Speaker: Paul Cupillard

Date: Thursday 27th of June 2024, 1:15pm.

Abstract:

Non-periodic homogenization has proved to be an accurate asymptotic method for computing long-wavelength equivalent media for the seismic wave equation, turning small-scale heterogeneities and geometric complexity into smooth elastic properties. Using homogenized media allows i) decreasing the computation cost of wave propagation simulation and ii) studying the apparent, small-scale-induced anisotropy. After illustrating these two aspects briefly, I propose to analyze in great detail the accuracy of body waves simulated in homogenized 3D models of the subsurface. First, the behaviour of head-, reflected and refracted waves with respect to source-receiver offset, maximum frequency and velocity contrast across a planar interface, is investigated. Then, I consider the SEG-EAGE overthrust model to exemplify how the accuracy of simulated body waves anti-correlates with the distance to seismic source and the amount of apparent anisotropy. In high apparent anisotropy regions, we show that the first-order correction provided by the homogenization theory significantly improves the computed wavefield. The overall results of this analysis better frame the use of homogenized media in seismic wave simulation.

Speaker: Giusi Ruggiero

Date: Thursday 20th of June 2024, 1:15pm.

Abstract:

Available soon

Speaker: Marius Rapenne

Date: Thursday 13th of June 2024, 1:15pm.

Abstract:

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Speaker: Julien Herrero

Date: Thursday 6th of June 2024, 1:15pm.

Abstract:

We introduce a new method for post-stack seismic inversion using Bayesian transdimensional methods in layered media. We employ a reversible jump Markov chain Monte Carlo algorithm, defined within a parsimonious Bayesian framework, to infer not only the values of the model parameters but also the optimal number of layers required to describe the data. The parameterization includes a layer inclination angle to locally infer the stratigraphy from a group of adjacent seismic traces. The forward method generates a set of synthetic seismic traces using a classical convolution model. These synthetic traces are then compared with actual seismic traces to infer the geological model parameters that are layer acoustic impedances, interface depths, and dipping angles. We run the inversion on synthetic data obtained from a two-dimensional reference geometry and show its ability to recover the reference model parameters. This suggests the potential of the method to discover stratigraphic gaps in seismic reservoir characterization tasks. More generally, this demonstrates the capability of transdimensional inversion in quantitative interpretation tasks. It opens opportunities for inversion of entire post-stack seismic sections, accommodating lateral variability with relatively low computational time.

Speaker: Paul Marchal

Date: Thursday 23th of May 2024, 1:15pm.

Abstract:

In mining, assessing uncertainties is an essential step throughout the resource development cycle, from exploration campaigns to remediation and development strategy planning. Indeed, geological data are only partially covering the subsurface and are subject to two main types of uncertainty:  i) sampling and measurement uncertainties, and ii) epistemic or conceptual uncertainties related to data interpretation. This paper focuses on the second ones. It aims to evaluate the diversity of conceptual interpretations that specialists and non-specialists have on data, and the potential impact this can have on the estimation of  uranium deposit geometries. For this purpose, a case study was carried out in the context of an unconformity-associated uranium deposit in the Athabasca basin. Based on a reference section from this area, a cross-section with synthetic drillcores was produced and given to 30 people to correlate and interpret. Our objectives are multiple: defining metrics for comparing data interpretations, assessing the differences in interpretations between expert and non-expert uranium geologists. We defined a set of mathematical criteria (50) based on 4 key characteristics: i) mineralized zones, ii) associated altered zones, iii) associated structural network, and iv) interpretation glyphs and annotations. Individual and group analysis of the defined criteria (t-SNE, MDS) were performed. Digital Leapfrog models are also compared with hand-drawn models. Primary results show that the group of uranium experts is less dispersed overall in terms of property variance. They  tend to propose mineralization zones that are more impacted by the influence of  faults and unconformities. They are finally prone to produce less parsimonious interpretations, incorporating more geological concepts.

Speaker: Amandine Fratani

Date: Thursday 25th of April 2024, 1:15pm.

Abstract:

Interpretation of faults is a requirement for a 3D geological modelling process. However, due to the incomplete observations caused by the gap between 2D seismic images or outcrops, the results of this stage can be ambiguous and uncertain. Recently, a proposition of solution based on a graph formalism has been expressed. In the graph, fault observations are represented as nodes, and edges carry the potential of pairwise associations computed from selected expert geological rules. Main limit of this work is that fault observations are not pairwise independent, therefore considering pair prevents the consideration of higher-order effects such as the distribution of the throw along several aligned nodes. We propose to consider a multiple-point likelihood computation to extend the graph where expert rules are replaced using machine learning on analog or partly observed data. The model is trained from a set of selected features such as the length of the fault trace or the throw value. Features are computed from fault traces extracted from 3D geological models. Association potential of k fault observations are then directly computed using the trained model. To prevent overfitting in our small geological models dataset, we propose to mimic a partly interpreted case: we split a 3D domain in two disjoint, contiguous sectors A and B, and use sector A as training and sector B for testing. This presentation will show first results on 2 and 3 points association using a Random Forest algorithm.

Speaker: Thomas Bodin

Date: Thursday 11th of April 2024, 1:15pm.

Abstract:

Earth sciences are by essence multidisciplinary as different fields study the same objects and processes at different scales. In seismology, the challenge is that tomographic models constructed from long period seismograms only “sees” the crust and the mantle as an effective medium, where the smallest scales are given by the resolution power of seismic waves. On the other hand, geodynamics and geochemistry “see” the Earth as a mixture of different chemical components, with the smallest scales given by chemical diffusion. In this presentation, I will show how modern tools from inverse theory, computational statistics, and data science can surpass qualitative interpretations of results obtained separately in different fields. Within a Bayesian framework, we will propose a fully probabilistic approach to explore the ensemble of small-scale earth models compatible with a set of observations and constraints.

Speaker: Matthieu Lusseyran

Date: Thursday 4th of April 2024, 1:15pm.

Abstract:

Understanding the damage processes in clay-bearing rocks is a decisive factor in geological engineering, and for instance considering nuclear waste deep geological repositories. But, more generally they may also contribute to localized deformation, and thus the rupture of fault gauges in seismic zones. However, owing to their complex mineralogy, multiscale microstructures and anisotropy, the mechanisms of clay-rich rock damage and their chronology are not yet well understood. Here we focus on the impact of micro-damage on ultrasonic wave propagation velocity, which is confronted with the corresponding full deformation fields calculated by digital image correlation (DIC). The aim is to associate the acoustic signature with the active deformation mechanisms identified by DIC. To this end, an integrated experimental approach is proposed to characterize localization and to identify the related deformation micro-mechanisms during uniaxial compression of natural clayey rock samples (Tournemire shales) with two simultaneous measurements: 1) the evolution of P-wave velocity within the sample by active acoustics, 2) the development of the 2D mechanical full field by digital image correlation. Both experimental techniques are well known, but the innovation of our approach is to combine simultaneously both measurements. Deformation localization is a multiscale problem, which obviously occurs at the sample scale, but also at the fines scales of the microstructure. Therefore, we developed two different experimental setups. On the one hand, during uniaxial compression with a standard MTS loading frame the macro-scale localization patterns are characterized by optical observations, which image resolution is well suited to the cm sample scale (sample diameter: 3.6 cm and double in length). On the other hand, in order to characterize the initiation of micro-damage at the microstructure scale of the composite type of rock, the same loading protocol is reproduced (while keeping the acoustic diagnosis) on smaller scale mm-sized specimens (sample diameter : 8 mm, double in length), using a home-designed miniature loading frame fit for an environmental scanning electron microscope (ESEM). The latter analysis is carried out under controlled relative humidity of RH = 80%, hence preventing the samples to dry out due to the high vacuum. A similar acoustic signature is identified at both scales of observation, in spite of the variations of experimental conditions imposed by the environmental SEM. We are therefore confident to be able to understand the fracturing process from micro-cracking initiation (microscale) to sample failure (macroscale), and to assess its impact on ultrasonic wave propagation.

Speaker: Janne T. Yliharyu

Date: Friday 22nd of March 2024, 1:15pm.

Abstract:

In many applications, it is essential to know what happens to the properties of a material if the water content is changed, i.e., if the material is wetted or dried [1]. One such example is bentonite clay, which is planned to be used as a buffer material in the geological final disposal of radioactive waste. Safety assessment of the multibarrier system, including the bentonite buffer, requires modelling and investigating the release barrier over long periods. These models benefit from information on the local water content of the bentonite layer. To facilitate modelling, we have developed X-ray tomographic methods to measure the time evolution of the local water content and applied the methods to bentonite clay samples in various conditions. This approach provides direct experimental data on the wetting and hydromechanical behavior of small-scale compacted bentonite samples, which can be used to validate material models. We present two approaches for measuring local water content. The first one is based on comparing successive tomographic images obtained during the wetting of the bentonite clay sample. This method involves measurement of the internal deformations of the sample, which is vital because, in general, materials will deform if the water content is changed. The second method is based on dual-energy imaging. Energy information is utilized to obtain quantitative information about the spatial distributions of bentonite and water. In both cases, we have applied a nonstandard beam hardening correction and careful calibrations of the mass attenuation coefficients. The deformation measurement method has been applied extensively to bentonite samples with different dry densities. Also, the influence of temperature on water transport has been studied. Based on the observations, the evolution of the water distribution is qualitatively reminiscent of diffusive transport. The first results from the dual-energy CT are promising, but further studies and validations are required before more profound conclusions can be drawn. These results suggest that deformation measurement enables more accurate water content estimates, but dual-energy CT can be applied in a broader range of material combinations.