluid-bearing rocks near active faults in Los Angeles.

Water scarcity is a pressing issue in California. We develop ambient noise differential adjoint tomography that improves the sensitivity to fluid-bearing rocks by canceling bias caused by noise sources. Here we image the shallow S-wave velocity structure using this method beneath a linear seismic array (LASSIE) in Los Angeles Basin, which shows significant velocity reduction marking a major regional water producer, the Silverado aquifer, along with other fluid-bearing structures.

Slow earthquake scaling reconsidered as a boundary between distinct modes of rupture propagation.

The scaling law for slow earthquakes, which is a linear relationship between seismic moment and duration, was proposed 15 y ago and initiated a debate on the difference in physical processes governing slow vs. fast (ordinary) earthquakes. Based on new observations across a wide period range, we show that linear scaling of slow earthquakes remains valid, but as a well-defined upper bound on moment rate of ~10 Nm/s.

A comprehensive suite of earthquake catalogues for the 2016-2017 Central Italy seismic sequence.

The protracted nature of the 2016-2017 central Italy seismic sequence, with multiple damaging earthquakes spaced over months, presented serious challenges for the duty seismologists and emergency managers as they assimilated the growing sequence to advise the local population. Uncertainty concerning where and when it was safe to occupy vulnerable structures highlighted the need for timely delivery of scientifically based understanding of the evolving hazard and risk. Seismic hazard assessment during complex sequences depends critically on up-to-date earthquake catalogues-i.

Deep-learning seismology.

Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep learning to seismology are likely to prove instructive for the geosciences, and perhaps other research areas more broadly.

Toward improved urban earthquake monitoring through deep-learning-based noise suppression.

Earthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array.

Statistical bounds on how induced seismicity stops.

Earthquakes caused by human activities receive scrutiny due to the risks and hazards they pose. Seismicity that occurs after the causative anthropogenic operation stops has been particularly problematic-both because of high-profile cases of damage caused by this trailing seismicity and due to the loss of control for risk management. With this motivation, we undertake a statistical examination of how induced seismicity stops.

Machine learning and earthquake forecasting-next steps.

A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving earthquake forecasting.

Ambient noise Love wave attenuation tomography for the LASSIE array across the Los Angeles basin.

The Los Angeles basin is located within the North America-Pacific plate boundary and contains multiple earthquake faults that threaten greater Los Angeles. Seismic attenuation tomography has the potential to provide important constraints on wave propagation in the basin and to provide supplementary information on structure in the form of the distribution of anelastic properties. On the basis of the amplitude information from seismic interferometry from the linear LASSIE array in the Los Angeles basin, we apply station-triplet attenuation tomography to obtain a 2D depth profile for the attenuation structure of the uppermost 0.

A risk-based approach for managing hydraulic fracturing-induced seismicity.

Risks from induced earthquakes are a growing concern that needs effective management. For hydraulic fracturing of the Eagle Ford shale in southern Texas, we developed a risk-informed strategy for choosing red-light thresholds that require immediate well shut-in. We used a combination of datasets to simulate spatially heterogeneous nuisance and damage impacts.

Revisiting evidence for widespread seismicity in the upper mantle under Los Angeles.

We revisit the finding of widespread deep seismicity in the upper mantle imaged with a dense, temporary nodal seismic array in Long Beach, California using back-projection to detect candidate events and trace randomization to develop a reliable imaging threshold for candidate detections. We find that nearly all detections of small events at depths greater than 20 kilometers in the upper mantle fall below the reliability threshold. We find a modest number of small, shallower events in the crust that appear to align with the active Newport-Inglewood Fault.

Laboratory earthquake forecasting: A machine learning competition.

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google's ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data.

Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking.

Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes. Here we present a global deep-learning model for simultaneous earthquake detection and phase picking. Performing these two related tasks in tandem improves model performance in each individual task by combining information in phases and in the full waveform of earthquake signals by using a hierarchical attention mechanism.

CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection.

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks.

Machine learning for data-driven discovery in solid Earth geoscience.

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth's behavior and by the inaccessibility of nearly all of Earth's subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging.

Stress drops of induced and tectonic earthquakes in the central United States are indistinguishable.

Induced earthquakes currently pose a significant hazard in the central United States, but there is considerable uncertainty about the severity of their ground motions. We measure stress drops of 39 moderate-magnitude induced and tectonic earthquakes in the central United States and eastern North America. Induced earthquakes, more than half of which are shallower than 5 km, show a comparable median stress drop to tectonic earthquakes in the central United States that are dominantly strike-slip but a lower median stress drop than that of tectonic earthquakes in the eastern North America that are dominantly reverse-faulting.

Earthquake detection through computationally efficient similarity search.

Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection-identification of seismic events in continuous data-is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods.

Shallow dynamic overshoot and energetic deep rupture in the 2011 Mw 9.0 Tohoku-Oki earthquake.

Strong spatial variation of rupture characteristics in the moment magnitude (M(w)) 9.0 Tohoku-Oki megathrust earthquake controlled both the strength of shaking and the size of the tsunami that followed. Finite-source imaging reveals that the rupture consisted of a small initial phase, deep rupture for up to 40 seconds, extensive shallow rupture at 60 to 70 seconds, and continuing deep rupture lasting more than 100 seconds.

A scaling law for slow earthquakes.

Recently, a series of unusual earthquake phenomena have been discovered, including deep episodic tremor, low-frequency earthquakes, very-low-frequency earthquakes, slow slip events and silent earthquakes. Each of these has been demonstrated to arise from shear slip, just as do regular earthquakes, but with longer characteristic durations and radiating much less seismic energy. Here we show that these slow events follow a simple, unified scaling relationship that clearly differentiates their behaviour from that of regular earthquakes.

Non-volcanic tremor and low-frequency earthquake swarms.

Non-volcanic tremor is a weak, extended duration seismic signal observed episodically on some major faults, often in conjunction with slow slip events. Such tremor may hold the key to understanding fundamental processes at the deep roots of faults, and could signal times of accelerated slip and hence increased seismic hazard. The mechanism underlying the generation of tremor and its relationship to aseismic slip are, however, as yet unresolved.

Low-frequency earthquakes in Shikoku, Japan, and their relationship to episodic tremor and slip.

Non-volcanic seismic tremor was discovered in the Nankai trough subduction zone in southwest Japan and subsequently identified in the Cascadia subduction zone. In both locations, tremor is observed to coincide temporally with large, slow slip events on the plate interface downdip of the seismogenic zone. The relationship between tremor and aseismic slip remains uncertain, however, largely owing to difficulty in constraining the source depth of tremor.