探地雷达（GPR）数据浅层地下结构勘探的有效地球物理手段之一。本研究使用的探地雷达中心频率100MHz，采样时窗设置为100ns。根据雷达数据中记录的GPS数据，得到了地面多极化探地雷达探测路线图，结合背景去除、增益、带通滤波的数据处理流程，与无人机探地雷达相同位置探测得到滑坡面信号，并发现实验点下方数米深度内存在两层不同性质的黄土，1.5m以浅的黄土内部较为不均匀，电磁波体散射能量强烈，1.5m以深的黄土更为均匀，几乎没有内部结构。该数据集适用于黄土滑坡地下地质结构识别、地下介质属性评估等领域，为兰州榆中地区黄土滑坡综合研究提供了重要数据支持。
", "ds_source": "数据来源于兰州市榆中县，采用100MHz全极化探地雷达进行黄土滑坡地下结构非破坏性探测，用于识别黄土滑坡地下落水洞、裂隙等结构，采样时窗设置为100ns，全过程严格遵守设备维护和操作规范，所有数据均按标准格式存档备份，确保数据的完整性、准确性和可追溯性，有效支撑后续地下结构分析和科研应用。
", "ds_process_way": "背景去除、增益、带通滤波、中值滤波、自适应滤波。
", "ds_quality": "采用包含背景去除、增益、带通滤波的数据处理流程，可以识别出黄土滑坡地区地下地质构造，包括滑坡面及落水洞结构，并与无人机探地雷达相同位置探测得到滑坡面信号。因此，本数据集可作为黄土滑坡地下结构探测的可靠指标。
", "ds_acq_start_time": "2025-08-15 00:00:00", "ds_acq_end_time": "2025-08-16 00:00:00", "ds_acq_place": "兰州榆中", "ds_acq_lon_east": 104.5, "ds_acq_lat_south": 35.83305555555556, "ds_acq_lon_west": 103.83305555555555, "ds_acq_lat_north": 36.166666666666664, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 30113086, "ds_files_count": 0, "ds_format": "*.zry *.xlsx", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d642e34d-9c7a-4df1-bd2c-c2157049a5cd.jpeg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "应要求由数据管理者决定", "ds_from_station": "", "organization_id": "bf138922-7121-438c-8d1b-19d5f751c907", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.2540" ], "quality_level": 0, "publish_time": "2026-05-25 11:03:00", "last_updated": "2026-05-25 11:03:00", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.loess.db7369.2026", "i18n": { "en": { "title": "Unmanned aerial vehicle ground penetrating radar dataset at Yuzhong, Lanzhou in 2025", "ds_format": "*.zry *.xlsx", "ds_source": "The data were collected from Yuzhong County, Lanzhou City, using a 100 MHz fully polarimetric ground‑penetrating radar (GPR) to perform non‑destructive detection of subsurface structures in loess landslides. The survey aimed to identify underground features such as sinkholes and fractures. The sampling time window was set to 100 ns. Throughout the operation, equipment maintenance and standard operating procedures were strictly followed. All data were archived and backed up in standardized formats to ensure their integrity, accuracy, and traceability, thereby providing reliable support for subsequent subsurface structural analysis and scientific research.", "ds_quality": "
By employing a data processing workflow that includes background removal, gain adjustment, bandpass filtering, median filtering, and adaptive filtering, underground geological structures in loess landslide areas—such as the landslide surface and subsurface water channels—can be identified. Furthermore, structures up to 7 meters underground can be accurately detected. Therefore, this dataset can serve as a reliable indicator for detecting underground structures in loess landslide regions.", "ds_ref_way": "", "ds_abstract": "
Ground Penetrating Radar (GPR) is an effective geophysical method for shallow subsurface structure exploration. The GPR system used in this study operates at a center frequency of 100 MHz with a sampling time window set to 100 ns. Based on the GPS data recorded in the radar measurements, a survey route map for the ground-based polarimetric GPR was generated. Through a data processing workflow that includes background removal, gain adjustment, and band-pass filtering, signals from the landslide surface were obtained at locations consistent with those surveyed by the drone-based GPR. The results reveal the presence of two distinct loess layers within a few meters below the experimental site: the loess above 1.5 m depth is relatively heterogeneous, exhibiting strong volumetric scattering of electromagnetic waves, whereas the loess below 1.5 m is more uniform with almost no internal structure. This dataset is suitable for applications such as identifying subsurface geological structures of loess landslides and assessing subsurface medium properties, providing essential data support for comprehensive research on loess landslides in the Yuzhong area of Lanzhou.", "ds_time_res": "", "ds_acq_place": "Yuzhong, Lanzhou", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Background removal, gain, bandpass filtering.", "ds_ref_instruction": "As requested, the decision shall be made by the data manager." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "探地雷达", "全极化" ], "ds_subject_tags": [ "空间物理探测" ], "ds_class_tags": [], "ds_locus_tags": [ "兰州", "榆中" ], "ds_time_tags": [ 2025 ], "ds_contributors": [ { "true_name": "冯晅", "email": "fengxuan@jlu.edu.cn", "work_for": "吉林大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "冯晅", "email": "fengxuan@jlu.edu.cn", "work_for": "吉林大学", "country": "中国" } ], "ds_managers": [ { "true_name": "冯晅", "email": "fengxuan@jlu.edu.cn", "work_for": "吉林大学", "country": "中国" } ], "category": "其他" }