Model-based separation filtering of magnetic data

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. L17-L23 ◽  
Author(s):  
Mark Pilkington ◽  
Duncan R. Cowan
Keyword(s):  
Power Spectrum ◽  
Data Interpretation ◽  
Source Distribution ◽  
Magnetic Data ◽  
Cutoff Wavelength ◽  
Potential Field Data ◽  
Source Effects ◽  
Model Based ◽  
Optimal Separation ◽  
Basaltic Layer

Separating the fields produced by sources at different depths is a common requirement in the interpretation of potential field data. Approaches to this problem are generally data- or model-based. Data-based methods require clear linear segments in the logarithmic power spectrum of the data corresponding to different components of the field. Various types of filters can then be designed to carry out the separation. When the logarithmic power spectrum shows no identifiable linear spectral segments, other approaches are necessary. We outline a model-based method that does not depend on power-spectral information but requires independent estimates of the average depths of the source distributions, e.g., from seismic interpretations. An ensemble of models using fractal source distributions is computed based on these known values, and filter parameters are determined that produce the closest fit (in a least-squares sense) to the theoretical fields that each source distribution generates. This approach is used to separate basement effects from intrasedimentary sources in magnetic data collected over the Colville Hills, Northwest Territories, Canada. Seismic data interpretation places crystalline basement at ∼10 km depth and an intrasedimentary basaltic layer at ∼2 km. Our approach results in an optimal separation filter with a cutoff wavelength of ∼12 km that appears to provide an effective separation of the two source effects.

Visual saliency and potential field data enhancements: Where is your attention drawn?

2014 ◽  
Vol 2 (4) ◽  
pp. SJ9-SJ21 ◽  
Author(s):  
Yathunanthan Sivarajah ◽  
Eun-Jung Holden ◽  
Roberto Togneri ◽  
Michael Dentith ◽  
Mark Lindsay
Keyword(s):  
Visual Attention ◽  
Image Enhancement ◽  
Feature Detection ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Data Interpretation ◽  
Detection Algorithm ◽  
Magnetic Data ◽  
Data Set ◽  
Potential Field Data

Interpretation of gravity and magnetic data for exploration applications may be based on pattern recognition in which geophysical signatures of geologic features associated with localized characteristics are sought within data. A crucial control on what comprises noticeable and comparable characteristics in a data set is how images displaying those data are enhanced. Interpreters are provided with various image enhancement and display tools to assist their interpretation, although the effectiveness of these tools to improve geologic feature detection is difficult to measure. We addressed this challenge by analyzing how image enhancement methods impact the interpreter’s visual attention when interpreting the data because features that are more salient to the human visual system are more likely to be noticed. We used geologic target-spotting exercises within images generated from magnetic data to assess commonly used magnetic data visualization methods for their visual saliency. Our aim was achieved in two stages. In the first stage, we identified a suitable saliency detection algorithm that can computationally predict visual attention of magnetic data interpreters. The computer vision community has developed various image saliency detection algorithms, and we assessed which algorithm best matches the interpreter’s data observation patterns for magnetic target-spotting exercises. In the second stage, we applied this saliency detection algorithm to understand potential visual biases for commonly used magnetic data enhancement methods. We developed a guide to choosing image enhancement methods, based on saliency maps that minimize unintended visual biases in magnetic data interpretation, and some recommendations for identifying exploration targets in different types of magnetic data.

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.

Automatic 3-D interpretation of potential field data using analytic signal derivatives

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Nicole Debeglia ◽  
Jacques Corpel
Keyword(s):  
Signal Amplitude ◽  
Vertical Gradient ◽  
Analytic Signal ◽  
Magnetic Data ◽  
Practical Implementation ◽  
Potential Field Data ◽  
Gravity And Magnetic ◽  
Second Derivatives ◽  
Gravity And Magnetic Data ◽  
Derivatives Of

A new method has been developed for the automatic and general interpretation of gravity and magnetic data. This technique, based on the analysis of 3-D analytic signal derivatives, involves as few assumptions as possible on the magnetization or density properties and on the geometry of the structures. It is therefore particularly well suited to preliminary interpretation and model initialization. Processing the derivatives of the analytic signal amplitude, instead of the original analytic signal amplitude, gives a more efficient separation of anomalies caused by close structures. Moreover, gravity and magnetic data can be taken into account by the same procedure merely through using the gravity vertical gradient. The main advantage of derivatives, however, is that any source geometry can be considered as the sum of only two types of model: contact and thin‐dike models. In a first step, depths are estimated using a double interpretation of the analytic signal amplitude function for these two basic models. Second, the most suitable solution is defined at each estimation location through analysis of the vertical and horizontal gradients. Practical implementation of the method involves accurate frequency‐domain algorithms for computing derivatives with an automatic control of noise effects by appropriate filtering and upward continuation operations. Tests on theoretical magnetic fields give good depth evaluations for derivative orders ranging from 0 to 3. For actual magnetic data with borehole controls, the first and second derivatives seem to provide the most satisfactory depth estimations.

Computer Application in Geosciences: Imaging of the Subsurface by Structural Coupled Inversion of Potential Field Data

2014 ◽  
Vol 644-650 ◽  
pp. 2670-2673
Author(s):  
Jun Wang ◽  
Xiao Hong Meng ◽  
Fang Li ◽  
Jun Jie Zhou
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Computer Application ◽  
Magnetic Data ◽  
Imaging Method ◽  
Inversion Algorithm ◽  
Constant Property ◽  
Near Surface ◽  
Potential Field Data

With the continuing growth in influence of near surface geophysics, the research of the subsurface structure is of great significance. Geophysical imaging is one of the efficient computer tools that can be applied. This paper utilize the inversion of potential field data to do the subsurface imaging. Here, gravity data and magnetic data are inverted together with structural coupled inversion algorithm. The subspace (model space) is divided into a set of rectangular cells by an orthogonal 2D mesh and assume a constant property (density and magnetic susceptibility) value within each cell. The inversion matrix equation is solved as an unconstrained optimization problem with conjugate gradient method (CG). This imaging method is applied to synthetic data for typical models of gravity and magnetic anomalies and is tested on field data.

Comment on “Grazing incidence X-ray fluorescence of periodic structures – a comparison between X-ray standing waves and geometrical optics calculations” by F. Reinhardt, S. H. Nowak, B. Beckhoff, J-C. Dousse and M. Schoengen, JAAS, 2014, 29, 1778

2015 ◽  
Vol 30 (12) ◽  
pp. 2548-2550
Author(s):  
W. Jark ◽  
D. Eichert
Keyword(s):  
Physical Model ◽  
Periodic Structures ◽  
Standing Waves ◽  
Grazing Incidence ◽  
Data Interpretation ◽  
Geometrical Optics ◽  
X Ray ◽  
Model Based ◽  
Published Paper

The data interpretation in the recently published paper with the above title is criticized and it is shown that an alternative more physical model based on diffraction in periodic structures can explain the data better and more consistently.

Inversion of airborne geophysics over the DO-27/DO-18 kimberlites — Part 1: Potential fields

2017 ◽  
Vol 5 (3) ◽  
pp. T299-T311 ◽  
Author(s):  
Sarah G. R. Devriese ◽  
Kristofer Davis ◽  
Douglas W. Oldenburg
Keyword(s):  
Magnetic Data ◽  
Gravity Gradiometry ◽  
3D Inversion ◽  
Potential Field Data ◽  
Electromagnetic Data ◽  
Magnetic Inversion ◽  
Geophysical Surveys ◽  
Airborne Geophysics ◽  
Joint Interpretation ◽  
Geologic Model

The Tli Kwi Cho (TKC) kimberlite complex contains two pipes, called DO-27 and DO-18, which were discovered during the Canadian diamond exploration rush in the 1990s. The complex has been used as a testbed for ground and airborne geophysics, and an abundance of data currently exist over the area. We have evaluated the historical and geologic background of the complex, the physical properties of interest for kimberlite exploration, and the geophysical surveys. We have carried out 3D inversion and joint interpretation of the potential field data. The magnetic data indicate high susceptibility at DO-18, and the magnetic inversion maps the horizontal extent of the pipe. DO-27 is more complicated. The northern part is highly magnetic and is contaminated with remanent magnetization; other parts of DO-27 have a low susceptibility. Low densities, obtained from the gravity and gravity gradiometry data, map the horizontal extents of DO-27 and DO-18. We combine the 3D density contrast and susceptibility models into a single geologic model that identifies three distinct kimberlite rock units that agree with drilling data. In further research, our density and magnetic susceptibility models are combined with information from electromagnetic data to provide a multigeophysical interpretation of the TKC kimberlite complex.

The Power Spectrum Estimation of the AR Model Based on Motor Imagery EEG

2013 ◽  
Vol 706-708 ◽  
pp. 1923-1927 ◽  
Author(s):  
Li Zhao ◽  
Yang He
Keyword(s):  
Power Spectrum ◽  
Motor Imagery ◽  
Ar Model ◽  
Spectrum Estimation ◽  
Power Spectrum Estimation ◽  
Estimation Algorithms ◽  
Model Based

This paper uses three common AR model power spectrum estimation algorithms which are the Yule-Walker method, the burg method and the improved covariance method. Taking Matlab as a tool, the corresponding algorithms are used to carry out the power spectrum estimation of motor imagery EEG, the relationships and distinctions between the spectrum charts are compared in order to find the relatively appropriate algorithm for analyzing the EEG, which aims at providing a theoretical guidance for processing the motor imagery EEG and laying a foundation for further research.

Misinterpreting proxy data for paleoclimate signals: A reply to Srivastava and Jovane, 2020

The Holocene ◽  
2020 ◽  
Vol 30 (12) ◽  
pp. 1874-1883
Author(s):  
Tanuj Shukla ◽  
Manish Mehta ◽  
Dwarika Prasad Dobhal ◽  
Archna Bohra ◽  
Bhanu Pratap ◽  
...  
Keyword(s):  
Late Holocene ◽  
Data Interpretation ◽  
High Energy ◽  
Magnetic Data ◽  
Climate Variations ◽  
Magnetic Mineralogy ◽  
Proxy Data ◽  
Glacier Valley ◽  
Future Work ◽  
Rule Out

Srivastava and Jovane (2020) have made several comments on our assessment of proxy data and challenged the outcome of Shukla et al. (2020) based mainly on interpretation of environmental magnetic parameters. We respond to their criticisms and re-evaluate our paper, remove ambiguities and validate our conclusions through additional proxies (grain-size and geochemistry). We welcome their comments and do not entirely rule out their interpretation for magnetic mineralogy. We highlight the importance of proxy validation for high-energy environments like Chorabari lake. However, single proxy data correlation is likely to produce biased results with no relevant meaning. The objective of our study was to understand complexities in the glacial-climate system by reconstructing late-Holocene climate variations using the glacial lake sediment records from the Mandakini River Basin, Central Himalaya, India. We presented the complexities in Shukla et al. (2020), and this was also highlighted by Srivastava and Jovane (2020). In response, we provide additional justification of proxy response and substantiate our results with present-day estimates from the Chorabari glacier valley. We disagree with the thesis put forward by Srivastava and Jovane (2020) in their conclusion as they overemphasize the interpretation of a single proxy. We maintain that the investigation of present-day glacial settings is an important precursor of paleoclimatic data interpretation and that this supports our conclusions. We will try to incorporate the important suggestions of Srivastava and Jovne (2020) relating to the interpretation of magnetic data in future work.

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