- Watch Recordings of Past Sessions
- Cutting-edge Methods for Seismic Imaging I – Abstracts
- Cutting-edge Methods for Seismic Imaging II – Abstracts
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Cutting-edge Methods for Seismic Imaging I
6 October 2020 at 10 AM Pacific
Featured Discussants: Jeroen Ritsema (University of Michigan) and Carl Tape (University of Alaska Fairbanks)
Markov Chain Monte Carlo Regional Tomography Models of Western North America: Findings from Alaska & Southern California
Presenting Author: Elizabeth Berg, University of Utah, Utah, USA
Authors: Elizabeth Berg, University of Utah, Utah, USA; Fan-Chi Lin, University of Colorado Boulder, Colorado, USA; Vera Schulte-Pelkum, University of Colorado Boulder, Colorado, USA; Weisen Shen, State University of New York at Stony Brook, New York, USA; Amir Allam, University of Utah, Utah, USA
We create isotropic shear velocity models of Southern California and Alaska with sensitivity from the near-surface into the upper mantle and resolve Moho depths. This is possible through data recorded on hundreds of stations provided by regional networks in Southern California in 2015, and from the USArray deployment in Alaska from 2014 to 2018. We use Rayleigh-wave measurements from earthquakes for long-periods (15s-100s), and from ambient noise cross-correlations for shorter periods (2s-24s periods). These measurements include Rayleigh-wave phase velocity via Helmholtz tomography and Eikonal tomography for earthquake and ambient noise data respectively, as well as Rayleigh-wave ellipticity. By combining surface wave measurements with azimuthally-corrected receiver functions we obtain sensitivity to near-station subsurface structure, including horizontal interfaces and sensitivity to regional 3-D subsurface structure. We leverage complementary sensitivity of these datasets in a Markov Chain Monte Carlo joint inversion and are able to quantify model uncertainty and gain understanding of sensitivity. In Southern California, we observe well-known large-scale features, including prominent sediment basins in the Central Valley, Los-Angeles, San Bernardino and Ventura basins. We also create self-consistent Moho depth maps across both regions. In Southern California, we observe thicker crust in the Peninsular and Sierra Nevada Ranges, and thinner crust in the Salton Trough and the Coast Ranges. In Alaska, we resolve every major basin, both shallow and deep, and we image the interaction of the Pacific slab and continental crust and its impact to create more or less prominent volcanism. In Alaska, we find thicker crust beneath the Brooks Range and thinner crust in the Yukon Composite Terrane and in interior Alaska.
Adjoint Tomography of New Zealand’s North Island Using an Automated, Open-source Workflow
Presenting Author: Bryant Chow, Victoria University of Wellington, Wellington, New Zealand
Authors: Bryant Chow, Victoria University of Wellington, Wellington, New Zealand; Yoshihiro Kaneko, GNS Science, Wellington, New Zealand, Kyoto University, Kyoto, Japan; Carl Tape, University of Alaska Fairbanks, Alaska, USA; Ryan Modrak, Los Alamos National Laboratory, New Mexico, USA; John Townend, Victoria University of Wellington, Wellington, New Zealand
We have developed an automated, open-source workflow for full-waveform inversion using spectral-element and adjoint methods (Chow et al., 2020). As a study area we choose the eastern North Island of New Zealand, which encompasses the Hikurangi subduction zone. The chosen domain offers a unique opportunity for imaging material properties near an active subduction zone, due to the availability of well-recorded earthquakes in close proximity to the plate interface. We have performed realistic synthetic inversions using New Zealand source and receiver distributions to determine a set of parameters usable in real-data inversions. We have then undertaken an iterative inversion using 5,000 measurements from a subset of long-period (10–30 s) earthquake waveform data to resolve broad-scale velocity changes with respect to an initial ray-based 3D velocity model of New Zealand. Velocity changes are resolved at shallow crustal depths in tectonically active areas, such as the Taupō Volcanic Zone, regions of geodetically detected slow slip, and in the vicinity of the locked-to-creeping transition within the subduction zone. Improved fits between data and synthetic waveforms motivate an ongoing large-scale inversion using 200 earthquakes recorded on as many as 100 broadband stations. Initial iterations are used to fit long-period waveforms (15–30 s), while progressively shorter periods are introduced to a target period of 3 s, corresponding to features on the order of several kilometers in size. We present ongoing efforts towards an accurate, high-resolution tomographic model of the North Island of New Zealand, which will be used to further understanding of enigmatic tectonic processes.
Seismic Traveltime Tomography Based on Stochastic Voronoi Cells Parameterization: Application from Local to Global Scales
Presenting Author: Hongjian Fang, Massachusetts Institute of Technology, Massachusetts, USA
Authors: Hongjian Fang, Massachusetts Institute of Technology, Massachusetts, USA; Malcolm White, University of Southern California, California, USA; Yehuda Ben-Zion, University of Southern California, California, USA; Rob van der Hilst, Massachusetts Institute of Technology, Massachusetts, USA
The amount of seismic arrival time data has increased dramatically in the past few decades. With the use of machine learning based automatic picking technique, the number is expected to grow more rapidly. However, current travel time tomography methods have to use a reduced data set to homogenize the data distribution and to decrease the computational costs. In this study, we propose a new tomography method that adopts a Poisson-Voronoi cells parameterization, which avoids the use of explicit regularization terms by decomposing the original high-dimensional problem into a series of low-dimensional sub-problems. Moreover, we use a subset of the whole dataset in each sub-problem, which is similar to the mini-batch technique in machine learning. The sub data set can be optimally selected in a way that can make the data sampling more homogeneous while making full use of the available data. We apply the method to seismic arrival times at different scales, from a local scale data set in the Ridgecrest area to a global scale in the Earth mantle. Detailed model features will be discussed in the talk.
Accelerating Global-Scale Full-Waveform Inversion
Presenting Author: Sölvi Thrastarson, ETH Zürich, Zürich, Switzerland
Authors: Sölvi Thrastarson, ETH Zürich, Zürich, Switzerland; Dirk-Philip van Herwaarden, ETH Zürich, Zürich, Switzerland; Lion Krischer, ETH Zürich, Zürich, Switzerland; Christian Boehm, ETH Zürich, Zürich, Switzerland; Martin van Driel, ETH Zürich, Zürich, Switzerland; Michael Afanasiev, ETH Zürich, Zürich, Switzerland; Andreas Fichtner, ETH Zürich, Zürich, Switzerland
Full-waveform inversion (FWI) has proven to produce subsurface images of previously unprecedented accuracies. However, with the requirement of multiple accurate seismic wavefield simulations, FWI is a computationally demanding procedure. Incorporating all available data into an FWI study is already a challenge. With the wealth of seismic data increasing exponentially, one has to think of ways to exploit the available data without hitting computational barriers. In this study, we present a novel approach to both numerical simulations and to the adjoint method which reduces the computational cost of FWI by an order of magnitude.
In smooth media it has been observed that wavefield complexity is highly anisotropic. The wavefield is relatively smooth in the lateral direction with respect to the source location, a feature which can be exploited in the meshing process. By adapting the meshing of the wavefield to the expected complexity, the number of required elements to fully resolve the wavefield can be reduced by an order of magnitude. This results in a mesh optimized for a single source location and medium. Accurate gradients can be computed via the discrete adjoint approach.
We will present a real data example of a global FWI where we were able to recover the large scale features of the Earth at the cost of less than 20 regular wavefield simulations. In the example, the wavefield adapted meshes are used in combination with a stochastic mini-batch optimization scheme where only a fraction of the dataset is used in each iteration. The example shows how global FWI can be done with a large dataset, in a computationally tractable manner.
Uncertainty Quantification in Full Waveform Tomography with Ensemble Data Assimilation – Methods and Anisotropy
Presenting Author: Julien Thurin, ISTerre, Université Grenoble Alpes, France
Authors: Julien Thurin, ISTerre, Université Grenoble Alpes, France; Romain Brossier, ISTerre, Université Grenoble Alpes, France; and Ludovic Métivier, LJK, CNRS, Université Grenoble Alpes, France
Uncertainty estimation and quality control are critically missing in most geophysical tomographic applications. The few solutions to cope with that issue are often left out in practical applications when these grow in scale and involve complex modeling. We present a joint full-waveform inversion and ensemble data assimilation scheme, allowing local Bayesian estimation of the solution that brings uncertainty estimation to the tomographic problem. This original methodology relies on a deterministic square root ensemble Kalman filter commonly used in the data assimilation community: the ensemble transform Kalman filter. Combined with a 2D visco-acoustic frequency-domain full-waveform inversion scheme, the resulting method allows access to a low-rank approximation of the posterior covariance matrix of the solution. It yields uncertainty information through an ensemble-representation, that can conveniently be mapped across the physical domain for visualization and interpretation. We present the combination of ensemble transform Kalman filter with full-waveform inversion along with the scheme design and algorithmic details that lead to our mixed application. Both synthetic and field-data results are considered, along with the biases that are associated with the limited rank ensemble representation. Finally, we review the open questions and development perspectives linked with data assimilation applications to the tomographic problem.
Cutting-edge Methods for Seismic Imaging II
10 November 2020 at 10 AM Pacific
Featured Discussants: Andrew Curtis, University of Edinburgh and Kees Wapenaar, Delft University of Technology
Characterization of a Magma Sill in the Mayotte Volcanic Zone Using Converted Waves
Presenting Author: Lise Retailleau, Université de Paris, Institut de Physique du Globe de Paris and Observatoire Volcanologique du Piton de la Fournaise, Institut de Physique du Globe de Paris
Authors: Lise Retailleau, Université de Paris, Institut de Physique du Globe de Paris, Observatoire Volcanologique du Piton de la Fournaise, Institut de Physique du Globe de Paris; Jean-Marie Saurel, Université de Paris, Institut de Physique du Globe de Paris; Jean Letort, Observatoire Midi Pyrénées, Université Paul Sabatier; Aude Lavayssière, Université de Paris, Institut de Physique du Globe de Paris; Claudio Satriano, Université de Paris, Institut de Physique du Globe de Paris; Wayne Crawford, Université de Paris, Institut de Physique du Globe de Paris; Jérome Vergne,Institut de Physique du Globe de Strasbourg, Université de Strasbourg; Arnaud Lemarchand; Université de Paris, Institut de Physique du Globe de Paris
The magmatic event and subsequent creation of a new volcanic edifice that occurred offshore from the Mayotte island in the North Mozambique channel (Indian Ocean) is unique in its emitted lava volume and duration since the Laki eruption in 1784.
The event was first noticed through the strong seismicity it generated. Indeed, multiple Volcano Tectonic (VT) earthquakes of magnitude superior to 4 were felt on the island during the first month of the crisis (May 2018). The seismic activity is still ongoing and regularly felt by the population of the Mayotte island.
Very long period events (VLP) have also been recorded. The 11 November 2018 VLP was recorded worldwide and highlighted the volcanic origin of the activity before a scientific cruise discovered the volcano in May 2019. On active volcanoes, VLP signals are often associated with fluid movement or fluid resonance in a structure of the volcanic system. Evidence of a reservoir above the main seismicity swarm has been proposed by recent Magneto-telluric and petrologic studies.
The signature of this structure also appears as multiple arrivals on recordings of VT earthquakes located beneath it. The P and S-waves propagating upward and crossing the layer undergo conversions and speed deceleration in the sill, generating the supplementary arrivals.
Combining arrival time analysis and wave modeling in teleseismic and regional settings, we characterize a possible sill and identify its geometry and main properties. The layered structure has a thickness of about 2km, topping at 20km depth.
Imaging Light Structures at Earth’s Core-mantle Boundary – In Heavy Detail
Presenting Author: Paula Koelemeijer, Royal Holloway University of London
Authors: Paula Koelemeijer, Royal Holloway University of London; Alex Robson, University of California, Berkeley; Harriet Lau, University of California, Berkeley; Barbara Romanowicz, University of California, Berkeley & College de France; Arwen Deuss, Utrecht University; Jeroen Ritsema, University of Michigan
Accurate estimates of density variations are essential for modeling mantle flow and to distinguish between a thermal or compositional origin of mantle heterogeneity. Particularly, constraining the density of the Large-Low-Velocity-Provinces (LLVPs) in the deep mantle is important as the properties and stability of these structures have major implications for the long term evolution of the mantle. However, resolving LLVP density via seismological observations is not straightforward. Body wave data are only indirectly sensitive to density, while the sensitivity of Earth’s normal modes to density is relatively small and also shows trade-offs with topography on the core-mantle boundary. Consequently, few models of lower mantle density exist, with the most recent studies based on specific normal modes (Koelemeijer et al., 2017) and tidal data (Lau et al., 2017) drawing opposite conclusions.
Here, I will review efforts from the last decades to constrain the nature of the LLVPs, focusing on density and CMB topography. Specifically, I will discuss how the spatial extent of any dense material may be restricted to smaller regions within the LLVPs. In addition, I will discuss our current knowledge of the large-scale CMB topography, which also holds clues as to what the LLVPs may be. Furthermore, I will demonstrate that including CMB topography reconciles recent density studies based on normal modes and Earth’s tides, while the presence of a dense layer is also able to explain both data types. Together, these results point towards primarily thermal LLVPs with deep, spatially confined dense material present at their base.
Imaging Stratified Structures Using Spectral and Temporal Autocorrelations of Earthquake-based Data: Applications to Antarctic Grounded and Floating Ice Caps
Presenting Author: Thanh-Son Phạm, The Australian National University
Authors: Thanh-Son Phạm, The Australian National University; Hrvoje Tkalčić, The Australian National University
The use of passive seismic data to study structures of the ice covers in some parts of the Earth has been limited. This is either due to the lack of over-ice seismic deployment or the inefficiency of existing seismic methodology in the presence of shallow but sharp stratifications often encountered in the icy environment or sedimentary basin. In this talk, we present a new class of single-component only analysis, the autocorrelation method, to image stratified ice structures beneath individual receivers. Instead of using continuous ambient noise records, we advocate the use of P-wave coda – the waveform records immediately following the P arrival at steep incidence – from distant earthquakes to obtain high-resolution images of the ice structures. The method is then demonstrated using seismic data from four major over-ice arrays in Antarctica in the last two decades. The stacked autocorrelogram in time domain over earthquakes is deployed to obtain distinct reflection signals from the ice-rock interface beneath grounded ice stations. Meanwhile, at stations deployed over a floating ice shelf, where a significant water layer must be included into the problem, the stacked autocorrelogram in the spectral domain, which is equivalent to the temporal autocorrelation via a Fourier transform, proves to be effective to obtain thickness of both ice and water layers. Because the autocorrelation method presented here requires single-component recordings only, it has great potential in cryoseismological studies, or imaging applications at places with limited logistical access, such as in space missions to icy planets, polar regions or in ocean-bottom deployments.
Imaging Yellowstone’s Crustal Magmatic System Using Ambient Noise Correlations
Presenting Author: Ross Maguire, Michigan State University, University of New Mexico
Authors: Ross Maguire, Michigan State University, University of New Mexico; Brandon Schmandt, University of New Mexico; Min Chen, Michigan State University; Chengxin Jiang, Australian National University; Justin Wilgus, University of New Mexico
Seismic tomography has provided key constraints on the geometry and melt fraction of Yellowstone’s magmatic system and can provide insight into where Yellowstone is in its volcanic life cycle. Recent tomography studies depict seismic wavespeed reductions consistent with a melt-poor, non-mobile, crustal magma reservoir with melt fractions of roughly 10% or less. However, the seismically determined melt fraction may be underestimated due to imperfect data coverage, inversion regularization and inaccurate forward modeling. Additionally, evidence from teleseismic body wave modeling hints that the melt fraction could be much higher than 10%. Here, we use 3D spectral element waveform modeling to illustrate that traditional surface wave tomography can dramatically underestimate the amplitude of seismic anomalies associated with a melt-rich reservoir, which could lead to misinterpretation of the volume and mobility of melt present in Yellowstone’s crustal reservoir. To overcome these limitations, we are developing a new model based on adjoint ambient noise tomography, which incorporates more realistic wave propagation physics compared with traditional inversion techniques. We present a new Vs model of Yellowstone’s crust and uppermost mantle which will serve as our starting point for an adjoint inversion. The model uses the most complete set of ambient noise cross-correlations assembled for the Yellowstone region thus far, including over 2000 distinct source-receiver pairs with exceptional signal-to-noise ratio. Key features of the model, such as the amplitude and geometry of the low wavespeed anomaly below the Yellowstone caldera, agree well with past studies based on body and surface wave tomography. Adjoint inversion of our ambient noise cross-correlation dataset will likely sharpen these features which should help improve our understanding of the melt fraction and distribution in Yellowstone’s magmatic system.
Matrix Approach Applied to Passive Seismic Data
Presenting Author: Rita Touma, ISTerre, Université Grenoble Alpes, Institut Langevin, ESPCI Paris, PSL University, CNRS
Authors: Rita Touma, ISTerre, Université Grenoble Alpes, Institut Langevin, ESPCI Paris, PSL University, CNRS; Thibaud Blondel, Institut Langevin, ESPCI Paris, PSL University, CNRS; Arnaud Derode, Institut Langevin, ESPCI Paris, PSL University, CNRS; Michel Campillo, ISTerre, Université Grenoble Alpes; Alexandre Aubry, Institut Langevin, ESPCI Paris, PSL University, CNRS
To understand fault systems, it is required to identify the structure of the crust and upper mantle. Seismic investigations have long been relying on active sources generating an incident wave-field from the Earth surface. The reflected wave-field is then recorded by sensors deployed at the surface. Nowadays, passive imaging has been adopted as an alternative of this source-receiver configuration by computing the correlations of ambient noise. This process allows to estimate the Green’s function between two receivers. We here present a passive imaging technique applied to data recorded along the Clark Branch of the San Jacinto Fault Zone (Ben-Zion et al., 2015). Inspired by previous works in optics and acoustics, we introduce a matrix approach of seismic imaging based on seismic noise cross correlations. Our method applies focusing operations at emission and reception (Blondel et al., 2018) allowing to project the reflection matrix recorded at the surface to depth (redatuming). Although seismic noise is dominated by surface waves, focusing operations allow to extract the body wave components that carry information about the reflectivity of in-depth structures. However, complex velocity distribution of the Earth’s crust results in phase distortions, referred to as aberrations in the imaging process. Phase distortions prevent the imaging of the true reflectivity of the subsurface leading to unphysical features and blurry images. To overcome these issues, we introduce a new operator: the so-called distortion matrix. It connects any virtual source induced by focusing at emission with the distorted part of the reflected wave-front in the spatial Fourier domain. A time-reversal analysis of the distortion matrix is applied to correct for high-order aberrations. A 3D image of the fault is retrieved with optimal resolution and contrast. The damage volume extends from the surface to the first few hundred meters. Strata layers located at depths on both sides of the fault were also identified.