Structurally tailored 3D anisotropic controlled-source electromagnetic resistivity inversion with cross-gradient criterion and simultaneous model calibration

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. E387-E402 ◽  
Author(s):  
Max A. Meju ◽  
Randall L. Mackie ◽  
Federico Miorelli ◽  
Ahmad Shahir Saleh ◽  
Roger V. Miller
Keyword(s):  
Field Data ◽  
A Priori ◽  
Calibration Data ◽  
A Priori Information ◽  
Data Set ◽  
Low Contrast ◽  
Controlled Source ◽  
Low Resistivity ◽  
Band Limited ◽  
Priori Information

Geologic interpretation of 3D anisotropic resistivity models from conventional marine controlled-source electromagnetic (CSEM) data inversion faces difficulties in low-resistivity contrast sediments and structurally complex environments that typify the new frontiers for hydrocarbon exploration. Currently, the typically reconstructed horizontal resistivity [Formula: see text] and vertical resistivity [Formula: see text] models often have conflicting depth structures that are difficult to explain in terms of subsurface geology, and the resulting resistivities may not be close to the true formation resistivities required for estimating reservoir parameters. We have investigated the concept that an objective geologically oriented or structurally tailored inversion can be achieved by requiring that the cross-product of the gradient of horizontal resistivity and the gradient of the vertical resistivity is equal to zero at significant geologic boundaries. We incorporate this boundary-shape criterion in our 3D inverse problem formulations, implemented within nonlinear model-space and conjugate-gradient contexts, for cases in which a priori calibration data from wells and/or seismically derived subsurface boundaries are available and for cases in which these are lacking. The resulting fit-for-purpose solutions serve to better analyze the peculiarity of a given data set. We applied these algorithms to synthetic and field CSEM data sets representing a fold-thrust environment with low-resistivity and low-contrast sediments. The resulting [Formula: see text] and [Formula: see text] models from cross-gradient joint inversion of synthetic data of appropriate frequency bandwidth without a priori information are structurally similar and consistent with the test models, whereas those from the inversions of band-limited field data are consistent with the available seismic and resistivity well-log data. This particular approach will thus be useful for lithologic correlation in frontier regions with limited a priori information using broadband CSEM data. For these band-limited field data, we found that the anisotropic bulk resistivities of the low-contrast sediments are better determined by incorporating a priori calibration data from triaxial resistivity logs and seismic horizons.

Deep learning spectral enhancement considering features of seismic field data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Yonggyu Choi ◽  
Yeonghwa Jo ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Input Data ◽  
Field Data ◽  
A Priori ◽  
Resolution Enhancement ◽  
Training Data ◽  
A Priori Information ◽  
Spectral Enhancement ◽  
Priori Information

The resolution of seismic data dictates the ability to identify individual features or details in a given image, and the temporal (vertical) resolution is a function of the frequency content of a signal. To improve thin-bed resolution, broadening of the frequency spectrum is required; this has been one of the major objectives in seismic data processing. Recently, many researchers have proposed machine learning based resolution enhancement and showed their applicability. However, since the performance of machine learning depends on what the model has learned, output from training data with features different from the target field data may be poor. Thus, we present a machine learning based spectral enhancement technique considering features of seismic field data. We used a convolutional U-Net model, which preserves the temporal connectivity and resolution of the input data, and generated numerous synthetic input traces and their corresponding spectrally broadened traces for training the model. A priori information from field data, such as the estimated source wavelet and reflectivity distribution, was considered when generating the input data for complementing the field features. Using synthetic tests and field post-stack seismic data examples, we showed that the trained model with a priori information outperforms the models trained without a priori information in terms of the accuracy of enhanced signals. In addition, our new spectral enhancing method was verified through the application to the high-cut filtered data and its promising features were presented through the comparison with well log data.

Particle swarm optimization of 2D magnetotelluric data

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. E125-E141 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
A Priori ◽  
Particle Swarm ◽  
Stability And Convergence ◽  
Real Field ◽  
A Priori Information ◽  
Data Set ◽  
Swarm Optimization ◽  
Priori Information

We implement the particle swarm optimization (PSO) algorithm for the two-dimensional (2D) magnetotelluric (MT) inverse problem. We first validate PSO on two synthetic models of different complexity and then apply it to an MT benchmark for real-field data, the COPROD2 data set (Canada). We pay particular attention to the selection of the PSO input parameters to properly address the complexity of the 2D MT inverse problem. We enhance the stability and convergence of the solution of the geophysical problem by applying the hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC). Moreover, we parallelize the code to reduce the computation time because PSO is a computationally demanding global search algorithm. The inverse problem was solved for the synthetic data both by giving a priori information at the beginning and by using a random initialization. The a priori information was given to a small number of particles as the initial position within the search space of solutions, so that the swarming behavior was only slightly influenced. We have demonstrated that there is no need for the a priori initialization to obtain robust 2D models because the results are largely comparable with the results from randomly initialized PSO. The optimization of the COPROD2 data set provides a resistivity model of the earth in line with results from previous interpretations. Our results suggest that the 2D MT inverse problem can be successfully addressed by means of computational swarm intelligence.

A COMBINATION SCHEME FOR FUZZY CLUSTERING

2002 ◽  
Vol 16 (07) ◽  
pp. 901-912 ◽  
Author(s):  
EVGENIA DIMITRIADOU ◽  
ANDREAS WEINGESSEL ◽  
KURT HORNIK
Keyword(s):  
A Priori ◽  
Fuzzy Cluster ◽  
A Priori Information ◽  
Cluster Validity ◽  
Data Set ◽  
Cluster Algorithms ◽  
Combination Scheme ◽  
Priori Information ◽  
Theoretical Considerations ◽  
Structurally Stable

In this paper we present a voting scheme for fuzzy cluster algorithms. This voting method allows us to combine several runs of cluster algorithms resulting in a common partition. This helps us to tackle the problem of choosing the appropriate clustering method for a data set where we have no a priori information about it. We mathematically derive the algorithm from theoretical considerations. Experiments show that the voting algorithm finds structurally stable results. Several cluster validity indexes show the improvement of the voting result in comparison to simple fuzzy voting.

scholarly journals A method to derive the Site Atmospheric State Best Estimate (SASBE) of ozone profiles from radiosonde and passive microwave data

2015 ◽  
Vol 8 (3) ◽  
pp. 3399-3422 ◽  
Author(s):  
E. Maillard Barras ◽  
A. Haefele ◽  
R. Stübi ◽  
D. Ruffieux
Keyword(s):  
Brightness Temperature ◽  
A Priori ◽  
A Priori Information ◽  
Data Set ◽  
Ozone Data ◽  
Best Estimate ◽  
Vertical Range ◽  
Ozone Profile ◽  
Atmospheric State ◽  
Priori Information

Abstract. We present a method to derive the site atmospheric state best estimate (SASBE) of the ozone profile combining brightness temperature spectra around the 142 GHz absorption line of ozone measured by the microwave radiometer SOMORA and ozone profiles measured by the radiosonde (RS). The SASBE ozone profile is obtained using the radiosonde ozone profile as a priori information in an optimal estimation retrieval of the SOMORA radiometer. The resulting ozone profile ranges from ground up to 65 km altitude and makes optimal use of the available information at each altitude. The high vertical resolution of the radiosonde profile can be conserved and the uncertainty of the SASBE is well defined at each altitude. A SASBE ozone profile dataset has been generated for Payerne, Switzerland, with a temporal resolution of 3 profiles a week for the time period ranging from 2011 to 2013. The relative difference of the SASBE ozone profiles to the AURA/MLS ozone profiles lies between −3 to 6% over the vertical range of 20–65 km. Above 20 km, the agreement between the SASBE and AURA/MLS ozone profiles is better than the agreement between the operational SOMORA ozone data set and AURA/MLS. Below 20 km the SASBE ozone data are identical to the radiosonde measurements. The same method has been applied to ECWMF-ERA interim ozone profiles and SOMORA data to generate a SASBE dataset with a time resolution of 4 profiles per day. These SASBE ozone profiles agree between −4 and +8% with AURA/MLS. The improved agreement of the SASBE datasets with AURA/MLS above 20 km demonstrates the benefit of better a priori information in the retrieval of ozone from brightness temperature data.

Algorithms for automatic detection and recognition of low contrast radar objects using noise-like broadband signals

2021 ◽  
Author(s):  
E.V. Egorova ◽  
A.N. Ribakov ◽  
M.Kh. Aksayitov
Keyword(s):  
Spectral Methods ◽  
Block Diagram ◽  
A Priori ◽  
Original Method ◽  
Optimal Algorithms ◽  
A Priori Information ◽  
Low Contrast ◽  
Priori Information ◽  
Detection And Recognition

An algorithm for automatic detection and recognition of low-contrast ground targets using noise-like broadband signals and the use of combined processing of radar signals against the background of interference is presented; the proposed original method for processing signals from airborne objects during their detection and determination of their coordinates synthesizes optimal algorithms for detecting radar objects in the case of a priori information about useful signals and interference, as well as the ability to determine the range and speed of movement; the block diagram of the mathematical model of signal processing is considered on the basis of the developed algorithms for identifying stationary targets against the background of local objects by the radar portrait, as well as by the envelope of the radar signal; the results of testing mathematical modeling of the algorithm for recognizing signals from stationary targets and a forest with an equal probability of the appearance of these targets in the analyzed space are presented. The results of domestic theoretical and experimental research today characterize the main areas of research in the field of detection and recognition of various radar objects. The main research tool of most works is the search and development of promising mathematical models of objects and the modeling of secondary radiation for their recognition, which in some cases allows obtaining additional information about these objects. Correlation and spectral methods of their processing are currently being considered in relation to the noise sounding signal of a radar station. This article analyzes the application of correlation and spectral methods in processing noise signals with the identification of the disadvantages and advantages of each of the methods; the functioning of the block diagram of the known single-channel noise radar stations with sequential spectral processing of the total signal is considered. The proposed original method for processing signals from airborne objects during their detection and determination of their coordinates synthesizes optimal algorithms for detecting targets in the case of a priori information about useful signals and interference, as well as the ability to determine the distance and speed of movement. It should be noted the promising application of combined processing of radar signals against the background of interference, taking into account simultaneously the spatial, polarization, temporal and frequency features of the signals reflected from objects. With regard to the problem of recognizing the shape of objects, both in Russia and abroad, intensive work is being carried out to improve the resolution of on-board radars with a synthesized broadband antenna array, while raising the range resolution and increasing the angular resolution allow obtaining long-range portraits of these objects, as well as seeing them. elements and obtain images of targets. In the study of methods for detecting radar objects based on Gaussian noise signals with a large base, it is shown that such signals are promising for detecting subtle objects at ranges greater than with conventional monopulse radar. When receiving noise signals with a large base, spectral methods of signal extraction turn out to be more advantageous in comparison with the known correlation method of signal processing. Based on the use of noise signals, recognition of ground and air objects is realized, while the method of long-range portraits can have an advantage over the envelope method. Based on the results of mathematical modeling, the possibility of automatic recognition of stationary ground objects by two different methods was confirmed with a high probability of their recognition.

Reconstruction of borehole radar images by a modified f–k migration

2020 ◽  
Vol 221 (3) ◽  
pp. 1626-1634
Author(s):  
Jianjian Huo ◽  
Binzhong Zhou ◽  
Qing Zhao ◽  
Iain M Mason
Keyword(s):  
Final Section ◽  
Early Stage ◽  
A Priori ◽  
A Priori Information ◽  
Data Set ◽  
Radar Images ◽  
Imaging Tool ◽  
Bedding Planes ◽  
Borehole Radar ◽  
Priori Information

SUMMARY Borehole radar (BHR) is an effective imaging tool. It can be used to detect and map faults, fractures, folds, domes, partings and mine workings. Most BHRs have azimuthally omnidirectional radiation patterns. The echoes sensed by such BHRs may come from any direction. Considering a radar in a straight borehole that passes through a stack of flat reflection planes, V-shaped events or crosses appear on the time section. One of the arms of each cross is a real image while the other is an ambiguity of known origin. Directional ambiguities such as these obstruct efforts to interpret the data. In this paper, we address this difficulty by using a modified f–k migration algorithm to translate crosses into lines on the final section that are consistent with a priori information about for example bedding. Compared with conventional strategies, for example migration + f–k dip filter, this approach integrates the two separated processes into one and is straightforward, computationally effective and simple to implement. The method is demonstrated using a synthetic model and a real BHR field data set. It allows the interpreter to use a priori information about fault swarms or plausible bedding planes at an early stage. The reconstructed BHR image helps the search for geological anomalies such as fractures, partings, domes and rolls that could be a hazard for mining.

Improvements to elastic full-waveform inversion using cross-gradient constraints

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R105-R115 ◽  
Author(s):  
Edgar Manukyan ◽  
Hansruedi Maurer ◽  
André Nuber
Keyword(s):  
Field Data ◽  
A Priori ◽  
Waveform Inversion ◽  
Structural Similarity ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
A Priori Information ◽  
Individual Parameter ◽  
Full Waveform ◽  
Priori Information

Seismic full-waveform inversion (FWI) is potentially a powerful method for obtaining high-resolution subsurface images, but the results are often distorted by nonlinear effects and parameter trade-offs. Such distortions can be particularly severe in the case of multiparameter FWI, such as elastic FWI, in which inversion is performed simultaneously for P- and S-wave velocities and density. The problem can be alleviated by adding constraints in the form of plausible a priori information. A usually well-justified constraint includes the structural similarity of different model parameters; i.e., an anomalous body likely exhibits variations in all elastic properties, although their magnitudes may be different. To consider such types of a priori information, we have developed a structurally constrained elastic FWI, which is based on minimization of the cross products of gradients of different model parameters. Our synthetic 2D experiments show that structurally constrained FWI can significantly improve model reconstruction. It is also demonstrated that our approach still leads to improved results, even when the structural similarity between the individual parameter types is not exactly met. Inversions of field data show that in comparison to conventional FWI, structurally constrained FWI is able to match the field data equally well while requiring less structural complexity of the subsurface.

3D inversion of a scalar radio magnetotelluric field data set

Geophysics ◽  
2003 ◽  
Vol 68 (3) ◽  
pp. 791-802 ◽  
Author(s):  
Gregory A. Newman ◽  
Stephan Recher ◽  
Bülent Tezkan ◽  
Fritz M. Neubauer
Keyword(s):  
Field Data ◽  
A Priori ◽  
3D Analysis ◽  
Borehole Data ◽  
Erroneous Conclusion ◽  
Data Set ◽  
Near Surface ◽  
Lateral Extent ◽  
Priori Information ◽  
2D Data

A radio magnetotelluric (MT) field data set, acquired in scalar mode, over a buried waste site has been successfully analyzed using a 3D MT inversion scheme using nonlinear conjugate gradients. The results of this analysis demonstrate the utility of the scheme where more than 4800 data points collected on multiple measurement profiles have been inverted simultaneously. The resulting image clearly detects the buried waste; when receiver profiles cross pit boundaries, the image maps the lateral extent of the pit. However, the base of the pit is poorly resolved, and depends upon the starting model used to launch the inversion. Hence, critical information on whether contamination is leaching into a resistive gravel bed lining the base of the pit, as well as the deeper geological horizons consisting of brown coal, clay, and tertiary sands, is inconclusive. Nevertheless, by incorporating within the inversion process a priori information of the background media that is host to the waste, sharper images of the base of the pit are obtained, which are in good agreement with borehole data. The 3D analysis applied in this paper overcomes previous limitations in the radio magnetotelluric (RMT) method using 2D data analysis and inversion. With 3D analysis, it is unnecessary to make assumptions regarding geological strike, and near‐surface statics can be accommodated in both source polarizations. Our findings also indicate that 2D MT interpretation can overestimate the pit's depth extent. This may lead to the erroneous conclusion that the geological horizons beneath the pit have been contaminated.

scholarly journals Inversion of phase times for hypocenters and shallow crustal velocities, Mojave Desert, California

1979 ◽  
Vol 69 (2) ◽  
pp. 387-396 ◽  
Author(s):  
James A. Hileman
Keyword(s):  
Mojave Desert ◽  
Velocity Structure ◽  
A Priori ◽  
Relative Importance ◽  
Local Velocity ◽  
A Priori Information ◽  
Linear Least Squares ◽  
Data Set ◽  
Velocity Models ◽  
Priori Information

abstract Phase times for an ensemble of 20 aftershocks of the June 1, 1975, Galway Lake earthquake were inverted to determine hypocenters and local velocity structure simultaneously. A maximum likelihood formulation of linear, least-squares inversion was used so that the data were weighted according to their estimated variances. A trade-off parameter controlled the relative importance of the RMS error and the amount by which the model changed at each iteration. Individual aftershocks must be selected so that a wide variety of travel paths are represented. Initial trials disclosed that careful constraints are necessary for allowed changes in station delays. Fixed velocity boundaries, based on a priori information, were used for each of several starting models, and only layer velocities were allowed to vary. Each of the trials indicated that shallow crustal velocities in the vincinity of Galway Lake are somewhat lower than those of the usual velocity models. The velocities are not strongly constrained by this data set, but the results are consistent with a subsequent, detailed refraction survey by other workers. Mapping the travel-time surface in time-depth-distance space helps clarify the limitations inherent in a given problem.

3-D inversion of magnetic data

Geophysics ◽  
1996 ◽  
Vol 61 (2) ◽  
pp. 394-408 ◽  
Author(s):  
Yaoguo Li ◽  
Douglas W. Oldenburg
Keyword(s):  
Objective Function ◽  
A Priori ◽  
Magnetic Data ◽  
A Priori Information ◽  
Data Set ◽  
Earth Models ◽  
Priori Information ◽  
Model Region ◽  
D Model ◽  
Physical Realizability

We present a method for inverting surface magnetic data to recover 3-D susceptibility models. To allow the maximum flexibility for the model to represent geologically realistic structures, we discretize the 3-D model region into a set of rectangular cells, each having a constant susceptibility. The number of cells is generally far greater than the number of the data available, and thus we solve an underdetermined problem. Solutions are obtained by minimizing a global objective function composed of the model objective function and data misfit. The algorithm can incorporate a priori information into the model objective function by using one or more appropriate weighting functions. The model for inversion can be either susceptibility or its logarithm. If susceptibility is chosen, a positivity constraint is imposed to reduce the nonuniqueness and to maintain physical realizability. Our algorithm assumes that there is no remanent magnetization and that the magnetic data are produced by induced magnetization only. All minimizations are carried out with a subspace approach where only a small number of search vectors is used at each iteration. This obviates the need to solve a large system of equations directly, and hence earth models with many cells can be solved on a deskside workstation. The algorithm is tested on synthetic examples and on a field data set.

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