Conventional geophysical radar measurements are ground-based, termed as ground penetrating radar (GPR). GPR data acquisitions are usually time-consuming, restricting their applications for the environmental monitoring of large areas (e.g., mega-farms). In addition, the conventional GPR processing methods are not intelligent, not allowing real-time investigations of environmental issues. Dr. Askari's team with the collaboration of Prof. Zekavat’s team from the Worcester Polytechnic Institute, Worcester, MA is developing an enhanced drone-based radar, in which real-time measurements become feasible through machine learning techniques. This research aims at estimating soil moisture to the root zone for mega farms. Using soil moisture maps obtained from our method, we can optimize irrigation for big farms. This research is funded by the United States Department of Agriculture (USDA). 

The near S-wave velocity is a critical geophysical property used to determine fundamental geotechnical parameters such as soil’s shear modulus, natural frequency, and effective vertical stress. The S-wave’s anisotropy can be used to identify near-surface fractures and characterize groundwater flows. In near-surface studies, the surface wave method (SWM) has been greatly used to estimate the S-wave velocity. SWM uses the dispersion properties (either phase or group velocity) of Love or Rayleigh waves to obtain an S-wave velocity model of the subsurface via an inversion procedure. My team has conducted extensive research to develop novel approaches to enhance the processing and penetration depth of SWM with a focus on environmental applications.

Currently, my team is working on ultrahigh frequency SWM (frequency range from 100 Hz to a few kilohertz). The ultrahigh-frequency SWM will allow super high-resolution S-wave velocity imaging up to 1m below the surface, which will have broad applications in agriculture and forestry.

In Jeng et al., we assessed the capability of SWM to forecast near-surface fractures within low-permeable bedrocks. We showed that by optimizing data acquisition and incorporating higher modes of the Rayleigh and Love wave phase velocities in inversion, it is possible to study the S-wave velocity within the bedrock (i.e., 0-90 m below the surface). Moreover, by considering anisotropic inversion, we calculated seismic radial anisotropy of subsurface layers. Seismic radial anisotropy is defined concerning the difference between the velocity of a vertically polarized S-wave (SV, from the dispersion analysis of the Rayleigh waves) and one polarized horizontally (SH, from the dispersion analysis of the Love wave). We evaluated the correlation of seismic radial anisotropy with near-surface fractures at two sites with different bedrock lithologies (one metamorphic-igneous and the other sedimentary). The seismic radial anisotropies at these two sites showed a strong correlation with near-surface fractures, and thus, can be used as a strong attribute to identify near-surface fractures.

Given the strong correlation of  seismic radial anisotropy with subsurface fractures, in Chatterjee et al., we showed that it can be used as secondary information in stochastic simulations to forecast near-surface fractures. We developed a pixel-based multiple-point simulation algorithm that borrows information from the training image of the fracture and anchors it within the secondary radial anisotropy data using a wavelet-based multiple-point simulation framework. We successfully applied our method to the radial anisotropy data from Jeng et al. to forecast fractures in the two bedrock aquifers.

In Esfahani et al., we developed an optimized method to estimate the group velocity of surface waves. Accurate estimation of either phase or group velocity of surface waves is a critical step in SWM. In near-surface studies, the conventional approach utilizes the phase velocity of surface waves in inversion. Although many studies have shown the advantages of using group velocity in inversion, the estimation of the group velocity is not straightforward due to the uncertainties of selecting an optimum Gaussian window of frequencies. We introduced a new approach for the estimation of the group velocity using the sparse S transform (SST) and sparse linear Radon transform (SLRT). In SST, the width of the Gaussian window is optimally calculated, and thus, it is robust to estimate the group velocity for any frequencies or velocity ranges. Using both synthetic and field data, we demonstrated the capability of our approach to calculate high-resolution distinguishable dispersion curves of the group velocity.