Multimineral non-linear inversion method using geochemical logging data based on Tikhonov regularization

2017 ◽  
Author(s):  
S.S. Wang ◽  
L.Z. Xiao ◽  
A.Z. Yue ◽  
X. Li
Keyword(s):  
Tikhonov Regularization ◽  
Inversion Method ◽  
Linear Inversion ◽  
Non Linear

Non linear inversion of gravity gradients and the GGI gradiometer

2011 ◽  
Vol 3 (4) ◽  
Author(s):  
Manik Talwani
Keyword(s):  
Iterative Procedure ◽  
Least Square ◽  
Inversion Method ◽  
Gravity Gradients ◽  
Linear Inversion ◽  
Non Linear ◽  
Best Fitting ◽  
The Difference ◽  
Differential Curvature ◽  
Fitting Model

AbstractAll gradiometers currently operating for exploration in the field are based on Lockheed Martin’s GGI gradiometer. The working of this gradiometer is described and a method for robust non linear inversion of gravity gradients is presented. The inversion method involves obtaining the gradient response of a trial body consisting of vertical rectangular prisms. The inversion adjusts the depth to the tops or bases of the prisms. In the trial model all the prisms are not required to have the same area of cross section or the same density (which can also be allowed to vary with depth). The depth to the tops and bottoms of each prism can also be different. This response is compared with the observed values of gradient and through an iterative procedure, the difference is minimized in a least square sense to arrive at a best fitting model by varying the position of the tops or bottoms of the prisms. Each gradient can be individually inverted or one or more gradients can be jointly inverted. The method is extended to invert gravity values individually or jointly with gradient values. The use of Differential Curvature, a quantity which is directly obtained by current gradiometers in use and which is an invariant under a rotation in the horizontal plane, is emphasized. Synthetic examples as well as a field example of inversion are given.

scholarly journals A New Hybrid Inversion Method for 2D Nuclear Magnetic Resonance Combining TSVD and Tikhonov Regularization

2021 ◽  
Vol 7 (2) ◽  
pp. 18
Author(s):  
Germana Landi ◽  
Fabiana Zama ◽  
Villiam Bortolotti
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Tikhonov Regularization ◽  
Large Scale ◽  
Regularization Parameter ◽  
High Sensitivity ◽  
Inversion Method ◽  
Nmr Relaxometry ◽  
Value Decomposition ◽  
Nuclear Magnetic

This paper is concerned with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) relaxometry. This is a large-scale and ill-posed inverse problem with many potential applications in biology, medicine, chemistry, and other disciplines. However, the large amount of data and the consequently long inversion times, together with the high sensitivity of the solution to the value of the regularization parameter, still represent a major issue in the applicability of the NMR relaxometry. We present a method for two-dimensional data inversion (2DNMR) which combines Truncated Singular Value Decomposition and Tikhonov regularization in order to accelerate the inversion time and to reduce the sensitivity to the value of the regularization parameter. The Discrete Picard condition is used to jointly select the SVD truncation and Tikhonov regularization parameters. We evaluate the performance of the proposed method on both simulated and real NMR measurements.

2-D and 3-D resistivity image reconstruction using crosshole data

Geophysics ◽  
1992 ◽  
Vol 57 (10) ◽  
pp. 1270-1281 ◽  
Author(s):  
Hiromasa Shima
Keyword(s):  
Field Test ◽  
Numerical Models ◽  
Electrical Potential ◽  
Nonlinear Least Squares ◽  
Three Dimensions ◽  
Inversion Method ◽  
Good Resolution ◽  
Inversion Algorithm ◽  
Linear Inversion ◽  
Resistivity Model

Theoretical changes in the distribution of electrical potential near subsurface resistivity anomalies have been studied using two resistivity models. The results suggest that the greatest response from such anomalies can be observed with buried electrodes, and that the resistivity model of a volume between boreholes can be accurately reconstructed by using crosshole data. The distributive properties of crosshole electrical potential data obtained by the pole‐pole array method have also been examined using the calculated partial derivative of the observed apparent resistivity with respect to a small cell within a given volume. The results show that for optimum two‐dimensional (2-D) and three‐dimensional (3-D) target imaging, in‐line data and crossline data should be combined, and an area outside the zone of exploration should be included in the analysis. In this paper, the 2-D and 3-D resistivity images presented are reconstructed from crosshole data by the combination of two inversion algorithms. The first algorithm uses the alpha center method for forward modeling and reconstructs a resistivity model by a nonlinear least‐squares inversion. Alpha centers express a continuously varying resistivity model, and the distribution of the electrical potential from the model can be calculated quickly. An initial general model is determined by the resistivity backprojection technique (RBPT) prior to the first inversion step. The second process uses finite elements and a linear inversion algorithm to improve the resolution of the resistivity model created by the first step. Simple 2-D and 3-D numerical models are discussed to illustrate the inversion method used in processing. Data from several field studies are also presented to demonstrate the capabilities of using crosshole resistivity exploration techniques. The numerical experiments show that by using the combined reconstruction algorithm, thin conductive layers can be imaged with good resolution for 2-D and 3-D cases. The integration of finite‐element computations is shown to improve the image obtained by the alpha center inversion process for 3-D applications. The first field test uses horizontal galleries to evaluate complex 2-D features of a zinc mine. The second field test illustrates the use of three boreholes at a dam site to investigate base rock features and define the distribution of an altered zone in three dimensions.

Non Linear Inversion for the Hydrostatic Structure of the Solar Interior

1999 ◽  
pp. 51-52
Author(s):  
K. I. Marchenkov ◽  
I. W. Roxburgh ◽  
S. V. Vorontsov
Keyword(s):  
Solar Interior ◽  
Linear Inversion ◽  
Non Linear
Sign in / Sign up

Export Citation Format

Share Document