T
Torleif Dahlin
Researcher at Lund University
Publications - 202
Citations - 6685
Torleif Dahlin is an academic researcher from Lund University. The author has contributed to research in topics: Electrical resistivity tomography & Aquifer. The author has an hindex of 34, co-authored 202 publications receiving 5893 citations.
Papers
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A comparison of smooth and blocky inversion methods in 2-D electrical imaging surveys
TL;DR: In this paper, the L 2 norm based least squares optimisation method is used to map moderately complex structures with arbitrary resistivity distributions, and the blocky or L 1 norm optimization method can be used for such situations.
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A numerical comparison of 2D resistivity imaging with 10 electrode arrays
Torleif Dahlin,Bing Zhou +1 more
TL;DR: In this article, numerical simulations are used to compare the resolution and efficiency of 2D resistivity imaging surveys for 10 electrode arrays, including pole-pole (PP), pole-dipole (PD), half-Wenner (HW), Wenner-α (WN), Schlumberger (SC), dipole-dipsole (DD), WenNER-β (WB), γ -array (GM), multiple or moving gradient array (GD) and midpoint-potential-referred measurement (MPR) arrays.
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2D resistivity surveying for environmental and engineering applications
TL;DR: The need for detailed geological knowledge, for geotechnical, hydrogeological, and environmental protection purposes, has increased in the past decades and can be expected to continue to rise.
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The development of DC resistivity imaging techniques
TL;DR: The development of direct current resistivity imaging techniques has been rapid in the last years as mentioned in this paper, and has led to a greatly expanded practical applicability of the method, which applies to data acquisition as well as inverse modelling techniques.
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A comparison of the Gauss–Newton and quasi-Newton methods in resistivity imaging inversion
M. H. Loke,Torleif Dahlin +1 more
TL;DR: In this paper, the smoothness-constrained least-squares method is used for two-dimensional and three-dimensional (3D) inversion of apparent resistivity data sets.