The application of extended Euler deconvolution method in the interpretation of potential field data

2014 ◽  
Vol 107 ◽  
pp. 188-194 ◽  
Author(s):  
Guoqing Ma
2014 ◽  
Vol 33 (4) ◽  
pp. 448-450 ◽  
Author(s):  
Leonardo Uieda ◽  
Vanderlei C. Oliveira ◽  
Valéria C. F. Barbosa

In this tutorial, we will talk about a widely used method of interpretation for potential-field data called Euler de-convolution. Our goal is to demonstrate its usefulness and, most important, to call attention to some pitfalls encountered in interpretation of the results. The code and synthetic data required to reproduce our results and figures can be found in the accompanying IPython notebooks ( ipython.org/notebook ) at dx.doi.org/10.6084/m9.figshare.923450 or github.com/pinga-lab/paper-tle-euler-tutorial . The note-books also expand the analysis presented here. We encourage you to download the data and try them on your software of choice. For this tutorial, we will use the implementation in the open-source Python package Fatiando a Terra ( fatiando.org ).


2016 ◽  
Author(s):  
Arvind Singh ◽  
Upendra Kumar Singh

Abstract. This paper deals the application of Continuous Wavelet Transform (CWT) and Euler deconvolution methods to estimate the source depth using magnetic anomalies. These methods are utilised mainly to focus on the fundamental issue for mapping the major coal seam and locating magnetic lineaments. These methods are tested and demonstrated on synthetic data and finally applied on field data from Jharia coal field. Prepared magnetic anomaly map that reflects clear tectonics control and nature of the underlying basement, demarcation of the basin, geological faults by steep gradients of magnetic anomaly. Analysis suggests that the CWT have a great utility in the magnetic data interpretation and the correlation between magnetic anomalies and geological features such as faults/joints and intrusive bodies over the basin. The CWT provides the consistent and reliable depth of the underlying basement with the results of Euler deconvolution and Tiltdepth methods without any priory information that is correlated well with borehole samples (Raja Rao, 1987). One of the fundamental issues is to detect differences in susceptibility and density between rocks that contain ore deposits or hydrocarbons or coal. These differences are reflected in the gravity and magnetic anomalies and also delineation of structural features, which are interpreted using several techniques (Blakely and Simpson, 1986). One of the most important objective in the interpretation of potential field data is to improve the resolution of underlying source, delineating lateral change in magnetic susceptibilities that provides information not only on lithological changes but also on structural trends. Especially, mapping the edges of causative bodies is fundamental to the application of potential field data to geological mapping. The edge detection techniques are used to distinguish between different sizes and different depths of the geological discontinuities (Cooper and Cowan 2006, 2008; Perez et al. 2005; Ardestani 2010; Hsu et al. 1996, 2002; Holschneider et al., 2003). The derivatives of magnetic data are used to enhance the edges of anomalies and improve significantly the visibility of such features. Sedimentary layer dominates the gravity and magnetic signature over Jharia Coal field (Verma et al., 1973, 1976, 1979). Thus the difference between the depths estimated using Euler deconvolution method (EDM) (Thompson 1982; Reid et al. 1990) and Tilt Depth Method (TDM) technique (Salem et al., 2007; Cooper 2004, 2011) may help to detect the thickness of the coalbed. Wavelet transform and Euler deconvolution method has been theoretically demonstrated on magnetic data. These methods provide source parameters such as the location, depth, geometry of geological bodies and interfaces in an easy and effective way. However, it may be more difficult to characterize the source properties in cases of extended sources (Sailhac et al., 2009). These methods executed over Jharia coal field, Dhanbad, India. This area forms an east west trending belt of Gondwana basin of Damodar valley at the north eastern part of India. This study region is mostly coal rich area of Gondwana basin. Analysis on Jharia coal field suggests that the magnetic anomalies provide encouraging results which are well correlated with available gravity data and some borehole informations.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Luan Thanh Pham ◽  
Ozkan Kafadar ◽  
Erdinc Oksum ◽  
Ahmed M. Eldosouky

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li

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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