Distributed Acoustic Sensing (DAS) observations at Harper Adams University

Structure and Concepts: This dataset integrates high-resolution
fiber-optic seismic sensing with detailed agronomic and meteorological
records. The data are structured into three primary components: Passive
Seismic Interferometry Data: Reconstructed Auto-Correlation Functions
(ACFs) derived from a 51-channel Distributed Acoustic Sensing (DAS) array.
The dataset includes minutely-averaged ACFs, bandpass-filtered between
15–60 Hz. These records capture temporal seismic velocity variations over
a 40-hour period. Meteorological Time-Series: Synchronized weather data
from two sources (Harper Adams University and Newport station) featuring
30-to-60-minute resolution air temperature, humidity, wind speed, and
cumulative/hourly rainfall. Crucially, it includes soil temperature
profiles at 10, 30, and 100 cm depths. Agronomic and Soil Physical
Properties: Spatial metadata for a randomized block design experiment,
including traffic management systems and tillage depths (Zero, Shallow,
Deep). Physical values include bulk density samples separated by 10 cm
depth increments and site-specific drainage maps. Value and Content: The
dataset provides a unique link between controlled mechanical soil
disturbance (tillage/traffic) and real-time geophysical observables. It
captures the transition of soil through distinct hydrological regimes
(wetting, drainage, and evapotranspiration). Values are provided in
tabular formats (.csv) and processed seismic formats suitable for
time-lapse interferometric analysis. Reuse Potential: This dataset is
highly suitable for: Validating hydromechanical models that couple seismic
velocity to soil moisture. Testing DAS "edge computing"
workflows and data-reduction techniques. Benchmarking ambient noise
interferometry algorithms in high-frequency, shallow-subsurface
environments. Studying the impacts of regenerative vs. conventional
farming on soil structural integrity. Legal and Ethical Considerations:
The data were collected at a designated agricultural research facility
(Harper Adams University, UK). No human subject data or sensitive private
information is included. The dataset is intended for open research use and
contains no proprietary software dependencies; seismic processing was
performed using open-source tools (NoisePy4DAS).

结构与核心概念：本数据集整合了高分辨率光纤地震传感技术与详实的农艺、气象观测记录，分为三个主要组成部分：
1. 被动地震干涉测量数据：源自51通道分布式声学传感（Distributed Acoustic Sensing，DAS）阵列的重构自相关函数（Auto-Correlation Functions，ACFs）。数据集包含经15–60 Hz带通滤波的每分钟平均自相关函数，记录了40小时内的时序地震波速变化。
2. 气象时间序列数据：来自哈珀亚当斯大学与纽波特站两个来源的同步气象观测数据，分辨率为30至60分钟，涵盖气温、湿度、风速以及累积/逐小时降雨量。尤为关键的是，数据集还包含10 cm、30 cm与100 cm深度的土壤温度剖面。
3. 农艺与土壤物理属性：随机区组设计试验的空间元数据，涵盖农田通行管理系统与耕作深度（零耕、浅耕、深耕）。物理参数包含按10 cm深度分层采集的容重样本，以及场地专属排水分布图。

数据价值与内容：本数据集建立了受控机械土壤扰动（耕作/农机通行扰动）与实时地球物理观测之间的独特关联，捕捉了土壤在不同水文状态（增湿、排水与蒸发蒸腾）下的动态转变过程。数据以表格格式（.csv）与适用于时序干涉分析的处理后地震数据格式提供。

复用潜力：本数据集可应用于以下研究场景：验证将地震波速与土壤含水率耦合的水文力学模型；测试分布式声学传感（DAS）“边缘计算”工作流与数据降维技术；在高频浅地表环境中基准测试环境噪声干涉测量算法；研究再生农业与传统农业对土壤结构完整性的影响。

法律与伦理考量：本数据集采集自英国哈珀亚当斯大学的指定农业研究设施，未包含人类受试者数据或敏感隐私信息，面向开放研究用途，且无专有软件依赖——地震数据处理采用开源工具NoisePy4DAS完成。