With a focus on geo-imaging applications for the energy transition, we are looking for affordable, but still accurate seismic imaging methodologies. One of those recently developed methods is Joint Migration Inversion, which involves the joint estimation of the seismic reflectivity image and the background propagation velocity model. This method operates in the frequency domain and is based on recursive wavefield propagation, while including all scattering and transmission effects. The involved full wavefield modeling engine is the most time-consuming part of the JMI process, so accelerating this has direct impact on the overall costs. One option is making use of the fact that the modeling can be done independently per frequency component, such that we can model the data for a subset of these frequencies and use interpolation to obtain the data at missing frequencies. This papers studies the use of a neural network (NN) approach for this interpolation process. We investigate the accuracy of the interpolation process under different sub-sampling ratios and using regular or irregular subsampling. The counter-intuitive result is that regular subsampling gives slightly better results. Moreover, we demonstrate that we can go down to 66% missing frequencies with the currently used NN based on the cGAN approach.

Seismic Imaging (SI) survey design for onshore applications faces challenges such as accessibility and poor data quality due to unexpected (near-)surface conditions. In this paper, we explore the correlation between the surface conditions provided by Land-Cover (LC) maps generated using Remote Sensing (RS) data and different settings of Seismic Processing (SP) parameters. The study involves a 2D seismic line related to geothermal exploration from the Netherlands.

In the present research, the synthesis of highly stable multiwalled carbon nanotubes in aqueous media is developed using a one-pot, covalent and green functionalization technique to improve the heat transfer and hydrodynamic behavior of a horizontal stainless-steel tube subjected to a uniform heat flow at its outer surface. Instead of using corrosive inorganic acids, the free radical grafting of gallic acids is used in this procedure. In this work, GA-functionalized multiwalled carbon nanotubes water-based colloidal suspensions (nanofluids) were prepared at three different weight concentrations. Various characterization techniques comprising Fourier transform infrared spectroscopy, Raman spectroscopy, High resolution transmission Electron Microscopy, and zeta-potential measurements were performed and confirmed the success of MWCNT functionalization. The thermophysical properties were evaluated experimentally and validated using empirical correlations available in the literature. To prove the colloidal suspension stability, ultraviolet-visible spectroscopy was used, and the results showed that nanofluid was stable for almost 60 days. When the GAMWCNT concentration was increased, there was a significant improvement in the thermal conductivity. Moreover, the nanofluids' dynamic viscosity experienced a slight increment up to 7.30% when the GAMWCNTs were loaded relative to distilled water. Following the confirmation of the experimental setup's analytical correlations, tests for the colloidal GAMWCNT suspension flowing through a heated horizontal tube were carried out in a fully developed turbulent state. A significant enhancement in the convective heat transfer coefficient was obtained, with only minor growth in the relative pumping power by 33.05 and 1.19%, respectively. More importantly, the reported positive performance index indicator for all the Reynolds numbers of ranges shows the possibility of using the synthesized GAMWCNT aqueous suspensions as an alternative working fluid in heat transfer systems.