Gaussian envelope for 3D geomagnetic data inversion

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 996-1007 ◽  
Author(s):  
Fabio Caratori Tontini ◽  
Osvaldo Faggioni ◽  
Nicolò Beverini ◽  
Cosmo Carmisciano
Keyword(s):  
Synthetic Data ◽  
Iteration Scheme ◽  
Source Distribution ◽  
Magnetic Data ◽  
Real Field ◽  
Inversion Method ◽  
Data Set ◽  
Data Inversion ◽  
Geomagnetic Data ◽  
Subjective Input

We describe an inversion method for 3D geomagnetic data based on approximation of the source distribution by means of positive constrained Gaussian functions. In this way, smoothness and positivity are automatically imposed on the source without any subjective input from the user apart from selecting the number of functions to use. The algorithm has been tested with synthetic data in order to resolve sources at very different depths, using data from one measurement plane only. The forward modeling is based on prismatic cell parameterization, but the algebraic nonuniqueness is reduced because a relationship among the cells, expressed by the Gaussian envelope, is assumed to describe the spatial variation of the source distribution. We assume that there is no remanent magnetization and that the magnetic data are produced by induced magnetization only, neglecting any demagnetization effects. The algorithm proceeds by minimization of a χ2 misfit function between real and predicted data using a nonlinear Levenberg‐Marquardt iteration scheme, easily implemented on a desktop PC, without any additional regularization. We demonstrate the robustness and utility of the method using synthetic data corrupted by pseudorandom generated noise and a real field data set.

Warping least-squares inversion for 4D velocity change: Gulf of Mexico case study

2019 ◽  
Vol 7 (2) ◽  
pp. SB23-SB31
Author(s):  
Chang Li ◽  
Mark Meadows ◽  
Todd Dygert
Keyword(s):  
Gulf Of Mexico ◽  
Least Squares ◽  
Synthetic Data ◽  
Inversion Method ◽  
Velocity Change ◽  
Data Set ◽  
Phase Constraints ◽  
Free Case ◽  
Velocity Changes

We have developed a new trace-based, warping least-squares inversion method to quantify 4D velocity changes. There are two steps to solve for these velocity changes: (1) dynamic warping with phase constraints to align the baseline and monitor traces and (2) least-squares inversion for 4D velocity changes incorporating the time shifts and 4D amplitude differences (computed after trace alignment by warping). We have demonstrated this new inversion workflow using simple synthetic layered models. For the noise-free case, phase-constrained warping is superior to standard, amplitude-based warping by improving trace alignment, resulting in more accurate inverted velocity changes (less than 1% error). For synthetic data with 6% rms noise, inverted velocity changes are reasonably accurate (less than 10% error). Additional inversion tests with migrated finite-difference data shot over a realistic anticline model result in less than 10% error. The inverted velocity changes on a 4D field data set from the Gulf of Mexico are more interpretable and consistent with the dynamic reservoir model than those estimated from the conventional time-strain method.

2.5‐D inversion of frequency‐domain electromagnetic data generated by a grounded‐wire source

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1753-1768 ◽  
Author(s):  
Yuji Mitsuhata ◽  
Toshihiro Uchida ◽  
Hiroshi Amano
Keyword(s):  
Frequency Domain ◽  
Regularization Parameter ◽  
Synthetic Data ◽  
Magnetic Data ◽  
Statistical Criterion ◽  
Model Parameters ◽  
Inversion Algorithm ◽  
Gas Field ◽  
Data Set ◽  
Transient Electromagnetic

Interpretation of controlled‐source electromagnetic (CSEM) data is usually based on 1‐D inversions, whereas data of direct current (dc) resistivity and magnetotelluric (MT) measurements are commonly interpreted by 2‐D inversions. We have developed an algorithm to invert frequency‐Domain vertical magnetic data generated by a grounded‐wire source for a 2‐D model of the earth—a so‐called 2.5‐D inversion. To stabilize the inversion, we adopt a smoothness constraint for the model parameters and adjust the regularization parameter objectively using a statistical criterion. A test using synthetic data from a realistic model reveals the insufficiency of only one source to recover an acceptable result. In contrast, the joint use of data generated by a left‐side source and a right‐side source dramatically improves the inversion result. We applied our inversion algorithm to a field data set, which was transformed from long‐offset transient electromagnetic (LOTEM) data acquired in a Japanese oil and gas field. As demonstrated by the synthetic data set, the inversion of the joint data set automatically converged and provided a better resultant model than that of the data generated by each source. In addition, our 2.5‐D inversion accounted for the reversals in the LOTEM measurements, which is impossible using 1‐D inversions. The shallow parts (above about 1 km depth) of the final model obtained by our 2.5‐D inversion agree well with those of a 2‐D inversion of MT data.

Automatic boundary extraction from magnetic field data using triangular meshes

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. J47-J60 ◽  
Author(s):  
Nathan Leon Foks ◽  
Yaoguo Li
Keyword(s):  
Magnetic Field ◽  
Synthetic Data ◽  
Magnetic Data ◽  
Lake Area ◽  
Boundary Extraction ◽  
Data Set ◽  
Magnetic Field Data ◽  
Line Segments ◽  
Extraction Algorithm ◽  
The Magnetic Field

Boundary extraction is a collective term that we use for the process of extracting the locations of faults, lineaments, and lateral boundaries between geologic units using geophysical observations, such as measurements of the magnetic field. The process typically begins with a preprocessing stage, where the data are transformed to enhance the visual clarity of pertinent features and hence improve the interpretability of the data. The majority of the existing methods are based on raster grid enhancement techniques, and the boundaries are extracted as a series of points or line segments. In contrast, we set out a methodology for boundary extraction from magnetic data, in which we represent the transformed data as a surface in 3D using a mesh of triangular facets. After initializing the mesh, we modify the node locations, such that the mesh smoothly represents the transformed data and that facet edges are aligned with features in the data that approximate the horizontal locations of subsurface boundaries. To illustrate our boundary extraction algorithm, we first apply it to a synthetic data set. We then apply it to identify boundaries in a magnetic data set from the McFaulds Lake area in Ontario, Canada. The extracted boundaries are in agreement with known boundaries and several of the regions that are completely enclosed by extracted boundaries coincide with regions of known mineralization.

Magnetization clustering inversion — Part 2: Assessing the uncertainty of recovered magnetization directions

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. J17-J29 ◽  
Author(s):  
Jiajia Sun ◽  
Yaoguo Li
Keyword(s):  
Magnetic Measurements ◽  
Synthetic Data ◽  
Magnetic Data ◽  
Proof Of Concept ◽  
High Confidence ◽  
Data Set ◽  
Number Of Clusters ◽  
Copper Gold ◽  
Standard Deviations ◽  
Low Uncertainty

The unknown magnetization directions in the presence of remanence have posed great challenges for interpreting magnetic data. Estimating magnetization directions based on magnetic measurements, therefore, has been an active area of research within the applied geophysics community. Despite the availability of several methods for estimating magnetization directions, quantifying the uncertainty of such estimates has remained untackled. We have investigated the use of the magnetization-clustering inversion (MCI) method for the purpose of assessing the uncertainty of the recovered magnetization directions. Specifically, we have leveraged the fact that the number of clusters that one expects to see among the magnetization directions recovered from MCI needs to be supplied by a user. We propose to implement a sequence of MCIs by assuming a series of different cluster numbers, and subsequently, to calculate the standard deviations of the recovered magnetization directions at each location in a model as a practical way of quantifying the uncertainty of the estimated magnetization directions. We have developed two different methods for the calculations of the standard deviations, and have also investigated the maximum number of clusters that one needs to consider to reliably assess the uncertainty. After the proof-of-concept study on a synthetic data set, we applied our methods to a field data set from an iron-oxide-copper-gold deposit exploration in the Carajás Mineral Province, Brazil. The high-confidence zones that correspond to low-uncertainty zones indicate a high spatial correspondence with the mineralization zones inferred from the drillholes and geology.

A rapid four-dimensional resistivity data inversion method using temporal segmentation

2020 ◽  
Vol 221 (1) ◽  
pp. 586-602 ◽  
Author(s):  
Bin Liu ◽  
Yonghao Pang ◽  
Deqiang Mao ◽  
Jing Wang ◽  
Zhengyu Liu ◽  
...  
Keyword(s):  
Current Data ◽  
Inversion Method ◽  
Data Sets ◽  
Temporal Segmentation ◽  
Data Set ◽  
Subsurface Structures ◽  
Data Inversion ◽  
Rapid Changes ◽  
High Level ◽  
Acquisition Cycle

SUMMARY 4-D electrical resistivity tomography (ERT), an important geophysical method, is widely used to observe dynamic processes within static subsurface structures. However, because data acquisition and inversion consume large amounts of time, rapid changes that occur in the medium during a single acquisition cycle are difficult to detect in a timely manner via 4-D inversion. To address this issue, a scheme is proposed in this paper for restructuring continuously measured data sets and performing GPU-parallelized inversion. In this scheme, multiple reference time points are selected in an acquisition cycle, which allows all of the acquired data to be sequentially utilized in a 4-D inversion. In addition, the response of the 4-D inversion to changes in the medium has been enhanced by increasing the weight of new data being added dynamically to the inversion process. To improve the reliability of the inversion, our scheme uses actively varied time-regularization coefficients, which are adjusted according to the range of the changes in model resistivity; this range is predicted by taking the ratio between the independent inversion of the current data set and historical 4-D inversion model. Numerical simulations and experiments show that this new 4-D inversion method is able to locate and depict rapid changes in medium resistivity with a high level of accuracy.

A ray inversion method for refraction analysis

Geophysics ◽  
1985 ◽  
Vol 50 (11) ◽  
pp. 1701-1720 ◽  
Author(s):  
Glyn M. Jones ◽  
D. B. Jovanovich
Keyword(s):  
Field Data ◽  
Opposite Sign ◽  
Synthetic Data ◽  
Head Wave ◽  
Inversion Method ◽  
Data Set ◽  
Near Surface ◽  
Left And Right ◽  
A New Technique ◽  
Good Agreement

A new technique is presented for the inversion of head‐wave traveltimes to infer near‐surface structure. Traveltimes computed along intersecting pairs of refracted rays are used to reconstruct the shape of the first refracting horizon beneath the surface and variations in refractor velocity along this boundary. The information derived can be used as the basis for further processing, such as the calculation of near‐surface static delays. One advantage of the method is that the shape of the refractor is determined independently of the refractor velocity. With multifold coverage, rapid lateral changes in refractor geometry or velocity can be mapped. Two examples of the inversion technique are presented: one uses a synthetic data set; the other is drawn from field data shot over a deep graben filled with sediment. The results obtained using the synthetic data validate the method and support the conclusions of an error analysis, in which errors in the refractor velocity determined using receivers to the left and right of the shots are of opposite sign. The true refractor velocity therefore falls between the two sets of estimates. The refraction image obtained by inversion of the set of field data is in good agreement with a constant‐velocity reflection stack and illustrates that the ray inversion method can handle large lateral changes in refractor velocity or relief.

scholarly journals Interpretation of gravity anomalies due to simple geometric-shaped structures based on quadratic curve regression

2018 ◽  
Vol 48 (2) ◽  
pp. 161-178 ◽  
Author(s):  
Mohammed Tlas ◽  
Jamal Asfahani
Keyword(s):  
Gaussian White Noise ◽  
Synthetic Data ◽  
Gravity Anomalies ◽  
Real Data ◽  
Real Field ◽  
Model Parameters ◽  
Data Set ◽  
Simple Method ◽  
Residual Gravity ◽  
Quadratic Curve

Abstract An easy and very simple method to interpret residual gravity anomalies due to simple geometrical shaped models such as a semi-infinite vertical rod, an infinite horizontal rod, and a sphere has been proposed in this paper. The proposed method is mainly based on the quadratic curve regression to best-estimate the model parameters, e.g. the depth from the surface to the center of the buried structure (sphere or infinite horizontal rod) or the depth from the surface to the top of the buried object (semi-infinite vertical rod), the amplitude coefficient, and the horizontal location from residual gravity anomaly profile. The proposed method has been firstly tested on synthetic data set corrupted and contaminated by a Gaussian white noise level to demonstrate the capability and the reliability of the method. The results acquired show that the estimated parameters values derived by this proposed method are very close to the assumed true parameters values. Next, the validity of the presented method is demonstrated on synthetic data set and 3 real data sets from Cuba, Sweden and Iran. A comparable and acceptable agreement is indicated between the results derived by this method and those from the real field data information.

scholarly journals Minerals and ore deposits exploration using meta-heuristic based optimization on magnetic data

2020 ◽  
Vol 50 (2) ◽  
pp. 161-199
Author(s):  
Mohamed GOBASHY ◽  
Maha ABDELAZEEM ◽  
Mohamed ABDRABOU
Keyword(s):  
Mineral Exploration ◽  
Random Noise ◽  
Synthetic Data ◽  
Ore Deposits ◽  
Mine Planning ◽  
Magnetic Data ◽  
Real Field ◽  
Model Parameters ◽  
Tectonic Structures ◽  
Whale Optimization

The difficulties in unravelling the tectonic structures, in some cases, prevent the understanding of the ore bodies' geometry, leading to mistakes in mineral exploration, mine planning, evaluation of ore deposits, and even mineral exploitation. For that reason, many geophysical techniques are introduced to reveal the type, dimension, and geometry of these structures. Among them, electric methods, self-potential, electromagnetic, magnetic and gravity methods. Global meta-heuristic technique using Whale Optimization Algorithm (WOA) has been utilized for assessing model parameters from magnetic anomalies due to a thin dike, a dipping dike, and a vertical fault like/shear zone geological structure. These structures are commonly associated with mineralization. This modern algorithm was firstly applied on a free-noise synthetic data and to a noisy data with three different levels of random noise to simulate natural and artificial anomaly disturbances. Good results obtained through the inversion of such synthetic examples prove the validity and applicability of our algorithm. Thereafter, the method is applied to real case studies taken from different ore mineralization resembling different geologic conditions. Data are taken from Canada, United States, Sweden, Peru, India, and Australia. The obtained results revealed good correlation with previous interpretations of these real field examples.

Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from the east Texas Woodbine gas sand

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 326-336 ◽  
Author(s):  
Subhashis Mallick
Keyword(s):  
Genetic Algorithm ◽  
Biological Evolution ◽  
Synthetic Data ◽  
Posteriori Probability ◽  
Inversion Method ◽  
Model Parameters ◽  
East Texas ◽  
Data Set ◽  
A Posteriori Probability ◽  
Prestack Inversion

In this paper, a prestack inversion method using a genetic algorithm (GA) is presented, and issues relating to the implementation of prestack GA inversion in practice are discussed. GA is a Monte‐Carlo type inversion, using a natural analogy to the biological evolution process. When GA is cast into a Bayesian framework, a priori information of the model parameters and the physics of the forward problem are used to compute synthetic data. These synthetic data can then be matched with observations to obtain approximate estimates of the marginal a posteriori probability density (PPD) functions in the model space. Plots of these PPD functions allow an interpreter to choose models which best describe the specific geologic setting and lead to an accurate prediction of seismic lithology. Poststack inversion and prestack GA inversion were applied to a Woodbine gas sand data set from East Texas. A comparison of prestack inversion with poststack inversion demonstrates that prestack inversion shows detailed stratigraphic features of the subsurface which are not visible on the poststack inversion.

scholarly journals 3D induced-polarization data inversion for complex resistivity

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. F157-F171 ◽  
Author(s):  
Michael Commer ◽  
Gregory A. Newman ◽  
Kenneth H. Williams ◽  
Susan S. Hubbard
Keyword(s):  
Environmental Remediation ◽  
Large Scale ◽  
Forward Modeling ◽  
Induced Polarization ◽  
Inversion Method ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Data Set ◽  
Complex Resistivity ◽  
Data Inversion

The conductive and capacitive material properties of the subsurface can be quantified through the frequency-dependent complex resistivity. However, the routine three-dimensional (3D) interpretation of voluminous induced polarization (IP) data sets still poses a challenge due to large computational demands and solution nonuniqueness. We have developed a flexible methodology for 3D (spectral) IP data inversion. Our inversion algorithm is adapted from a frequency-domain electromagnetic (EM) inversion method primarily developed for large-scale hydrocarbon and geothermal energy exploration purposes. The method has proven to be efficient by implementing the nonlinear conjugate gradient method with hierarchical parallelism and by using an optimal finite-difference forward modeling mesh design scheme. The method allows for a large range of survey scales, providing a tool for both exploration and environmental applications. We experimented with an image focusing technique to improve the poor depth resolution of surface data sets with small survey spreads. The algorithm’s underlying forward modeling operator properly accounts for EM coupling effects; thus, traditionally used EM coupling correction procedures are not needed. The methodology was applied to both synthetic and field data. We tested the benefit of directly inverting EM coupling contaminated data using a synthetic large-scale exploration data set. Afterward, we further tested the monitoring capability of our method by inverting time-lapse data from an environmental remediation experiment near Rifle, Colorado. Similar trends observed in both our solution and another 2D inversion were in accordance with previous findings about the IP effects due to subsurface microbial activity.

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