Inverse and Forward Modelling of Shallow-Marine Stratigraphy

This thesis presents the development and application of a numerical inverse and forward model of stratigraphy applied to shallow-marine wave-dominated sedimentary systems. The approach links a “process-based” forward model of stratigraphy (i.e. BARSIM, developed by J.E.A. Storms, University of Delft) to a fully non-linear stochastic inverse scheme. The inverse problem has been formulated using a Bayesian framework in order to sample the full range of uncertainty and explicitly build in prior knowledge. The methodology combines Reversible Jump Markov chain Monte Carlo and Simulated Tempering algorithms which are able to deal with variable dimensional inverse problems and multi-modal posterior probability distributions, respectively. The numerical scheme requires the construction of a likelihood function to quantify the agreement between simulated and observed data (e.g. sediment ages and thicknesses, grain-size distributions). Prior to real case study applications, the method has been successfully validated on different scenarios built from synthetic data, in which the impact of data distribution, quantity and quality on the uncertainty of the inferred environmental parameters were investigated. The numerical scheme has then been applied to two case studies: the outcrop-constrained Lower Cretaceous “Standardville” parasequence of the Aberdeen Member of the Blackhawk Formation (Boock Cliffs, Utah, U.S.A.) and the Emsian sub-surface data of South Algeria. The inverse modelling scheme successfully reproduced stratigraphic architecture in both cases, within the constraints of the input data quality. The inferences of the relative sea level, sediment supply and wave regime histories contribute to the understanding of mechanisms that produced the observed stratigraphy. Of equal importance, the inverse results allowed complete characterisation of uncertainties in these forcing parameters and in the stratigraphic architecture developed in between data constraints. These results suggest that the inverse model may ultimately provide a process-based geological complement to standard geostatistical tools for the static characterization of hydrocarbon reservoirs

Inverse and Forward Modelling of Shallow-Marine Stratigraphy

This thesis presents the development and application of a numerical inverse and forward model of stratigraphy applied to shallow-marine wave-dominated sedimentary systems. The approach links a 'process-based' forward model of stratigraphy (i.e. BARSIM, developed by J.E.A. Storms, University of Delft) to a fully non-linear stochastic inverse scheme. The inverse problem has been formulated using a Bayesian framework in order to sample the full range of uncertainty and explicitly build in prior knowledge. The methodology combines Reversible Jump Markov chain Monte Carlo and Simulated Tempering algorithms which are able to deal with variable dimensional inverse problems and multi-modal posterior probability distributions, respectively. The numerical scheme requires the construction of a likelihood function to quantify the agreement between simulated and observed data (e.g. sediment ages and thicknesses, grain-size distributions). Prior to real case study applications, the method has been successfully validated on different scenarios built from synthetic data, in which the impact of data distribution, quantity and quality on the uncertainty of the inferred environmental parameters were investigated. The numerical scheme has then been applied to two case studies: the outcrop-constrained Lower Cretaceous 'Standardville' parasequence of the Aberdeen Member of the Blackhawk Formation (Boock Cliffs, Utah, U.S.A.) and the Emsian sub-surface data of South Algeria. The inverse modelling scheme successfully reproduced stratigraphic architecture in both cases, within the constraints of the input data quality. The inferences of the relative sea level, sediment supply and wave regime histories contribute to the understanding of mechanisms that produced the observed stratigraphy. Of equal importance, the inverse results allowed complete characterisation of uncertainties in these forcing parameters and in the stratigraphic architecture developed in between data constraints. These results suggest that the inverse model may ultimately provide a process-based geological complement to standard geostatistical tools for the static characterization of hydrocarbon reservoirs.EThOS - Electronic Theses Online ServiceImperial College (Janet Watson Scholarship) and Total E&P UKGBUnited Kingdo

Intra-parasequence architecture of an interpreted asymetrical wave-dominated delta

International audienceAlthough modern wave-dominated shorelines exhibit complex geomorphologies, their ancient counterparts are typically described in terms of shoreface-shelf parasequences with a simple internal architecture. This discrepancy can lead to poor discrimination between, and incorrect identification of, different types of wave-dominated shoreline in the stratigraphic record. Documented in this paper are the variability in facies characteristics, high-resolution stratigraphic architecture and interpreted palaeo-geomorphology within a single parasequence that is interpreted to record the advance of an ancient asymmetrical wave-dominated delta. The Standardville (Ab1) parasequence of the Aberdeen Member, Blackhawk Formation is exposed in the Book Cliffs of central Utah, USA. This parasequence, and four others in the Aberdeen Member, record the eastward progradation of north/south-trending, wave-dominated shorelines. Within the Standardville (Ab1) parasequence, distal wave-dominated shoreface-shelf deposits in the eastern part of the study area are overlain across a downlap surface by southward prograding fluvial-dominated delta-front deposits, which have previously been assigned to a separate 'stranded lowstand parasequence' formed by a significant, allogenic change in relative sea-level. High-resolution stratigraphic analysis of these deposits reveals that they are instead more likely to record a single episode of shoreline progradation characterized by alternating periods of normal regressive and forced regressive shoreline trajectory because of minor cyclical fluctuations in relative sea-level. Interpreted normal regressive shoreline trajectories within the wavedominated shoreface-shelf deposits are marked by aggradational stacking of bedsets bounded by non-depositional discontinuity surfaces. Interpreted forced regressive shoreline trajectories in the same deposits are characterized by shallow incision of fluvial distributary channels and strongly progradational stacking of bedsets bounded by erosional discontinuity surfaces that record enhanced wave-base scour. Fluvial-dominated deltafront deposits most probably record the regression of a lobate delta parallel to the regional shoreline into an embayment that was sheltered from wave influence. Wave-dominated shoreface-shelf and fluvial-dominated delta-front deposits occur within the same parasequence, and their interpretation as the respective updrift and downdrift flanks of a single asymmetrical wave-dominated delta that periodically shifted its position provides the most straightforward explanation of the distribution and relative orientation of these two deposit types

Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems

We present an overview of Markov chain Monte Carlo, a sampling method for model inference and uncertainty quantification. We focus on the Bayesian approach to MCMC, which allows us to estimate the posterior distribution of model parameters, without needing to know the normalising constant in Bayes' theorem. Given an estimate of the posterior, we can then determine representative models (such as the expected model, and the maximum posterior probability model), the probability distributions for individual parameters, and the uncertainty about the predictions from these models. We also consider variable dimensional problems in which the number of model parameters is unknown and needs to be inferred. Such problems can be addressed with reversible jump (RJ) MCMC. This leads us to model choice, where we may want to discriminate between models or theories of differing complexity. For problems where the models are hierarchical (e.g. similar structure but with a different number of parameters), the Bayesian approach naturally selects the simpler models. More complex problems require an estimate of the normalising constant in Bayes' theorem (also known as the evidence) and this is difficult to do reliably for high dimensional problems. We illustrate the applications of RJMCMC with 3 examples from our earlier working involving modelling distributions of geochronological age data, inference of sea-level and sediment supply histories from 2D stratigraphic cross-sections, and identification of spatially discontinuous thermal histories from a suite of apatite fission track samples distributed in 3D

Characterization of controls on high-resolution stratigraphic architecture in wave-dominated shoreface-shelf parasequences using inverse numerical modelling

International audienceA new inverse numerical modeling method is used to constrain the environmental parameters (e.g., relative-sealevel, sediment-supply, and wave climate histories) that control stratigraphic architecture in wave-dominated shallow-marine deposits. The method links a ''process-response'' forward stratigraphic model that simulates wave and storm processes (BARSIM) to a combination of inverse methods formulated in a Bayesian framework that allows full characterization of uncertainties. This method is applied for the first time to a real geologic dataset, collected at outcrop from two shoreface-shelf parasequences in the Aberdeen Member, Blackhawk Formation of the Book Cliffs, east-central Utah, USA. The environmental parameters that controlled the observed stratigraphic architecture are quantified, and key aspects of stratigraphic architecture are successfully predicted from limited data. Stratigraphic architecture at parasequence-stacking and intra-parasequence scales was driven principally by relative sea level (varying by up to about 55 m) and sediment supply (varying by up to 70 m2/yr), whose interplay determines the shoreline trajectory. Within zones of distinctive shoreline trajectory, variations in wave climate (of up to about 3 m in fairweather-wave height) controlled superimposed variations in sandstone and shale content (e.g., the development of upward-coarsening and upward-fining bedsets). The modeling results closely match the observed stratigraphic architecture, but their quality is limited by: (1) the formulation and assumptions of the forward-modeling algorithms, and (2) the observed data distribution and quality, which provide poor age constraint

Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems

International audienceWe present an overview of Markov chain Monte Carlo, a sampling method for model inference and uncertainty quantification.We focus on the Bayesian approach to MCMC, which allows us to estimate the posterior distribution of model parameters, without needing to know the normalising constant in Bayes' theorem. Given an estimate of the posterior, we can then determine representative models (such as the expected model, and the maximum posterior probability model), the probability distributions for individual parameters, and the uncertainty about the predictions from these models. We also consider variable dimensional problems in which the number of model parameters is unknown and needs to be inferred. Such problems can be addressed with reversible jump (RJ) MCMC. This leads us to model choice, where we may want to discriminate between models or theories of differing complexity. For problems where the models are hierarchical (e.g. similar structure but with a different number of parameters), the Bayesian approach naturally selects the simpler models. More complex problems require an estimate of the normalising constant in Bayes' theorem (also known as the evidence) and this is difficult to do reliably for high dimensional problems. We illustrate the applications of RJMCMC with 3 examples from our earlier working involving modelling distributions of geochronological age data, inference of sea-level and sediment supply histories from 2D stratigraphic cross-sections, and identification of spatially discontinuous thermal histories from a suite of apatite fission track samples distributed in 3D

Papiers de Pauline Viardot.XIXe-XXe s. I-II Lettres adressées à Pauline Viardot. I Abertich-Rubinstein.

Maupoil, Marcelle Chamerot, Mme Pierre. Manuscrit(s) provenant d'elleViardot, Pauline Garcia, Mme Louis, cantatrice. Lettre(s) reçue(s)Contient : Abertich, J.. Lettre(s) ; Agar, Marie-Léonide Charvin, dite. Lettre(s) ; Arago, Emmanuel, homme politique. Lettre(s) ; Arditi, Luigi. Lettre(s) ; Augier, Émile. Lettre(s) ; Augier, Émile. Lettre(s) reçue(s) ; Pingard, Julia, secrétaire général de l'Institut de France. Lettre(s) ; Bériot fils, Charles De. Lettre(s) ; Berlioz, Hector. Lettre(s) ; Bida, Alexandre. Lettre(s) ; Rattazzi, Marie Letizia Bonaparte-Wyse, Mme de Solms, puis Mme Urbain Rattazzi, puis Mme Luis de Rute, écrit sous le nom de Mme. Lettre(s) ; Bonnat, Léon, peintre, membre de l'Institut. Lettre(s) ; Bouilly, Jean-Nicolas. Lettre(s) ; Bourgault-Ducoudray, Louis-Albert. Lettre(s) ; Brandt, Marianne. Lettre(s) ; Brohan, Augustine, actrice. Lettre(s) ; Browning, Robert. Lettre(s) ; Capoul, Victor, chanteur. 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Lettre(s) ; Duprez, Gilbert-Louis, artiste lyrique. Lettre(s) ; Chamerot, Georges. Lettre(s) reçue(s) ; Fauré, Gabriel. Lettre(s) ; Febvre, Frédéric, acteur. Lettre(s) ; Flaubert, Gustave. Note(s) ; Franck, César. Lettre(s) ; Gallet, Louis, écrivain. Lettre(s) ; Garcia, Joaquina Sitchès, Mme Manuel. Lettre(s) ; Garcia fils, Manuel. Lettre(s) ; Garcia, Manuel. Portrait(s) ; George, Marguerite-Joséphine Weimer, dite Mlle, actrice. Lettre(s) ; Gérôme, Jean-Léon, peintre. Lettre(s) ; Godard, Benjamin. Lettre(s) ; Godard, Benjamin. Lettre(s) reçue(s) ; Talazac, Adrien, chanteur. Lettre(s) ; Gounod, Charles, compositeur. Lettre(s) ; Gounod, Charles, compositeur. Dessin(s) ; Gouvy, Théodore, compositeur. Lettre(s) ; Grote, W.. Lettre(s) ; Grove, Georges, musicologue. Lettre(s) ; Guillaume III, roi des Pays-Bas. Lettre(s) ; Guiraud, Ernest, compositeur. Lettre(s) ; Hébert, Ernest, membre de l'Institut. Lettre(s) ; Hiller, Ferdinand, compositeur. Lettre(s) ; Humbolt, Baron Alexandre de. Lettre(s) ; Ingres, Jean-Auguste-Dominique, peintre. Lettre(s) ; Jaille, Marie. Lettre(s) ; Janin, Jules, de l'Académie française. Lettre(s) ; Jaubert, Caroline. Lettre(s) ; Joachim, Joseph, violoniste. Lettre(s) ; Joncières, Félix-Ludger Rossignol de Joncières, dit Victorin, compositeur. Lettre(s) ; Ketten, Henry, pianiste. Lettre(s) ; Krauss, Marie-Gabrielle, cantatrice. Lettre(s) ; Lamoureux, L.. Lettre(s) ; Lanfrey, Pierre, homme politique. Lettre(s) ; Lassen, Édouard, compositeur. Lettre(s) ; Lederma, Mariano de. Lettre(s) rimée ; Lefebvre, Charles-Édouard, compositeur. Lettre(s) ; Lemoine-Montigny, Rose Chéri, Mme Adolphe. Lettre(s) ; Léonard, Hubert. Lettre(s) ; Lévi, Hermann, chef d'orchestre. Lettre(s) ; Lévy, Émile, peintre. Lettre(s) ; Liszt, Franz. Lettre(s) ; Lvoff, Général Alexis. Lettre(s) ; Maignan, Albert, peintre. Lettre(s) ; Mallet, Félicia. Lettre(s) ; Manin, Daniele, homme politique. Lettre(s) ; Marmontel, Antoine-François, pianiste. Lettre(s) ; Mars, Anne Boutet, dite Mlle. Lettre(s) ; Massart, A.. Lettre(s) ; Massenet, Jules, compositeur. Lettre(s) ; Menzel, Adolphe-Frédéric-Erdmann, peintre. Lettre(s) ; Mévil, Charles. Lettre(s) ; Meyerbeer, Jakob Liebman Beer, dit Giacomo, compositeur. Lettre(s) ; Meyerbeer, Minna. Lettre(s) ; Michelet, Jules, membre de l'Institut. Lettre(s) ; Millais, Sir John Everett, peintre. Lettre(s) ; Millet, Aimé. Lettre(s) ; Niemann-Seebach, Maria, actrice. Lettre(s) ; Novello, Clara. Lettre(s) ; Paris, Gaston, de l'Académie française. Lettre(s) ; Pasdeloup, Jules. Lettre(s) ; Reyer, Ernest Rey, dit, compositeur. Lettre(s) ; Péladan, Joséphin. Lettre(s) ; Régnier de La Brière, François-Joseph, pseud. de Tousez, acteur. Lettre(s) ; Reinecke, Karl-Heinrich-Carsten, compositeur. Lettre(s) ; Renan, Ernest. Lettre(s) ; Richter, Hans. Lettre(s) ; Rietz, Julius, compositeur. Lettre(s) ; Ristori, Adélaïde, marquise Capranica del Grillo. Lettre(s) ; Rossini, Gioachino, compositeur. Lettre(s) ; Rubini, Giovanni Battista, chanteur. Lettre(s) ; Rubinstein, Antoine, pianiste-compositeur. Lettre(s)Numérisation effectuée à partir d'un document original