Updating prior geologic uncertainty with GPR traveltime tomographic data

ENIGMA IT

Absolute frequency measurements for hyperfine structure determination of the R(26) 62-0 transition at 501.7 nm in molecular iodine

The absolute frequencies of the hyperfine components of the R(26) 62-0 transition in molecular iodine at 501.7 nm are measured for the first time with an optical clockwork based on a femtosecond laser frequency comb generator. The set-up is composed of an Ar+ laser locked to a hyperfine component of the R(26) 62-0 transition detected in a continuously pumped low-pressure cell (0.33 Pa). The detected resonances show a linewidth of 45 kHz (half-width at half-maximum). The uncertainty of the frequency measurement is estimated to be 250 Hz

Competition between Spin Echo and Spin Self-Rephasing in a Trapped Atom Interferometer

We perform Ramsey interferometry on an ultracold 87Rb ensemble confined in an optical dipoletrap. We use a \pi-pulse set at the middle of the interferometer to restore the coherence of the spinensemble by canceling out phase inhomogeneities and creating a spin echo in the contrast. However,for high atomic densities, we observe the opposite behavior: the \pi-pulse accelerates the dephasingof the spin ensemble leading to a faster contrast decay of the interferometer. We understand thisphenomenon as a competition between the spin-echo technique and an exchange-interaction drivenspin self-rephasing mechanism based on the identical spin rotation effect. Our experimental data iswell reproduced by a numerical model

Correction of the distortion in frequency-modulation spectroscopy

A theoretical expression of the detected signal in frequency modulation spectroscopy with a residual amplitude modulation (RAM) is computed. The line shape distortion induced by the RAM is shown to be essentially suppressed for a proper choice of the modulation and detection parameters. The experimental tests are carried out in saturation spectroscopy of I2 at 514.5 nm. Experimental limitations are analysed

Frequency modulated laser beam with highly efficient intensity stabilisation

We analyse the limitation of the amplitude modulation rejection due to the spatial modulation of the output beam of an acousto-optic modulator used in an active laser beam stabilisation system when a frequency modulation of a few megahertz is applied to this modulator. We show how to overcome this problem, using a single mode optical fibre at the output of the modulator. A residual amplitude modulation of 10-5 is achieved

Mid-IR frequency measurement using an optical frequency comb and a long-distance remote frequency reference

We have built a frequency chain which enables to measure the absolute frequency of a laser emitting in the 28-31 THz frequency range and stabilized onto a molecular absorption line. The set-up uses an optical frequency comb and an ultrastable 1.55 $\mu$m frequency reference signal, transferred from LNE-SYRTE to LPL through an optical link. We are now progressing towards the stabilization of the mid-IR laser via the frequency comb and the extension of this technique to quantum cascade lasers. Such a development is very challenging for ultrahigh resolution molecular spectroscopy and fundamental tests of physics with molecules

Constraining gradient-based inversion with a variational autoencoder to reproduce geological patterns

Given the sparsity of geophysical data it is useful to rely on prior information on the expected geological patterns to constrain the inverse problem and obtain a realistic image of the subsurface. By using several examples of such patterns (e.g. those obtained from a training image), deep generative models learn a low-dimensional latent space that can be seen as a reparameterization of the original high-dimensional parameters and then inversion can be done in this latent space. Examples of such generative models are the variational autoencoder (VAE) and the generative adversarial network (GAN). Both usually include deep neural networks within their architecture and have shown good performance in reproducing high-dimensional structured subsurface models. However, they both use a highly nonlinear function to map from latent space to the original high-dimensional parameter space which hinders the optimization of the objective function during inversion. Particularly, such nonlinearity may give rise to local minima where gradient-based inversion gets trapped and therefore fails to reach the global minimum. GAN has been previously used with gradient-based inversion in a linear traveltime tomography synthetic test where it was shown to often fail in reaching a consistent RMSE (compared to the added noise) because optimization converges to local minima. On the other hand, inversion with MCMC and GAN was shown to reach acceptable RMSE values. When applicable, however, a gradient-based inversion is preferred because of its lower computational demand. We propose using VAE together with gradient-based inversion and show that optimization reaches lower RMSE values on average compared to GAN in a linear traveltime tomography synthetic case. We also compare the subsurface models that are generated during the iterations of the optimization to explore the effect of the different latent spaces used by GAN and VAE. We identify a trade-off between a strict following of the patterns and getting trapped in local minima during optimization, i.e. VAE seems to be able to break some continuous channels in order to not get trapped in local minima whereas GAN does not break channels. Finally, we perform some synthetic tests with nonlinear traveltime tomography and show that gradient-based inversion with VAE is able to recover a similar global structure to the true model but its final RMSE values are still far from the added noise level

Imaging the subsurface to inform hydrological models : a geophysicist's perspective

Heterogeneity plays a major role in subsurface processes from the local scale (preferential infiltration and flow paths, fractures) to the catchment scale (presence of lateral and vertical variability, multiple horizons, bedrock interface, etc.). If high-resolution direct observations are often available through drillholes, CPT or installing in-situ monitoring probes, those local measurements only provide punctual or 1D information. Within this context, geophysical techniques can provide relevant spatially-distributed information (2D, 3D or even 4D) with a much larger coverage than direct measurements. However, geophysical information remains indirect and must be translated into the sought parameter through petrophysical or transfer functions. Geophysicists are facing two important issues when imaging the subsurface: 1) Generating images of the subsurface that are consistent in terms of soil or geological structures; 2) Integrating the geophysical information into hydrological models. Both issues will be discussed in this contribution. Geophysical imaging is the result of an inversion process whose solution is non-unique. This problem is generally solved using a regularization approach introducing some a priori characteristics of the model. The dominant choice is still the smoothness constraint inversion, which often introduces a too simplistic representation of the subsurface, and decreases the potential of geophysics to discriminate between different facies. In the first part of this contribution, we will analyze what can be expected from geophysical methods in terms of characterization of the heterogeneity. We will illustrate how the inversion method affects the discrimination potential of geophysics, and how we can improve the geophysical image by accounting for prior information. We will see how the discrimination potential decreases with the loss of resolution. Finally, we will investigate how recent methodologies using machine learning can improve our ability to image the subsurface. Given the high spatial coverage of geophysical methods, they have a huge potential to inform hydrological models in terms of heterogeneity. However, the limitations related to geophysical inversion also make the geophysical model uncertain and the risk to propagate erroneous information exists. In the second part of this contribution, we will illustrate how to incorporate geophysical data into hydrological models to unravel their spatial complexity. At the early stage of a project, several scenarios regarding spatial heterogeneity are often possible (orientation of fractures, number of facies to consider, interconnection within one facies, etc.), and this can largely influence the outcomes of the hydrological models. In this context, geophysical data can be used to verify the consistency of some scenarios without requiring any inversion in a process called falsification. Once realistic scenarios have been identified, geophysical data can be used to spatially constrain hydrological models. However, this should ideally account for the uncertainty related to geophysical inversion. One possibility is to use a fully-coupled approach where geophysical data are integrated directly in the hydrological model inversion. This requires nevertheless a transfer function to relate hydrological and geophysical variables. As an alternative, a sequential approach using a probabilistic framework accounting for the imperfect geophysical data can be used. The latter requires co-located measurements