Mesures sismiques à faible profondeur : une approche intégrée

Des mesures sismiques à faible profondeur ont été réalisées dans le cadre d’une étude hydrogéophysique en Outaouais pour investiguer le sous-sol. À cet effet, une approche intégrée qui regroupe plusieurs méthodes de traitement du signal sismique a été développée. Dans cette approche, l’ensemble des modes de propagation sismique est considéré en effectuant l’analyse conjointe des ondes de surface, des ondes réfractées critiquement et des ondes réfléchies de compression P et de cisaillement polarisées verticalement SV. Cette approche intégrée est beaucoup plus robuste que si chaque méthode était considérée séparément. Elle permet d’estimer la distribution spatiale des vitesses de propagation des ondes P et S de la sous-surface et d’obtenir des profils en réflexion qui permettent d’identifier les contacts stratigraphiques. Quatre études de cas dans la région de l’Outaouais sont présentées pour illustrer cette approche. Pour l’une de ces études à Buckingham, un essai de pénétration au piézocône sismique a été réalisé afin de valider les modèles de vitesse obtenus à partir de l’approche développée. Cette approche intégrée est applicable entre autres aux domaines de l’environnement, de la géotechnique, des risques naturels et de l’hydrogéologie

Seismic modeling and inversion using half-precision floating-point numbers

New processors are increasingly supporting half-precision floating-point numbers, often with a significant throughput gain over single-precision operations. Seismic modeling, imaging, and inversion could benefit from such an acceleration, but it is not obvious how the accuracy of the solution can be preserved with a very narrow 16-bit representation. By scaling the finite-difference expression of the isotropic elastic wave equation, we have found that a stable solution can be obtained despite the very narrow dynamic range of the half-precision format.We develop an implementation with the CUDA platform, which, on most recent graphics processing units (GPU), is nearly twice as fast and uses half the memory of the equivalent single-precision version. The error on seismograms caused by the reduced precision is shown to correspond to a negligible fraction of the total seismic energy and is mostly incoherent with seismic phases. Finally, we find that this noise does not adversely impact full-waveform inversion nor reverse time migration, which both benefit from the higher throughput of half-precision computation

Seismic velocity estimation: A deep recurrent neural-network approach

ABSTRACT: Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than 44  m/s for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building

A Review on Applications of Time-Lapse Electrical Resistivity Tomography Over the Last 30 Years : Perspectives for Mining Waste Monitoring

"Mining operations generate large amounts of wastes which are usually stored into large-scale storage facilities which pose major environmental concerns and must be properly monitored to manage the risk of catastrophic failures and also to control the generation of contaminated mine drainage. In this context, non-invasive monitoring techniques such as time-lapse electrical resistivity tomography (TL-ERT) are promising since they provide large-scale subsurface information that complements surface observations (walkover, aerial photogrammetry or remote sensing) and traditional monitoring tools, which often sample a tiny proportion of the mining waste storage facilities. The purposes of this review are as follows: (i) to understand the current state of research on TL-ERT for various applications; (ii) to create a reference library for future research on TL-ERT and geoelectrical monitoring mining waste; and (iii) to identify promising areas of development and future research needs on this issue according to our experience. This review describes the theoretical basis of geoelectrical monitoring and provides an overview of TL-ERT applications and developments over the last 30 years from a database of over 650 case studies, not limited to mining operations (e.g., landslide, permafrost). In particular, the review focuses on the applications of ERT for mining waste characterization and monitoring and a database of 150 case studies is used to identify promising applications for long-term autonomous geoelectrical monitoring of the geotechnical and geochemical stability of mining wastes. Potential challenges that could emerge from a broader adoption of TL-ERT monitoring for mining wastes are discussed. The review also considers recent advances in instrumentation, data acquisition, processing and interpretation for long-term monitoring and draws future research perspectives and promising avenues which could help improve the design and accuracy of future geoelectric monitoring programs in mining wastes.

A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings

Accurate and large-scale assessment of volumetric water content (VWC) plays a critical role in mining waste monitoring to mitigate potential geotechnical and environmental risks. In recent years, time-lapse electrical resistivity tomography (TL-ERT) has emerged as a promising monitoring approach that can be used in combination with traditional invasive and point-measurements techniques to estimate VWC in mine tailings. Moreover, the bulk electrical conductivity (EC) imaged using TL-ERT can be converted into VWC in the field using petrophysical relationships calibrated in the laboratory. This study is the first to assess the scale effect on the accuracy of ERT-predicted VWC in tailings. Simultaneous and co-located monitoring of bulk EC and VWC are carried out in tailings at five different scales, in the laboratory and in the field. The hydrogeophysical datasets are used to calibrate a petrophysical model used to predict VWC from TL-ERT data. Overall, the accuracy of ERT-predicted VWC is , and the petrophysical models determined at sample-scale in the laboratory remain valid at larger scales. Notably, the impact of temperature and pore water EC evolution plays a major role in VWC predictions at the field scale (tenfold reduction of accuracy) and, therefore, must be properly taken into account during the TL-ERT data processing using complementary hydrogeological sensors. Based on these results, we suggest that future studies using TL-ERT to predict VWC in mine tailings could use sample-scale laboratory apparatus similar to the electrical resistivity Tempe cell presented here to calibrate petrophysical models and carefully upscale them to field applications