DOI: 10.5061/dryad.0vt4b8hcb
title: Distributed Acoustic Sensing (DAS) observations at Harper Adams University

description: 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).
description: # Distributed Acoustic Sensing (DAS) observations at Harper Adams
 University This dataset integrates high-frequency distributed acoustic
 sensing (DAS) with synchronized agronomic and meteorological records to
 characterize the hydromechanical response of agricultural soils. The data
 were collected in March 2023 at Harper Adams University, UK, following a
 mechanical tillage event. By analyzing ambient seismic noise, the dataset
 tracks relative seismic velocity changes (dv/ v) as a proxy for soil
 moisture dynamics and structural changes across various traffic and
 tillage management systems. The results provide a mechanistic framework
 for understanding how tillage impairs soil moisture retention and drought
 resilience. ## Description of the data and file structure The dataset is
 organized into a large-scale raw seismic file and a processed directory
 containing environmental and physical metadata. ### 1. Raw Data:
 `farmDAS_concat_100hz.hdf5` It mainly contains a 2D HDF5 array
 representing the raw strain-rate measurements. This file is intended for
 researchers who wish to apply alternative seismic interferometry to the
 raw wavefield. * **Sub-objects of HDF5 dataset:** * Key "data":
 dimension is {50, 12960000}, representing the DAS data saved for 129600
 seconds with 100 Hz sampling rate, and at 50 locations. * Key
 "dt": sampling rate (single scalar value) * Key
 "timestamp": a list of 2408 strings of the time stamp
 representing the starting time of every minute. ### 2. Processed Data:
 `processed.zip` This archive contains the interpreted data products
 required to reproduce the study’s figures and models. **A.
 Autocorrelations (ACFs: autocorr_15_60Hz_3chs500pts.hdf5):** *
 **Terminology:** ACFs represent the "fingerprint" of the
 subsurface. By comparing these over time (Passive Seismic Interferometry),
 we measure dv/v (relative velocity change). A decrease in dv/v generally
 indicates soil wetting or loosening. * **Data definition:** minutely
 averaged ACFs and downsampled in time by a factor of 5. Today 482 samples
 in clock time and 500 points in lag time. * **Window:** Correlation lag
 times from -5 to +5 seconds. * **Sub-objects of HDF5 dataset [key,
 dimension {clock time every 5 minutes, lag time every 0.01 s}]:** 
 corr_all_time_ch18,       {482, 500} corr_all_time_ch33,       {482, 500}
 corr_all_time_ch44,       {482, 500} corr_all_time_stretched1_ch18,  {482,
 500} corr_all_time_stretched1_ch33,  {482, 500}
 corr_all_time_stretched1_ch44,  {482, 500} corr_all_time_stretched2_ch18, 
 {482, 500} corr_all_time_stretched2_ch33,  {482, 500}
 corr_all_time_stretched2_ch44,  {482, 500} corr_all_time_stretched3_ch18, 
 {482, 350} corr_all_time_stretched3_ch33,  {482, 350}
 corr_all_time_stretched3_ch44,  {482, 350} **B. dv/v
 (final_peaks_deRatio_3iterations.h5):** * **Data
 definition: **dv/v measured by stretching ACFs, and
 normalized by measuring the ratio between each dv/v to the reference. *
 **Sub-objects of HDF5 dataset:** key "deratio_dvv": dimension is
 {50, 482}, representing dv/v measured at 482 clock time points and 50
 locations. **C. Rain proxy by power spectral density
 (integrated_psd.hdf5):** * **Data definition:** minutely integrated power
 spectral density (PSD) of 80-140 Hz of the raw data spectrum. *
 **Sub-objects of HDF5 dataset:** * Key "file_list": 2408 strings
 representing the filenames of the raw minute-long DAS data. - Key
 "freq Dataset": 129 float numbers representing the discrete
 frequencies between 80-140 Hz. * Key "int_PSD": dimension is
 {51, 2408}, representing integrated PSDs at 2408 minutes and 51 locations
 (including an unused last end point). **D. Weather Records 1
 (met_HarperAdams_merged.csv):** * Key "Date": day/month/year *
 Key "Time": HHMM * Key "10cm Soil Temperature": soil
 temperature at 10 cm depth, in degrees Celsius (°C) * Key "30cm Soil
 Temperature": soil temperature at 30 cm depth, in degrees Celsius
 (°C) * Key "100cm Soil Temperature": soil temperature at 100 cm
 depth, in degrees Celsius (°C) * Key "Rainfall Total since
 0900": cumulative gauges with daily manual calibration, in mm. * Key
 "Humidity": air relative humidity (normalized to 100%) **E.
 Weather Records 2 (met_newport_700m.csv):** * Key "Time":
 hour:minute, in every 30 minutes * Key "Temperature": air
 temperature in degrees Celsius (°C) * Key "Wind": wind direction
 (S: south, N: north, E: east, W: west) * Key "Wind Speed": wind
 speed in m/s * Key "Condition": weather condition **F. Soil
 Properties (soil_data_interp.csv):** * Key "tillage_depth":
 depth of tillage at each plot (0-25 cm) * Key "tire_pressure":
 traffic tire pressure (70-150 kPa) * Key "porosity_10cm": soil
 porosity at 10 cm depth derived from the bulk density measurements * Key
 "porosity_20cm": soil porosity at 20 cm depth derived from the
 bulk density measurements * Key "porosity_30cm": soil porosity
 at 30 cm depth derived from the bulk density measurements ##
 Sharing/Access information Data was derived from the following sources: *
 Weather history for Newport, Shropshire:
 [https://www.timeanddate.com/weather/@7296017/historic](https://www.timeanddate.com/weather/@7296017/historic) * Harper Adams University Meteorological Station (Internal Records). ## Code/Software The processing workflow for this dataset is primarily Python-based. **Core Software:** * **NoisePy4DAS (v1.0):** Used for computing the time-lapse cross-correlations and autocorrelations. * **Python (v3.8+):** Including packages `h5py` for HDF5 manipulation, `pandas/numpy` for meteorological data analysis, and `obspy` for seismic data handling. **Workflow:** The workflow selects one out of every five 1-minute raw segments to reduce data volume, applies one-bit normalization to the waveforms to remove transient noise (like vehicle pass-bys), and computes the ACFs in the frequency domain before transforming them back to the time domain for phase-shift analysis. The code for reproducing the analysis can be accessed through the Denolle Lab GitHub repository: [https://github.com/Denolle-Lab/FarmDAS](https://github.com/Denolle-Lab/FarmDAS)