3D inversion of potential field data using a marginalizing probabilistic method

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. G93-G106 ◽  
Author(s):  
Meixia Geng ◽  
Xiangyun Hu ◽  
Henglei Zhang ◽  
Shuang Liu
Keyword(s):  
Covariance Matrix ◽  
Field Data ◽  
Weighting Function ◽  
Probabilistic Method ◽  
A Priori ◽  
Inversion Method ◽  
Spatial Gradient ◽  
Underlying Assumption ◽  
Potential Field Data ◽  
Probabilistic Inversion

Probabilistic inversion methods have proven effective in solving many geophysical inverse problems. Structural orientation and spatial extent information can be efficiently incorporated the probabilistic inversion by the use of parameter covariances to produce a geologically realistic model. However, the use of a single model covariance matrix, with the underlying assumption of the presence of only one type of feature (e.g., similar size, shape, and orientation) in the subsurface, limits the ability of probabilistic inversions to recover geologically sound models. An approach based on marginalizing the probabilistic inversion is presented, which makes it possible to partition the inverse domain into various zones, each of which can have its own covariance matrix depending upon the features and/or depths of the sources. Moreover, a spatial gradient weighting function is introduced to enhance or attenuate the structural complexity in different zones. Thus, sources with different shapes, sizes, depths, and densities (or magnetic susceptibilities) can be simultaneously reconstructed. The sensitivity of the solutions to uncertainties in the a priori information, including the orientation, depth, and horizontal position as well as subdivision of the inversion domain, is analyzed. We found through synthetic examples and field data that the developed inversion method was a valid tool for exploration geophysics in presence of a priori geologic information.

Self-potential Data Interpretation for Two Co-axial Structures Utilizing the RMS Parameter

2020 ◽  
Vol 25 (1) ◽  
pp. 15-23
Author(s):  
Khalid S. Essa ◽  
Zein E. Diab ◽  
Mahmoud Elhussein
Keyword(s):  
A Priori ◽  
Source Parameters ◽  
Data Interpretation ◽  
The Body ◽  
Inversion Method ◽  
Model Parameters ◽  
Polarization Angle ◽  
Potential Field Data ◽  
Rms Error ◽  
Self Potential

We have developed an algorithm to obtain the model parameters for two co-axial structures from self-potential data. The method uses the first numerical horizontal derivatives calculated from the observed self-potential anomaly employing filters of sequential window lengths (s-values) so as to gauge the model constraints for the shallow and deep structures. In addition, this algorithm uses a standard inversion method for solving a non-linear equation based on the lowest root-mean-square (RMS) error of the estimated model parameters. The body constraints are the depth, polarization angle and electric dipole moment of each structure. Our approach models the self-potential dataset as an aggregation of spheres, horizontal cylinders, and vertical cylinders. These simple bodies are used to approximate, without a priori expectations, the furthermost plausible position and/or area of intersection. In other words, the bodies are used to estimate the true values of the source parameters for the two-co-axial bodies at different s-values. Minimizing the RMS error has the advantage of optimizing all model factors. The proposed technique is tested using a numerical model with and without noise and on self-potential field data acquired at a site in Germany. In all cases, the assessed body parameters are reasonable approximations of the known values.

Generalized radial inversion of 2D potential‐field data

Geophysics ◽  
2004 ◽  
Vol 69 (6) ◽  
pp. 1405-1413 ◽  
Author(s):  
João B. C. Silva ◽  
Valéria C. F. Barbosa
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Physical Property ◽  
Quantitative Information ◽  
Fixed Number ◽  
Polar Coordinates ◽  
Inversion Method ◽  
Potential Field Data ◽  
Convexity Constraint ◽  
Interpretation Model

We introduce a new 2D method for inverting potential‐field data with model constraints designed by the interpreter. Our method uses an interpretation model consisting of a source with polygonal cross‐section whose vertices are described by polar coordinates with an origininside the source. With this coordinate system, constraints in an inversion are easier to develop and apply. Our inversion method assumes a known physical property contrast for the source and estimates the radii associated with the polygon vertices for a fixed number of equally spaced angles from 0° to 360°. A wide variety of constraints may be used to stabilize the solutions by introducing information about the source shape. The method recovers stable solutions whose shapes range from almost circular or pear‐shaped to elongated in one or more directions. The convexity constraint applied to the source shape, despite requiring no quantitative information, is more versatile than the other constraints. The convexity constraint efficiently recovers source geometries that are either isometric or elongated in one direction.

The Method of Curvature Attribute applied in the Depth Inversion of the Geological Bodies Edge by Potential Field Data

2020 ◽  
Author(s):  
Jinlan Liu ◽  
Wanyin Wang ◽  
Shengqing Xiong
Keyword(s):  
Objective Function ◽  
Potential Field ◽  
Field Data ◽  
Small Scale ◽  
Inversion Method ◽  
Potential Field Data ◽  
Upward Continuation ◽  
Extraction Algorithm ◽  
Key Technologies ◽  
Key Techniques

<p>It is vital to quickly and effectively determine the extent and depth of geological body by using potential field data in gravity and magnetic survey. In this study, three key techniques studying the extent and depth of geological sources based on curvature attribute are studied: the optimal solutions to the objective function, the edge of geological bodies and picking out solutions. Firstly, the optimal solution to the objective function is studied, that is, the key extraction algorithm about the curvature attribute. The Huber norm is introduced into the extraction algorithm of curvature attribute, which more accurately detect the depth of edge of the geological bodies. Secondly, the normalized vertical derivative of the total horizontal derivative (NVDR-THDR) technique is introduced into curvature attribute, which shows more continuous results about the edge position of the geological bodies and more sensitive to the small-scale tectonic structure. Finally, we study the way to pick out the inversion solution, that is, to solve the multi-solution equations in the inversion. The upward continuation of a certain height with strict physical significance was introduced into the inversion method, which was used to suppress the noise, and the final and actual inversion depth was equal to the inversion depth minus the height of upward continuation. And the average value of threshold limitation technology of the potential fields data was also introduced into this method. Using the two technologies, solutions of non-field source edge positions were eliminated, and make the inversion solutions closer to the actual situation. Through the above three key techniques, the accuracy, continuity and recognition to the small-scale structure of the inversion result are optimized. The theoretical models are used to verify the effectiveness of the above key technologies, the results show that the three key technologies have achieved good results, and the combined models are used to verify the effectiveness of the optimized inversion method. The measured aeromagnetic data were used to inversing the edge depth of the intrusive rock in a mining area, and the inversion results are in good agreement with the rock depth revealed by borehole.</p>

scholarly journals OpenMP Implementation of a Novel Potential-Field-Data Source-Growth-Based Inversion Approach for 3D Salt Imaging in Deepwater Gulf of Mexico

2020 ◽  
Vol 10 (14) ◽  
pp. 4798
Author(s):  
Naín Vera ◽  
Carlos Couder-Castañeda ◽  
Jorge Hernández ◽  
Alfredo Trujillo-Alcántara ◽  
Mauricio Orozco-del-Castillo ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Potential Field ◽  
Field Data ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Complex Salt ◽  
Research Field ◽  
Inversion Method ◽  
Potential Field Data ◽  
Simultaneous Inversion

Potential-field-data imaging of complex geological features in deepwater salt-tectonic regions in the Gulf of Mexico remains an open active research field. There is still a lack of resolution in seismic imaging methods below and in the surroundings of allochthonous salt bodies. In this work, we present a novel three-dimensional potential-field-data simultaneous inversion method for imaging of salt features. This new approach incorporates a growth algorithm for source estimation, which progressively recovers geological structures by exploring a constrained parameter space; restrictions are posed from a priori geological knowledge of the study area. The algorithm is tested with synthetic data corresponding to a real complex salt-tectonic geological setting commonly found in exploration areas of deepwater Gulf of Mexico. Due to the huge amount of data involved in three-dimensional inversion of potential field data, the use of parallel computing techniques becomes mandatory. In this sense, to alleviate computational burden, an easy to implement parallelization strategy for the inversion scheme through OpenMP directives is presented. The methodology was applied to invert and integrate gravity, magnetic and full tensor gradient data of the study area.

Magnetic amplitude inversion for depth-to-basement and apparent magnetization-intensity estimates

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. J1-J11
Author(s):  
Marlon C. Hidalgo-Gato ◽  
Valéria C. F. Barbosa ◽  
Vanderlei C. Oliveira
Keyword(s):  
Field Data ◽  
Regional Scale ◽  
Inversion Method ◽  
Magnetization Direction ◽  
Potential Field Data ◽  
Amplitude Inversion ◽  
Basement Rocks ◽  
Amplitude Data ◽  
Magnetization Intensity ◽  
Magnetic Amplitude

We have developed an inversion method to recover the depth and the total magnetization intensity of the basement under a sedimentary basin using the amplitude of the magnetic anomaly vector (amplitude data). Because the amplitude data are weakly dependent on the magnetization direction, our method is suitable for interpreting areas with remanent magnetization. Our method assumes constant magnetized basement rocks overlain by nonmagnetic sediments. To overcome the inherent ambiguity of potential field data, we assume knowledge of the average depth of the basement and use it as a constraint to regularize the inversion. A sensitivity analysis with synthetic data shows the weak dependency of the magnetic amplitude inversion on the magnetization direction. Different combinations of magnetization directions recover the interface separating sediments from basement rocks. Test on field data over the Foz do Amazonas Basin, Brazil, recovers the shape of the basement relief without any knowledge about the magnetization intensity and direction. The estimated basement relief reveals a smooth basement framework with basement highs in the central part of the area. In a regional-scale perspective, the deeper and constant estimated basement relief at the northernmost limit of the area may suggest changing in crustal domains from a hyperextended continental crust to homogeneous oceanic crust.

Gravity modeling for crustal-scale models of rifted continental margins using a constrained 3D inversion method

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. G25-G39 ◽  
Author(s):  
Meixia Geng ◽  
J. Kim Welford ◽  
Colin G. Farquharson ◽  
Xiangyun Hu
Keyword(s):  
Weighting Function ◽  
Gravity Data ◽  
A Priori ◽  
Probabilistic Approach ◽  
Gravity Modeling ◽  
Inversion Method ◽  
Continental Margins ◽  
Linear Equality Constraints ◽  
Boundary Information ◽  
Regional Gravity

We have developed a new constrained inversion method that is based on a probabilistic approach for resolving crustal structure from regional gravity data. The smoothness of estimated structures is included in the inversion by using a model covariance matrix, and the sparse boundary information obtained from seismic data is incorporated in the inversion by using linear equality constraints. Moreover, constraints on the average anomalous densities expected for different crustal layers are applied instead of using a depth-weighting function. Bathymetric data and sediment thicknesses are included in the inversion by using an a priori model. Using the proposed method, model structures with sharp boundaries can be obtained while the existing boundary information and sparse seismic constraints are honored. We determine through a synthetic example and a real-world example that the proposed constrained inversion method is a valid tool for studying crustal-scale structures.

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