Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

Abstract

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

Bayesian seismic tomography using normalizing flows

“…We can use any finite set of such samples to approximate statistical properties such as methods have long been applied to solve inverse problems (Press 1968;Anderssen & Seneta 1971;Mosegaard & Tarantola 1995;Sambridge 1999;Malinverno 2002). In the more sophisticated Reversible Jump McMC (rj-McMC -Green 1995;Green & Mira 2001;Green 2003), the parametrization including dimensionality of the parameter vector is also treated as unknown and is constrained by the data during inversion (Bodin & Sambridge 2009;Bodin et al 2012;Galetti et al 2015Galetti et al , 2017Zhang et al 2018Zhang et al , 2020. This can lead to huge gains in efficiency by reducing dimensionality to only parameters that are justifiably necessary to explain the data.…”

Bayesian Seismic Tomography using Normalizing Flows

“…Mosegaard and Tarantola (1995) first introduced McMC to geophysical community, after which the method was applied widely to solve seismic inverse problems (Sambridge, 1999;Malinverno, 2002). In the more sophisticated Reversible Jump McMC (rj-McMC -Green, 1995;Green & Mira, 2001;Green, 2003), the parametrization including dimensionality of the parameter vector is also treated as unknown and is constrained by the data during inversion (Bodin & Sambridge, 2009;Bodin et al, 2012;Galetti et al, 2015Galetti et al, , 2017Zhang et al, 2018Zhang et al, , 2020. This can lead to huge gain in efficiency by reducing dimensionality to only parameters that are justifiably necessary to explain the data.…”

Surface wave dispersion inversion using an energy likelihood function

“…Thus the parameterization of the seismic velocity model can itself be determined by the data and prior information. The method has been applied in a range of geophysical applications [24,3,46,13,34,14,52,53,54]. In this study we use the method to solve the surface wave dispersion inversion problem.…”