Diego Rovetta is a research geophysicist working for Aramco on multi-geophysics integration and joint inversion. He previously worked as a researcher for Politecnico di Milano, collaborating with different companies (eni, Saipem, Schlumberger, etc.), and as a geophysicist at the WesternGeco Center of Excellence for electromagnetics. In 2002, he received his MSc. degree in Telecommunications Engineering from Politecnico di Milano, studying non-linear inverse problem applications using Simulated Annealing. In 2006, he obtained his Ph.D. from the same institute in Information Engineering focusing on geophysical inverse problems. Diego is currently the president of the EAGE local chapter in the Netherlands.
Diego Rovetta, Apostolos Kontakis, Daniele Colombo** *Delft Global Research Center, Aramco Overseas Company B.V. **Geophysics Technology, EXPEC Advanced Research Center, Saudi Aramco
Oil and gas exploration in desert environments requires an accurate description of the near surface, typically characterized by a complex geology strongly affecting the quality of the seismic data. High resolution details of the shallow subsurface can be obtained by analyzing the surface waves (SW) and their phase velocity variation with the frequency (dispersion curves). A detailed near surface velocity model can be obtained by the inversion of the dispersion curves, or by their joint inversion with other geophysical measurements. However, the necessary step of extracting manually the dispersion curves from the seismic data can be a highly inefficient and cumbersome task for large seismic surveys. We recently proposed to use machine learning techniques to automatize this extraction procedure and we tested different supervised (neural networks) and unsupervised (clustering) algorithms after integrating them in a workflow specifically designed to extract SW dispersion curves from the frequency-phase velocity spectrum. In particular, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) proved to be the best algorithm when it comes to balancing accuracy, robustness against noise, efficiency and automation. When tested on the SEAM Arid model synthetic dataset, this method extracts dispersion curves that are matching the theoretical ones with pretty good accuracy, and once inverted for velocities they are successfully recovering the near-surface features with high resolution. We also applied this algorithm to a field dataset acquired in a desert environment, providing geology-consistent velocities through single-domain SW inversion and first break travel times and SW joint inversion. Finally, we extended the picking workflow from the fundamental to higher-order modes. We believe that the integration of machine learning algorithms into the dispersion curve picking procedure makes it feasible to use SW information for a high resolution characterization of the near surface in complex geology environments.