Acoustic penetrometer for subsoil investigation

Mitchell, James K.; Villet, Willem C. B.; Tringale, Philip T.; Chan, C. K.

doi:10.1121/1.389881

The Journal of the Acoustical Society of America

1983

DOI: 10.1121/1.389881

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Acoustic penetrometer for subsoil investigation

James K. Mitchell¹,

Willem C. B. Villet²,

Philip T. Tringale³

et al.

Abstract: assignor to Schlumberger Technology Corporation 4 J .anuary 1983 (Class 367/34); flied 6 December 1979 This sonic logging tool consists of a transmitter and several receivers spaced one above the other. First portions of each received signal are correlated with each other using an assumed velocity, and the process is repeated using lesser and greater velocities. The best correlation is selected as the first provisional velocity. The process is repeated for second, third etc. portions of each waveform. The resu… Show more

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Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

Grunwald,

Murad,

Farrington

et al. 2024

Sensors

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We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications.

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Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

Grunwald,

Murad,

Farrington

et al. 2024

Sensors

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show abstract

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Acoustic penetrometer for subsoil investigation

Cited by 1 publication

References 0 publications

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

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