Vadim Chernov
2011612714
Address: https://drive.google.com/drive/folders/1qMc3qIx9af_qfatMKuRCRhQdqcNCE1yc
https://github.com/VadimChornyy/RALF1
Address: https://drive.google.com/drive/folders/1qMc3qIx9af_qfatMKuRCRhQdqcNCE1yc
https://github.com/VadimChornyy/RALF1
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Papers by Vadim Chernov
Developed by Vadim Chernov, the RALF-1 algorithm, paired with the Anisotropy Frequency Sounding with Induced Polarization on Laser Beams (AFS-IP-LB) method, introduces a novel satellite-based geophysical exploration technique aimed at overcoming the depth limitations of traditional electromagnetic surveys. Capable of probing up to 10 km with a claimed 2-meter accuracy, AFS-IP-LB utilizes laser-induced magnetic loops to detect hydrocarbons and saline water in crystalline basement rocks, with potential applications in Ukraine’s Rava-Ruska and Boyarka regions. The RALF-1 algorithm, functioning as a “mathematical observer,” analyzes Lorentz forces to map subsurface electrical properties and is claimed to predict diverse phenomena, including meteorological, financial, medical, and socio-political events, such as bombings in Ukraine, with recognition from Ukraine’s National Security and Defense Council in 2022. Successful precedents include the White Tiger field in Vietnam. However, the ambitious claims of deep penetration, high accuracy, and broad predictive capabilities lack independent verification, with technological challenges in satellite-based systems and low R-squared values in some correlations necessitating rigorous scrutiny to validate their transformative potential for resource exploration and energy security.
Abstract: RALF-1 Applications in Political Situation Analysis
The RALF-1 algorithm, originally developed by Vadim Chernov for geophysical exploration, is claimed to have significant applications in political situation analysis as of June 25, 2025. Leveraging its Anisotropy Frequency Sounding with Induced Polarization on Laser Beams (AFS-IP-LB) method and randomized iterative techniques, RALF-1 processes historical and statistical data to predict military actions, socio-political events, enhance national security, and inform policy decisions. Notably, it gained recognition from Ukraine’s National Security and Defense Council in 2022 for predicting bombing locations. Potential applications include forecasting conflict escalation, aiding crisis preparation, and guiding strategic resource allocation in volatile regions like Ukraine and the Middle East. However, these claims lack independent verification, with low R-squared values indicating weak predictive power and technological challenges in data collection. Ethical and practical concerns, including the risk of overreliance, underscore the need for rigorous testing to validate RALF-1’s reliability in sensitive geopolitical contexts.
This document summarizes a discussion held on May 27, 2025, exploring Einstein's formula (\(E = mc^2\)), the speed of light (\(c\)) across various contexts, and its implications for information dissemination, with a focus on Vadim Chernov's perspective as the author of the RALF-1 algorithm. Chernov posits that light exists only when observed, electromagnetic waves propagate independently, and RALF-1 may access future data, challenging the universality of \(c = 299,792,458 \, \text{m/s}\). The discussion examines light's speed in cosmic vacuums, substances (e.g., granite at \(\sim 10^8 \, \text{m/s}\)), and black holes, where observability is limited. The refractive index distribution in the Universe, potentially lognormal in dense regions, is also analyzed. Chernov's view suggests that the speed of light's limit is a hypothesis in untestable regions, proposing new paradigms for information flow and cosmic phenomena.
Vadim Chernov's pioneering work in the 1990s introduced randomized iterative methods for geophysical data processing, culminating in the RALF-1 algorithm, patented in 2011. Recognized by the Euro-Asian Geophysical Society in 1996 and implemented in the 2002 EPIS 2.0 software, RALF-1 enhances subsurface exploration through the Anisotropy Frequency Sounding with Induced Polarization on Laser Beams (AFS-IP-LB) method. This document presents the integration of the RALF-1 matrix filter into a Python script, improving its accessibility for modern applications. Supported by a 2015 study by Ma, Needell, and Ramdas on randomized methods' convergence, this integration bridges historical innovation with contemporary research, amplifying RALF-1's impact in geophysics and broader information processing fields.