ORCID Profile
0000-0003-2617-3420
Current Organisations
Fleet Space Technologies
,
University of Oxford
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Oxford University Press (OUP)
Date: 16-02-2023
DOI: 10.1093/GJI/GGAD074
Abstract: The spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point processes continue to provide an important tool for analysing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogues. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time-varying seismicity rates, we introduce a deep Gaussian process (GP) formulation for the background rate as an extension to ETAS. GPs are a robust non-parametric model for function spaces with covariance structure. By conditioning the length-scale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently s led by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Matérn GPs. We illustrate our method using synthetic ex les, and show that the deep-GP-ETAS model successfully captures multiscale temporal behaviour in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA 2019 July 5 Mw 7.1 event catalogue, showing that deep-GP-ETAS can successfully characterize a classical aftershock sequence and the 2016–2019 Cahuilla, CA earthquake swarm, which shows two distinct phases of aseismic forcing concordant with a fluid injection-driven initial sequence, arrest of the fluid along a physical barrier and release following the largest Mw 4.4 event of the sequence.
Publisher: American Geophysical Union (AGU)
Date: 30-12-2021
DOI: 10.1029/2021GL096503
Abstract: Earthquake ground motion depends strongly on near‐surface structure, which is challenging to image in urban areas at high resolution. Distributed acoustic sensing (DAS) is an emerging technique that provides a scalable solution by converting preexisting fiber‐optic cables into dense seismic arrays. After the July 2019 M7.1 Ridgecrest earthquake, we converted an underground dark fiber across the city of Ridgecrest, CA, into a DAS array. The recorded aftershocks show substantial lateral variability in site lification over only 8‐km in distance. To understand the cause of such variability, we used three months of continuous data, dominated by traffic‐generated seismic noise, to image near‐surface structure along the fiber path. We find that the lateral variations of earthquake shaking correlate well with the shallow shear velocity model at sub‐kilometer scales, in particular micro‐basins filled with soft sediments. These results highlight the great potential of DAS for high‐resolution seismic hazard mapping in urban areas.
Publisher: Springer Science and Business Media LLC
Date: 17-12-2015
DOI: 10.1038/SREP18416
Abstract: The core mantle boundary (CMB) separates Earth’s liquid iron outer core from the solid but slowly convecting mantle. The detailed structure and dynamics of the mantle within ~300 km of this interface remain enigmatic: it is a complex region, which exhibits thermal, compositional and phase-related heterogeneity, isolated pockets of partial melt and strong variations in seismic velocity and anisotropy. Nonetheless, characterising the structure of this region is crucial to a better understanding of the mantle’s thermo-chemical evolution and the nature of core-mantle interactions. In this study, we examine the heterogeneity spectrum from a recent P-wave tomographic model, which is based upon trans-dimensional and hierarchical Bayesian imaging. Our tomographic technique avoids explicit model parameterization, smoothing and d ing. Spectral analyses reveal a multi-scale wavelength content and a power of heterogeneity that is three times larger than previous estimates. Inter alia , the resulting heterogeneity spectrum gives a more complete picture of the lowermost mantle and provides a bridge between the long-wavelength features obtained in global S-wave models and the short-scale dimensions of seismic scatterers. The evidence that we present for strong, multi-scale lowermost mantle heterogeneity has important implications for the nature of lower mantle dynamics and prescribes complex boundary conditions for Earth’s geodynamo.
Publisher: Seismological Society of America (SSA)
Date: 15-01-2020
DOI: 10.1785/0220190266
Abstract: Historical seismic data are essential to fill in the gaps in geophysical knowledge caused by the low rate of significant seismic events. Handling historical data in the context of geophysical inverse problems requires special care, due to the large errors in the data collection process. Using Oldham’s data for the discovery of Earth’s core as a case study, we illustrate how a hierarchical Bayesian model selection methodology using leave-one-out cross validation can robustly and efficiently answer quantitative questions using even poor-quality geophysical data. We find that there is statistically significant evidence for the existence of the core using only the P-wave data that Oldham effectively discarded in his discussion.
Publisher: American Geophysical Union (AGU)
Date: 31-08-2022
DOI: 10.1029/2022GL099943
Abstract: The structure of the lowermost mantle and the core‐mantle boundary (CMB) has profound implications for Earth's evolution and current‐day dynamics. Whilst tomographic studies of V S show good agreement in the lowermost mantle, consensus as to V P and especially CMB radius has not yet been reached. We perform a hierarchical Bayesian inversion for V P in the lowermost 300 km of the mantle and the radius of the CMB using differential travel time data. Concurrent with finding V P perturbations of 0.56% RMS litude that spatially agree with previous studies in areas of low posterior variance, we find 4.5 km RMS litude CMB radius perturbations with a broadly north‐south hemispherical character, with spherical harmonic power evenly distributed between degrees 1–3. These results suggest that CMB radial processes are set by a longer scale process than the V P perturbations.
Publisher: Seismological Society of America (SSA)
Date: 10-09-2019
DOI: 10.1785/0120190187
Publisher: McGill University Library and Archives
Date: 11-08-2023
DOI: 10.26443/SEISMICA.V2I2.385
Abstract: Template matching has become a cornerstone technique of observational seismology. By taking known events, and scanning them against a continuous record, new events smaller than the signal-to-noise ratio can be found, substantially improving the magnitude of completeness of earthquake catalogues. Template matching is normally used in an array setting, however as we move into the era of planetary seismology, we are likely to apply template matching for very small arrays or even single stations. Given the high impact of planetary seismology studies on our understanding of the structure and dynamics of non-Earth bodies, it is important to assess the reliability of template matching in the small-n setting. Towards this goal, we estimate a lower bound on the rate of false positives for single-station template matching by examining the behaviour of correlations of totally uncorrelated white noise. We find that, for typical processing regimes and match thresholds, false positives are likely quite common. We must therefore be exceptionally careful when considering the output of template matching in the small-n setting.
Publisher: Seismological Society of America (SSA)
Date: 25-09-2017
DOI: 10.1785/0120170051
Publisher: American Geophysical Union (AGU)
Date: 28-01-2022
DOI: 10.1029/2021JB023103
Abstract: The proliferation of dense arrays promises to improve our ability to image geological structures at the scales necessary for accurate assessment of seismic hazard. However, combining the resulting local high‐resolution tomography with existing regional models presents an ongoing challenge. We developed a framework based on the level‐set method that infers where local data provide meaningful constraints beyond those found in regional models ‐ for ex le the Community Velocity Models (CVMs) of southern California. This technique defines a volume within which updates are made to a reference CVM, with the boundary of the volume being part of the inversion rather than explicitly defined. By penalizing the complexity of the boundary, a minimal update that sufficiently explains the data is achieved. To test this framework, we use data from the Community Seismic Network, a dense permanent urban deployment. We inverted Love wave dispersion and lification data, from the Mw 6.4 and 7.1 2019 Ridgecrest earthquakes. We invert for an update to CVM‐S4.26 using the Tikhonov Ensemble S ling scheme, a highly efficient derivative‐free approximate Bayesian method. We find the data are best explained by a deepening of the Los Angeles Basin with its deepest part south of downtown Los Angeles, along with a steeper northeastern basin wall. This result offers new progress toward the parsimonious incorporation of detailed local basin models within regional reference models utilizing an objective framework and highlights the importance of accurate basin models when accounting for the lification of surface waves in the high‐rise building response band.
Publisher: Oxford University Press (OUP)
Date: 11-08-2021
DOI: 10.1093/GJI/GGAB309
Abstract: We introduce a scheme for probabilistic hypocentre inversion with Stein variational inference. Our approach uses a differentiable forward model in the form of a physics informed neural network, which we train to solve the Eikonal equation. This allows for rapid approximation of the posterior by iteratively optimizing a collection of particles against a kernelized Stein discrepancy. We show that the method is well-equipped to handle highly multimodal posterior distributions, which are common in hypocentral inverse problems. A suite of experiments is performed to examine the influence of the various hyperparameters. Once trained, the method is valid for any seismic network geometry within the study area without the need to build traveltime tables. We show that the computational demands scale efficiently with the number of differential times, making it ideal for large-N sensing technologies like Distributed Acoustic Sensing. The techniques outlined in this manuscript have considerable implications beyond just ray tracing procedures, with the work flow applicable to other fields with computationally expensive inversion procedures such as full waveform inversion.
Publisher: Wiley
Date: 10-06-2022
Publisher: California Digital Library (CDL)
Date: 19-02-2023
DOI: 10.31223/X5G362
Abstract: Template matching has become a cornerstone technique of observational seismology. By taking known events, and scanning them against a continuous record, new events smaller than the signal-to-noise ratio can be found, substantially improving the magnitude of completeness of earthquake catalogues. Template matching is normally used in an array setting, however as we move into the era of planetary seismology, we are likely to apply template matching for very small arrays or even single stations. Given the high impact of planetary seismology studies on our understanding of the structure and dynamics of non-Earth bodies, it is important to assess the reliability of template matching in the small-n setting. Towards this goal, we estimate a lower bound on the rate of false positives for single-station template matching by examining the behaviour of correlations of totally uncorrelated white noise. We find that, for typical processing regimes and match thresholds, false positives are likely quite common. We must therefore be exceptionally careful when considering the output of template matching in the small-n setting.
Publisher: Oxford University Press (OUP)
Date: 21-10-2019
DOI: 10.1093/GJI/GGZ472
Abstract: Tomography is one of the cornerstones of geophysics, enabling detailed spatial descriptions of otherwise invisible processes. However, due to the fundamental ill-posedness of tomography problems, the choice of parametrizations and regularizations for inversion significantly affect the result. Parametrizations for geophysical tomography typically reflect the mathematical structure of the inverse problem. We propose, instead, to parametrize the tomographic inverse problem using a geologically motivated approach. We build a model from explicit geological units that reflect the a priori knowledge of the problem. To solve the resulting large-scale nonlinear inverse problem, we employ the efficient Ensemble Kalman Inversion scheme, a highly parallelizable, iteratively regularizing optimizer that uses the ensemble Kalman filter to perform a derivative-free approximation of the general iteratively regularized Levenberg–Marquardt method. The combination of a model specification framework that explicitly encodes geological structure and a robust, derivative-free optimizer enables the solution of complex inverse problems involving non-differentiable forward solvers and significant a priori knowledge. We illustrate the model specification framework using synthetic and real data ex les of near-surface seismic tomography using the factored eikonal fast marching method as a forward solver for first arrival traveltimes. The geometrical and level set framework allows us to describe geophysical hypotheses in concrete terms, and then optimize and test these hypotheses, helping us to answer targeted geophysical questions.
Publisher: Oxford University Press (OUP)
Date: 15-09-2020
DOI: 10.1093/GJI/GGAA397
Abstract: Bayesian methods, powered by Markov Chain Monte Carlo estimates of posterior densities, have become a cornerstone of geophysical inverse theory. These methods have special relevance to the deep Earth, where data are sparse and uncertainties are large. We present a strategy for efficiently solving hierarchical Bayesian geophysical inverse problems for fixed parametrizations using Hamiltonian Monte Carlo s ling, and highlight an effective methodology for determining optimal parametrizations from a set of candidates by using efficient approximations to leave-one-out cross-validation for model complexity. To illustrate these methods, we use a case study of differential traveltime tomography of the lowermost mantle, using short period P-wave data carefully selected to minimize the contributions of the upper mantle and inner core. The resulting tomographic image of the lowermost mantle has a relatively weak degree 2—instead there is substantial heterogeneity at all low spherical harmonic degrees less than 15. This result further reinforces the dichotomy in the lowermost mantle between relatively simple degree 2 dominated long-period S-wave tomographic models, and more complex short-period P-wave tomographic models.
Publisher: California Digital Library (CDL)
Date: 31-01-2022
DOI: 10.31223/X5F03K
Abstract: The proliferation of dense arrays promises to improve our ability to image geological structures at the scales necessary for accurate assessment of seismic hazard. However, combining the resulting local high-resolution tomography with existing regional models presents an ongoing challenge. We developed a framework based on the level-set method that infers where local data provide meaningful constraints beyond those found in regional models - e.g. the Community Velocity Models (CVMs) of southern California. This technique defines a volume within which updates are made to a reference CVM, with the boundary of the volume being part of the inversion rather than explicitly defined. By penalizing the complexity of the boundary, a minimal update that sufficiently explains the data is achieved. To test this framework, we use data from the Community Seismic Network, a dense permanent urban deployment. We inverted Love wave dispersion and lification data, from the Mw 6.4 and 7.1 2019 Ridgecrest earthquakes. We invert for an update to CVM-S4.26 using the Tikhonov Ensemble S ling scheme, a highly efficient derivative-free approximate Bayesian method. We find the data are best explained by a deepening of the Los Angeles Basin with its deepest part south of downtown Los Angeles, along with a steeper northeastern basin wall. This result offers new progress towards the parsimonious incorporation of detailed local basin models within regional reference models utilizing an objective framework and highlights the importance of accurate basin models when accounting for the lification of surface waves in the high-rise building response band.
Publisher: Oxford University Press (OUP)
Date: 10-2015
DOI: 10.1093/GJI/GGV361
Abstract: The problem of decomposing irregular data on the sphere into a set of spherical harmonics is common in many fields of geosciences where it is necessary to build a quantitative understanding of a globally varying field. For ex le, in global seismology, a compressional or shear wave speed that emerges from tomographic images is used to interpret current state and composition of the mantle, and in geomagnetism, secular variation of magnetic field intensity measured at the surface is studied to better understand the changes in the Earth's core. Optimization methods are widely used for spherical harmonic analysis of irregular data, but they typically do not treat the dependence of the uncertainty estimates on the imposed regularization. This can cause significant difficulties in interpretation, especially when the best-fit model requires more variables as a result of underestimating data noise. Here, with the above limitations in mind, the problem of spherical harmonic expansion of irregular data is treated within the hierarchical Bayesian framework. The hierarchical approach significantly simplifies the problem by removing the need for regularization terms and user-supplied noise estimates. The use of the corrected Akaike Information Criterion for picking the optimal maximum degree of spherical harmonic expansion and the resulting spherical harmonic analyses are first illustrated on a noisy synthetic data set. Subsequently, the method is applied to two global data sets sensitive to the Earth's inner core and lowermost mantle, consisting of PKPab-df and PcP-P differential traveltime residuals relative to a spherically symmetric Earth model. The posterior probability distributions for each spherical harmonic coefficient are calculated via Markov Chain Monte Carlo s ling the uncertainty obtained for the coefficients thus reflects the noise present in the real data and the imperfections in the spherical harmonic expansion.
Location: Australia
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Jack Muir.