scholarly journals Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code

2021 ◽  
Vol 14 (11) ◽  
pp. 6681-6709
Author(s):  
Jérémie Giraud ◽  
Vitaliy Ogarko ◽  
Roland Martin ◽  
Mark Jessell ◽  
Mark Lindsay
Keyword(s):  
Open Source ◽  
Potential Field ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Source Inversion ◽  
Bound Constraints ◽  
Data Set ◽  
Potential Field Data ◽  
Geological Information ◽  
Geological Constraints

Abstract. The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review of the different techniques for the utilisation of structural, petrophysical, and geological information in single physics and joint inversion as implemented in the Tomofast-x open-source inversion platform. We detail the range of constraints that can be applied to the inversion of potential field data. The inversion examples we show illustrate a selection of scenarios using a realistic synthetic data set inspired by real-world geological measurements and petrophysical data from the Hamersley region (Western Australia). Using Tomofast-x's flexibility, we investigate inversions combining the utilisation of petrophysical, structural, and/or geological constraints while illustrating the utilisation of the L-curve principle to determine regularisation weights. Our results suggest that the utilisation of geological information to derive disjoint interval bound constraints is the most effective method to recover the true model. It is followed by model smoothness and smallness conditioned by geological uncertainty and cross-gradient minimisation.

scholarly journals Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code

2021 ◽  
Author(s):  
Jeremie Giraud ◽  
Vitaliy Ogarko ◽  
Roland Martin ◽  
Mark Jessell ◽  
Mark Lindsay
Keyword(s):  
Open Source ◽  
Potential Field ◽  
Joint Inversion ◽  
Source Inversion ◽  
Bound Constraints ◽  
Potential Field Data ◽  
Geological Information ◽  
Interval Bound ◽  
Quantitative Integration ◽  
Geological Constraints

Abstract. The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review of the different techniques for the utilisation of structural, petrophysical and geological information in single physics and joint inversion as implemented in the Tomofast-x open-source inversion platform. We detail the range of constraints that can be applied to the inversion of potential field data. The inversion examples we show illustrate a selection of scenarios using a realistic synthetic dataset inspired by real-world geological measurements and petrophysical data from the Hamersley region (Western Australia). Using Tomofast-x’s flexibility, we investigate inversions combining the utilisation of petrophysical, structural and/or geological constraints while illustrating the utilisation of the L-curve principle to determine regularisation weights. Our results suggest that the utilisation of geological information to derive disjoint interval bound constraints is the most effective method to recover the true model. It is followed by model smoothness and smallness conditioned by geological uncertainty, and cross-gradient minimisation.

scholarly journals Geophysical tutorial: Euler deconvolution of potential-field data

2014 ◽  
Vol 33 (4) ◽  
pp. 448-450 ◽  
Author(s):  
Leonardo Uieda ◽  
Vanderlei C. Oliveira ◽  
Valéria C. F. Barbosa
Keyword(s):  
Open Source ◽  
Potential Field ◽  
Field Data ◽  
Synthetic Data ◽  
Euler Deconvolution ◽  
Potential Field Data ◽  
Python Package

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 ).

scholarly journals A computation scheme based on field attenuation rate for improving regional-residual separation of potential field data set

2019 ◽  
Author(s):  
Jun Wang ◽  
Xiaohong Meng ◽  
Fang Li
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Synthetic Data ◽  
Regional Trend ◽  
Data Set ◽  
Potential Field Data ◽  
Attenuation Rate ◽  
Original Field ◽  
Computation Scheme ◽  
The Difference

Abstract To further improve the accuracy of regional-residual separation of potential field data set, this paper presents a novel computation scheme based on different attenuation rate of the fields induced from deep and shallow sources respectively. For the new scheme, the observations are first upward continued to a plane above it to get an updated field. Then, the difference between the original field and the updated field is calculated. Next, a controlling parameter is set to select those data points whose amplitudes have been much reduced. The adverse effects from the residual anomalies on the fitting of the regional trend can be reduced by removing the identified local points from the original field. Finally, a low-order polynomial is utilised for approximating the regional trend, and the corresponding residual field can be obtained by simple subtraction. Compared with gradient-based methods, the proposed new scheme has better noise adaptability for distinguishing different anomalies. The accuracy of the presented scheme was tested on synthetic data with and without noise. All tests showed that the new scheme reduces subjectivity and inaccuracy of the conventional methods significantly. In addition, the scheme was applied to Bouguer gravity anomaly of the Dida orebodies in Jilin Province, northeast China. This application also verified the superiority of the proposed scheme.

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.

scholarly journals GRAV-MAG SUITE - An open source MATLAB-based program for processing potential field data

2018 ◽  
Author(s):  
F.R. Castro ◽  
S.P. Oliveira ◽  
J. de Souza ◽  
F.J.F. Ferreira
Keyword(s):  
Open Source ◽  
Potential Field ◽  
Field Data ◽  
Potential Field Data

Seismic‐controlled nonlinear extrapolation of well parameters using neural networks

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 2035-2041 ◽  
Author(s):  
Zhengping Liu ◽  
Jiaqi Liu
Keyword(s):  
Neural Networks ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Real Data ◽  
Wide Band ◽  
Seismic Section ◽  
Data Set ◽  
Multiple Parameters ◽  
Sonic Log ◽  
Well Data

We present a data‐driven method of joint inversion of well‐log and seismic data, based on the power of adaptive mapping of artificial neural networks (ANNs). We use the ANN technique to find and approximate the inversion operator guided by the data set consisting of well data and seismic recordings near the wells. Then we directly map seismic recordings to well parameters, trace by trace, to extrapolate the wide‐band profiles of these parameters using the approximation operator. Compared to traditional inversions, which are based on a few prior theoretical operators, our inversion is novel because (1) it inverts for multiple parameters and (2) it is nonlinear with a high degree of complexity. We first test our algorithm with synthetic data and analyze its sensitivity and robustness. We then invert real data to obtain two extrapolation profiles of sonic log (DT) and shale content (SH), the latter a unique parameter of the inversion and significant for the detailed evaluation of stratigraphic traps. The high‐frequency components of the two profiles are significantly richer than those of the original seismic section.

Multiple joint inversion of seismic, magnetotelluric and potential field data in Santos basin

2011 ◽  
Author(s):  
Luis A. Gallardo ◽  
Sergio L. Fontes ◽  
Vinicius R. Pinto ◽  
Max Meju ◽  
Patricia de Lugao
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Joint Inversion ◽  
Potential Field Data

Reduction of potential field data to a horizontal plane

Geophysics ◽  
1990 ◽  
Vol 55 (5) ◽  
pp. 549-555 ◽  
Author(s):  
Mark Pilkington ◽  
W. E. S. Urquhart
Keyword(s):  
Potential Field ◽  
Horizontal Plane ◽  
Field Data ◽  
Synthetic Data ◽  
Mirror Image ◽  
Source Distribution ◽  
Aeromagnetic Survey ◽  
Potential Field Data ◽  
Equivalent Sources ◽  
Topographic Gradients

Most existing techniques for potential field data enhancement and interpretation require data on a horizontal plane. Hence, when observations are made on an irregular surface, reduction to a horizontal plane is necessary. To effect this reduction, an equivalent source distribution that models the observed field is computed on a mirror image of the observation surface. This irregular mirror image surface is then replaced by a horizontal plane and the effect of the equivalent sources is computed on the required horizontal level. This calculated field approximates the field reduced to a horizontal plane. The good quality of this approximation is demonstrated by two‐dimensional synthetic data examples in which the maximum errors occur in areas of steep topographic gradients and increased magnetic field intensity. The approach is also applied to a portion of a helicopter‐borne aeromagnetic survey from the Gaspé region in Quebec, Canada, where the results are a horizontal shifting of anomaly maxima of up to 150 m and changes in anomaly amplitudes of up to 100 nT.

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