Multi‐measurement integration for near‐surface geological characterization

Abstract

Geological characterization of the near‐surface is challenging because often shallow layers are unconsolidated and their properties have rapid and significant variations in vertical and lateral directions. This creates challenges for a majority of geophysical methods, which have limitations in the presence of a complex distribution of physical properties and that give optimal results over limited depth intervals, which are different for the different methods. The shallow near‐surface depth region is often problematic for standard seismic exploration methods. As a result, geological characterization through individual measurements is often compromised. The solution proposed here is the integration of multiple measurements of surface and deep subsurface properties, with the goal of generating a geologically meaningful lithostructural model of the near‐surface. The underlying idea is based on the observation that deep subsurface structural features often leave an impression on the surface. This provides an opportunity for structural mapping at the surface and below the bottom of the near‐surface zone to guide the processing and interpretation of data from the near‐surface. We show how the integration of multiple data sets from the measurement of different physical properties can be used to generate and enhance specific near‐surface products such as drilling risk maps or P‐wave velocity models for static corrections or for velocity modelling. The analysis proves that integration of multiple data sets can provide a solution when individual measurements, such as refraction traveltimes, do not provide sufficient information to characterize the lithostructure of the near‐surface. Satellite surface imagery and deep seismic data can provide the structural framework for the processing and interpretation of shallow seismic data. Simultaneous joint inversion of shallow P‐ and S‐wave data reveals the vertical and lateral velocity structures in the near‐surface, which are usually concealed when only refraction data are used. Finally, the correlation of multiple data sets in a correct spatial reference provides a quality control for the data set and allows for the lithostructural interpretation of data in the near‐surface.

Reconstruction, with tunable sparsity levels, of shear wave velocity profiles from surface wave data

“…Surface wave data are routinely used for the reconstruction of the elastic properties of the subsoil (in particular, the shear-wave velocity) at depths ranging from near-surface (Xia et al 1999;Laake & Strobbia 2012;Park et al 2017) to crustal scales (Dorman & Ewing 1962;Beucler et al 2003;Ekström 2011;Ars et al 2019). These data, usually, consist of dispersion curves extracted from the seismograms through, for example, integral transforms (Vignoli & Cassiani 2010;Vignoli et al 2011;Pan et al 2019).…”

Model‐based attenuation for scattered dispersive waves

“…it appears that some of the diffractions are related to sharp variations of the surface‐wave velocity: in other words, some near‐surface boundaries correspond to a variation of surface‐wave velocity and they also produce scattering. The near‐surface velocity model, together with remote‐sensing derived surface geomorphology (Laake and Strobbia ), can identify topographic irregularities, lithological boundaries and structural features: however a deterministic prediction of the scattering is not realistically possible. The scale of the features that can generate scattering is very small, beyond the resolution of the velocity model; some relevant features, like cracks, do not have individually an effect on the velocity, they only have a combined effect on it (Pecorari ).…”