Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery

Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. To alleviate these issues, we investigate a double-scale supervised impedance inversion method based on the gated recurrent encoder-decoder network (GREDN). We first train the decoder network of GREDN called the forward operator, which can map impedance to seismic data. We then implement the well-trained decoder as a constraint to train the encoder network of GREDN called the inverse operator. Besides matching the output of the encoder with broadband pseudo-well impedance labels, data generated by inputting the encoder output into the known decoder match the observed narrowband seismic data. Both the broadband impedance information and the already-trained decoder largely limit the solution space of the encoder. Finally, after training, only the derived optimal encoder is applied to unseen seismic traces to yield broadband impedance volumes. The proposed approach is fully data-driven and does not involve the initial model, seismic wavelet and model-driven operator. Tests on the Marmousi model illustrate that the proposed double-scale supervised impedance inversion method can effectively recover low-frequency components of the impedance model, and demonstrate that low frequencies of the predicted impedance originate from well logs. Furthermore, we apply the strategy of combining the double-scale supervised impedance inversion method with a model-driven impedance inversion method to process field seismic data. Tests on a field data set show that the predicted impedance results not only reveal a classical tectonic sedimentation history, but also match the corresponding results measured at the locations of two wells.

DETECTION OF AGE-RELATED MACULAR DEGENERATION FROM OCT IMAGES USING DOUBLE SCALE CNN ARCHITECTURE

An ocular disease that affects the elderly is Age-related Macular Degeneration (AMD). Because of the aging population in society, AMD incidence is increasing; early diagnosis is vital to avoid vision loss in the elderly. It is a challenging process to organize a comprehensive eye screening system for detecting AMD. This paper proposes a novel Double Scale Convolutional Neural Network (DSCNN) architecture for an accurate AMD diagnosis. The architecture proposed is a DSCNN with six convolutional layers for classifying AMD or normal images. The double-scale convolution layer enables many local structures to be generated with two different filter sizes. In this proposed network, the sigmoid function is used as the classifier. The proposed CNN network is trained on the Mendeley data set and tested on four data sets, namely Mendeley, OCTID, Duke, SD-OCT Noor data set, and achieved an accuracy of 99.46%, 98.08%, 96.66%, and 94.89% respectively. The comparison with alternative methods provided results showing the efficacy of the proposed algorithm in detecting AMD. Although the proposed model is trained only on the Mendeley data set, it achieved good detection accuracy when evaluated with other data sets. This indicates the proposed model’s ability to classify AMD/Normal images from different data sets. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in detecting AMD. The proposed architecture can be applied in the rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters, the proposed CNN can be implemented in real-time.

Tow Deformation Behaviors in Resin-Impregnated Glass Fibers under Different Flow Rates

With the rapid development of high-performance fibers such as carbon, enhanced glass fibers in structural applications, the use of fiber-reinforced composite (FRC) materials has also increased in many areas. Liquid composite molding (LCM) is a widely used manufacturing process in composite manufacturing; however, the rapid impregnation of resin in the reinforcing fibers during processing poses a significant issue. The optimization of resin impregnation is related to tow deformations in the reinforcing fibers. The present study therefore focuses on this tow deformation. The permeability behaviors in double-scale porous media were observed under different flow rates and viscosity conditions to examine the overall tendencies of structural changes in the reinforcement. The permeability results showed hysteresis with increasing and decreasing flow rate conditions of 50–800 mm3/s, indicating structural changes in the reinforcement. The tow behaviors of the double-scale porous media with respect to the thickness and flow rate were investigated in terms of the representative indices of the minor axis (tow thickness) and major axis. The minor axis and major axis of the tow showed decreasing and increasing trends of 2–5% and 2%, respectively, with minimum and maximum values at different positions along the reinforcement, affected by the different hydrodynamic entry lengths. Finally, the deformed tow behavior was observed microscopically to examine the behavior of the tow at different flow rates.

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

Double-scale diffusive wave model dedicated to spatial river observation and associated covariance kernel for variational data assimilation

<p>The spatial altimetry provides an important amount of water surface height data from multi-missions satellites (especially Jason-3, Sentinel-3A/B and the forthcoming NASA-CNES SWOT mission). To exploit at best the potential of spatial altimetry, the present study proposes on the derivation of a model adapted to spatial observations scale; a diffusive-wave type model but adapted to a double scale [1].</p><p>Moreover, Green-like kernel can be employed to derived covariance operators, therefore they may provide an approximation of the covariance kernel of the background error in Variational Data Assimilation processes. Following the derivation of the aforementioned original flow model, we present the derivation of a Green kernel which provides an approximation of the covariance kernel of the background error for the bathymetry (i.e. the control variable) [2].</p><p>This approximation of the covariance kernel is used to infer the bathymetry in the classical Saint-Venant’s (Shallow-Water) equations with better accuracy and faster convergence than if not introducing an adequate covariance operator [3].</p><p>Moreover, this Green kernel helps to analyze the sensitivity of the double-scale diffusive waves (or even the Saint-Venant’s equations) with respect to the bathymetry.</p><p>Numerical results are analyzed on real like datasets (derived from measurements of the Rio Negro, Amazonia basin).</p><p>The double-scale diffusive wave provide more accurate results than the classical version. Next, in terms of inversions, the derived physically-based covariance operators enable to improve the inferences, compared to the usual exponential one.</p><p>[1] T. Malou, J. Monnier "Double-scale diffusive wave equations dedicated to spatial river observations". In prep.</p><p>[2] T. Malou, J. Monnier "Physically-based covariance kernel for variational data assimilation in spatial hydrology". In prep.</p><p>[3] K. Larnier, J. Monnier, P.-A. Garambois, J. Verley. "River discharge and bathymetry estimations from SWOT altimetry measurements". Inv. Pb. Sc. Eng (2020).</p>