Journal articles on the topic 'Probabilistic inversions'
To see the other types of publications on this topic, follow the link: Probabilistic inversions.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Probabilistic inversions.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Bobe, Christin, Daan Hanssens, Thomas Hermans, and Ellen Van De Vijver. "Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data." Algorithms 13, no. 6 (June 18, 2020): 144. http://dx.doi.org/10.3390/a13060144.
Full textAbstract:
Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies.
2
Manassero, M. C., J. C. Afonso, F. Zyserman, S. Zlotnik, and I. Fomin. "A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation." Geophysical Journal International 223, no. 3 (September 1, 2020): 1837–63. http://dx.doi.org/10.1093/gji/ggaa415.
Full textAbstract:
SUMMARY Simulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.
3
Bredesen, Kenneth, Ian Herbert, Florian Smit, Ask Frode Jakobsen, Peter Frykman, and Anders Bruun. "Characterizing a Wedged Chalk Prospect in the Danish Central Graben Using Direct Probabilistic Inversion." Geosciences 12, no. 5 (April 29, 2022): 194. http://dx.doi.org/10.3390/geosciences12050194.
Full textAbstract:
A novel direct probabilistic inversion using seismic pre-stack data as input to characterize a wedged chalk reservoir prospect was demonstrated from the Upper Cretaceous unit, Danish North Sea. The objective was to better resolve the lateral extent and pinch-out of the chalk prospect in a frontier exploration setting and compare the results with a more traditional deterministic inversion and geostatistical reservoir modeling. The direct probabilistic inversion results provided additional reservoir insights that were challenging to obtain from the more traditional workflows and are also more flexible for associated uncertainty assessments. Hence, this study demonstrates the usefulness of such direct probabilistic inversions even with suboptimal data availability.
4
Geng, Meixia, Xiangyun Hu, Henglei Zhang, and Shuang Liu. "3D inversion of potential field data using a marginalizing probabilistic method." GEOPHYSICS 83, no. 5 (September 1, 2018): G93—G106. http://dx.doi.org/10.1190/geo2016-0683.1.
Full textAbstract:
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.
5
Rosa, Daiane R., Juliana M. C. Santos, Rafael M. Souza, Dario Grana, Denis J. Schiozer, Alessandra Davolio, and Yanghua Wang. "Comparing different approaches of time-lapse seismic inversion." Journal of Geophysics and Engineering 17, no. 6 (November 4, 2020): 929–39. http://dx.doi.org/10.1093/jge/gxaa053.
Full textAbstract:
Abstract Time-lapse (4D) seismic inversion aims to predict changes in elastic rock properties, such as acoustic impedance, from measured seismic amplitude variations due to hydrocarbon production. Possible approaches for 4D seismic inversion include two classes of method: sequential independent 3D inversions and joint inversion of 4D seismic differences. We compare the standard deterministic methods, such as coloured and model-based inversions, and the probabilistic inversion techniques based on a Bayesian approach. The goal is to compare the sequential independent 3D seismic inversions and the joint 4D inversion using the same type of algorithm (Bayesian method) and to benchmark the results to commonly applied algorithms in time-lapse studies. The model property of interest is the ratio of the acoustic impedances, estimated for the monitor, and base surveys at each location in the model. We apply the methods to a synthetic dataset generated based on the Namorado field (offshore southeast Brazil). Using this controlled dataset, we can evaluate properly the results as the true solution is known. The results show that the Bayesian 4D joint inversion, based on the amplitude difference between seismic surveys, provides more accurate results than sequential independent 3D inversion approaches, and these results are consistent with deterministic methods. The Bayesian 4D joint inversion is relatively easy to apply and provides a confidence interval of the predictions.
6
Giraud, Jeremie, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier. "Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization." Solid Earth 10, no. 1 (January 25, 2019): 193–210. http://dx.doi.org/10.5194/se-10-193-2019.
Full textAbstract:
Abstract. We introduce a workflow integrating geological modelling uncertainty information to constrain gravity inversions. We test and apply this approach to the Yerrida Basin (Western Australia), where we focus on prospective greenstone belts beneath sedimentary cover. Geological uncertainty information is extracted from the results of a probabilistic geological modelling process using geological field data and their inferred accuracy as inputs. The uncertainty information is utilized to locally adjust the weights of a minimum-structure gradient-based regularization function constraining geophysical inversion. Our results demonstrate that this technique allows geophysical inversion to update the model preferentially in geologically less certain areas. It also indicates that inverted models are consistent with both the probabilistic geological model and geophysical data of the area, reducing interpretation uncertainty. The interpretation of inverted models reveals that the recovered greenstone belts may be shallower and thinner than previously thought.
7
Pendrel, John, and Henk Schouten. "Facies — The drivers for modern inversions." Leading Edge 39, no. 2 (February 2020): 102–9. http://dx.doi.org/10.1190/tle39020102.1.
Full textAbstract:
It is common practice to make facies estimations from the outcomes of seismic inversions and their derivatives. Bayesian analysis methods are a popular approach to this. Facies are important indicators of hydrocarbon deposition and geologic processes. They are critical to geoscientists and engineers. The application of Bayes’ rule maps prior probabilities to posterior probabilities when given new evidence from observations. Per-facies elastic probability density functions (ePDFs) are constructed from elastic-log and rock-physics model crossplots, over which inversion results are superimposed. The ePDFs are templates for Bayesian analysis. In the context of reservoir characterization, the new information comes from seismic inversions. The results are volumes of the probabilities of occurrences of each of the facies at all points in 3D space. The concepts of Bayesian inference have been applied to the task of building low-frequency models for seismic inversions without well-log interpolation. Both a constant structurally compliant elastic trend approach and a facies-driven method, where models are constructed from per-facies trends and initial facies estimates, have been tested. The workflows make use of complete 3D prior information and measure and account for biases and uncertainties in the inversions and prior information. Proper accounting for these types of effects ensures that rock-physics models and inversion data prepared for reservoir property analysis are consistent. The effectiveness of these workflows has been demonstrated by using a Gulf of Mexico data set. We have shown how facies estimates can be effectively used to build reasonable low-frequency models for inversion, which obviate the need for well-log interpolation and provide full 3D variability. The results are more accurate probability-based net-pay estimates that correspond better to geology. We evaluate the workflows by using several measures including precision, confidence, and probabilistic net pay.
8
Stähler, S. C., and K. Sigloch. "Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation." Solid Earth Discussions 5, no. 2 (July 23, 2013): 1125–62. http://dx.doi.org/10.5194/sed-5-1125-2013.
Full textAbstract:
Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves, but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 STFs by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits to propagate these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible.
9
Stähler, S. C., and K. Sigloch. "Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation." Solid Earth 5, no. 2 (November 17, 2014): 1055–69. http://dx.doi.org/10.5194/se-5-1055-2014.
Full textAbstract:
Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Seismic source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 source time functions (STFs) by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits propagating these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible.
10
Hauser, Juerg, James Gunning, and David Annetts. "Probabilistic inversion of airborne electromagnetic data under spatial constraints." GEOPHYSICS 80, no. 2 (March 1, 2015): E135—E146. http://dx.doi.org/10.1190/geo2014-0389.1.
Full textAbstract:
Probabilistic 1D inversions of airborne electromagnetic (AEM) surveys allow an exhaustive search of model space for each station, but they often assume that there is no spatial correlation between neighboring stations. This can result in abrupt transverse model discontinuities when attempting to construct a 3D model. In contrast to this, fully spatially regularized deterministic inversions can take spatial correlation between 1D models into account, but they do not explore the model space sufficiently to be able to evaluate model robustness. The Bayesian parametric bootstrap (BPB) approach that we developed is a practical compromise between computationally expensive exhaustive search techniques and computationally efficient deterministic inversions. Using a 1D kernel, we inverted for the interfaces, layer properties, and related uncertainties, taking lateral spatial correlations and additional prior information into account. Numerical examples revealed that a BPB technique was likely to explore the model space sufficiently for nonpathological situations. Using a subset of a large AEM survey collected in northwest Australia for aquifer mapping, we show how the BPB approach can be used to produce a spatially coherent map of the base of the Broome sandstone aquifer. The recovered uncertainties, which are likely to be one of the main sources of uncertainty in any groundwater model, exhibited the well-known increase in uncertainty of a depth to interface with increasing depth to the interface.
11
Shubhashis, S., M. Choubey, and A. C. Rao. "A PSEUDO PROBABILISTIC APPROACH TO TO DETECT DISTINCT INVERSIONS OF KINEMATIC CHAINS." Transactions of the Canadian Society for Mechanical Engineering 21, no. 2 (June 1997): 85–96. http://dx.doi.org/10.1139/tcsme-1997-0006.
Full textAbstract:
Isomorphism in kinematic chains has been the subject of intensive investigations, but the detection of distinct inversions has received lesser attention. The methods to detect distinct mechanisms reported to-date require extra or separate computational efforts. The method presented saves both computational effort and time. Moreover, it incorporates more information on the type, number and disposition of the links of kinematic chains and is computationaly easier. A pseudo probability scheme (pseudo means it appears to be , but not exactly) is developed to uniquely represent the kinematic chain and the same is used directly to detect distinct inversions of the kinematic chain. The capability of the method has been illustrated by applying it to detect distinct inversions of kinematic chains with simple as well as multiple joints, the latter being a less explored area so far. The method is applied to linkage (kinematic chains with lower pairs) mechanisms with revolute joints only.
12
Earp, S., A. Curtis, X. Zhang, and F. Hansteen. "Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data." Geophysical Journal International 223, no. 3 (August 8, 2020): 1741–57. http://dx.doi.org/10.1093/gji/ggaa328.
Full textAbstract:
SUMMARY Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network (NN) based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of NNs called mixture density networks (MDNs), to invert dispersion curves for shear wave velocity models and their non-linearized uncertainty. MDNs are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties as additional inputs to the MDN gives substantially more reliable velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26 772 locations to produce a 3-D velocity model in 21 s on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3-D subsurface imaging and monitoring.
13
Qin, Hui, Zhengzheng Wang, Yu Tang, and Tiesuo Geng. "Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data." Remote Sensing 13, no. 2 (January 10, 2021): 215. http://dx.doi.org/10.3390/rs13020215.
Full textAbstract:
The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters.
14
Qin, Hui, Zhengzheng Wang, Yu Tang, and Tiesuo Geng. "Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data." Remote Sensing 13, no. 2 (January 10, 2021): 215. http://dx.doi.org/10.3390/rs13020215.
Full textAbstract:
The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters.
15
Earp, Stephanie, and Andrew Curtis. "Probabilistic neural network-based 2D travel-time tomography." Neural Computing and Applications 32, no. 22 (May 6, 2020): 17077–95. http://dx.doi.org/10.1007/s00521-020-04921-8.
Full textAbstract:
Abstract Travel-time tomography for the velocity structure of a medium is a highly nonlinear and nonunique inverse problem. Monte Carlo methods are becoming increasingly common choices to provide probabilistic solutions to tomographic problems but those methods are computationally expensive. Neural networks can often be used to solve highly nonlinear problems at a much lower computational cost when multiple inversions are needed from similar data types. We present the first method to perform fully nonlinear, rapid and probabilistic Bayesian inversion of travel-time data for 2D velocity maps using a mixture density network. We compare multiple methods to estimate probability density functions that represent the tomographic solution, using different sets of prior information and different training methodologies. We demonstrate the importance of prior information in such high-dimensional inverse problems due to the curse of dimensionality: unrealistically informative prior probability distributions may result in better estimates of the mean velocity structure; however, the uncertainties represented in the posterior probability density functions then contain less information than is obtained when using a less informative prior. This is illustrated by the emergence of uncertainty loops in posterior standard deviation maps when inverting travel-time data using a less informative prior, which are not observed when using networks trained on prior information that includes (unrealistic) a priori smoothness constraints in the velocity models. We show that after an expensive program of network training, repeated high-dimensional, probabilistic tomography is possible on timescales of the order of a second on a standard desktop computer.
16
Dassios, Angelos, and Jia Wei Lim. "Recursive formula for the double-barrier Parisian stopping time." Journal of Applied Probability 55, no. 1 (March 2018): 282–301. http://dx.doi.org/10.1017/jpr.2018.17.
Full textAbstract:
Abstract In this paper we obtain a recursive formula for the density of the double-barrier Parisian stopping time. We present a probabilistic proof of the formula for the first few steps of the recursion, and then a formal proof using explicit Laplace inversions. These results provide an efficient computational method for pricing double-barrier Parisian options.
17
Geng, Meixia, J. Kim Welford, Colin G. Farquharson, Alexander L. Peace, and Xiangyun Hu. "3-D joint inversion of airborne gravity gradiometry and magnetic data using a probabilistic method." Geophysical Journal International 223, no. 1 (June 18, 2020): 301–22. http://dx.doi.org/10.1093/gji/ggaa283.
Full textAbstract:
SUMMARY A probabilistic approach is presented for jointly inverting gravity gradient and magnetic data for 3-D subsurface distributions of density and magnetic susceptibility. The coupling of the physical property models is incorporated in the inversion by using a cross-covariance matrix of density and magnetic susceptibility. This enables structural similarity such as the orientation and spatial extent of the sources and cross-variance between the two physical properties to be incorporated. A user-defined correlation coefficient can control the level of similarity between the two models. By applying a marginalizing algorithm in the joint inversion, the inversion domain is allowed to be partitioned into various zones, each of which can have its own covariance, cross-covariance matrix, as well as correlation coefficient, depending upon the feature and similarity of sources. Thus, sources with different shapes, sizes and relationships between the two physical properties can be simultaneously recovered. The validity of the method is verified using three synthetic examples, which demonstrate how incorrect parameters of the cross-covariance matrix affect the inverted results. Finally, the proposed method is successfully applied to full tensor gradiometry and magnetic data collected over the Budgell Harbour Stock (BHS) intrusion in north-central Newfoundland, Canada. Compared with models generated from independent inversions, better definition and localization of the main intrusion, as well as associated lamprophyre dykes at shallow depth, are achieved by using the joint inversion. The resolved physical properties for the intrusions show good agreement with field observations of lamprophyre dykes in proximity to the BHS.
18
Reinwald, Michael, Moritz Bernauer, Heiner Igel, and Stefanie Donner. "Improved finite-source inversion through joint measurements of rotational and translational ground motions: a numerical study." Solid Earth 7, no. 5 (October 21, 2016): 1467–77. http://dx.doi.org/10.5194/se-7-1467-2016.
Full textAbstract:
Abstract. With the prospects of seismic equipment being able to measure rotational ground motions in a wide frequency and amplitude range in the near future, we engage in the question of how this type of ground motion observation can be used to solve the seismic source inverse problem. In this paper, we focus on the question of whether finite-source inversion can benefit from additional observations of rotational motion. Keeping the overall number of traces constant, we compare observations from a surface seismic network with 44 three-component translational sensors (classic seismometers) with those obtained with 22 six-component sensors (with additional three-component rotational motions). Synthetic seismograms are calculated for known finite-source properties. The corresponding inverse problem is posed in a probabilistic way using the Shannon information content to measure how the observations constrain the seismic source properties. We minimize the influence of the source receiver geometry around the fault by statistically analyzing six-component inversions with a random distribution of receivers. Since our previous results are achieved with a regular spacing of the receivers, we try to answer the question of whether the results are dependent on the spatial distribution of the receivers. The results show that with the six-component subnetworks, kinematic source inversions for source properties (such as rupture velocity, rise time, and slip amplitudes) are not only equally successful (even that would be beneficial because of the substantially reduced logistics installing half the sensors) but also statistically inversions for some source properties are almost always improved. This can be attributed to the fact that the (in particular vertical) gradient information is contained in the additional motion components. We compare these effects for strike-slip and normal-faulting type sources and confirm that the increase in inversion quality for kinematic source parameters is even higher for the normal fault. This indicates that the inversion benefits from the additional information provided by the horizontal rotation rates, i.e., information about the vertical displacement gradient.
19
Bai, Peng, Giulio Vignoli, and Thomas Mejer Hansen. "1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error." Remote Sensing 13, no. 19 (September 28, 2021): 3881. http://dx.doi.org/10.3390/rs13193881.
Full textAbstract:
Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.
20
Houck, Richard T., Adrian Ciucivara, and Scott Hornbostel. "Accuracy and effectiveness of three-dimensional controlled source electromagnetic data inversions." GEOPHYSICS 80, no. 2 (March 1, 2015): E83—E95. http://dx.doi.org/10.1190/geo2014-0142.1.
Full textAbstract:
Unconstrained 3D inversion of marine controlled source electromagnetic data (CSEM) data sets produces resistivity volumes that have an uncertain relationship to the true subsurface resistivity at the scale of typical hydrocarbon reservoirs. Furthermore, CSEM-scale resistivity is an ambiguous indicator of hydrocarbon presence; not all resistivity anomalies are caused by hydrocarbon reservoirs, and not all hydrocarbon reservoirs produce a distinct resistivity anomaly. We have developed a method for quantifying the effectiveness of resistivities from CSEM inversion in detecting hydrocarbon reservoirs. Our approach uses probabilistic rock-physics modeling to update information from a preexisting prospect assessment, based on uncertain resistivities from CSEM. The result is an estimate the probability of hydrocarbon presence that accounts for uncertainty in the resistivity and in rock properties. Examples using synthetic and real CSEM data sets demonstrate that the effectiveness of CSEM inversion in identifying hydrocarbon reservoirs depends on the interaction between the uncertainty associated with the inversion-derived resistivity and the range of rock and fluid properties that were expected for the targeted prospect. Resistivity uncertainty that has a small effect on hydrocarbon probability for one set of rock property distributions may have a large effect for a different set of rock properties. Depending on the consequences of this interaction, resistivities from CSEM inversion might reduce the risk associated with predictions of hydrocarbon presence, but they cannot be expected to guarantee a specific well outcome.
21
Arregui, I., and M. Goossens. "No unique solution to the seismological problem of standing kink magnetohydrodynamic waves." Astronomy & Astrophysics 622 (January 24, 2019): A44. http://dx.doi.org/10.1051/0004-6361/201833813.
Full textAbstract:
The aim of this paper is to point out that the classic seismological problem using observations and theoretical expressions for the periods and damping times of transverse standing magnetohydrodynamic waves in coronal loops is better referred to as a reduced seismological problem. “Reduced” emphasises the fact that only a small number of characteristic quantities of the equilibrium profiles can be determined. Reduced also implies that there is no unique solution to the full seismological problem. Even the reduced seismological problem does not allow a unique solution. Bayesian inference results support our mathematical arguments and offer insight into the relationship between the algebraic and the probabilistic inversions.
22
Scalzo, Richard, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps. "Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models." Geoscientific Model Development 15, no. 9 (May 9, 2022): 3641–62. http://dx.doi.org/10.5194/gmd-15-3641-2022.
Full textAbstract:
Abstract. Parametric geological models such as implicit or kinematic models provide low-dimensional, interpretable representations of 3-D geological structures. Combining these models with geophysical data in a probabilistic joint inversion framework provides an opportunity to directly quantify uncertainty in geological interpretations. For best results, care must be taken with the intermediate step of rendering parametric geology in a finite-resolution discrete basis for the geophysical calculation. Calculating geophysics from naively voxelized geology, as exported from commonly used geological modeling tools, can produce a poor approximation to the true likelihood, degrading posterior inference for structural parameters. We develop a simple integrated Bayesian inversion code, called Blockworlds, showcasing a numerical scheme to calculate anti-aliased rock properties over regular meshes for use with gravity and magnetic sensors. We use Blockworlds to demonstrate anti-aliasing in the context of an implicit model with kinematic action for simple tectonic histories, showing its impact on the structure of the likelihood for gravity anomaly.
23
Tilmann, F. J., H. Sadeghisorkhani, and A. Mauerberger. "Another look at the treatment of data uncertainty in Markov chain Monte Carlo inversion and other probabilistic methods." Geophysical Journal International 222, no. 1 (May 6, 2020): 388–405. http://dx.doi.org/10.1093/gji/ggaa168.
Full textAbstract:
SUMMARY In probabilistic Bayesian inversions, data uncertainty is a crucial parameter for quantifying the uncertainties and correlations of the resulting model parameters or, in transdimensional approaches, even the complexity of the model. However, in many geophysical inference problems it is poorly known. Therefore, it is common practice to allow the data uncertainty itself to be a parameter to be determined. Although in principle any arbitrary uncertainty distribution can be assumed, Gaussian distributions whose standard deviation is then the unknown parameter to be estimated are the usual choice. In this special case, the paper demonstrates that a simple analytical integration is sufficient to marginalise out this uncertainty parameter, reducing the complexity of the model space without compromising the accuracy of the posterior model probability distribution. However, it is well known that the distribution of geophysical measurement errors, although superficially similar to a Gaussian distribution, typically contains more frequent samples along the tail of the distribution, so-called outliers. In linearized inversions these are often removed in subsequent iterations based on some threshold criterion, but in Markov chain Monte Carlo (McMC) inversions this approach is not possible as they rely on the likelihood ratios, which cannot be formed if the number of data points varies between the steps of the Markov chain. The flexibility to define the data error probability distribution in McMC can be exploited in order to account for this pattern of uncertainties in a natural way, without having to make arbitrary choices regarding residual thresholds. In particular, we can regard the data uncertainty distribution as a mixture between a Gaussian distribution, which represent valid measurements with some measurement error, and a uniform distribution, which represents invalid measurements. The relative balance between them is an unknown parameter to be estimated alongside the standard deviation of the Gauss distribution. For each data point, the algorithm can then assign a probability to be an outlier, and the influence of each data point will be effectively downgraded according to its probability to be an outlier. Furthermore, this assignment can change as the McMC search is exploring different parts of the model space. The approach is demonstrated with both synthetic and real tomography examples. In a synthetic test, the proposed mixed measurement error distribution allows recovery of the underlying model even in the presence of 6 per cent outliers, which completely destroy the ability of a regular McMC or linear search to provide a meaningful image. Applied to an actual ambient noise tomography study based on automatically picked dispersion curves, the resulting model is shown to be much more consistent for different data sets, which differ in the applied quality criteria, while retaining the ability to recover strong anomalies in selected parts of the model.
24
Zhang, Xin, Corinna Roy, Andrew Curtis, Andy Nowacki, and Brian Baptie. "Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion." Geophysical Journal International 222, no. 3 (May 9, 2020): 1639–55. http://dx.doi.org/10.1093/gji/ggaa230.
Full textAbstract:
SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.
25
Haller, Kathleen M., Morgan P. Moschetti, Charles S. Mueller, Sanaz Rezaeian, Mark D. Petersen, and Yuehua Zeng. "Seismic Hazard in the Intermountain West." Earthquake Spectra 31, no. 1_suppl (December 2015): S149—S176. http://dx.doi.org/10.1193/103114eqs173m.
Full textAbstract:
The 2014 national seismic-hazard model for the conterminous United States incorporates new scientific results and important model adjustments. The current model includes updates to the historical catalog, which is spatially smoothed using both fixed-length and adaptive-length smoothing kernels. Fault-source characterization improved by adding faults, revising rates of activity, and incorporating new results from combined inversions of geologic and geodetic data. The update also includes a new suite of published ground motion models. Changes in probabilistic ground motion are generally less than 10% in most of the Intermountain West compared to the prior assessment, and ground-motion hazard in four Intermountain West cities illustrates the range and magnitude of change in the region. Seismic hazard at reference sites in Boise and Reno increased as much as 10%, whereas hazard in Salt Lake City decreased 5–6%. The largest change was in Las Vegas, where hazard increased 32–35%.
26
Moschetti, Morgan P., Peter M. Powers, Mark D. Petersen, Oliver S. Boyd, Rui Chen, Edward H. Field, Arthur D. Frankel, et al. "Seismic Source Characterization for the 2014 Update of the U.S. National Seismic Hazard Model." Earthquake Spectra 31, no. 1_suppl (December 2015): S31—S57. http://dx.doi.org/10.1193/110514eqs183m.
Full textAbstract:
We present the updated seismic source characterization (SSC) for the 2014 update of the National Seismic Hazard Model (NSHM) for the conterminous United States. Construction of the seismic source models employs the methodology that was developed for the 1996 NSHM but includes new and updated data, data types, source models, and source parameters that reflect the current state of knowledge of earthquake occurrence and state of practice for seismic hazard analyses. We review the SSC parameterization and describe the methods used to estimate earthquake rates, magnitudes, locations, and geometries for all seismic source models, with an emphasis on new source model components. We highlight the effects that two new model components—incorporation of slip rates from combined geodetic-geologic inversions and the incorporation of adaptively smoothed seismicity models—have on probabilistic ground motions, because these sources span multiple regions of the conterminous United States and provide important additional epistemic uncertainty for the 2014 NSHM.
27
Alder, C., E. Debayle, T. Bodin, A. Paul, L. Stehly, and H. Pedersen. "Evidence for radial anisotropy in the lower crust of the Apennines from Bayesian ambient noise tomography in Europe." Geophysical Journal International 226, no. 2 (February 19, 2021): 941–67. http://dx.doi.org/10.1093/gji/ggab066.
Full textAbstract:
SUMMARY Probing seismic anisotropy of the lithosphere provides valuable clues on the fabric of rocks. We present a 3-D probabilistic model of shear wave velocity and radial anisotropy of the crust and uppermost mantle of Europe, focusing on the mountain belts of the Alps and Apennines. The model is built from Love and Rayleigh dispersion curves in the period range 5–149 s. Data are extracted from seismic ambient noise recorded at 1521 broad-band stations, including the AlpArray network. The dispersion curves are first combined in a linearized least squares inversion to obtain 2-D maps of group velocity at each period. Love and Rayleigh maps are then jointly inverted at depth for shear wave velocity and radial anisotropy using a Bayesian Monte Carlo scheme that accounts for the trade-off between radial anisotropy and horizontal layering. The isotropic part of our model is consistent with previous studies. However, our anisotropy maps differ from previous large scale studies that suggested the presence of significant radial anisotropy everywhere in the European crust and shallow upper mantle. We observe instead that radial anisotropy is mostly localized beneath the Apennines while most of the remaining European crust and shallow upper mantle is isotropic. We attribute this difference to trade-offs between radial anisotropy and thin (hectometric) layering in previous studies based on least-squares inversions and long period data (>30 s). In contrast, our approach involves a massive data set of short period measurements and a Bayesian inversion that accounts for thin layering. The positive radial anisotropy (VSH > VSV) observed in the lower crust of the Apennines cannot result from thin layering. We rather attribute it to ductile horizontal flow in response to the recent and present-day extension in the region.
28
Arthern, Robert J. "Exploring the use of transformation group priors and the method of maximum relative entropy for Bayesian glaciological inversions." Journal of Glaciology 61, no. 229 (2015): 947–62. http://dx.doi.org/10.3189/2015jog15j050.
Full textAbstract:
AbstractIce-sheet models can be used to forecast ice losses from Antarctica and Greenland, but to fully quantify the risks associated with sea-level rise, probabilistic forecasts are needed. These require estimates of the probability density function (PDF) for various model parameters (e.g. the basal drag coefficient and ice viscosity). To infer such parameters from satellite observations it is common to use inverse methods. Two related approaches are in use: (1) minimization of a cost function that describes the misfit to the observations, often accompanied by explicit or implicit regularization, or (2) use of Bayes’ theorem to update prior assumptions about the probability of parameters. Both approaches have much in common and questions of regularization often map onto implicit choices of prior probabilities that are made explicit in the Bayesian framework. In both approaches questions can arise that seem to demand subjective input. One way to specify prior PDFs more objectively is by deriving transformation group priors that are invariant to symmetries of the problem, and then maximizing relative entropy, subject to any additional constraints. Here we investigate the application of these methods to the derivation of priors for a Bayesian approach to an idealized glaciological inverse problem.
29
Taira, Toru, and Kazunori Yoshizawa. "Upper-mantle discontinuities beneath Australia from transdimensional Bayesian inversions using multimode surface waves and receiver functions." Geophysical Journal International 223, no. 3 (September 21, 2020): 2085–100. http://dx.doi.org/10.1093/gji/ggaa442.
Full textAbstract:
SUMMARY Radially anisotropic S-wave structures under the permanent seismic stations in Australia are reconstructed using multimode surface waves (SWs) and receiver functions (RFs) in a framework of the Bayesian inference. We have developed a fully nonlinear method of joint inversions incorporating P-RFs and multimode Rayleigh and Love waves, based on the transdimensional Hierarchical Bayesian formulation. The method allows us to estimate a probabilistic Earth model taking account of the complexity and uncertainty of Earth structure, by treating the model parameters and data errors as unknowns. The Parallel Tempering algorithm is employed for the effective parameter search based on the reversible-jump Markov Chain Monte Carlo method. The use of higher modes enables us to enhance the sensitivity to the depth below the continental asthenosphere. Synthetic experiments indicate the importance of higher mode SWs for the better recovery of radial anisotropy in the whole depth range of the upper mantle. The method is applied to five Global Seismographic Network stations in Australia. While the S-wave models in eastern Australia show shallow lithosphere–asthenosphere boundary (LAB) above 100 km depth, those in central and Western Australia exhibit both mid-lithosphere discontinuities (MLDs) and LAB. Also, seismic velocity jumps equivalent to the Lehmann discontinuity (L-D) are found in all seismic stations. The L-D under the Australian continents is found at around 200–300 km depth, depending on locations. Radial anisotropy in the depth range between LAB and L-D tends to show faster SH anomalies, which may indicate the effects of horizontal shear underneath the fast-moving Australian plate.
30
Almakari, M., H. Chauris, F. Passelègue, P. Dublanchet, and A. Gesret. "Fault’s hydraulic diffusivity enhancement during injection induced fault reactivation: application of pore pressure diffusion inversions to laboratory injection experiments." Geophysical Journal International 223, no. 3 (September 22, 2020): 2117–32. http://dx.doi.org/10.1093/gji/ggaa446.
Full textAbstract:
SUMMARY In situ observations of fluid induced fault slip reactivation, as well as the analysis of induced seismicity have demonstrated the complexity of fluid–fault interactions under geological conditions. If fluid flow commonly reactivates faults in the form of aseismic slip or earthquakes, the resulting shear deformation causes strong modifications of the hydraulic properties. In this context, the relationship between slip front and fluid front on deep faults remains not fully understood. In this study, we investigate shear induced fluid flow and hydraulic diffusivity enhancement during fracture shearing in the laboratory. We use a series of injection reactivation tests, conducted under triaxial conditions, at different confining pressures (30, 60 and 95 MPa). The evolution of the fluid pressure along the saw-cut Andesite rock sample was monitored by two pressure sensors, at two opposite locations of the experimental fault. We estimate the history of the effective hydraulic diffusivity (and its associated uncertainties) governing the experimental fault, using the pressure history at two points on the fault. For this, we develop a deterministic and a probabilistic inversion procedure, which is able to reproduce the experimental data for a wide time range of the different experiments. In this study, the hydraulic diffusivity increases by one order of magnitude through the injection experiment. Hydraulic diffusivity changes are mainly governed by the reduction of the effective normal stress acting on the fault plane, with a second-order effect of the shear slip.
31
Feng, Chengjun, Guangliang Gao, Shihuai Zhang, Dongsheng Sun, Siyu Zhu, Chengxuan Tan, and Xiaodong Ma. "Fault slip potential induced by fluid injection in the Matouying enhanced geothermal system (EGS) field, Tangshan seismic region, North China." Natural Hazards and Earth System Sciences 22, no. 7 (July 12, 2022): 2257–87. http://dx.doi.org/10.5194/nhess-22-2257-2022.
Full textAbstract:
Abstract. The Tangshan region is one of the most seismically active areas in the North China, and the 1976 M 7.8 earthquake occurred on 28 July near the Tangshan fault zone. The Matouying enhanced geothermal system (EGS) field is located ∼90 km away from the city of Tangshan. Since late 2020, preliminary hydraulic stimulation tests have been conducted at depths of ∼3965–4000 m. Fluid injection into geothermal reservoir facilitates a heat exchanger system. However, fluid injection may also induce earthquakes. In anticipation of the EGS operation at the Matouying uplift, it is essential to assess how the fault slip potential of the nearby active and quiescent faults will change in the presence of fluid injection. In this study, we first characterize the ambient stress field in the Tangshan region by performing stress tensor inversions using 98 focal-mechanism data (ML≥2.5). Then, we estimate the principal stress magnitudes near the Matouying EGS field by analyzing in situ stress measurements at shallow depths (∼600–1000 m). According to these data, we perform a quantitative risk assessment using the Mohr–Coulomb framework in order to evaluate how the main active faults might respond to hypothetical injected-related pore pressure increases due to the upcoming EGS production. Our results mainly show that most earthquakes in the Tangshan seismic region have occurred on the faults that have relatively high fault slip potential in the present ambient stress field. At well distances of less than 15 km, the probabilistic fault slip potential on most of the boundary faults increases with continuing fluid injection over time, especially on the faults with well distances of ∼6–10 km. The probabilistic fault slip potential (fsp) increases linearly with the fluid injection rate. However, the fsp values decrease exponentially with increased unit permeability. The case study of the Matouying EGS field has important implications for deep geothermal exploitation in China, especially for Gonghe EGS (in Qinghai Province) and Xiong'an New Area (in Hebei Province) geothermal reservoirs that are close to the Quaternary active faults. Ongoing injection operations in the regions should be conducted with these understandings in mind.
32
Li, Liyong, and Hamdi A. Tchelepi. "Conditional Statistical Moment Equations for Dynamic Data Integration in Heterogeneous Reservoirs." SPE Reservoir Evaluation & Engineering 9, no. 03 (June 1, 2006): 280–88. http://dx.doi.org/10.2118/92973-pa.
Full textAbstract:
Summary An inversion method for the integration of dynamic (pressure) data directly into statistical moment equations (SMEs) is presented. The method is demonstrated for incompressible flow in heterogeneous reservoirs. In addition to information about the mean, variance, and correlation structure of the permeability, few permeability measurements are assumed available. Moreover, few measurements of the dependent variable are available. The first two statistical moments of the dependent variable (pressure) are conditioned on all available information directly. An iterative inversion scheme is used to integrate the pressure data into the conditional statistical moment equations (CSMEs). That is, the available information is used to condition, or improve the estimates of, the first two moments of permeability, pressure, and velocity directly. This is different from Monte Carlo (MC) -based geostatistical inversion techniques, where conditioning on dynamic data is performed for one realization of the permeability field at a time. In the MC approach, estimates of the prediction uncertainty are obtained from statistical post-processing of a large number of inversions, one per realization. Several examples of flow in heterogeneous domains in a quarter-five-spot setting are used to demonstrate the CSME-based method. We found that as the number of pressure measurements increases, the conditional mean pressure becomes more spatially variable, while the conditional pressure variance gets smaller. Iteration of the CSME inversion loop is necessary only when the number of pressure measurements is large. Use of the CSME simulator to assess the value of information in terms of its impact on prediction uncertainty is also presented. Introduction The properties of natural geologic formations (e.g., permeability) rarely display uniformity or smoothness. Instead, they usually show significant variability and complex patterns of correlation. The detailed spatial distributions of reservoir properties, such as permeability, are needed to make performance predictions using numerical reservoir simulation. Unfortunately, only limited data are available for the construction of these detailed reservoir-description models. Consequently, our incomplete knowledge (uncertainty) about the property distributions in these highly complex natural geologic systems means that significant uncertainty accompanies predictions of reservoir flow performance. To deal with the problem of characterizing reservoir properties that exhibit such variability and complexity of spatial correlation patterns when only limited data are available, a probabilistic framework is commonly used. In this framework, the reservoir properties (e.g., permeability) are assumed to be a random space function. As a result, flow-related properties such as pressure, velocity, and saturations are random functions. We assume that the available information about the permeability field includes a few measurements in addition to the spatial correlation structure, which we take here as the two-point covariance. This incomplete knowledge (uncertainty) about the detailed spatial distribution of permeability is the only source of uncertainty in our problem. Uncertainty about the detailed distribution of the permeability field in the reservoir leads to uncertainty in the computed predictions of the flow field (e.g., pressure).
33
Dosso, Stan. "Probabilistic geoacoustic inversion." Journal of the Acoustical Society of America 113, no. 4 (April 2003): 2189–90. http://dx.doi.org/10.1121/1.4808810.
Full text
34
Molinari, Irene, Roberto Tonini, Stefano Lorito, Alessio Piatanesi, Fabrizio Romano, Daniele Melini, Andreas Hoechner, et al. "Fast evaluation of tsunami scenarios: uncertainty assessment for a Mediterranean Sea database." Natural Hazards and Earth System Sciences 16, no. 12 (December 6, 2016): 2593–602. http://dx.doi.org/10.5194/nhess-16-2593-2016.
Full textAbstract:
Abstract. We present a database of pre-calculated tsunami waveforms for the entire Mediterranean Sea, obtained by numerical propagation of uniformly spaced Gaussian-shaped elementary sources for the sea level elevation. Based on any initial sea surface displacement, the database allows the fast calculation of full waveforms at the 50 m isobath offshore of coastal sites of interest by linear superposition. A computationally inexpensive procedure is set to estimate the coefficients for the linear superposition based on the potential energy of the initial elevation field. The elementary sources size and spacing is fine enough to satisfactorily reproduce the effects of M> = 6.0 earthquakes. Tsunami propagation is modelled by using the Tsunami-HySEA code, a GPU finite volume solver for the non-linear shallow water equations. Like other existing methods based on the initial sea level elevation, the database is independent on the faulting geometry and mechanism, which makes it applicable in any tectonic environment. We model a large set of synthetic tsunami test scenarios, selected to explore the uncertainty introduced when approximating tsunami waveforms and their maxima by fast and simplified linear combination. This is the first time to our knowledge that the uncertainty associated to such a procedure is systematically analysed and that relatively small earthquakes are considered, which may be relevant in the near-field of the source in a complex tectonic setting. We find that non-linearity of tsunami evolution affects the reconstruction of the waveforms and of their maxima by introducing an almost unbiased (centred at zero) error distribution of relatively modest extent. The uncertainty introduced by our approximation can be in principle propagated to forecast results. The resulting product then is suitable for different applications such as probabilistic tsunami hazard analysis, tsunami source inversions and tsunami warning systems.
35
Shahraeeni, Mohammad S., and Andrew Curtis. "Fast probabilistic nonlinear petrophysical inversion." GEOPHYSICS 76, no. 2 (March 2011): E45—E58. http://dx.doi.org/10.1190/1.3540628.
Full textAbstract:
We have developed an extension of the mixture-density neural network as a computationally efficient probabilistic method to solve nonlinear inverse problems. In this method, any postinversion (a posteriori) joint probability density function (PDF) over the model parameters is represented by a weighted sum of multivariate Gaussian PDFs. A mixture-density neural network estimates the weights, mean vector, and covariance matrix of the Gaussians given any measured data set. In one study, we have jointly inverted compressional- and shear-wave velocity for the joint PDF of porosity, clay content, and water saturation in a synthetic, fluid-saturated, dispersed sand-shale system. Results show that if the method is applied appropriately, the joint PDF estimated by the neural network is comparable to the Monte Carlo sampled a posteriori solution of the inverse problem. However, the computational cost of training and using the neural network is much lower than inversion by sampling (more than a factor of 104 in this case and potentially a much larger factor for 3D seismic inversion). To analyze the performance of the method on real exploration geophysical data, we have jointly inverted P-wave impedance and Poisson’s ratio logs for the joint PDF of porosity and clay content. Results show that the posterior model PDF of porosity and clay content is a good estimate of actual porosity and clay-content log values. Although the results may vary from one field to another, this fast, probabilistic method of solving nonlinear inverse problems can be applied to invert well logs and large seismic data sets for petrophysical parameters in any field.
36
Rossi, Giovanni Battista, and Francesco Crenna. "Probabilistic inversion: a preliminary discussion." Journal of Physics: Conference Series 588 (February 16, 2015): 012039. http://dx.doi.org/10.1088/1742-6596/588/1/012039.
Full text
37
Du, C., D. Kurowicka, and R. M. Cooke. "Techniques for generic probabilistic inversion." Computational Statistics & Data Analysis 50, no. 5 (March 2006): 1164–87. http://dx.doi.org/10.1016/j.csda.2005.01.002.
Full text
38
Sripanich, Yanadet, Sergey Fomel, Jeannot Trampert, William Burnett, and Thomas Hess. "Probabilistic moveout analysis by time warping." GEOPHYSICS 85, no. 1 (January 1, 2020): U1—U20. http://dx.doi.org/10.1190/geo2018-0797.1.
Full textAbstract:
Parameter estimation from reflection moveout analysis represents one of the most fundamental problems in subsurface model building. We have developed an efficient moveout inversion method based on the process of automatic flattening of common-midpoint (CMP) gathers using local slopes. We find that as a by-product of this flattening process, we can also estimate reflection traveltimes corresponding to the flattened CMP gathers. This traveltime information allows us to construct a highly overdetermined system and subsequently invert for moveout parameters including normal-moveout velocities and quartic coefficients related to anisotropy. We use the 3D generalized moveout approximation (GMA), which can accurately capture the effects of complex anisotropy on reflection traveltimes as the basis for our moveout inversion. Due to the cheap forward traveltime computations by GMA, we use a Monte Carlo inversion scheme for improved handling of the nonlinearity between the reflection traveltimes and moveout parameters. This choice also allows us to set up a probabilistic inversion workflow within a Bayesian framework, in which we can obtain the posterior probability distributions that contain valuable statistical information on estimated parameters such as uncertainty and correlations. We use synthetic and real data examples including the data from the SEAM Phase II unconventional reservoir model to demonstrate the performance of our method and discuss insights into the problem of moveout inversion gained from analyzing the posterior probability distributions. Our results suggest that the solutions to the problem of traveltime-only moveout inversion from 2D CMP gathers are relatively well constrained by the data. However, parameter estimation from 3D CMP gathers associated with more moveout parameters and complex anisotropic models are generally nonunique, and there are trade-offs among inverted parameters, especially the quartic coefficients.
39
Cooke, Roger M., Maarten Nauta, Arie H. Havelaar, and Ine van der Fels. "Probabilistic inversion for chicken processing lines." Reliability Engineering & System Safety 91, no. 10-11 (October 2006): 1364–72. http://dx.doi.org/10.1016/j.ress.2005.11.054.
Full text
40
Hwang, Yong Keun, Helena Zirczy, and Sudhish Bakku. "Quantitative seismic reservoir modeling — Model-based probabilistic inversion for optimal field development." Leading Edge 38, no. 10 (October 2019): 786–90. http://dx.doi.org/10.1190/tle38100786.1.
Full textAbstract:
Full-field reservoir models provide key input to annual business plans and reserve booking. They support the long-term field development plan by enabling well target optimization, identification of infill opportunities, water-flood management, and well-surveillance and intervention strategies. It is crucial to constrain the model with all available static and dynamic data to improve its predictive power for confident decision making. Across Shell's global deepwater portfolio, a model-based probabilistic seismic amplitude-variation-with-offset (AVO) inversion methodology is used to constrain reservoir properties as part of a comprehensive quantitative seismic reservoir modeling workflow. Promise, a proprietary probabilistic inversion tool, estimates values of reservoir properties and quantifies their uncertainties through repeated forward modeling and automated quality checking of synthetic against recorded seismic data. During workflow execution, available geologic, petrophysical, and geophysical data are incorporated. As a consequence, the reservoir models are consistent with all relevant subsurface data following their update through inversion. Model-based inversion establishes a direct link between static model properties and elastic impedances. Probabilistic inversion output is an ensemble of posterior static models. The inversion process automatically sorts through the ensemble. It can directly provide low, mid, and high cases of the inverted models that are ready to be used in hydrocarbon volume estimation and multiscenario dynamic modeling for history matching and production forecasting. For successful and efficient delivery of full-field reservoir models with uncertainty assessment using model-based probabilistic AVO inversion, early integration of interdisciplinary subsurface data and cross-business collaboration are key.
41
Kararepanov, N. "FEATURES OF THE VERSIONAL PROCESS IN THE USE OF METHODS OF FORENSIC COGNITION." East European Scientific Journal 2, no. 01(77) (February 17, 2022): 63–68. http://dx.doi.org/10.31618/essa.2782-1994.2022.2.77.239.
Full textAbstract:
The article presents some theoretical questions about the content and structure of the theory as scientific knowledge. Methods of forensic cognition are used in obtaining reliable knowledge about the objects of research (traces). The movement of the process of cognition is carried out from the effect to the alleged cause, which forces to put forward a certain number of versions. And the aggregation of traces, as a process of their systematic selection for the holistic perception of past events, consists, from the point of view of the situational approach, in eliminating the problem situation (lack of reliable information) and transforming it into a simple one.
Attention is drawn to the nature of the validity of the version, which seems to be the insufficiency and incompleteness of contradictory and probabilistic information. Overcoming problem situations in the search, study, and use of traces of crime events is associated with a complex structure of the process of putting forward leads (stages). The beginning of such dynamics is characterized by the total collection of the entire non-systemic array of information. As a result of the heuristic thought process, the incoming information is analyzed and gives grounds for putting forward some already sufficiently substantiated assumptions in the form of versions. Versions are checked, filtered, and transformed into reliable information.
Strategic uncertainty, which forms a conflict situation, has a completely different character. Retro scient and predictive versions are useful in our research. Commenting on the complexity of the relationship between versions, it should be noted that more often different assumptions can be incompatible (inversions, disjunctions) and compatible (conjunctions and). At the same time, it is necessary to distinguish incompatible versions from counter versions, as a special kind of them.
The versional process of searching for and studying traces of certain events has a pronounced heuristic character. The application of versions is particularly successful in circumstances where the content is limited as much as possible. These propositions manifest a new paradigm based on the constant change of one scientific direction from another. This is how the essence of version theory is expressed, based on an effective heuristic scientific concept.
42
Hakim, Gregory J. "A Probabilistic Theory for Balance Dynamics." Journal of the Atmospheric Sciences 65, no. 9 (September 1, 2008): 2949–60. http://dx.doi.org/10.1175/2007jas2499.1.
Full textAbstract:
Abstract Balance dynamics are proposed in a probabilistic framework, assuming that the state variables and the master, or control, variables are random variables described by continuous probability density functions. Balance inversion, defined as recovering the state variables from the control variables, is achieved through Bayes’ theorem. Balance dynamics are defined by the propagation of the joint probability of the state and control variables through the Liouville equation. Assuming Gaussian statistics, balance inversion reduces to linear regression of the state variables onto the control variables, and assuming linear dynamics, balance dynamics reduces to a Kalman filter subject to perfect observations given by the control variables. Example solutions are given for an elliptical vortex in shallow water having unity Rossby and Froude numbers, which produce an outward-propagating pulse of inertia–gravity wave activity. Applying balance inversion to the potential vorticity reveals that, because potential vorticity and divergence share well-defined patterns of covariability, the inertia–gravity wave field is recovered in addition to the vortical field. Solutions for a probabilistic balance dynamics model applied to the elliptical vortex reveal smaller errors (“imbalance”) for height control compared to potential vorticity control. Important attributes of the probabilistic balance theory include quantification of the concept of balance manifold “fuzziness,” and clear state-independent definitions of balance and imbalance in terms of the range of the probabilistic inversion operators. Moreover, the theory provides a generalization of the notion of balance that may prove useful for problems involving moist physics, chemistry, and tropical circulations.
43
Wong, Tak Kwong, and Sheung Chi Phillip Yam. "A probabilistic proof for Fourier inversion formula." Statistics & Probability Letters 141 (October 2018): 135–42. http://dx.doi.org/10.1016/j.spl.2018.05.028.
Full text
44
Teugels, Jozef L. "Probabilistic Proofs of Some Real Inversion Formulas." Mathematische Nachrichten 146, no. 7-12 (1990): 149–57. http://dx.doi.org/10.1002/mana.19901460708.
Full text
45
Shahraeeni, Mohammad S., Andrew Curtis, and Gabriel Chao. "Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data." GEOPHYSICS 77, no. 3 (May 1, 2012): O1—O19. http://dx.doi.org/10.1190/geo2011-0340.1.
Full textAbstract:
A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.
46
Park, Seongjun, Inho Baek, and Tae-Kyung Hong. "Six Major Historical Earthquakes in the Seoul Metropolitan Area during the Joseon Dynasty (1392–1910)." Bulletin of the Seismological Society of America 110, no. 6 (July 14, 2020): 3037–49. http://dx.doi.org/10.1785/0120200004.
Full textAbstract:
ABSTRACT Earthquake records in the historical literature provide valuable information on the seismic hazard potentials for long recurrence times. The Seoul metropolitan area is the center of the economy and infrastructure in South Korea. Six major earthquakes that occurred around the Seoul metropolitan area during the Joseon dynasty in 1392–1910 are analyzed using a probabilistic joint inversion method based on seismic damage records and earthquake-felt reports. The inversion yields sets of event locations and magnitudes with probabilities. The joint inversion method is validated with synthetic and instrumentally observed data sets. The historical earthquakes are found to be located around the Seoul metropolitan area. The magnitudes of the earthquakes range from ML 5.3 to 6.8 at the peak probabilistic locations. These historical earthquakes suggest considerable seismic hazard potentials in the Seoul metropolitan area.
47
Hunziker, Jürg, Eric Laloy, and Niklas Linde. "Inference of multi-Gaussian relative permittivity fields by probabilistic inversion of crosshole ground-penetrating radar data." GEOPHYSICS 82, no. 5 (September 1, 2017): H25—H40. http://dx.doi.org/10.1190/geo2016-0347.1.
Full textAbstract:
In contrast to deterministic inversion, probabilistic Bayesian inversion provides an ensemble of solutions that can be used to quantify model uncertainty. We have developed a probabilistic inversion approach that uses crosshole first-arrival traveltimes to estimate an underlying geostatistical model, the subsurface structure, and the standard deviation of the data error simultaneously. The subsurface is assumed to be represented by a multi-Gaussian field, which allows us to reduce the dimensionality of the problem significantly. Compared with previous applications in hydrogeology, novelties of this study include an improvement of the dimensionality reduction algorithm to avoid streaking artifacts, it is the first application to geophysics and the first application to field data. The results of a synthetic example show that the model domain enclosed by one borehole pair is generally too small to provide reliable estimates of geostatistical variables. A real-data example based on two borehole pairs confirms these findings and demonstrates that the inversion procedure also works under realistic conditions with, for example, unknown measurement errors.
48
Jordan, Thomas H., and Alan Juarez. "Stress–strain characterization of seismic source fields using moment measures of mechanism complexity." Geophysical Journal International 227, no. 1 (June 5, 2021): 591–616. http://dx.doi.org/10.1093/gji/ggab218.
Full textAbstract:
SUMMARY Earthquake ruptures and seismic sequences can be very complex, involving slip in various directions on surfaces of variable orientation. How is this geometrical complexity in seismic energy release, here called mechanism complexity, governed by tectonic stress? We address this question using a probabilistic model for the distribution of double couples that is consistent with three assumptions commonly used in regional stress inversions: the tectonic stress is constant, slip vectors are aligned with the maximum shear traction in the plane of slip, and higher shear traction promotes more seismic energy release. We characterize the moment-tensor field of a stress-aligned source process in terms of an ordered set of principal-stress directions, a stress shape factor R, and a strain-sensitivity parameter $\kappa $. The latter governs the dependence of the seismic moment density on the shear-traction magnitude and therefore parametrizes the seismic strain response to the driving stress. These stress–strain characterization (SSC) parameters can be determined from moment measures of mechanism complexity observed in large earthquakes and seismic sequences. The moment measures considered here are the ratio of the Aki moment to the total seismic moment and the five fractions of the total-moment defined by linear mappings of the moment-tensor field onto an orthonormal basis of five deviatoric mechanisms. We construct this basis to be stress-oriented by choosing its leading member to be the centroid moment tensor (CMT) mechanism and three others representing orthogonal rotations of the CMT mechanism. From the projections of the stress-aligned field onto this stress-oriented basis, we derive explicit expressions for the expected values of the moment-fraction integrals as functions of R and $\kappa $. We apply the SSC methodology to a 39-yr focal mechanism catalogue of the San Jacinto Fault (SJF) zone and to realizations from the Graves–Pitarka stochastic rupture model. The SJF data are consistent with the SSC model, and the recovered parameters, $R = {\rm{ }}0.45 \pm 0.050$ and $\kappa = {\rm{ }}5.7 \pm 1.75$, indicate moderate mechanism complexity. The parameters from the Graves–Pitarka realizations, $R = {\rm{\ }}0.49 \pm 0.005,{\rm{\ \ }}\kappa = {\rm{\ }}9.5 \pm 0.375,$ imply lower mechanism complexity than the SJF catalogue, and their moment measures show inconsistencies with the SSC model that can be explained by differences in the modelling assumptions.
49
Hauser, Juerg, James Gunning, and David Annetts. "Probabilistic inversion of airborne electromagnetic data for basement conductors." GEOPHYSICS 81, no. 5 (September 2016): E389—E400. http://dx.doi.org/10.1190/geo2016-0128.1.
Full textAbstract:
Probabilistic inversion of airborne electromagnetic data is often approximated by a layered earth using a computationally efficient 1D kernel. If the underlying framework accounts for prior beliefs on spatial correlation, the inversion will be able to recover spatially coherent interfaces and associated uncertainties. Greenfield exploration using airborne electromagnetic data, however, often seeks to identify discrete economical targets. In mature exploration provinces, such bodies are frequently obscured by thick, conductive regolith, and the response of such economic basement conductors presents a challenge to any layered earth inversion. A well-known computationally efficient way to approximate the response of a basement conductor is to use a thin plate. Here we have extended a Bayesian parametric bootstrap approach, so that the basement of a spatially varying layered earth can contain a thin plate. The resulting Bayesian framework allowed for the inversion of basement conductors and associated uncertainties, but more importantly, the use of model selection concepts to determine if the data supports a basement conductor model or not. Recovered maps of basement conductor probabilities show the expected patterns in uncertainty; for example, a decrease in target probability with increasing depth. Such maps of target probabilities generated using the thin plate approximation are a potentially valuable source of information for the planning of exploration activity, such as the targeting of drillholes to confirm the existence of a discrete conductor in a greenfield exploration scenario. We have used a field data set from northwest Queensland, Australia, to illustrate how the approach allowed inversion for a basement conductor and related uncertainties in a spatially variable layered earth, using the information from multiple survey lines and prior beliefs of geology.
50
White, Luther, and John Castagna. "Stochastic fluid modulus inversion." GEOPHYSICS 67, no. 6 (November 2002): 1835–43. http://dx.doi.org/10.1190/1.1527083.
Full textAbstract:
A probabilistic inversion approach is used with Gassmann's equation to determine pore fluid modulus using elastic wave velocity without reference information from a rock saturated with a second fluid of known modulus. Numerical examples show that even when uncertainties in input parameters are relatively large, useful estimates of fluid modulus can be obtained. For a well‐log data example, water saturation derived from the inverted fluid modulus compares favorably to saturations derived from well log analysis.