First Break - Volume 42, Issue 7, 2024

This study presents a specially designed dripping rainfall simulator, functional in both laboratory and field settings, developed to research water infiltration processes relevant to landslide studies. The simulator incorporates several advanced features, including adjustable rainfall parameters and precise monitoring and measurement capabilities for a range of experimental setups. The system’s calibration was achieved by measuring the volume of water over a set period, correlating it with the rainfall intensity. Experiments were conducted on a slope surface for up to five hours at a constant rainfall intensity. During this time, 3D electrical resistivity measurements were taken to assess the influence of rainfall on resistivity data, offering insights into the subsurface dynamics of water infiltration. The findings suggest that the combination of dripping rainfall simulation and 3D electrical resistivity analysis holds promise for advancing landslide risk reduction research. This paper provides an in-depth overview of the simulator’s design, functionality, and performance, emphasising its applicability for comprehensive landslide investigations.

Offshore Benin, and the wider Keta Basin, remains an underex-plored area of the West Africa Transform Margin. The evolution of the different sediment fairways and their depocentres can be identified on structure maps from the mapping of a regional 2D seismic dataset. The 3D seismic offshore Benin supports the 2D interpretation but in addition, allows for a more complete evaluation through detailed seismic attribute analysis. The use of 3D spectral decomposition highlights the changes in fairway directions with clear imaging of the channel systems and their orientations, correlating with the thicknesses observed from regional 2D seismic mapping.

The transform faults strongly control the overall structur-ation of offshore Benin and the depositional style during the Cretaceous period. The onshore Togo and Benin river systems supply sediment directly to the basin in a north to south direction which is limited and directed by the transform movement and ridges outboard. Towards the end of the syn-transform deposition, the main fairway input changes from a directly northern source to a north eastern source in the Dahomey Embayment. Finally, in the Cenozoic, the Niger River system drainage increased leading to the Benin Ultra Deep area to form part of the Niger prodelta, with a predominant easterly sourced sediment input. The transform faults are no longer active and no longer control sediment distribution, leading to an unconfined channelised system.

Seismic data with a low-frequency content is crucial for full waveform imaging (FWI) as it can improve the resolution of subsurface features and offer insight into the underlying geological characteristics. A lack of low-frequency content may cause cycle-skipping, which can distort the results of the inversion process. Low frequency content in seismic data is usually estimated using seismic processing-based deghosting techniques. In this study, an attempt is made to reconstruct the low-frequency content using an artificial intelligence approach through a deep learning algorithm. A convolutional neural network (CNN) approach was used to automatically extrapolate the low-frequency content of the band-limited common-shot-gather data, without the need for preprocessing steps. The model was first tested and validated with synthetic data. The optimised model was applied to the Sadewa field, Indonesia, and the obtained low-frequency extrapolated data were used as input for the FWI process. The results show that the proposed algorithm can reliably extrapolate the low-frequency content of the field data with minimal errors and exhibits good agreement with the deghosting results. The FWI results also demonstrate that our proposed method can be a reliable and efficient substitute for determining the low-frequency component of seismic reflection data.