The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Continuous monitoring of the lunar or Martian subsurface using on-board pattern recognition and neural processing of Rover geophysical data


    Beteiligte:
    Mcgill, J. W. (Autor:in) / Glass, C. E. (Autor:in) / Sternberg, B. K. (Autor:in)


    Erscheinungsdatum :

    1990-01-01


    Medientyp :

    Sonstige


    Format :

    Keine Angabe


    Sprache :

    Englisch




    Searching for Subsurface Lunar Water Ice Using a Nuclear-Powered Rover

    Randolph, J. E. / Abelson, R. D. / Oxnevad, K. I. et al. | British Library Conference Proceedings | 2005


    Lunar rover

    LIU QIANG / LI JIAN / LIU GANG et al. | Europäisches Patentamt | 2024

    Freier Zugriff


    Moving system of lunar rover and lunar rover

    LIU ZHEN / GAO HAIBO / HU LANQING | Europäisches Patentamt | 2023

    Freier Zugriff

    Geological Investigation of Lunar and Martian Subsurface Using Laser Drilling System

    Gahan, Brian / Batarseh, Samih / Reilly, James et al. | AIAA | 2004