Showing papers by "Jing Li published in 2023"
TL;DR: In this article , the authors explore the potential for community-level intervention to guide the evolution of rural landscapes under the World Heritage Sustainable Tourism Programme (WHTP) and reveal that community-Level interveners should facilitate community development by recognising the important role of rural communities.
Abstract: Abstract Although the World Heritage Sustainable Tourism Programme has great potential for addressing the Sustainable Development Goals, it faces a continual lack of on-the-ground community-level tools. This paper explores the potential for community-level intervention to guide the evolution of rural landscapes under the World Heritage Sustainable Tourism Programme. This community-level intervention comprises three phases (knowledge coproduction, perspective planning and community action) and nine stages (village representative assembly, internalising knowledge workshop, field trip and casual interviews, knowledge demonstration, joint fieldwork, perspective discussions, tangible landscape element design, intangible landscape element coordination, and effectiveness evaluation). Our case study, Dragon Tail Village, reveals that community-level interveners should facilitate community development by recognising the important role of rural communities—co-owners of heritage sites—and rural landscapes—sets of attributes with heritage value. Our findings therefore improve the understanding of the World Heritage Sustainable Tourism Programme’s driving rationale for community development.
TL;DR: In this paper , the authors used qualitative research triangulation to explain the causality between community exclusion and local distrust of the protected area and build an explanation concerning the political and economic context.
TL;DR: In this article , the authors conducted a quantitative analysis of the dielectric properties of the buried lens structure observed during the Chang'E-4 mission, revealing that the relative permittivity and loss tangent of the structure is 10.5 and 0.037, respectively.
Abstract: The Moon-based ground-penetrating radar (GPR) onboard the Chang’E-4 (CE-4) Yutu-2 rover has deployed on the Moon’s far side, providing an unprecedented opportunity to study the shallow surface geological process and the history of the volcanic eruption of the Moon. The high-frequency radar observed a buried lens structure $\sim $ 27 m below the lunar surface, interpreted as paleoregolith by previous studies. In this study, we conducted a quantitative analysis of the dielectric properties of the buried lens structure observed during the CE-4 mission. Our results reveal that the relative permittivity and loss tangent of the structure is $\sim $ 10.5 and $\sim $ 0.037, respectively. Comparing our estimated dielectric properties with those of Apollo samples and radar-observed lava flows shows that the buried lens is neither regolith nor ejecta material but possibly basalt flow. In addition, we speculate that the buried basalt flow may represent the latest volcanic eruption on the Moon, possibly from the Eratosthenian-aged volcano that occurred approximately 2.5–2.2 billion years ago. Finally, we update the interpretation of the regolith stratigraphic structure observed by the Yutu-2 high-frequency radar at the CE-4 landing site.
TL;DR: Li et al. as discussed by the authors proposed an elastic wave-equation traveltime (WT) inversion algorithm based on the autoencoder (AE) to invert the $P$ -velocity model.
Abstract: Due to unexpected environmental variations and poor consistencies in land data acquisition, complex near-surface seismograms are usually polluted unreasonably with a low signal-to-noise ratio (SNR). These complicated circumstances bring more challenges in identifying accurate first arrivals for the following wave-equation traveltime (WT) inversion. Recently, the autoencoder (AE) is a typical unsupervised learning network, whose basic principle is to compress the input seismic data for their intrinsic features in the latent space with an encoder and, thereafter, to decipher these features for seismic profiles as the output with a decoder. This process is fully automatic with high stability and is not very sensitive to data quality. In this article, we propose an elastic WT inversion algorithm based on the AE method (AEWT) to invert the $P$ -velocity model. Compared with the standard WT, the AEWT method automatically extracts the intrinsic features of the refractions with AE as reference data for the misfit functionals. Feature images in the latent space show similar but intensified sensitivity to the traveltimes with respect to velocity perturbations. We present one synthetic and two field data tests for comparing the proposed AEWT and the standard WT tomograms to investigate the locations of a buried fault and the depth of a buried sinkhole. All these experiments demonstrate that the proposed elastic AEWT method can reduce errors caused by low SNR and obtain a more reliable and stable $P$ -velocity tomogram.