Bayesian Monte Carlo Inversion Research Articles

Amphibious Shear Wave Structure Beneath the Alaska‐Aleutian Subduction Zone From Ambient Noise Tomography

AbstractI present a 3‐D isotropic shear wave velocity model of the crust and uppermost mantle beneath the Alaska‐Aleutian subduction zone offshore of the Alaska Peninsula, based on seismic data recorded by the Alaska Amphibious Community Seismic Experiment (AACSE) array and some other networks. The model derives from Rayleigh wave phase speed measurements extracted from ambient seismic noise. A new three‐station interferometry (Zhang et al., 2020) approach is applied to improve the data coverage of ambient noise surface waves. Based on the ambient noise Rayleigh wave dispersion data, a Bayesian Monte Carlo inversion is performed to produce the shear wave velocity model. There are several prominent structures captured by the model, including: (1) The major sedimentary basins across the study region are identified by model. (2) Crustal thickness estimates are related with the geological structures. (3) The imaged slab edge is consistent with both the Slab 2.0 model (Hayes et al., 2018) and earthquake locations. And lots of geological and tectonic features related to subduction zone are captured, including the serpentinized forearc and partial melting zone beneath the Aleutian arc volcanoes. (4) Near the Shumagin gap, reduction in Vs is observed at the uppermost part of the incoming Pacific plate, consistent with the active source study of Shillington et al. (2015). The Vs reduction reflects hydration of the oceanic plate which could be related to local seismicity variation.

High‐Resolution Crustal and Uppermost Mantle Structure Beneath Central Mongolia From Rayleigh Waves and Receiver Functions

AbstractIn this study, I present a high‐resolution 3‐D Vs model of the crust and uppermost mantle beneath central Mongolia, based on seismic data recorded at the Central Mongolia Seismic Experiment array and some other networks. The model is constructed by a Bayesian Monte Carlo inversion that jointly interprets Rayleigh wave phase speeds and receiver functions. The Rayleigh wave dispersion measurements are derived from both ambient noise and teleseismic earthquake data. To improve ambient noise data coverage, a recently developed three‐station interferometry (Zhang et al., 2020, https://doi.org/10.1093/gji/ggaa046) technique is applied to produce high‐quality three‐station interferograms that augment the ambient noise data base. There are several prominent geological and tectonic features captured by the model, including (1) the Khövsgöl rift system is imaged as a high‐speed anomaly in the upper crust, whose southern edge follows the Bulnay fault. (2) The estimated crustal thickness map identifies relatively thick crust beneath the Khövsgöl rift system, the Hangay Dome, and the Gobi‐Altai Mountains. (3) A crustal high‐velocity anomaly is imaged beneath the Taryatu‐Chulutu volcanic field, which is interpreted as a solidified intrusive magma body. (4) Dispersed low‐velocity anomalies in the mantle identify locations with possible partial melting. The melting zones could be related to small‐scale asthenospheric upwelling and convective removal of lithosphere, which produces uplift of the Hangay Dome.

High-resolution Bayesian spatial autocorrelation (SPAC) quasi-3-D Vs model of Utah FORGE site with a dense geophone array

SUMMARY We expand the application of spatial autocorrelation (SPAC) from typical 1-D Vs profiles to quasi-3-D imaging via Bayesian Monte Carlo inversion (BMCI) using a dense nodal array (49 nodes) located at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site. Combinations of 4 and 9 geophones in subarrays provide for 36 and 25 1-D Vs profiles, respectively. Profiles with error bars are determined by calculating coherency functions that fit observations in a frequency range of 0.2–5 Hz. Thus, a high-resolution quasi-3-D Vs model from the surface to 2.0 km depth is derived and shows that surface-parallel sedimentary strata deepen to the west, consistent with a 3-D seismic reflection survey. Moreover, the resulting Vs profile is consistent with a Vs profile derived from distributed acoustic sensing (DAS) data located in a borehole at the FORGE site. The quasi-3-D velocity model shows that the base of the basin dips ∼22° to the west and topography on the basement interface coincident with the Mag Lee Wash suggests that the bedrock interface is an unconformity.

A Bayesian Monte Carlo inversion of spatial auto-correlation (SPAC) for near-surface Vs structure applied to both broad-band and geophone data

SUMMARY We propose a new Bayesian method to reveal the Vs structure of the near surface of the earth using spatial autocorrelation (SPAC) functions and apply this new method to synthetic, broad-band and geophone data sets. The principle of SPAC is introduced, and an implementation of the Bayesian Monte Carlo inversion (BMCI) for modelling SPAC coherency functions is described. To demonstrate its effectiveness, BMCI is applied to synthetic tests, data from 14 SPAC array sites in the Salt Lake Valley (SLV), Utah, and two arrays (one broad-band and one geophone) located in south central Utah. The Vs models derived from previous SPAC analysis of the 14 SLV sites differ by 10 per cent at most from those determined by BMCI and lie within uncertainties determined for the BMCI models. These agreements demonstrate the effectiveness of the BMCI method. The synthetic tests and applications to the SLV SPAC data show BMCI has great potential to resolve Vs structure down to at least 400 m. To achieve resolution for deeper Vs structure, longer duration deployments, wider array apertures, and additional seismometers or geophones can be used. Additionally, when the target frequencies are greater than 0.1 Hz, there is no apparent disadvantage in using geophone data for BMCI compared to broad-band data. Most significantly, BMCI places a quantifiable constraint on the uncertainties of the Vs models as well as Vs30.