MODELLING THE SHALLOW CRUSTAL STRUCTURE OF THE GUERRERO SEISMIC GAP FROM GEODETIC DATA
The subduction zone in the Mexican Pacific comprises a complex tectonic environment of interacting convergent active margin plates. Along the Middle American Trench (MAT), the Cocos and Rivera plates subduct beneath the North American and Caribbean plates, generating an irregular distribution of seismicity due to variations in the subduction angle of the slab. The Guerrero Seismic Gap (GGap) is a ~140 km segment at the Cocos-North America plate boundary. Since 1911 there has been no record of a large subduction thrust earthquake in the NW portion of the GGap, and taking into account the seismic evolution and subduction dynamics, specialists see a possible scenario of a Mw ~8.2 earthquake in the area. Therefore, understanding the nature of the rupture process in the crust is a fundamental question of this study.
Gravity techniques are accurate methods to investigate the crustal configuration and define the structure in the subducting slab. In this project, data from the global satellite model of Sandwell et al., 2014, we intend to generate a model of the density distribution in the shallow crust in the GGap area. The shallow crust is of particular interest because lateral heterogeneity in the slab zone modifies subduction dynamics. These heterogeneities comprise seafloor structures (e.g., seamounts) and may be key to studying the seismogenic zone of the Mexican subduction and better assessing its risk. Some studies show how the bathymetric relief on the seafloor, when it enters subduction, modifies the mechanical properties at the interface between the subducting plate and the overriding plate. This arrangement can be an important factor because it can affect the distribution of large earthquakes.
Gravimetric inversion methods can solve subsurface mapping problems by determining the density and/or depth of the layers that comprise it. Here, we will use statistical methods of gravity inversion to relate the parameters (density) to the observed data. To do this, we will use a Bayesian approach, defining our likelihood functions, evaluating the forward map, and our prior function, smoothed using Markov Random Fields, which restricts the field values to their neighboring dependencies.
The objective of this work is to conduct a detailed analysis of the surface crust, its configuration, and its relationship with seismicity in the area, in order to improve understanding of subduction dynamics and seismic risk assessment.