No abstract

Most seismic studies of changes in traveltimes are of a qualitative nature and a major challenge in four dimensions is to use the information contained in time shifts to quantify the nature of velocity changes in the subsurface layers. We propose a 4D tomographic inversion method that uses time shifts from prestack seismic data to estimate parameters describing the 2D velocity field after changes have occurred. Prestack data allow for the usage of many offsets, thus increasing the information input for the inversion. The velocity changes are parameterized by a chosen number of Gaussian functions in two dimensions and weighted least-squares inversion is used to estimate the parameters describing these functions. We have found that the parameters describing the position and shape of the Gaussian velocity anomalies can be estimated with this method for simple synthetic cases. For more complex cases with overlapping Gaussian functions, resolution of the parameters can be difficult and in these cases our recommendation is to find the best fit for a simple smooth anomaly to a more complex real world. The method is tested on a real data set from a [Formula: see text] injection project above the Sleipner field in the North Sea, where quantification of changes is important for monitoring purposes. We have found that the noise levels in prestack traveltime data are on the high side for large-scale analysis; however, we estimate reasonable [Formula: see text] layer thickness and velocity compared to previous work in a nearby area.

Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Both conventional marine seismic and wide azimuth data acquisition lack near offset coverage, which limits imaging in these settings. A new marine source over cable survey, with split-spread configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, wavefield reconstruction in the near offsets is challenging in the shallow part of the seismic record due to the high temporal frequencies and coarse sampling that leads to severe spatial aliasing. We investigate deep learning as a tool for the reconstruction problem, beyond spatial aliasing. Our method is based on a convolutional neural network (CNN) approach trained in the wavelet domain in order to reconstruct the wavefield across the streamers. We demonstrate the performance of the proposed method on broadband synthetic data and TopSeis field data from the Barents Sea. From our synthetic example, we show that the CNN can be learned in the inline direction and applied in the crossline direction, and that the approach preserves the characteristics of the geological model in the migrated section. In addition, we compare our method to an industry-standard Fourier-based method, where the CNN approach shows an improvement in the RMS error close to a factor of two. In our field data example, we show that the approach manages to reconstruct the wavefield across the streamers in the shot domain, and displaying promising characteristics of a reconstructed 3D wavefield.

Some of the key tasks in seismic processing involve suppressing multiples and noise that interfere with primary events. Conventional multiple attenuation on seismic prestack data is time-consuming and subjective. As an alternative, we propose model-driven processing using a convolutional neural network trained on synthetically modeled training data. The crucial part of our approach is to generate appropriate training data. Here, we compute a generic data set with pairs of synthetic gathers with and without multiples. Because we generate the primaries first and then add multiples, we ensure that we have perfect target data without any multiple energy. To compute generic and realistic training data, we include elements of wave propagation physics and implement a randomized flexibility of settings such as the wavelet, frequency content, degree of random noise, and amplitude variation with offset effects with each gather pair. A fully convolutional neural network is trained on the synthetic data in order to learn to suppress the noise and multiples. Evaluations of the approach on benchmark data indicate that our trained network is faster than conventional multiple attenuation because it can be run efficiently on a modern GPU, and it has the potential to better preserve primary amplitudes. Multiple removal with model-driven processing is demonstrated on seismic field data, and the results are compared to conventional multiple attenuation using a commercial Radon algorithm. The model-driven approach performs well when applied to real common-depth point gathers, and it successfully removes multiples, even where the multiples interfere with the primary signals on the near offsets.