Implications of Laterally Varying Scattering Properties for Subsurface Monitoring With Coda Wave Sensitivity Kernels: Application to Volcanic and Fault Zone Setting

Abstract

Monitoring changes of seismic properties at depth can provide a first‐order insight into Earth’s dynamic evolution. Coda wave interferometry is the primary tool for this purpose. This technique exploits small changes of waveforms in the seismic coda and relates them to temporal variations of attenuation or velocity at depth. While most existing studies assume statistically homogeneous scattering strength in the lithosphere, geological observations suggest that this hypothesis may not be fulfilled in active tectonic or volcanic areas. In a numerical study we explore the impact of a non‐uniform distribution of scattering strength on the spatio‐temporal sensitivity of coda waves. Based on Monte Carlo simulation of the radiative transfer process, we calculate sensitivity kernels for three different observables, namely travel‐time, decorrelation, and intensity. Our results demonstrate that laterally varying scattering properties can have a profound impact on the sensitivities of coda waves. Furthermore, we demonstrate that the knowledge of the mean intensity, specific intensity, and energy flux, governed by spatial variation of scattering strength, is key to understanding the decorrelation, travel‐time, and scattering kernels, respectively. A number of previous works on coda wave sensitivity kernels neglect the directivity of energy fluxes by employing formulas extrapolated from the diffusion approximation. In this work, we demonstrate and visually illustrate the importance of the use of specific intensity for the travel‐time and scattering kernels, in the context of volcanic and fault zone setting models. Our results let us foresee new applications of coda wave monitoring in environments of high scattering contrast.

Continuous isolated noise sources induce repeating waves in the coda of ambient noise correlations

“…Note that repeating direct waves repeatedly travel the same path. A similar effect occurs in the presence of a strong nearby scatterer (van Dinther et al, 2021). As the multiply scattered part of the correlation wavefield reaches the strong scatterer, spatial sensitivity focuses along the path between stations and scatterer.…”

“…Monitoring applications, on the other hand, rely on estimating relative velocity changes by repeatedly computing correlation wavefields throughout time and measuring changes in the arrival time of their coda (Wegler and Sens-Schönfelder, 2007;Sens-Schönfelder and Larose, 2010). Current strategies often rely on the assumption that the coda of a given correlation wavefield is comprised of multiply scattered waves, originating from the master station, which also dictates its spatial sensitivity (Planès et al, 2014;Margerin et al, 2016;van Dinther et al, 2021). If the spatial sensitivity of the coda is known, seismic velocity changes can be located (Obermann et al, 2014;Mao et al, 2022).…”

Imaging of Small‐Scale Heterogeneity and Absorption Using Adjoint Envelope Tomography: Results From Laboratory Experiments

“…The concept of AET is in full analogy to the adjoint method in waveform tomography (Fichtner et al, 2006;Tarantola, 1984;Tromp et al, 2005). The inversion of seismogram envelopes for heterogeneity and absorption models starts with the definition of the misfit function that quantifies how well the model predictions match the observed data.…”

“…Combined with seismic interferometry of ambient noise, this has been used for continuous monitoring of subtle changes in volcanoes (Sens‐Schönfelder & Wegler, 2006), fault zones (Brenguier et al., 2008), environmentally stressed areas (Sens‐Schönfelder & Eulenfeld, 2019), and even assessing groundwater storage (Illien et al., 2021; Mao et al., 2022). The spatial sensitivity of this coda wave‐based monitoring, however, depends on the distribution of the heterogeneity that generates the coda waves (T. Zhang et al., 2021; van Dinther et al., 2021).…”

“…Instead of the diffusion constant and dissipation used in the diffusion model, the spatial distribution of fluctuation strength ɛ in the random medium and the intrinsic quality factor Q −1 describe the spatial variability of scattering and absorption. The simulation of energy propagation with spatially variable properties using RTT allowed us to introduce the adjoint method initially developed in FWI (Fichtner et al, 2006(Fichtner et al, , 2010Tarantola, 1984;Tromp et al, 2005) for the imaging of scattering and absorption properties with scattered waves (T. Zhang & Sens-Schönfelder, 2022). ZHANG ET AL.…”