Marine A. Denolle

Full-3-D Tomography for Crustal Structure in Southern California Based on the Scattering-Integral and the Adjoint-Wavefield Methods

We have successfully applied full-3-D tomography (F3DT) based on a combination of the scattering-integral method (SI-F3DT) and the adjoint-wavefield method (AW-F3DT) to iteratively improve a 3-D starting model, the Southern California Earthquake Center (SCEC) Community Velocity Model version 4.0 (CVM-S4). In F3DT, the sensitivity (Frechet) kernels are computed using numerical solutions of the 3-D elastodynamic equation and the nonlinearity of the structural inversion problem is accounted for through an iterative tomographic navigation process. More than half-a-million misfit measurements made on about 38,000 earthquake seismograms and 12,000 ambient-noise correlagrams have been assimilated into our inversion. After 26 F3DT iterations, synthetic seismograms computed using our latest model, CVM-S4.26, show substantially better fit to observed seismograms at frequencies below 0.2 Hz than those computed using our 3-D starting model CVM-S4 and the other SCEC CVM, CVM-H11.9, which was improved through 16 iterations of AW-F3DT. CVM-S4.26 has revealed strong crustal heterogeneities throughout Southern California, some of which are completely missing in CVM-S4 and CVM-H11.9 but exist in models obtained from previous crustal-scale 2-D active-source refraction tomography models. At shallow depths, our model shows strong correlation with sedimentary basins and reveals velocity contrasts across major mapped strike-slip and dip-slip faults. At middle to lower crustal depths, structural features in our model may provide new insights into regional tectonics. When combined with physics-based seismic hazard analysis tools, we expect our model to provide more accurate estimates of seismic hazards in Southern California.

Strong Ground Motion Prediction Using Virtual Earthquakes

Sedimentary basins increase the damaging effects of earthquakes by trapping and amplifying seismic waves. Simulations of seismic wave propagation in sedimentary basins capture this effect; however, there exists no method to validate these results for earthquakes that have not yet occurred. We present a new approach for ground motion prediction that uses the ambient seismic field. We apply our method to a suite of magnitude 7 scenario earthquakes on the southern San Andreas fault and compare our ground motion predictions with simulations. Both methods find strong amplification and coupling of source and structure effects, but they predict substantially different shaking patterns across the Los Angeles Basin. The virtual earthquake approach provides a new approach for predicting long-period strong ground motion. Ambient seismic noise helps predict the ground motion associated with future large earthquakes. Noise in Motion A large earthquake along the southern San Andreas Fault has the potential to cause serious damage to the city of Los Angeles, USA. Earthquake simulations in this region, which lies in a sedimentary basin capable of amplifying shaking, predict strong ground motion but they lack validation with observational data. Denolle et al. (p. 399) developed an independent method to predict ground motion using virtual earthquakes and information gleaned from background seismic noise. This ambient seismic field—generated by sources such as the oceans and atmosphere—produces differences in ground motion in the Los Angeles Basin compared to simulations, but suggests that locally shaking could on average be 3 times larger than the surrounding areas.

Dynamics of the 2015 M7.8 Nepal earthquake

The 2015 M7.8 Nepal earthquake ruptured part of the Main Himalayan Thrust beneath Kathmandu. To study the dynamics of this event, we compute P wave spectra of the main shock and of two large aftershocks to estimate stress drop and radiated energy. We find that surface reflections (depth phases) of these shallow earthquakes produce interference that severely biases spectral measurements unless corrections are applied. Measures of earthquake dynamics for the main shock are within the range of estimates from global and regional earthquakes. We explore the azimuthal and temporal variations of radiated energy and highlight unique aspects of the M7.8 rupture. The beginning of the earthquake likely experienced a dynamic weakening mechanism immediately followed by an abrupt change in fault geometry. Correlation of backprojection results with frequency-dependent variations in the radiated energy rate and with the suggested geometry of the Main Himalayan Thrust yields new constraints on dynamic ruptures through geometrical barriers.

Long‐period seismic amplification in the Kanto Basin from the ambient seismic field

Tokyo, like many seismically threatened cities, is situated atop a sedimentary basin that has the potential to trap and amplify seismic waves from earthquakes. We study amplification in the Kanto Basin by exploiting the information carried by the ambient seismic field. We use 375 seismic stations from the high sensitivity seismograph network across central Honshu as virtual sources and 296 seismic stations of the Metropolitan Seismic Observation network shallow borehole seismometers within the basin as receivers to map the basin response. We find a linear relationship between ground motion and basin depth at periods of 2–10 s that could be used to represent 3-D basin effects in ground motion prediction equations. We also find that the strength of basin seismic amplification depends strongly on the direction of illumination by seismic waves.

New perspectives on self-similarity for shallow thrust earthquakes

Scaling of dynamic rupture processes from small to large earthquakes is critical to seismic hazard assessment. Large subduction earthquakes are typically remote, and we mostly rely on teleseismic body waves to extract information on their slip rate functions. We estimate the P wave source spectra of 942 thrust earthquakes of magnitude Mw 5.5 and above by carefully removing wave propagation effects (geometrical spreading, attenuation, and free surface effects). The conventional spectral model of a single-corner frequency and high-frequency falloff rate does not explain our data, and we instead introduce a double-corner-frequency model, modified from the Haskell propagating source model, with an intermediate falloff of f−1. The first corner frequency f1 relates closely to the source duration T1, its scaling follows M0∝T13 for Mw<7.5, and changes to M0∝T12 for larger earthquakes. An elliptical rupture geometry better explains the observed scaling than circular crack models. The second time scale T2 varies more weakly with moment, M0∝T25, varies weakly with depth, and can be interpreted either as expressions of starting and stopping phases, as a pulse-like rupture, or a dynamic weakening process. Estimated stress drops and scaled energy (ratio of radiated energy over seismic moment) are both invariant with seismic moment. However, the observed earthquakes are not self-similar because their source geometry and spectral shapes vary with earthquake size. We find and map global variations of these source parameters.

Solving the Surface‐Wave Eigenproblem with Chebyshev Spectral Collocation

Stable and accurate surface‐wave calculations present a long‐standing numerical and analytical challenge to seismologists. In a layered medium, we can describe the surface‐wave behavior with harmonic time dependence in terms of an eigenproblem where the eigenvalues and eigenfunctions define the surface‐wave modes. Numerous studies have explored diverse numerical approaches to solve this eigenproblem, but they tend to suffer from numerical difficulties that limit the complexity of the medium, frequency range of applicability, or accuracy of the solution. We propose an equivalent formulation that replaces the conventional stress‐displacement vector with an alternative one, in order to cast the eigenproblem in a standard form that is linear in the eigenvalues. We discretize the system and boundary conditions using a Chebyshev spectral collocation method, leading to a finite‐dimensional generalized matrix eigenvalue problem that can be solved directly. No iterations are required to satisfy the boundary conditions or to isolate the eigenmodes. Collocation methods allow for the solution of the eigenproblem for general depth‐dependent elastic properties, including continuous depth variations of the properties as well as material interfaces. We illustrate the use of our technique to calculate dispersion curves, theoretical waveform time series, and to estimate the change from retrograde to prograde particle motion at the surface for a complex 1D structure under Los Angeles.

Convolutional neural network for earthquake detection and location

ConvNetQuake is the first neural network for detection and location of earthquakes from seismograms. The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes. Today’s most elaborate methods scan through the plethora of continuous seismic records, searching for repeating seismic signals. We leverage the recent advances in artificial intelligence and present ConvNetQuake, a highly scalable convolutional neural network for earthquake detection and location from a single waveform. We apply our technique to study the induced seismicity in Oklahoma, USA. We detect more than 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey. Our algorithm is orders of magnitude faster than established methods.