scholarly journals Application of the normalized largest eigenvalue of structure tensor in the interpretation of potential field tensor data

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Yuan Yuan ◽  
Xiangyu Zhang ◽  
Wenna Zhou ◽  
Guochao Wu ◽  
Weidong Luo
Keyword(s):  
Potential Field ◽  
Continuation Method ◽  
Structure Tensor ◽  
Downward Continuation ◽  
Southwest Indian Ridge ◽  
Magnetic Data ◽  
Depth Information ◽  
Field Tensor ◽  
Largest Eigenvalue ◽  
Tensor Data

Abstract Obtaining horizontal edges and the buried depths of geological bodies, using potential field tensor data directly is an outstanding question. The largest eigenvalue of the structure tensor is one of the commonly used edge detectors for delineating the horizontal edges without depth information of the potential field tensor data. In this study, we presented a normalized largest eigenvalue of structure tensor method based on the normalized downward continuation (NDC) to invert the source location parameters without any priori information. To improve the stability and accuracy of the NDC calculation, the Chebyshev–Pade´ approximation downward continuation method was introduced to obtain the potential field data on different depth levels. The new approach was tested on various models data with and without noise, which validated that it can simultaneously obtain the horizontal edges and the buried depths of the geological bodies. The satisfactory results demonstrated that the normalized largest eigenvalue of structure tensor can describe the locations of geological sources and decrease the noise interference magnified by the downward continuation. Finally, the method was applied to the gravity data over the Humble salt dome in USA, and the near-bottom magnetic data over the Southwest Indian Ridge. The results show a good correspondence to the results of previous work.

A novel method for computing the vertical gradients of the potential field: Application to downward continuation

2019 ◽  
Author(s):  
Kha Van Tran ◽  
Trung Nhu Nguyen
Keyword(s):  
Series Expansion ◽  
Taylor Series ◽  
Potential Field ◽  
Continuation Method ◽  
Taylor Series Expansion ◽  
Expansion Method ◽  
Downward Continuation ◽  
Source Distribution ◽  
Vertical Derivative ◽  
Series Expansion Method

Summary Downward continuation is a very useful technique in the interpretation of potential field data. It would enhance the short wavelength of the gravity anomalies or accentuate the details of the source distribution. Taylor series expansion method has been proposed to be one of the best downward continued methods. However, the method using high-order vertical derivatives leads to low accuracy and instability results in many cases. In this paper, we propose a new method using a combination of Taylor series expansion and upward continuation for computing vertical derivatives. This method has been tested on the gravitational anomaly of infinite horizontal cylinder in both cases with and without random noise for higher accurate and stable than Hilbert transform method and Laplace equation method, especially in the case of noise input data. This vertical derivative method is applied successfully to calculate the downward continuation according to Taylor series expansion method. The downward continuation is also tested on both complex synthetic models and real data in the East Vietnam Sea (South China Sea). The results reveal that by calculating this new vertical derivative, the downward continuation method gave higher accurate and stable than the previous downward continuation methods.

Some comments on potential field tensor data

2003 ◽  
Vol 34 (1-2) ◽  
pp. 57-62 ◽  
Author(s):  
Philip Heath ◽  
Graham Heinson ◽  
Stewart Greenhalgh
Keyword(s):  
Potential Field ◽  
Field Tensor ◽  
Tensor Data

scholarly journals A Magnetic Potential-field Downward Continuation Method Based on Accelerated Landweber Iteration

2021 ◽  
Vol 1885 (4) ◽  
pp. 042003
Author(s):  
Ze Wang ◽  
Qi Zhang ◽  
Mengchun Pan ◽  
Dixiang Chen ◽  
Zhongyan Liu ◽  
...  
Keyword(s):  
Potential Field ◽  
Continuation Method ◽  
Magnetic Potential ◽  
Downward Continuation

An improved and stable downward continuation of potential field data: The truncated Taylor series iterative downward continuation method

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. J75-J86 ◽  
Author(s):  
HengLei Zhang ◽  
Dhananjay Ravat ◽  
XiangYun Hu
Keyword(s):  
Taylor Series ◽  
Potential Field ◽  
Field Data ◽  
Memory Performance ◽  
Continuation Method ◽  
Taylor Series Expansion ◽  
Downward Continuation ◽  
Continuation Methods ◽  
Potential Field Data ◽  
The Difference

We present a stable downward continuation strategy based on combining the ideas of the Taylor series expansion and the iterative downward continuation methods in a single method with better downward continuation and/or computer time/memory performance for potential field data containing noise. In the new truncated Taylor series iterative downward continuation (TTSIDC) method, a correction is made on the continuing plane by downward continuing the difference between the observed and the calculated field. The process is iteratively repeated until the difference meets the convergence conditions. It is tested on synthetic and field data and compared to other downward continuation methods. The proposed method yields sharper images and estimates more accurate amplitudes than most of the existing methods, especially for downward continuation over larger distances. The TTSIDC method also gives comparable results to the method of downward continuation using the least-squares inversion (DCLSI); however, the DCLSI method’s requirements of computer memory and time are substantially greater than our TTSIDC method, rendering the DCLSI method impractical for data sets of routine size on desktop computers commonly available today.

scholarly journals A Stable and High-Precision Downward Continuation Method of Magnetic Data

2021 ◽  
Vol 11 (22) ◽  
pp. 10881
Author(s):  
Zhiwen Zhou ◽  
Jun Wang ◽  
Xiaohong Meng ◽  
Yuan Fang
Keyword(s):  
High Precision ◽  
Continuation Method ◽  
Target Surface ◽  
Lower Surface ◽  
Downward Continuation ◽  
Calculation Procedure ◽  
Continuation Methods ◽  
Magnetic Data ◽  
Equivalent Source ◽  
Original Observation

Downward continuation is an effective technique that can be used to transform the magnetic data measured on one surface to the data that would be measured on another arbitrary lower surface. However, it suffers from amplitude attenuation and is susceptible to noise, especially when the continuation distance is large. To solve these problems, we present a stable and high-precision downward continuation method combining the ideas of equivalent source technique, Tikhonov regularization, radial logarithmic power spectrum analysis, and constrained strategy. To implement this method, the observed data is used to construct the equivalent source in the study area, and the small amount of measured magnetic data at the lower surface (relative to the original observation surface) is employed to constrain the calculation procedure simultaneously. Then the magnetic data at the target surface can be obtained by using a forward calculation procedure instead of the risky downward continuation procedure. The proposed method is tested on both synthetic model data and real magnetic data collected in the South China sea. Various obtained results demonstrate that the method reported in this study has higher accuracy and better noise resistance than the traditional downward continuation methods.

An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Maysam Abedi
Keyword(s):  
Magnetic Field ◽  
Magnetic Susceptibility ◽  
Least Squares ◽  
Minimization Problem ◽  
Potential Field ◽  
Field Data ◽  
Least Squares Method ◽  
Porphyry Copper ◽  
Magnetic Data ◽  
Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit.  The real case study located in  Semnan province of  Iran  consists  of  an arc-shaped  porphyry  andesite  covered  by  sedimentary  units  which  may  have  potential  of  mineral  occurrences, especially  porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Potential Field ◽  
Adaptive Sampling ◽  
Field Data ◽  
Computational Cost ◽  
Large Field ◽  
Magnetic Data ◽  
Data Sampling ◽  
Data Types ◽  
Data Set ◽  
Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

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