Supporting information and data for "Chapter 1: Constraining Hydraulic Permeability at Great Depth by Using Magnetotellurics" in Pepin JD (2019) New Approaches to Geothermal Resource Exploration and Characterization (PhD dissertation)
收藏DataCite Commons2025-06-01 更新2025-06-15 收录
下载链接:
https://datadryad.org/dataset/doi:10.5061/dryad.3bk3j9kfh
下载链接
链接失效反馈官方服务:
资源简介:
This supporting information includes additional text, figures, and tables
regarding the newly derived sodium-chloride fluid resistivity model and
the magnetotelluric (MT) inversion methodology. It also includes
additional simulated electrical resistivity results that are not
explicitly presented in the main text; the three simulations featured in
the main text are selected to represent this larger set of simulations.
Six supporting datasets are also described in this document. The first is
a compilation of previously published laboratory-measured electrical
resistivity data taken at various temperatures and salinities for
sodium-chloride fluids. While all of this data is considered reliable, the
measurement accuracy is variable, since the data were compiled from work
that was published over a time period ranging from 1907 to 2009. These
data are used to derive a thin-plate spline model that permits estimation
of sodium-chloride fluid resistivity over an extensive range of salinities
(6 to 321,420 mg/L) and temperatures (0 to 309°C). A table of fluid
resistivity, as estimated by the spline model, that covers salinities from
0.001 to 5.5 mol/L over a temperature range of 0 to 300°C is also
provided. A zip folder containing an R script and the necessary input
files to use this spline model independently is included as well. Tables
containing the hydrologic modeling results are provided for the three
simulations featured in the main text. Lastly, the MT forward responses
and calculated inversion fits to those responses are enclosed. The forward
responses include both noise-free MT curves and those with 2% Gaussian
noise added; the noisy curves were used exclusively for the inverse
analysis.
提供机构:
Dryad
创建时间:
2020-03-06



