Erica Galetti

Constructing new seismograms from old earthquakes: Retrospective seismology at multiple length scales

If energy emitted by a seismic source such as an earthquake is recorded on a suitable backbone array of seismometers, source-receiver interferometry (SRI) is a method that allows those recordings to be projected to the location of another target seismometer, providing an estimate of the seismogram that would have been recorded at that location. Since the other seismometer may not have been deployed at the time at which the source occurred, this renders possible the concept of “retrospective seismology” whereby the installation of a sensor at one period of time allows the construction of virtual seismograms as though that sensor had been active before or after its period of installation. Here we construct such virtual seismograms on target sensors in both industrial seismic and earthquake seismology settings, using both active seismic sources and ambient seismic noise to construct SRI propagators, and on length scales ranging over 5 orders of magnitude from ∼40 m to ∼2500 km. In each case we compare seismograms constructed at target sensors by SRI to those actually recorded on the same sensors. We show that spatial integrations required by interferometric theory can be calculated over irregular receiver arrays by embedding these arrays within 2-D spatial Voronoi cells, thus improving spatial interpolation and interferometric results. The results of SRI are significantly improved by restricting the backbone receiver array to include approximately those receivers that provide a stationary-phase contribution to the interferometric integrals. Finally, we apply both correlation-correlation and correlation-convolution SRI and show that the latter constructs fewer nonphysical arrivals.

Mapping the material microstructure of safety critical components using ultrasonic phased arrays

Traditional imaging algorithms within the ultrasonic NDE community typically assume that the material being inspected is homogeneous. Obviously, when the medium is of a heterogeneous or anisotropic nature this assumption can contribute to the poor detection, sizing and characterisation of defects. Knowledge of the internal structure and properties of the material would allow corrective measures to be taken. The work presented here endeavours to reconstruct coarsened maps of the locally anisotropic grain structure of industrially representative samples from ultrasonic phased array data. This is achieved via application of the reversible-jump Markov Chain Monte Carlo (rj-MCMC) method: an ensemble approach within a Bayesian framework. The resulting maps are used in conjunction with the total focussing method and the reconstructed flaws are used as a quantitative measure of the success of this methodology. Using full matrix capture data arising from a finite element simulation of a phased array inspection of an austenitic weld, a 71% improvement in flaw location and an 11dB improvement in SNR is achieved using no a priori knowledge of the materials internal structure.

Seismic Attenuation from Ambient Noise across the North Sea Ekofisk Permanent Array

Quality factor (Q) or equivalently attenuationα 1⁄4 1 Qdescribes the amount of energy lost per cycle as a wave travels through a medium. This is important to correct seismic data amplitudes for near-surface effects, to locate subsurface voids or porosity, to aid seismic interpretation, or for characterizing other rock and fluid properties. Seismic attenuation can be variable even when there are no discernible changes in seismic velocity or density (Yıldırım et al., 2017, https://doi.org/10.1016/j.jappgeo.2016.11.010) and so provides independent information about subsurface heterogeneity. This study uses ambient noise recordings made on the Ekofisk Life of Field Seismic array to estimate Q structure in the near surface. We employ the method of X. Liu et al. (2015, https://doi.org/10.1093/gji/ggv357), which uses linear triplets of receivers to estimate Q—ours is the first known application of the method to estimate the Q structure tomographically. Estimating Q requires an estimate of phase velocity which we obtain using the method of Bloch and Hales (1968, https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/58/3/1021/116607/) followed by traveltime tomography. The Q structure at Ekofisk has features which can be related to local geology, showing that surface ambient noise recordings may provide a new and robust method to image Q. Our results suggest that there is a nonlinear relationship between Q and compression. They also may explain why it has been found that in the period range of 1 to 2 s considered here, ambient noise cross correlations along paths that span the North Sea Basin are unreliable: Such Q values would attenuate almost all ambient seismic energy during such a traverse.

Transdimensional Monte Carlo Electrical Resistivity Tomography

The ability to accurately assess and estimate the uncertainty of the solution to an inverse problem is an important aspect of geophysical inversion. Within this paper, we present a stochastic inversion method for electrical resistivity tomography (ERT) which makes use of Bayesian theory, the reversible-jump Markov chain Monte Carlo algorithm, and model parameterisation with Voronoi cells, to produce an ensemble of valid solutions which are distributed according to the posterior probability density function. By solving the forward problem at each Markov chain iteration and allowing the model cells to vary in number, shape and size throughout the inversion, we ensure that the physics of the forward problem is never linearised, and hence that any parametrisation- and modelling-related bias is naturally reduced to a minimum. In addition, being fully non-linear, this method provides an accurate representation of subsurface resistivity structures as well as a measure of their associated uncertainties. Within this paper, we introduce the theory and method behind our inversion algorithm and present an example of its application to a synthetic dataset. We also benchmark our results by comparing them to those obtained from a more traditional, iterated-linearised inversion scheme.

To: “Introduction to special section: Ambient noise,” Sjoerd de Ridder, Florent Brenguier, Farnoush Forghani, Erica Galetti, Nori Nakata, and Cornelis Weemstra, Interpretation, 4, no. 3, SJi, doi: 10.1190/INT-2016-0627-SPSEINTRO.1.

The author names for the following two summaries originally appeared as Pascal and Yuan and Pascal and Halliday , but they should have read: Edme and Yuan formulate a novel acquisition and processing technique to derive surface-wave dispersion curves from seismic ambient noise. The authors show

Introduction to special section: Ambient noise

Many efforts of geophysical processing have traditionally been devoted to the separation, attenuation, and elimination of noise from seismic acquisition data. However, one geophysicist’s noise is another geophysicist’s signal. [Aki (1957)][1] formulated the spatial autocorrelation method, which