We present jointly inverted models of P-wave velocity (Vp) and electrical resistivity for a two-dimensional profile centered on the San Andreas Fault Observatory at Depth (SAFOD). Significant structural similarity between main features of the separately inverted Vp and resistivity models is exploited by carrying out a joint inversion of the two datasets using the normalized cross-gradient constraint. This constraint favors structurally similar Vp and resistivity images that adequately fit the seismic and magnetotelluric (MT) datasets. The new inversion code, tomoDDMT, merges the seismic inversion code tomoDD and the forward modeling and sensitivity kernel subroutines of the MT inversion code OCCAM2DMT. TomoDDMT is tested on a synthetic dataset and demonstrates the code’s ability to more accurately resolve features of the input synthetic structure relative to the separately inverted resistivity and velocity models. Using tomoDDMT, we are able to resolve a number of key issues raised during drilling at SAFOD. We are able to infer the distribution of several geologic units including the Salinian granitoids, the Great Valley sequence, and the Franciscan Formation. The distribution and transport of fluids at both shallow and great depths is also examined. Low values of velocity/resistivity attributed to a feature known as the Eastern Conductor (EC) can be explained in two ways: the EC is a brine-filled, high porosity region, or this region is composed largely of clay-rich shales of the Franciscan. The Eastern Wall, which lies immediately adjacent to the EC, is unlikely to be a fluid pathway into the San Andreas Fault’s seismogenic zone due to its observed higher resistivity and velocity values.
Additional publication details
|Publication Subtype||Journal Article|
|Title||Joint inversion of seismic and magnetotelluric data in the Parkfield Region of California using the normalized cross-gradient constraint|
|Series title||Pure and Applied Geophysics|
|Contributing office(s)||Crustal Geophysics and Geochemistry Science Center|
|Other Geospatial||Parkfield Region|
|Google Analytic Metrics||Metrics page|