1887

Abstract

Summary

GPR is one of the geophysical methods most used to explore and characterize the shallow surface, in particular, to study eolian and fluvial deposits in sandy environments. A usual prospecting strategy is to acquire longitudinal profiles and transects, with the goal of determining the geometry of the structures along and through a determined vertical plane, which is often parallel to the predominant wind direction of a given period. Normally, the data are acquired by using the reflection mode and the constant offset configuration, and then processed through standard procedures. With this methodology, detailed images of the reflectors in the soil can be obtained, from which the interpretation is performed. A complementary practice, which has been little used in the area of GPR, is to calculate attributes of the data. The main objectives of using attributes are to reveal and quantify different properties of the reflection patterns that improve its interpretation. In this work, we analyze different attributes of the GPR data sections, to investigate present eolian-fluvial interaction deposits. In particular, we show that attributes as the rms frequency, apparent dip, curvature and parallelism produce information that is useful to differentiate similar sedimentary units and characterize them in detail.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201902369
2019-09-08
2020-03-29
Loading full text...

Full text loading...

References

  1. Barnes, A
    , 2016. Handbook of poststack Seismic attributes. Geophysical references series No. 21, Society of Exploration Geophysicists, Yale, USA. Pp. 254.
    [Google Scholar]
  2. Chopra, S. and Marfurt, K.
    , 2007. Seismic attributes for prospect identification and reservoir characterization. SEG geophysical developments series, no. 11, Yale, USA. Pp. 464.
    [Google Scholar]
  3. Du, H., Wang, Z., & Mao, D.
    , 2018. Characteristics of Sand Dune Pattern and Fluvial-Aeolian Interaction in Horqin Sandy Land, Northeast Plain of China. Chinese Geographical Science, 1–12.
    [Google Scholar]
  4. Fu, T., Tan, L., Wu, Y., Wen, Y., Li, D., Duan, J.
    , 2018. Quantitative analysis of ground penetrating radar data in the Mu Us Sandland. Aeolian Research32, 218–227.
    [Google Scholar]
  5. Liu, B. and Coulthard, T.J.
    , 2017. Modelling the interaction of eolian and fluvial processes with a combined cellular model of sand dunes and river systems. Computers and Geosciences106, 1–9.
    [Google Scholar]
  6. Neal, A.
    , 2004. Ground-penetrating radar and its use in sedimentology: principles, problems and progress. Earth-science reviews, 66, 261–330.
    [Google Scholar]
  7. Tripaldi, A. y Limarino, C.O.
    , 2008. Ambientes de interacción eólica-fluvial en valles intermontanos: ejemplos actuales y antiguos. Latin American Journal of Sedimentology and Basin Analysis15: 43–66.
    [Google Scholar]
  8. Zabala, Medina P., Bonomo, N., Osella, A.M., Salvo Bernárdez, S., Limarino, O.
    , 2019. GPR prospecting of fluvial-eolian interaction deposits in the Bermejo Valley, NW Argentina. 24th European Meeting of Environmental and Engineering Geophysics. DOI: 10.3997/2214‑4609.201802474.
    https://doi.org/10.3997/2214-4609.201802474 [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902369
Loading
/content/papers/10.3997/2214-4609.201902369
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error