Crustal Structure Beneath Mexico City From Joint Inversion of Receiver Functions and Dispersion Curves

Over the years, it has been clear to seismologists that the crustal structure beneath Mexico City is highly complex. This fact may influence the seismic waves' behavior when traveling through it. Consequently, many studies have been done to understand this crustal structure. This article presents a high-resolution S-wave velocity model obtained from joint inversion of radial receiver functions and group-velocity dispersion curves beneath Mexico City (VMRFDC). This model includes azimuthal variations observed with receiver functions, which may be interpreted as crustal discontinuities. Receiver functions were retrieved from velocity and acceleration records, and dispersion curves were collected from a previous tomographic study. We performed a geostatistical analysis that revealed a horizontal spatial correlation of the crustal structure for up to 7.5 km. This distance was obtained from the S-wave velocity omnidirectional semivariogram range per depth. Using ordinary kriging, we utilized this spatial correlation to estimate S-wave velocities per depth and introduced two novel techniques: a resolution test for kriging and a geostatistical-dependent smoothing technique. Finally, we compare our results with previous works, making our model consistent with geological and geophysical features reported in the literature.

Automated summary

Over the years, seismologists have recognized the complex crustal structure beneath Mexico City, which can affect the behavior of seismic waves. This study presents a high-resolution S-wave velocity model obtained by combining radial receiver functions and group-velocity dispersion curves. The obtained model includes azimuthal variations that indicate crustal discontinuities. Through geostatistical analysis, the horizontal spatial correlation of the crustal structure was determined, and two innovative techniques were introduced for estimating S-wave velocities and improving the accuracy of the model.