This inventor holds 1 USPTO granted patent. Top assignee: Engiscent Pte. Ltd.. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Innovations in Seismic Exploration by Alexander Vlasov
Introduction
Alexander Vlasov is an innovative inventor based in Bishkek, Kyrgyzstan. He has made significant contributions to the field of seismic exploration through his groundbreaking patent. His work focuses on utilizing advanced algorithms and deep learning techniques to enhance the accuracy of subsurface property reconstruction.
Latest Patents
Vlasov holds a patent for a "Full waveform inversion algorithm for subsurface physical property reconstruction using generative deep learning approach." This patent addresses the inverse problem in seismic exploration by leveraging both the Radon transform and deep learning techniques. The method employs Generative Adversarial Networks (GANs) and specialized neural network architectures known as R-nets. The first R-net efficiently handles hyperbolic Radon Transform, aiding in data preprocessing, while the second R-net generates detailed subsurface models. The reconstructed properties include the velocity of pressure wave propagation, velocity of shear wave propagation, impedance, and density. This novel approach significantly enhances the accuracy and efficiency of the reconstruction process, particularly beneficial for pre-stack depth migration.
Career Highlights
Vlasov's career is marked by his dedication to advancing seismic exploration technologies. His innovative methods have led to a substantial reduction in the time and resources traditionally needed for depth-velocity model construction. This has resulted in the generation of superior subsurface models at a faster pace.
Collaborations
Vlasov collaborates with Dmitry Mikhailov, contributing to the development of cutting-edge technologies in the field. Their partnership has fostered a creative environment that encourages innovation and the exploration of new ideas.
Conclusion
Alexander Vlasov's contributions to seismic exploration through his patented algorithms demonstrate the potential of combining traditional methods with modern deep learning techniques. His work not only enhances the accuracy of subsurface models but also streamlines the reconstruction process, paving the way for future advancements in the field.