Identification of the Sediment Thickness Variation of a Tidal Mudflat in the South Yellow Sea via GPR
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| Publicado en: | Remote Sensing vol. 17, no. 23 (2025), p. 3785-3806 |
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17233785 |2 doi | |
| 035 | |a 3280962394 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Chen, Wentao |u Land Science Research Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 202312100030@nuist.edu.cn (W.C.); | |
| 245 | 1 | |a Identification of the Sediment Thickness Variation of a Tidal Mudflat in the South Yellow Sea via GPR | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>Three distinct radar facies (F1–F3) were successfully identified from GPR profiles and correlated with sediment properties, enabling high-resolution discrimination of subsurface sedimentary units. <list-item> The Hilbert instantaneous phase method achieved the highest accuracy in sediment interface detection, with relative errors below 6% in 64% of sediment layers and positioning errors under 5 cm in most horizons. </list-item> What are the implication of the main findings? <list list-type="bullet"> <list-item> </list-item>The integration of radar facies interpretation with borehole data provides a reliable approach for inferring lithological properties in deep strata where GPR signal quality declines. <list-item> The demonstrated performance of the Hilbert-based method supports its use as a robust tool for high-precision, non-invasive subsurface mapping in similar coastal depositional environments. </list-item> The tidal mudflat of the South Yellow Sea is characterized by complex sediment environments that preserve rich paleoenvironmental signals, making it an important area for understanding land–sea interactions and promoting sustainable coastal development. Thus, accurate identification of sediment sequences and layer thicknesses becomes crucial for interpreting sediment dynamics and paleoenvironmental reconstruction. While borehole data have elucidated local sediment facies, their spatially discontinuous nature hinders a holistic reconstruction of regional depositional history. To overcome this limitation, ground-penetrating radar (GPR) surveys were conducted across the tidal mudflat of the South Yellow Sea, enabling systematic correlation between radar reflection patterns and sediment architectures. Based on the relationship between the dielectric permittivity and wave velocity, short-time Fourier transform (STFT) was applied to derive the peak-weighted average frequency in the frequency domain for individual soil layers, revealing its dependence on dielectric properties. Sediment interfaces and layer thicknesses were determined using three methods: the radar image waveform method, the Hilbert spectrum instantaneous phase method, and the generalized S-transform time–frequency analysis method. The results indicate the following: (1) GPR enables high-fidelity imaging of subsurface stratigraphy, successfully resolving three distinct radar facies: F1: high-amplitude, horizontal, continuous reflections with parallel waveforms; F2: moderate-to-high-amplitude, sinuous continuous reflections with parallelism; and F3: medium-amplitude, discontinuous chaotic reflections. (2) All three methods effectively characterize subsurface soil stratification, but positioning accuracy decreases systematically with depth. Excluding anomalous errors at one site, the relative error for most layers within the 1 m depth is below 15%, and remains ≤25% at the 1–2 m depth. Beyond the 2 m depth, reliable stratification becomes unattainable due to severe signal attenuation. (3) Comparative analysis demonstrates that the Hilbert spectral instantaneous phase method significantly enhances GPR signals, achieving an optimal performance with positioning errors consistently below 5 cm for most soil layers. The application of this approach along the tidal mudflat of the South Yellow Sea significantly enhances the precision of sediment layer boundary identification. Our analysis systematically interpreted radar facies, demonstrating the effectiveness of the Hilbert spectrum instantaneous phase method in delineating soil stratification. These findings offer reliable technical support for interpreting GPR data in comparable sediment environments. | |
| 651 | 4 | |a Yellow River | |
| 651 | 4 | |a Yellow Sea | |
| 651 | 4 | |a China | |
| 653 | |a Boreholes | ||
| 653 | |a Amplitudes | ||
| 653 | |a Mud flats | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Wave velocity | ||
| 653 | |a Waveforms | ||
| 653 | |a Investigations | ||
| 653 | |a Coastal development | ||
| 653 | |a Radar | ||
| 653 | |a Stratification | ||
| 653 | |a Sediments | ||
| 653 | |a Frequency dependence | ||
| 653 | |a Electrical properties | ||
| 653 | |a Stratigraphy | ||
| 653 | |a Radar imaging | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Soil layers | ||
| 653 | |a Accuracy | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Time-frequency analysis | ||
| 653 | |a Signal quality | ||
| 653 | |a Ground penetrating radar | ||
| 653 | |a Dielectric properties | ||
| 653 | |a Sustainable development | ||
| 653 | |a Frequency analysis | ||
| 653 | |a Rivers | ||
| 653 | |a Subsurface mapping | ||
| 653 | |a Thickness | ||
| 700 | 1 | |a Zhao Chengyi |u Land Science Research Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 202312100030@nuist.edu.cn (W.C.); | |
| 700 | 1 | |a Zheng Guanghui |u Land Science Research Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 202312100030@nuist.edu.cn (W.C.); | |
| 700 | 1 | |a Zhu Jianting |u Department of Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, WY 82071, USA | |
| 700 | 1 | |a Li, Xinran |u Land Science Research Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 202312100030@nuist.edu.cn (W.C.); | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 23 (2025), p. 3785-3806 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280962394/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3280962394/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280962394/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |