  Subsurface applications including geothermal, geological carbon
sequestration, oil and gas, etc., typically involve maximizing either the
extraction of energy or the storage of fluids. Characterizing the subsurface is
extremely complex due to heterogeneity and anisotropy. Due to this complexity,
there are uncertainties in the subsurface parameters, which need to be
estimated from multiple diverse as well as fragmented data streams. In this
paper, we present a non-intrusive sequential inversion framework, for
integrating data from geophysical and flow sources to constraint subsurface
Discrete Fracture Networks (DFN). In this approach, we first estimate bounds on
the statistics for the DFN fracture orientations using microseismic data. These
bounds are estimated through a combination of a focal mechanism (physics-based
approach) and clustering analysis (statistical approach) of seismic data. Then,
the fracture lengths are constrained based on the flow data. The efficacy of
this multi-physics based sequential inversion is demonstrated through a
representative synthetic example.
