Dialogue on the Role of Artificial Intelligence in Scientific Research: Inverse Problems in Seismic and Electrical Exploration

This preprint presents a scientific dialogue between geophysicist Sergey A. Gritsenko and GPT-5.6 Sol on the role of artificial intelligence in scientific research, using inverse problems in seismic and electrical exploration as a case study.A substantial part of the discussion is devoted to the work of representatives of the Novosibirsk school of geophysics—M. I. Epov, V. N. Glinskikh, K. V. Sukhorukova, and their colleagues—in the field of electrical and electromagnetic well logging. S. A. Gritsenko also belongs to the Novosibirsk school, with his research focused on seismic exploration and inverse problems. The dialogue considers two-dimensional and quasi-three-dimensional inversion, multilayer models, invasion zones, anisotropy, dielectric permittivity and its frequency dispersion, as well as the inverse problem for spontaneous potential using analytical and sigmoid functions.These approaches are compared with the seismic inverse problem for a viscoelastic medium, in which medium parameters are determined from the observed space-time wavefield. The discussion emphasizes the fundamental distinction between recovering parameters of a two-dimensional geoelectric model from one-dimensional well-log observations using a priori parameterization and recovering distributed parameters of a viscoelastic medium from the complete wavefield.The dialogue also considers full-waveform inversion (FWI), neural-network methods, and the possibility of seeking not the complete medium model, but those characteristics of the medium that are actually determined by the observations.The central methodological conclusion is that the order of solving an inverse problem should be changed: first, one should establish which characteristics of the medium are actually contained in the original observations and can be determined from them, and only then construct an algorithm for their recovery. If the complete medium model cannot be determined from the data, the inverse problem should instead focus on those functions, parameters, or combinations of parameters that are identifiable from the observed field.The dialogue also serves as an example of joint scientific research by a human and artificial intelligence. AI is used not only for literature search and analysis, but also for comparing different approaches, analyzing differential equations, formulating hypotheses, and refining the general methodology for solving inverse problems.Keywords: inverse problems; geophysics; seismic exploration; electrical exploration; electrical well logging; viscoelastic medium; identifiability; full-waveform inversion; neural networks; artificial intelligence in scientific research; human–AI scientific dialogue; Novosibirsk school of geophysics Related publications: Inverse Dynamic Problem in a Viscoelastic Medium: https://doi.org/10.5281/zenodo.21723472 Model VSP Wavefields and Results of Solving Inverse Dynamic Problems for a Viscoelastic Medium: https://doi.org/10.5281/zenodo.21724624 Dialogues Concerning the Two Chief Principles — Reason and Life: https://doi.org/10.5281/zenodo.22914613 Transcript of Conversation: Human and DeepSeek: https://doi.org/10.5281/zenodo.22958905