scholarly journals Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 1: Method and synthetic examples

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R805-R814 ◽  
Author(s):  
Zhen Xing ◽  
Alfredo Mazzotti
Keyword(s):  
Genetic Algorithm ◽  
Rayleigh Wave ◽  
A Priori ◽  
Computing Time ◽  
A Priori Information ◽  
Fine Grid ◽  
Near Surface ◽  
Reference Models ◽  
Full Waveform ◽  
Priori Information

When reliable a priori information is not available, it is difficult to correctly predict near-surface S-wave velocity models from Rayleigh waves through existing techniques, especially in the case of complex geology. To tackle this issue, we have developed a new method: two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI). Adopting a two-grid parameterization of the model, the genetic algorithm inverts for unknown velocities and densities at the nodes of a coarse grid, whereas the forward modeling is performed on a fine grid to avoid numerical dispersion. A bilinear interpolation brings the coarse-grid results into the fine-grid models. The coarse inversion grid allows for a significant reduction in the computing time required by the genetic algorithm to converge. With a coarser grid, there are fewer unknowns and less required computing time, at the expense of the model resolution. To further increase efficiency, our inversion code can perform the optimization using an offset-marching strategy and/or a frequency-marching strategy that can make use of different kinds of objective functions and allows for parallel computing. We illustrate the effect of our inversion method using three synthetic examples with rather complex near-surface models. Although no a priori information was used in all three tests, the long-wavelength structures of the reference models were fairly predicted, and satisfactory matches between “observed” and predicted data were achieved. The fair predictions of the reference models suggest that the final models estimated by our genetic-algorithm FWI, which we call macromodels, would be suitable inputs to gradient-based Rayleigh-wave FWI for further refinement. We also explored other issues related to the practical use of the method in different work and explored applications of the method to field data.

scholarly journals Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 2: Application to two actual data sets

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R815-R825 ◽  
Author(s):  
Zhen Xing ◽  
Alfredo Mazzotti
Keyword(s):  
Genetic Algorithm ◽  
Rayleigh Wave ◽  
A Priori ◽  
Computing Time ◽  
Waveform Inversion ◽  
Actual Data ◽  
Data Sets ◽  
Wave Inversion ◽  
Full Waveform ◽  
Text Model

We have applied our two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI) to two actual data sets acquired in Luni (Italy) and Grenoble (France), respectively. Because our technique used 2D elastic finite-difference modeling for solving the forward problem, the observed data were 3D to 2D corrected prior to the inversion. To limit the computing time, both inversions focused on predicting low-resolution, smooth models by using quite coarse inversion grids. The wavelets for FWI were estimated directly from the observed data by using the Wiener method. In the Luni case, due to the strong dispersion effects on the data, to strengthen the inversion, envelopes and waveforms were considered in the objective function and an offset-marching strategy was applied. Though no a priori information was exploited, the outcomes of the Luni and Grenoble data inversion were fair. The predicted Luni [Formula: see text] model indicates a strong velocity increase from approximately 3 to 6 m, and velocity inversions have been detected at approximately 2 and 9 m depths. Analyzing the dispersion spectra, it results that the predicted Luni data reasonably reproduced the waveforms related to the fundamental mode and, likely, a small part of those related to the first higher mode. Concerning the Grenoble example, the predicted [Formula: see text] model coincides reasonably well with the long-wavelength structures presented in the [Formula: see text] profiles obtained from nearby boreholes. The data reconstruction is generally satisfactory, and when mismatches occur between the predicted and observed traces, the phase differences are always within half-periods. The fair inversion outcomes suggest that the predicted Luni and Grenoble models would likely be adequate initial models for local FWI, which could further increase the resolution and the details of the estimated [Formula: see text] models.

scholarly journals A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals

2019 ◽  
Vol 12 (7) ◽  
pp. 3943-3961 ◽  
Author(s):  
Ali Jalali ◽  
Shannon Hicks-Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele ◽  
Thomas von Clarmann
Keyword(s):  
Water Vapor ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Mixing Ratio ◽  
A Priori Information ◽  
Fine Grid ◽  
Large Power ◽  
Priori Information ◽  
Water Vapor Mixing Ratio

Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

DETERMINATION OF THE STACKING VELOCITY FIELD VIA OPTIMIZATION METHODS

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Danian Steinkirch de Oliveira ◽  
Milton José Porsani ◽  
Paulo Eduardo Miranda Cunha
Keyword(s):  
Genetic Algorithm ◽  
Velocity Field ◽  
A Priori ◽  
Optimization Methods ◽  
Optimization Method ◽  
A Priori Information ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Priori Information

ABSTRACT. We developed a strategy for automatic Semblance panels pick, that uses Genetic Algorithm optimization method. In conjunction with restrictions and penalties set from a priori information... RESUMO. Foi desenvolvida uma estratégia de pick automático dos painéis de Semblance , que usa método de otimização Algorítmo Genético. Em conjunto com restrições...

scholarly journals Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. H19-H31 ◽  
Author(s):  
Knud Skou Cordua ◽  
Thomas Mejer Hansen ◽  
Klaus Mosegaard
Keyword(s):  
Inverse Problem ◽  
Monte Carlo ◽  
A Priori ◽  
Waveform Inversion ◽  
Multiple Point ◽  
Full Waveform Inversion ◽  
A Posteriori ◽  
A Priori Information ◽  
Full Waveform ◽  
Priori Information

We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear inverse problems, such as full-waveform inversion. Sequential Gibbs sampling is a method that allows efficient sampling of a priori probability densities described by geostatistical algorithms based on either two-point (e.g., Gaussian) or multiple-point statistics. We outline the theoretical framework for a full-waveform inversion strategy that integrates the extended Metropolis algorithm with sequential Gibbs sampling such that arbitrary complex geostatistically defined a priori information can be included. At the same time we show how temporally and/or spatiallycorrelated data uncertainties can be taken into account during the inversion. The suggested inversion strategy is tested on synthetic tomographic crosshole ground-penetrating radar full-waveform data using multiple-point-based a priori information. This is, to our knowledge, the first example of obtaining a posteriori realizations of a full-waveform inverse problem. Benefits of the proposed methodology compared with deterministic inversion approaches include: (1) The a posteriori model variability reflects the states of information provided by the data uncertainties and a priori information, which provides a means of obtaining resolution analysis. (2) Based on a posteriori realizations, complicated statistical questions can be answered, such as the probability of connectivity across a layer. (3) Complex a priori information can be included through geostatistical algorithms. These benefits, however, require more computing resources than traditional methods do. Moreover, an adequate knowledge of data uncertainties and a priori information is required to obtain meaningful uncertainty estimates. The latter may be a key challenge when considering field experiments, which will not be addressed here.

PREDICTION OF SEVERE THUNDERSTORMS WITH MINIMAL A PRIORI INFORMATION

2005 ◽  
Vol 08 (01) ◽  
pp. 75-86 ◽  
Author(s):  
SUTAPA CHAUDHURI ◽  
SURAJIT CHATTOPADHYAY
Keyword(s):  
Genetic Algorithm ◽  
Monsoon Season ◽  
A Priori ◽  
A Priori Information ◽  
Northeastern Part ◽  
Severe Thunderstorms ◽  
Suitable Combination ◽  
Quantitative Manner ◽  
Priori Information

A genetic algorithm (GA) is introduced to fix ranges of parameters, in a quantitative manner, pertaining to forecasting the genesis of severe thunderstorms in the pre-monsoon season over Calcutta (22°32′, 88°20′) in the northeastern part of India (20°–24°N latitude, 85°–93°E longitude). The results depict that definite quantitative ranges of parameters can be made available before as well as after the occurrence of severe thunderstorms using a GA. Thus, a suitable combination of these ranges of parameters before the onset can represent a possible predictor of thunderstorm genesis.

scholarly journals A synthetic study to assess the applicability of full-waveform inversion to infer snow stratigraphy from upward-looking ground-penetrating radar data

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA213-WA223 ◽  
Author(s):  
Lino Schmid ◽  
Jürg Schweizer ◽  
John Bradford ◽  
Hansruedi Maurer
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
A Priori ◽  
Waveform Inversion ◽  
Liquid Water Content ◽  
A Priori Information ◽  
Full Waveform ◽  
Snow Stratigraphy ◽  
Priori Information ◽  
Ground Penetrating

Snow stratigraphy and liquid water content are key contributing factors to avalanche formation. Upward-looking ground-penetrating radar (upGPR) systems allow nondestructive monitoring of the snowpack, but deriving density and liquid water content profiles is not yet possible based on the direct analysis of the reflection response. We have investigated the feasibility of deducing these quantities using full-waveform inversion (FWI) techniques applied to upGPR data. For that purpose, we have developed a frequency-domain FWI algorithm in which we additionally took advantage of time-domain features such as the arrival times of reflected waves. Our results indicated that FWI applied to upGPR data is generally feasible. More specifically, we could show that in the case of a dry snowpack, it is possible to derive snow densities and layer thicknesses if sufficient a priori information is available. In case of a wet snowpack, in which it also needs to be inverted for the liquid water content, the algorithm might fail, even if sufficient a priori information is available, particularly in the presence of realistic noise. Finally, we have investigated the capability of FWI to resolve thin layers that play a key role in snow stability evaluation. Our simulations indicate that layers with thicknesses well below the GPR wavelengths can be identified, but in the presence of significant liquid water, the thin-layer properties may be prone to inaccuracies. These results are encouraging and motivate applications to field data, but significant issues remain to be resolved, such as the determination of the generally unknown upGPR source function and identifying the optimal number of layers in the inversion models. Furthermore, a relatively high level of prior knowledge is required to let the algorithm converge. However, we feel these are not insurmountable and the new technology has significant potential to improve field data analysis.

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.

scholarly journals Orbit computation of the TELECOM-2D satellite with a Genetic Algorithm

2014 ◽  
Vol 9 (S310) ◽  
pp. 146-149
Author(s):  
Florent Deleflie ◽  
David Coulot ◽  
Alain Vienne ◽  
Romain Decosta ◽  
Pascal Richard ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Orbit Determination ◽  
A Priori ◽  
Geostationary Satellite ◽  
Determination Method ◽  
Operational Method ◽  
A Priori Information ◽  
Priori Information ◽  
Orbit Computation ◽  
As If

AbstractIn order to test a preliminary orbit determination method, we fit an orbit of the geostationary satellite TELECOM-2D, as if we did not know any a priori information on its trajectory. The method is based on a genetic algorithm coupled to an analytical propagator of the trajectory, that is used over a couple of days, and that uses a whole set of altazimutal data that are acquired by the tracking network made up of the two TAROT telescopes. The adjusted orbit is then compared to a numerical reference. The method is described, and the results are analyzed, as a step towards an operational method of preliminary orbit determination for uncatalogued objects.

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