Enhancing the vertical resolution of lunar penetrating radar data using predictive deconvolutionOAEI
The Yutu-2 rover onboard the Chang’E-4 mission performed the first lunar penetrating radar detection on the farside of the Moon.The high-frequency channel presented us with many unprecedented details of the subsurface structures within a depth of approximately 50 m.However,it was still difficult to identify finer layers from the cluttered reflections and scattering waves.We applied deconvolution to improve the vertical resolution of the radar profile by extending the limited bandwidth associated with the emissive radar pulse.To overcome the challenges arising from the mixed-phase wavelets and the problematic amplification of noise,we performed predictive deconvolution to remove the minimum-phase components from the Chang’E-4 dataset,followed by a comprehensive phase rotation to rectify phase anomalies in the radar image.Subsequently,we implemented irreversible migration filtering to mitigate the noise and diminutive clutter echoes amplified by deconvolution.The processed data showed evident enhancement of the vertical resolution with a widened bandwidth in the frequency domain and better signal clarity in the time domain,providing us with more undisputed details of subsurface structures near the Chang’E-4 landing site.
Chao Li;JinHai Zhang;
CAS Engineering Laboratory for Deep Resources Equipment and Technology,Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China
Chang’E-4lunar penetrating radar data processingpredictive deconvolutionirreversible migration filtering
《Earth and Planetary Physics》 2024 (004)
P.570-578 / 9
supported by the National Natural Science Foundation of China(Grant Nos.42325406 and 42304187);the China Postdoctoral Science Foundation(Grant No.2023M733476);the CAS Project for Young Scientists in Basic Research(Grant No.YSBR082);the National Key R&D Program of China(Grant No.2022YFF0503203);the Key Research Program of the Institute of Geology and Geophysics;Chinese Academy of Sciences(Grant Nos.IGGCAS-202101 and IGGCAS-202401).
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