Identification of subsurface soil layers in Sutami Dam area and its surroundings using magnetic methods

We determined the sulfate desorption in representative soils of the São Paulo State, Brazil, and related it to the soil affinity for sulfate ions as estimated by the parameters kL and nF of the Langmuir and Freundlich equations, respectively. Although the parameters kL and nF were well correlated, the nonlinear decay of the sulfate desorption with the increase in the soil affinity by sulfate was better defined when the affinity was estimated by the parameter kL. Despite this, results of multiple regression linear analyses carried out for both kL and nF were quite similar and allowed us to verify that sulfate desorption tends to increase in subsurface soil layers as they become less acidic and/or have their phosphate-extractable sulfur contents increased trough successive fertilisations and/or amendments with S-containing products. At the same time, sulfate desorption in these soils tends to be restrained by the advance of weathering.

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Modeling Freezing and Thawing of Subsurface Soil Layers for Global Climate Models

Climate Models ◽

10.5772/32863 ◽

2012 ◽

Author(s):

Kazuyuki Saito

Keyword(s):

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Freezing And Thawing ◽

Soil Layers ◽

Subsurface Soil

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Estimation of Distribution of Subsurface Soil Layers in the Mexico City Earthquake Area Based on the Database

Geographic Information Systems (GIS) and Mapping—Practices and Standards ◽

10.1520/stp24195s ◽

2009 ◽

pp. 172-172-11

Author(s):

M Kawamura ◽

T Arai ◽

T Ozawa

Keyword(s):

Mexico City ◽

Soil Layers ◽

Subsurface Soil ◽

Estimation Of Distribution ◽

Earthquake Area

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Liquefaction potential evaluation for subsurface soil layers of Delhi region

Journal of the Geological Society of India ◽

10.1007/s12594-016-0473-y ◽

2016 ◽

Vol 88 (2) ◽

pp. 147-150 ◽

Cited By ~ 2

Author(s):

L. Thoithoi ◽

C. S. Dubey ◽

P. S. Ningthoujam ◽

D. P. Shukla ◽

R. P. Singh ◽

...

Keyword(s):

Liquefaction Potential ◽

Soil Layers ◽

Subsurface Soil ◽

Potential Evaluation

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Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran

Geologica Carpathica ◽

10.2478/v10096-011-0034-7 ◽

2011 ◽

Vol 62 (5) ◽

pp. 477-485 ◽

Cited By ~ 4

Author(s):

Farzad Farrokhzad ◽

Amin Barari ◽

Lars Ibsen ◽

Asskar Choobbasti

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Training Data ◽

Landslide Risk ◽

Northern Iran ◽

Soil Layers ◽

Subsurface Soil ◽

Soil Layering ◽

Artificial Neural

Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran This paper is concerned principally with the application of Artificial Neural Networks (ANN) in geotechnical engineering. In particular the application of ANN is discussed in more detail for subsurface soil layering and landslide analysis. Two ANN models are trained to predict subsurface soil layering and landslide risk using data collected from a study area in northern Iran. Given the three-dimensional coordinates of soil layers present in thirty boreholes as training data, our first ANN successfully predicted the depth and type of subsurface soil layers at new locations in the region. The agreement between the ANN outputs and actual data is over 90 % for all test cases. The second ANN was designed to recognize the probability of landslide occurrence at 200 sampling points which were not used in training. The neural network outputs are very close (over 92 %) to risk values calculated by the finite element method or by Bishop's method.

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