This work determines whether the amount of frequency components present in the data can be reduced, whilst still retaining image quality, whereas most efforts in seismological research are done in reducing spatial sampling. It is shown using a PCA on the frequency spectra of several data sets that indeed a large redundancy in frequency content is present in onshore seismic data, and an attempt is made to generate a distribution of frequencies in order of importance. Given this redundancy in the frequency spectrum of onshore seismic data, it has been attempted to reconstruct the missing frequencies by applying the Fourier transformation iteratively to the data. However, this transform does not take spatial sampling into account, which is aimed at to compensate for the missing frequencies. Therefore it has been elected to use a linear Radon transformation instead, which keeps components which are connected in space-time connected in the transform domain. A CGNE scheme has been set up to reconstruct the data, which performs very well along the almost linear asymptots in the shot records, up to a reduction of 70% of frequency components. This scheme iteratively applies the linear Radon transform to a shot record, weighing the data in the transform domain with an amplitude based norm. The energy that was spread out due to aliasing because of the missing frequencies is refocused to the main reflectors, especially along the asymptots of the reflection hyperbola. Missing frequencies are reconstructed, up to a scaling factor, and band gaps of up to 6Hz get filled in very well. Next, it is attempted in this work to give quantitative quality metrics, to make comparison between seismic images easier and based on data, rather than subjective visual inspection. Treating migration as a black box, several quality metrics have been devised for the migrated sections: correlation to the ground truth, contrast within an image, average length of found lines, and local SNR. Contrast is not a very good metric to compare between images as its average across an image is almost constant with reduction percentage. The other parameters are good metrics and show a clear trend that the fewer frequency components present in the shot records, the worse the quality of the final image. An increase in deterioration of image quality is observed around 70% reduction, which is in correspondence with the earlier found value for the shot records.

An onshore seismic survey is best conducted symmetrically, due to the reciprocity theorem of the wave field. Within the family of symmetric geometries the cross spread is most used. Recent developments show a marked increase of the number of available channels, nowadays 100,000+. This enables the use of point receivers, meaning that every geophone has its output recorded, instead of being in an array which is summed before sending the data.Geophones are ever increasing in capability, seeing an increase to lower corner frequencies and the use of batteries and GPS systems to make them cableless and suitable for point-receiver recording. MEMS are new on the market, with a noise floor of < 15 ng-per-square-root-of-Herz being available. The two main quantities which are not specified in the manufacturers’ data sheets of geophones, but which are highly relevant to the user are the noise floor and the dynamic operating range.The case study shows that most surveys nowadays are 3D cross-spread surveys with a source and receiver spacing of approximately 40m each.The most used data format in the geophysical industry is SEG-Y.A synthetic subsurface model has been generated, through which shots have been modelled. Using real field data from Saudi Aramco tests have been conducted to both increase the shot and receiver interval, and to add various amounts of noise to this data. The shots have subsequently been migrated to generate a subsurface image, for which two methods of quality control have been devised: the correlation of the image with respect to the full, no-noise model, and the number of lines as detected through the Canny edge method. The quantity spatial interval over SNR has been used to create a function for both QC methods; Corr = 117.862/( SI/SNR + 114.650), and Nl proportional to (SI/SNR)^1/4 for the number of lines. The fit for the number of lines has a very low R-squared, hence a new method must be sought for quality control.For the given model SI/SNR = 75 has been selected as a maximum for this ratio to obtain a migrated result of sufficient quality for it to be able to be interpreted.