Recent Submissions

  • Aiding self-supervised coherent noise suppression by the introduction of signal segmentation using blind-spot networks

    Liu, Sixiu; Birnie, Claire Emma; Alkhalifah, Tariq Ali; Bakulin, Andrey (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Blind-spot networks have been shown to be natural noise suppressors under the assumption that noise is unpredictable based on the information fed into the network during training. Trained in a self-supervised manner, such approaches only utilise the original raw data to determine to remove the noise. In this work, we propose two novel elements for enhancing blind-spot denoising: (1) the introduction of a 2-class segmentation task to aid the network in identification of interest areas of signals that require particular attention during denoising, and; (2) the introduction of a trace-wise noise mask designed to obscure the coherency of noise from being observed by the network. The joint scheme is achieved by introducing a joint loss function to balance between the two deep learning tasks. As such, the final joint scheme is the combination of a self-supervised, blind-spot denoising procedure and a supervised segmentation procedure. We illustrate how the joint scheme can improve the denoising performance of the network, hypothesising that this is due to the introduction of prior information guiding the denoising procedure to areas of focus. Preliminary results from synthetic data contaminated by trace-wise noise, show an increase in the structural similarity index from 0.989 to 0.995, when comparing the optimal jointscheme versus the pure denoising procedure. Future work will extend the procedure to field data where rule-based approaches will be used to generate the segmentation labels.
  • Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

    Corrales, Miguel; Izzatullah, Muhammad; Ravasi, Matteo; Hoteit, Hussein (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Reservoir characterization is a critical component in any oil and gas, geothermal, and CO2 sequestration project. A fundamental step in the process of characterizing the subsurface is represented by the inversion of petrophysical parameters from seismic data. However, this problem suffers from various uncertainty sources originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model and Zoeppritz equation that constitute the modelling operator, further complicates the inversion process. In this work, we propose a novel data-driven approach where well-log information is used to obtain optimal basis functions that link band-limited petrophysical reflectivities to pre-stack seismic data. Subsequently, the inversion of such band-limited reflectivities for petrophysical parameters is framed in a Bayesian framework where a generative adversarial network is used to produce a geologically realistic prior distribution. The trained prior distribution is updated using the Stein Variational Gradient Descent and a set of representative solutions is produced that is consistent with the uncertainties in the data and the nonlinear operators.
  • Using deep learning for automatic detection and segmentation of carbonate microtextures

    Birnie, Claire Emma; Chandra, Viswasanthi (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    The difficulties involved in studying micrometer-sized micrite crystals, and quantifying the associated impact on large scale geophysical properties, have long hindered our society’s understanding of both Middle Eastern and global microporous limestones. Instance segmentation procedures, from the field of deep learning, offer the ability to identify at a pixel-level each individual crystal within an SEM image, allowing for automated morphological analysis. We illustrate how the common Masked Region-based Convolution Neural Network from computer vision can be adapted to the task of identifying individual micrite crystal within gray-scale SEM images. Leveraging Transfer Learning, the ResNet50 neural architecture is used with weights initialized through a pre-training on Microsoft’s Common Objects in COntext (COCO) dataset. The resulting model accurately detects and separates a number of crystals observed within different SEM images. However the trained model is also shown to be highly susceptible to noise introduced as part of the imaging procedure, for example charging noise. Future work will aim to make the procedure more robust, reducing the impact of noise by adapting the pre-processing workflow and incorporating more noisy images into the training dataset.
  • Pwd-pinn: Slope-assisted seismic interpolation with physics-informed neural networks

    Brandolin, Francesco; Ravasi, Matteo; Alkhalifah, Tariq Ali (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Seismic data can be expressed as a superposition of local plane waves. A gather of traces can be described by the local plane wave differential equation (PDE), that allows to predict each of the traces from the previous one, given the knowledge of the local slope of the events. In the approach presented here, we train a neural network in an unsupervised manner to solve seismic interpolation problems using the local plane wave differential equation and the local slope estimated by the mean of plane wave destruction filters (PWD). The physics-informed neural network (PINN) maps the input grid points in time and space to the amplitudes of the wavefield whilst matching the information contained in the available traces. The proposed approach is tested on two seismic interpolation tasks using synthetic data, specifically, interpolation of data with large gaps and those aliased. Whilst the network shows remarkable interpolation capabilities in both experiments, it tends to struggle fitting aliased data with high frequency content. To mitigate this problem, we propose to include locally adaptive activation functions in the architecture. This leads to improved convergence and reconstruction accuracy.
  • Traveltime tomography and efficient physics-informed Bayesian inversion

    Qiao, Tian; Turkiyah, George; Schuster, Gerard T. (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Bayesian inversion provides the same information as regularized inversion of seismic data, except it also supplies a probability estimate of the solution throughout model space. The cost, however, is that Bayesian inversion is orders-of-magnitude more expensive than regularized inversion by a gradient optimization method. To mitigate this cost, we present an efficient physics-informed Bayesian inversion method that combines regularized inversion to get both the optimal solution and the posterior probability functions in model space. A gradient optimization method is used to efficiently estimate the maximum a posterior (MAP) solution, and so function evaluations are only needed around the MAP point in model space. This efficiently provides the posterior probability in that neighbourhood, and therefore avoids the tremendous expense of sampling points throughout the high-dimensional model space. We apply this physics-informed Bayesian inversion to VSP traveltime data. The tomogram is computed with the assistance of an analytic inverse, and the posterior probability estimate is computed with an order-of-magnitude less cost than standard Bayesian analysis. This procedure can also be adapted to refraction traveltime tomography for near-surface imaging.
  • Efficient Physics-Informed Bayesian Inversion of VSP and Hydrofrac Data

    Qiao, Tian; Turkiyah, George; Schuster, Gerard T. (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Bayesian inversion provides the same information as regularized inversion of seismic data, except it also supplies a probability estimate of the solution throughout model space. The cost, however, is that Bayesian inversion is orders-of-magnitude more expensive than regularized inversion by a gradient optimization method. To mitigate this cost, we present an efficient physics-informed Bayesian inversion method that combines regularized inversion to get both the optimal solution and the posterior probability functions in model space. A gradient-optimization method is used to efficiently estimate the maximum a posterior (MAP) solution, and so function evaluations are only needed around the MAP point in model space. This efficiently provides the posterior probability in that neighborhood, and therefore avoids the tremendous expense of sampling points throughout the high-dimensional model space. We present two applications of this physics-informed Bayesian inversion: VSP traveltime inversion and migration of passive seismic data. For 4D monitoring of hydrofrac operations, reuse of the previously computed traveltimes for probability estimates is orders-of-magnitude less expensive for computing migration and posterior images.
  • Robust joint inversion and segmentation of 4D seismic data

    Romero, Juan; Ravasi, Matteo; Luiken, Nick (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Seismic inversion is the leading method to map and quantify changes from time-lapse (4D) seismic datasets, with applications ranging from monitoring of hydrocarbon-producing fields to carbon capture and sequestration. Time-lapse seismic inversion is however, a notoriously ill-posed inverse problem: the band-limited nature of seismic data, alongside inaccuracies in the repeatability of consecutive acquisition surveys make it challenging to obtain high-resolution, clean estimates of 4D effects. Adding prior information to the inversion process in the form of properly crafted regularization (or preconditioning) is therefore essential to successfully extract weak signals that are usually buried under strong noise. In this work, we leverage the fact that 4D seismic inversion can be described as a coupled inversion of its baseline and monitor 3D seismic datasets. In existing approaches, the coupling is introduced by penalizing the squared L2-norm of difference between the baseline and the monitor acoustic impedances, as this is usually assumed to be small. A major downside of such a regularization is that, whilst reducing the overall level of noise in the estimated acoustic impedance differences, the resulting 4D effects are usually oversmoothed and their strength is underestimated. We instead propose to adapt the joint inversion and segmentation algorithm introduced by Ravasi and Birnie (2021) to the problem of 4D seismic inversion. Our technique produces two acoustic impedance models by inverting the corresponding 3D seismic datasets, regularized by Total-Variation. Moreover, the objective function to optimize is augmented with a segmentation term that renders solutions consistent with the expected 4D effects (obtained, for example, as part of a 4D feasibility study by means of Gassmann fluid substitution). A numerical experiment is presented to validate the effectiveness of the proposed approach and its superiority over state-of-the-art 4D inversion methods.
  • Boosting self-supervised blind-spot networks via transfer learning

    Birnie, Claire Emma; Alkhalifah, Tariq Ali (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Self-supervised procedures offer an appealing alternative to supervised denoising techniques that require noisy-clean pairs of training data. However, the capabilities of self-supervised denoising procedures are often limited by the requirement that noise cannot be predicted directly from neighbouring values in the training input samples. As such, there is often a trade-off with respect to the number of training epochs between learning to replicate the signal without learning to replicate the noise. Focusing on blind-spot networks that learn a pixel’s value based on neighbouring pixels, we propose to train a supervised model in a blind-spot manner such that the model learns how to predict a pixel’s clean value based off its noisy neighbouring traces. The weights of the trained model are then used to initialise a self-supervised model which is trained purely on noisy field data. In comparison to the fully self-supervised approach, we illustrate that pre-training with synthetic data results in increased noise suppression, alongside a lower level of signal leakage in the field data.
  • Deep Earth: Leveraging neural networks for seismic exploration objectives

    Alkhalifah, Tariq Ali; Birnie, Claire Emma; Harsuko, Randy; Wang, Hanchen; Ovcharenko, Oleg (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Machine learning has already made many inroads in developments related to acquisition, processing, imaging, inverting, and interpreting seismic data. In spite of the many success stories, its commercial use has been limited as the challenges mount. These challenges include cost of training, availability of training samples, the applicability of the trained model to real data (generalization), and more importantly, the availability of practitioners who actually know what the neural networks (NNs) are doing. Taking a step back, I will review what worked in deep learning and what we are still waiting on to work. We will look into the various ML algorithms, from supervised to unsupervised, transformers to contrastive learning, and identify the potential role of these various algorithms on seismic data, with examples. The examples include seismic data denoising, data extrapolation, first arrival picking, microseismic location, velocity inversion all on real data.
  • Large-scale Marchenko imaging with distance-aware matrix reordering, tile low-rank compression, and mixed-precision computations

    Ravasi, Matteo; Hong, Yuxi; Ltaief, Hatem; Keyes, David E.; Vargas, David (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    A variety of wave-equation-based seismic processing algorithms rely on the repeated application of the Multi- Dimensional Convolution (MDC) operator. For large-scale 3D seismic surveys, this comes with severe computational challenges due to the sheer size of high-density, full-azimuth seismic datasets required by such algorithms. We present a three-fold solution that greatly alleviates the memory footprint and computational cost of 3D MDC by leveraging a combination of i) distance-aware matrix reordering, ii) Tile Low-Rank (TLR) matrix compression, and iii) computations in mixed floating-point precision. By applying our strategy to a 3D synthetic dataset, we show that the size of kernel matrices used in the Marchenko redatuming and Multi-Dimensional Deconvolution equations can be reduced by a factor of 34 and 6, respectively. We also introduce a TLR Matrix-Vector Multiplication (TLR-MVM) algorithm that, as a direct consequence of such compression capabilities, is consistently faster than its dense counterpart by a factor of 4.8 to 36.1 (depending on the selected hardware). As a result, the associated inverse problems can be solved at a fraction of cost in comparison to state-of- the-art implementations that require a pass through the entire data at each MDC operation. This is achieved with minimal impact on the quality of the processing outcome.
  • Physics-based preconditioned multidimensional deconvolution in the time domain

    Vargas, David; Vasconcelos, Ivan; Ravasi, Matteo; Luiken, Nick (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Multi-Dimensional Deconvolution is a data-driven method that is at the center of key seismic processing applications - from suppressing multiples to inversion-based imaging. When posed in an interferometric context, it can grant access to overburden-free seismic virtual surveys at a given datum in the subsurface. As such, it constitutes an essential processing operation that achieves multiple imaging objectives simultaneously in redatuming or target-oriented imaging: e.g., suppressing multiples, removing complex overburden effects, and retrieving amplitude consistent image gathers for impedance inversion. Despite its potential, the deconvolution process relies on the solution of an ill-conditioned linear inverse problem sensitive to noise artifacts due to incomplete acquisition, limited sources, and band-limited data. Typically, this inversion is performed in the Fourier domain where the estimation of optimal regularization parameters hinders accurate waveform reconstruction. We reformulate the problem in the time domain - long believed to be computationally intractable - and introduce several physical constraints that naturally drive the inversion towards a reduced set of reliable, stable solutions. This allows to successfully reconstruct the overburden-free reflection response beneath a complex salt body from noise-contaminated data.
  • Deep-Learning Based Channel Estimation for RIS-Aided mmWave Systems with Beam Squint

    Abdallah, Asmaa; Celik, Abdulkadir; Mansour, Mohammad M.; Eltawil, Ahmed (IEEE, 2022-08-11) [Conference Paper]
    Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve overall network performance. However, CSI acquisition is non-trivial due to the passive nature of RIS, and the dimensions of the cascaded channel between transceivers increase with the large number of RIS elements, which requires high training overhead. Prior art has considered frequency-selective channel estimation without considering the beam squint effect in wideband systems, severely degrading channel estimation performance. This paper proposes a novel data-driven approach for estimating wideband cascaded channels of RIS-assisted multi-user millimeter-wave massive multiple-input multiple-output (MIMO) systems with limited training overhead, explicitly considering the effect of beam squint. To circumvent the beam squint effect, the proposed method exploits the common sparsity property among the different subcarriers as well as the double-structured sparsity property of the users’ angular cascaded channel matrices. The proposed data-driven cascaded channel estimation approach exploits denoising neural networks to detect channel supports accurately. Compared to beam squint effect agnostic traditional orthogonal matching pursuit (OMP) approaches, the proposed data-driven approach achieves 5-6dB less normalized mean square error (NMSE) and reduces the lower bound gap to only 1dB for the oracle least-square benchmark.
  • Waveguiding via Transformation Optics

    Elizarov, Maxim; Fratalocchi, Andrea (IEEE, 2022-08-09) [Conference Paper]
    We demonstrate that it is possible to surpass current limitations of nanophotonics and plasmonics by designing an artificial material which can emulate user-defined spatial refractive index distribution. The effective optical property of the material is engineered through the deformation of reflective substrate via transformation optics approach. We provide one of possible applications - subwavelength optical waveguide coupler device based on this technique.
  • Differentially Private ℓ1-norm Linear Regression with Heavy-tailed Data

    Wang, Di; Xu, Jinhui (IEEE, 2022-08-03) [Conference Paper]
    We study the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data. Specifically, we focus on the ℓ1-norm linear regression in the ϵ-DP model. While most of the previous work focuses on the case where the loss function is Lipschitz, here we only need to assume the variates has bounded moments. Firstly, we study the case where the ℓ2 norm of data has bounded second order moment. We propose an algorithm which is based on the exponential mechanism and show that it is possible to achieve an upper bound of O~(dnε−−√) (with high probability). Next, we relax the assumption to bounded θ-th order moment with some θ ∈ (1,2) and show that it is possible to achieve an upper bound of O~((dnε−−√)θ−1θ). Our algorithms can also be extended to more relaxed cases where only each coordinate of the data has bounded moments, and we can get an upper bound of O~(dnε−−√) and O~(d(nε)θ−1θ) in the second and θ-th moment case respectively.
  • Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)

    Pei, Shichao; Yu, Lu; Zhang, Xiangliang (IEEE, 2022-08-02) [Conference Paper]
    Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by using entity synonym set information and constructing a two-level network. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.
  • An industrial perspective on web scraping characteristics and open issues

    Chiapponi, Elisa; Dacier, Marc; Thonnard, Olivier; Fangar, Mohamed; Mattsson, Mattias; Rigal, Vincent (IEEE, 2022-07-25) [Conference Paper]
    An ongoing battle has been running for more than a decade between e-commerce websites owners and web scrapers. Whenever one party finds a new technique to prevail, the other one comes up with a solution to defeat it. Based on our industrial experience, we know this problem is far from being solved. New solutions are needed to address automated threats. In this work, we will describe the actors taking part in the battle, the weapons at their disposal, and their allies on either side. We will present a real-world setup to explain how e-commerce websites operators try to defend themselves and the open problems they seek solutions for.
  • CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions

    Abdal, Rameen; Zhu, Peihao; Femiani, John; Mitra, Niloy; Wonka, Peter (ACM, 2022-07-24) [Conference Paper]
    The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images. However, such editing operations are either trained with semantic supervision or annotated manually by users. In another development, the CLIP architecture has been trained with internet-scale loose image and text pairings, and has been shown to be useful in several zero-shot learning settings. In this work, we investigate how to effectively link the pretrained latent spaces of StyleGAN and CLIP, which in turn allows us to automatically extract semantically-labeled edit directions from StyleGAN, finding and naming meaningful edit operations, in a fully unsupervised setup, without additional human guidance. Technically, we propose two novel building blocks; one for discovering interesting CLIP directions and one for semantically labeling arbitrary directions in CLIP latent space. The setup does not assume any pre-determined labels and hence we do not require any additional supervised text/attributes to build the editing framework. We evaluate the effectiveness of the proposed method and demonstrate that extraction of disentangled labeled StyleGAN edit directions is indeed possible, revealing interesting and non-trivial edit directions.
  • BeeCast: A Device-to-Device Collaborative Video Streaming System

    Alghamdi, Asaad; Balah, Younes; AlBejadi, Mohammad; Felemban, Muhamad (IEEE, 2022-07-19) [Conference Paper]
    In this paper, we propose BeeCast, a collaborative video streaming system that facilitates collaborative video streaming for a group of mobile users with limited Internet connectivity. The novelty of the proposed system is the ability to watch the video on a shared screen or to watch the video on multiple screens. The latter option entails proposing a method to exchange the downloaded video segments among the users using device-to-device communication. The proposed system is composed of two components: BeeBuzzer, and BeePlanner. The BeeBuzzer component manages and coordinates the segment exchange among devices, while BeePlanner component enhances the overall Quality of Experience (QoE) through effective segment assignments decisions for each user. Simulation results show that using BeeCast in an unstable network produces a more consistent QoE than individual streaming while eliminating 80% of redundant network traffic.
  • RF Performance Assessment of Sub-8nm GaN-SOI-FinFET Using Power Gain Parameters

    Kumar, Ajay; Gupta, Neha; Goyal, Amit K.; Massoud, Yehia Mahmoud (IEEE, 2022-07-18) [Conference Paper]
    In this work, we have presented, a radio frequency (RF) assessment of nanoscale gallium nitride-silicon-on-insulator fin field-effect transistor (GaN-SOI-FinFET). All the performances of the device have been compared with conventional FinFET (Conv. FinFET) simultaneously. All the results show that the power gains have significantly improved in terms of Gma, Gms, stern stability factor (SS), GMT, and intrinsic delay in comparison to conventional FinFET. Current gain unilateral power gain and have also been evaluated for the extraction of fT (cut-off frequency) and fMAX respectively. fT and fMAX enhance by 88.8% and 94.6% respectively. This analysis has been done at several THz frequencies. The implementation of GaN in the channel reduces the parasitic capacitance and paves the way for high-performance RF applications.
  • Parallel space-time likelihood optimization for air pollution prediction on large-scale systems

    Salvaña, Mary Lai O.; Abdulah, Sameh; Ltaief, Hatem; Sun, Ying; Genton, Marc G.; Keyes, David E. (ACM, 2022-07-12) [Conference Paper]
    Gaussian geostatistical space-time modeling is an effective tool for performing statistical inference of field data evolving in space and time, generalizing spatial modeling alone at the cost of the greater complexity of operations and storage, and pushing geostatistical modeling even further into the arms of high-performance computing. It makes inferences for missing data by leveraging space-time measurements of one or more fields. We propose a high-performance implementation of a widely applied space-time model for large-scale systems using a two-level parallelization technique. At the inner level, we rely on state-of-the-art dense linear algebra libraries and parallel runtime systems to perform complex matrix operations required to evaluate the maximum likelihood estimation (MLE). At the outer level, we parallelize the optimization process using a distributed implementation of the particle swarm optimization (PSO) algorithm. At this level, parallelization is accomplished using MPI sub-communicators, such that the nodes in each sub-communicator perform a single MLE iteration at a time. To evaluate the effectiveness of the proposed methodology, we assess the accuracy of the newly implemented space-time model on a set of large-scale synthetic space-time datasets. Moreover, we use the proposed implementation to model two air pollution datasets from the Middle East and US regions with 550 spatial locations X730 time slots and 945 spatial locations X500 time slots, respectively. The evaluation shows that the proposed approach satisfies high prediction accuracy on both synthetic datasets and real particulate matter (PM) datasets in the context of the air pollution problem. We achieve up to 757.16 TFLOPS/s using 1024 nodes (75% of the peak performance) using 490K geospatial locations on Shaheen-II Cray XC40 system.

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