scholarly journals Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture

2016 ◽  
Vol 230 ◽  
pp. 294-306 ◽  
Author(s):  
R. Shaw ◽  
R.M. Lark ◽  
A.P. Williams ◽  
D.R. Chadwick ◽  
D.L. Jones
Keyword(s):  
Sensor Networks ◽  
Spatial Variation ◽  
Precision Agriculture ◽  
Efficient Design ◽  
Field Scale ◽  
Grassland Soil

scholarly journals Underground Microseismic Event Monitoring and Localization within Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Information Needs ◽  
Localization Algorithm ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Microseismic Events ◽  
The Stability ◽  
Stability And Accuracy

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

scholarly journals Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 938
Author(s):  
Ladislav Menšík ◽  
Lukáš Hlisnikovský ◽  
Pavel Nerušil ◽  
Eva Kunzová
Keyword(s):  
Precision Agriculture ◽  
Alluvial Soil ◽  
Coefficient Of Determination ◽  
Fast Method ◽  
Agricultural Practice ◽  
Regression Equations ◽  
Icp Oes ◽  
Risk Elements ◽  
The Relationship

The aim of the study was to compare the concentrations of risk elements (As, Cu, Mn, Ni, Pb, Zn) in alluvial soil, which were measured by a portable X-ray fluorescence analyser (pXRF) in situ (FIELD) and in the laboratory (LABORATORY). Subsequently, regression equations were developed for individual elements through the method of construction of the regression model, which compare the results of pXRF with classical laboratory analysis (ICP-OES). The accuracy of the measurement, expressed by the coefficient of determination (R2), was as follows in the case of FIELD–ICP-OES: Pb (0.96), Zn (0.92), As (0.72), Mn (0.63), Cu (0.31) and Ni (0.01). In the case of LABORATORY–ICP-OES, the coefficients had values: Pb (0.99), Zn (0.98), Cu and Mn (0.89), As (0.88), Ni (0.81). A higher dependence of the relationship was recorded between LABORATORY–ICP-OES than between FIELD–ICP-OES. An excellent relationship was recorded for the elements Pb and Zn, both for FIELD and LABORATORY (R2 higher than 0.90). The elements Cu, Mn and As have a worse tightness in the relationship; however, the results of the model have shown its applicability for common use, e.g., in agricultural practice or in monitoring the quality of the environment. Based on our results, we can say that pXRF instruments can provide highly accurate results for the concentration of risk elements in the soil in real time for some elements and meet the principle of precision agriculture: an efficient, accurate and fast method of analysis.

Soil Cleanup by In-Situ Aeration. XVII. Field-Scale Model with Distributed Diffusion

1994 ◽  
Vol 29 (10) ◽  
pp. 1251-1274 ◽  
Author(s):  
Céser Gómez-Lahoz ◽  
James M. Rodríguez-Maroto ◽  
David J. Wilson∗
Keyword(s):  
Scale Model ◽  
Field Scale ◽  
Soil Cleanup

scholarly journals Induced microearthquakes predict permeability creation in the brittle crust

2022 ◽  
Author(s):  
Ziyan Li ◽  
Derek Elsworth ◽  
Chaoyi Wang
Keyword(s):  
Patch Size ◽  
Dynamic Evolution ◽  
Permeability Evolution ◽  
Field Scale ◽  
Scale Invariant ◽  
Flow Paths ◽  
Porosity And Permeability ◽  
Hybrid Machine ◽  
Early Injection

Abstract Fracturing controls rates of mass, chemical and energy cycling within the crust. We use observed locations and magnitudes of microearthquakes (MEQs) to illuminate the evolving architecture of fractures reactivated and created in the otherwise opaque subsurface. We quantitatively link seismic moments of laboratory MEQs to the creation of porosity and permeability at field scale. MEQ magnitudes scale to the slipping patch size of remanent fractures reactivated in shear - with scale-invariant roughnesses defining permeability evolution across nine decades of spatial volumes – from centimeter to decameter scale. This physics-inspired seismicity-permeability linkage enables hybrid machine learning (ML) to constrain in-situ permeability evolution at verifiable field-scales (~10 m). The ML model is trained on early injection and MEQ data to predict the dynamic evolution of permeability from MEQ magnitudes and locations, alone. The resulting permeability maps define and quantify flow paths verified against ground truths of permeability.

Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks

Geoderma ◽  
2012 ◽  
Vol 170 ◽  
pp. 195-205 ◽  
Author(s):  
Gary C. Heathman ◽  
Michael H. Cosh ◽  
Eunjin Han ◽  
Thomas J. Jackson ◽  
Lynn McKee ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Spatiotemporal Analysis ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Point Scale
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