Recent Submissions

  • Natural processes dominate the pollution levels during COVID-19 lockdown over India.

    Madineni, Venkat Ratnam; Dasari, Hari Prasad; Karumuri, Ramakrishna; Viswanadhapalli, Yesubabu; Perumal, Prasad; Hoteit, Ibrahim (Scientific reports, Springer Science and Business Media LLC, 2021-07-24) [Article]
    The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20-50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70-80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60-70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20-40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40-60% increase in relative humidity further amplified aerosol concentrations. The results of this study suggest that besides emissions, natural processes including background meteorology and dynamics, play a crucial role in the pollution concentrations over the Indian sub-continent.
  • Bayesian seismic inversion: A fast sampling Langevin dynamics Markov chain Monte Carlo method

    Izzatullah, Muhammad; van Leeuwen, Tristan; Peter, Daniel (Geophysical Journal International, Oxford University Press (OUP), 2021-07-22) [Article]
    Summary In this study, we aim to solve the seismic inversion in the Bayesian framework by generating samples from the posterior distribution. This distribution incorporates the uncertainties in the seismic data, forward model, and prior information about the subsurface model parameters; thus, we obtain more information through sampling than through a point estimate (e.g., Maximum a Posteriori method). Based on the numerical cost of solving the forward problem and the dimensions of the subsurface model parameters and observed data, sampling with Markov chain Monte Carlo (MCMC) algorithms can be prohibitively expensive. Herein, we consider the promising Langevin dynamics MCMC algorithm. However, this algorithm has two central challenges: (1) the step size requires prior tuning to achieve optimal performance, and (2) the Metropolis-Hastings acceptance step is computationally demanding. We approach these challenges by proposing an adaptive step-size rule and considering the suppression of the Metropolis-Hastings acceptance step. We highlight the proposed method’s potential through several numerical examples and rigorously validate it via qualitative and quantitative evaluation of the sample quality based on the kernelized Stein discrepancy (KSD) and other MCMC diagnostics such as trace and autocorrelation function (ACF) plots. We conclude that, by suppressing the Metropolis-Hastings step, the proposed method provides fast sampling at efficient computational costs for large-scale seismic Bayesian inference; however, this inflates the second statistical moment (variance) due to asymptotic bias. Nevertheless, the proposed method reliably recovers important aspects of the posterior, including means, variances, skewness, and one- and-twodimensional marginals. With larger computational budget, exact MCMC methods (i.e., with a Metropolis-Hastings step) should be favored. The results thus obtained can be considered a feasibility study for promoting the approximate Langevin dynamics MCMC method for Bayesian seismic inversion on limited computational resources.
  • Direct micro-seismic event location and characterization from passive seismic data using convolutional neural networks

    Wang, Hanchen; Alkhalifah, Tariq Ali (GEOPHYSICS, Society of Exploration Geophysicists, 2021-07-19) [Article]
    The ample size of time-lapse data often requires significant event detection and source location efforts, especially in areas like shale gas exploration regions where a large number of micro-seismic events are often recorded. In many cases, the real-time monitoring and locating of these events are essential to production decisions. Conventional methods face considerable drawbacks. For example, traveltime-based methods require traveltime picking of often noisy data, while migration and waveform inversion methods require expensive wavefield solutions and event detection. Both tasks require some human intervention, and this becomes a big problem when too many sources need to be located, which is common in micro-seismic monitoring. Machine learning has recently been used to identify micro-seismic events or locate their sources once they are identified and picked. We propose to use a novel artificial neural network framework to directly map seismic data, without any event picking or detection, to their potential source locations. We train two convolutional neural networks on labeled synthetic acoustic data containing simulated micro-seismic events to fulfill such requirements. One convolutional neural network, which has a global average pooling layer to reduce the computational cost while maintaining high-performance levels, aims to classify the number of events in the data. The other network predicts the source locations and other source features such as the source peak frequencies and amplitudes. To reduce the size of the input data to the network, we correlate the recorded traces with a central reference trace to allow the network to focus on the curvature of the input data near the zero-lag region. We train the networks to handle single, multi, and no event segments extracted from the data. Tests on a simple vertical varying model and a more realistic Otway field model demonstrate the approach's versatility and potential.
  • Seasonal M2 Internal Tides in the Arabian Sea

    Ma, Jingyi; Guo, Daquan; Zhan, Peng; Hoteit, Ibrahim (Remote Sensing, MDPI AG, 2021-07-18) [Article]
    Internal tides play a crucial role in ocean mixing. To explore the seasonal features of mode-1 M2 internal tides in the Arabian Sea, we analyzed their propagation and energy distribution using along-track sea-level anomaly data collected by satellite altimeters. We identified four primary source regions of internal tides: Abd al Kuri Island, the Carlsberg Ridge, the northeastern Arabian Sea, and the Maldive Islands. The baroclinic signals that originate from Abd al Kuri Island propagate meridionally, whereas those originating from the west coast of India propagate southwestward. The strength and energy flux of the internal tides in the Arabian Sea exhibit significant seasonal and spatial variability. The internal tides generated during winter are more energetic and can propagate further than those generated in summer. Doppler shifting and horizontal variations in stratification can explain the differences in the internal tides’ seasonal distributions.
  • Frequency domain reflection waveform inversion with generalized internal multiple imaging

    Wang, Guanchao; Guo, Qiang; Alkhalifah, Tariq Ali; Wang, Shangxu (GEOPHYSICS, Society of Exploration Geophysicists, 2021-07-02) [Article]
    Full-waveform Inversion (FWI) has the potential to provide a high resolution detailed model of the earth’s subsurface, but it often fails to do so if the starting model is far from the true one. Reflection waveform inversion (RWI) is a popular method to build a sufficiently accurate initial model for FWI. In traditional RWI, the low-wavenumber updates are always computed and captured by smearing the data misfit along the reflection path with the help of migration/de-migration. However, the success of the RWI relies heavily on accurately reproducing the data in de-migration. Thus, we introduce a new generalized internal multiple imaging-based RWI implementation (GIMI-RWI), in which we avoid the Born modeling and update the primary reflection kernel directly. In the GIMI-RWI, we store one reflection kernel for each source-receiver pair, preserving the unique wave path for every single source-receiver trace. Subsequently, the convolution between the data residuals and the corresponding reflection kernel can build the tomographic velocity updates. In this situation, the long-wavelength tomographic updates are free of migration footprints, and will contribute a smoother background velocity to reduce the cycle-skipping risk and stabilize the followed full-waveform inversion process. Also, the GIMI-RWI method is source independent, as it entirely relies on the data. Using a synthetic example extracted from the Sigsbee2A model, we show the reliable performance of the GIMI-RWI technique.
  • Automatic identification model of micro-earthquakes and blasting events in Laohutai coal mine based on the measurement of source parameter difference

    Dong, Chen; Mai, Paul Martin (Measurement, Elsevier BV, 2021-07) [Article]
    The micro-seismic signal of coal mine is obviously affected by blasting signal, which seriously affects the identification accuracy of micro-seismic signal. For this purpose, automated identification and discrimination methods exist to monitor seismicity occurrence. In this study, seismic source properties of blasting events and micro-earthquakes in the Laohutai coal mine are quantified to more accurately distinguish between these two types of events and to investigate potential physical differences between them. Besides examining the space-time evolution of micro-earthquakes in the Laohutai coal mine, source parameters of blasting events and micro-earthquakes (corner frequency f0; spectral level Ω0; seismic moment M0; moment magnitude Mw; source radius R; stress drop △σ; apparent stress σa, radiated seismic energy E) are determined and scaling relationships between them are investigated. Our results show that the number of micro-earthquakes is closely related to the mining activity. Source-spectral characteristics of blasting events are well described by the Brune omega-square model and follow in general the classical scaling relations (i.e. increasing seismic moment with decreasing corner frequency), like the source-spectral characteristics of micro-earthquakes. Importantly, for events of same magnitude, corner frequency and stress drop of blasting events are larger than for micro-earthquakes. This observation helps to improve automatic identification and discrimination of micro-seismic and blast events, thereby providing important information for (real-time) seismic hazard monitoring and risk management.
  • Data-driven analysis of climate change in Saudi Arabia: trends in temperature extremes and human comfort indicators

    Odnoletkova, Natalia; Patzek, Tadeusz (Journal of Applied Meteorology and Climatology, American Meteorological Society, 2021-06-30) [Article]
    AbstractWe have analyzed the long-term temperature trends and extreme temperature events in Saudi Arabia between 1979 and 2019. Our study relies on the high resolution, consistent and complete ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). We evaluated linear trends in several climate descriptors, including temperature, dewpoint temperature, thermal comfort and extreme event indices. Previous works on this topic used data from weather station observations over limited time intervals and did not include temperature data for recent years. The years 2010-2019 have been the warmest decade ever observed by instrumental temperature monitoring and comprise the eight warmest years on record. Therefore the earlier results may be incomplete and their results no longer relevant. Our findings indicate that over the past four decades, Saudi Arabia has warmed up at a rate that is 50% higher than the rest of land mass in the Northern Hemisphere. Moreover, moisture content of the air has significantly increased in the region. The increases of temperature and humidity have resulted in the soaring of dew point temperature and thermal discomfort across the country. These increases are more substantial during summers, which are already very hot compared to winters. Such changes may be dangerous to people over vast areas of the country. If the current trend persists into the future, human survival in the region will be impossible without continuous access to air conditioning.
  • Impact of fracture geometry and topology on the connectivity and flow properties of stochastic fracture networks

    Zhu, Weiwei; Khirevich, Siarhei; Patzek, Tadeusz (Water Resources Research, American Geophysical Union (AGU), 2021-06-27) [Article]
    In a low permeability formation, connectivity of natural and induced fractures determines overall hydraulic diffusivity in fluid flow through this formation and defines effective rock permeability. Efficient evaluation of fracture connectivity is a nontrivial task. Here we utilize a topological concept of global efficiency to evaluate this connectivity. We address the impact of key geometrical properties of stochastic fracture networks (fracture lengths, orientations, apertures and positions of fracture centers) on the macro-scale flow properties of a shale-like formation. Six thousand different realizations have been generated to characterize these properties for each fracture network. We find that a reduced graph of a fracture network, which consists of the shortest paths from the inlet nodes (fractures) to all outlet nodes, contributes most to fluid flow. 3D fracture networks usually have higher global efficiency than 2D ones, because they have better connectivity. All geometrical properties of fractures influence quality of their connectivity. Aperture distribution impacts strongly global efficiency of a fracture network, and its influence is more significant when the system is dominated by large fractures. Fracture clustering lowers global efficiency in both 2D and 3D fracture networks. Global efficiency of 2D and 3D fracture networks also decreases with the increasing exponent of the power-law distribution of fracture lengths, which means that the connectivity of the system decreases with an increasing number of small fractures. Realistic fracture networks, composed of several sets of fractures with constrained preferred orientations, share all the characteristics of the stochastic fracture networks we have investigated.
  • Modeling of Water Generation from Air Using Anhydrous Salts

    Sibie, Shereen K.; El-Amin, Mohamed F.; Sun, Shuyu (Energies, MDPI AG, 2021-06-25) [Article]
    The atmosphere contains 3400 trillion gallons of water vapor, which would be enough to cover the entire Earth with a one-inch layer of water. As air humidity is available everywhere, it acts as an abundant renewable water reservoir, known as atmospheric water. The efficiency of an atmospheric water harvesting system depends on the sorption capacities of water-based absorption materials. Using anhydrous salts is an efficient process in capturing and delivering water from ambient air, especially under a condition of low relative humidity, as low as 15%. Many water-scarce countries, like Saudi Arabia, receive high annual solar radiation and have relatively high humidity levels. This study is focused on the simulation and modeling of the water absorption capacities of three anhydrous salts under different relative humidity environments: copper chloride (CuCl2), copper sulfate (CuSO4), and magnesium sulfate (MgSO4), to produce atmospheric drinking water in water-scarce regions. By using a mathematical model to simulate water absorption, this study attempts to compare and model the results of the current computed model with the laboratory experimental results under static and dynamic relative humidities. This paper also proposes a prototype of a system to produce atmospheric water using these anhydrous salts. A sensitivity analysis was also undertaken on these three selected salts to determine how the uniformity of their stratified structures, thicknesses, and porosities as applied in the mathematical model influence the results.
  • A Corrected Cubic Law for Single-phase Laminar Flow through Rough-walled Fractures

    He, Xupeng; Sinan, Marwa; Kwak, Hyung; Hoteit, Hussein (Advances in Water Resources, Elsevier BV, 2021-06-19) [Article]
    Hydraulic properties of natural fractures are essential parameters for the modeling of fluid flow and transport in subsurface fractured porous media. The cubic law, based on the parallel-plate concept, has been traditionally used to estimate the hydraulic properties of individual fractures. This upscaling approach, however, is known to overestimate the fractures hydraulic properties. Dozens of methods have been proposed in the literature to improve the accuracy of the cubic law. The relative performance of these various methods is not well understood. In this work, a comprehensive review and benchmark of almost all commonly used cubic law-based approaches in the literature, covering 43 methods is provided. We propose a new corrected cubic law for incompressible, single-phase laminar flow through rough-walled fractures. The proposed model incorporates corrections to the hydraulic fracture aperture based on the flow tortuosity and local roughness of the fracture walls. We identify geometric rules relative to the local characteristic of the fracture and apply an efficient algorithm to subdivide the fracture into segments, accordingly. High-resolution simulations for Navier-Stokes equations, computed in parallel, for synthetic fractures with various ranges of surface roughness and apertures are then performed. The numerical solutions are used to assess the accuracy of the proposed model and compare it with the other 43 approaches, where we demonstrate its superior accuracy. The proposed model retains the simplicity and efficiency of the cubic law but with pronounced improvement to its accuracy. The data set used in the benchmark, including more than 7500 fractures, is provided in open-access.
  • Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)

    Pathak, Raju; Dasari, Hari Prasad; El Mohtar, Samah; Subramanian, Aneesh; Sahany, Sandeep; Mishra, Saroj K; Knio, Omar; Hoteit, Ibrahim (Frontiers in Climate, Frontiers, 2021-06-16) [Article]
    Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud forcing, ice and liquid water path). A first-order sensitivity analysis reveals that dcs contributes approximately 40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute approximately 20% and 11%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). Our study has implications for enhancing our understanding of the physical mechanisms associated with cloud processes leading to uncertainty in model simulations and further helps to improve the models used for their assessment.
  • Quality Evaluation of Epoxy Pore Casts Using Silicon Micromodels: Application to Confocal Imaging of Carbonate Samples

    Hassan, Ahmed; Yutkin, Maxim; Chandra, Viswasanthi; Patzek, Tadeusz (Applied Sciences, MDPI AG, 2021-06-16) [Article]
    Pore casting refers to filling the void spaces of porous materials with an extraneous fluid, usually epoxy resin, to obtain a high-strength composite material, stabilize a fragile porous structure, produce a three-dimensional replica of the pore space, or provide imaging contrast. Epoxy pore casting may be accompanied by additional procedures, such as etching, in which the material matrix is dissolved, leaving a clean cast. Moreover, an epoxy resin may be mixed with fluorophore substances to allow fluorescence imaging. Our work aims to investigate and optimize the epoxy pore casting procedure parameters, for example, impregnation pressure. We use silicon micromodels as a reference to validate the key parameters of high-pressure resin impregnation. We demonstrate possible artifacts and defects that might develop during impregnation with resin, e.g., resin shrinkage and gas trapping. In the end, we developed an optimized protocol to produce high-quality resin pore casts for high-resolution 3D imaging and the description of microporosity in micritic carbonates. In our applications, the high-quality pore casts were acid-etched to remove the non-transparent carbonate material, making the pore casts suitable for imaging with Confocal Laser Scanning Microscopy (CLSM). In addition, we evaluate the quality of our etching procedure using micro-computed tomography (micro-CT) scans of the pre- and post-etched samples and demonstrate that the etched epoxy pore casts represent the pore space of microporous carbonate rock samples with high fidelity.
  • Evaluation of minerals being deposited in the Red Sea using gravimetric, size distribution, and mineralogical analysis of dust deposition samples collected along the Red Sea coastal plain

    Shevchenko, Illia; Engelbrecht, Johann; Mostamandi, Suleiman; Stenchikov, Georgiy L. (Aeolian Research, Elsevier BV, 2021-06-15) [Article]
    The effect of atmospheric dust on the Earth's climate and air quality is especially severe in the major dust-source regions of the globe, such as the Arabian Peninsula. To better quantify the impact of dust over this region, we established the dust deposition measurement sites at King Abdullah University of Science and Technology (KAUST) and an AErosol RObotic NETwork (AERONET) station. We measured and analyzed dust deposition for 61 months from 2014 to 2019, totaling 442 samples, in 6 different locations on the KAUST campus (22.3 N; 39.1E). The analyses include gravimetric measurements, X-Ray Diffraction (XRD) mineral analyses, and particle size distribution measurements. The intercomparisons of the samples collected from different sampling sites show that the dust deposition rates on campus are spatially uniform. Particle size and mass measurements of deposition dust samples are found to be uncorrelated with the concurrent AERONET measurements. Deposition sample sieving (D < 56 µm), applied since May 2019, make the measurements more consistent but do not significantly affect particles' size distribution with diameters D < 20 μm. Large particles with D > 20 µm are typically of local origin, since they deposit quickly. The annual mean deposition rate is about 11 g m-2 mo-1, with higher spring and fall rates and reduced rates in summer. The mineralogical analysis shows an abundance of quartz and feldspar with lesser amounts of micas, gypsum, clays, carbonate, halite, and iron oxides. Gypsum traces are probably produced either in the atmosphere or in the deposited sample by the reaction between carbonates and sulfur dioxide. The deposition of dust particles with D < 20 µm in the Red Sea totals 8.6 Mt annually. This comprises 1.05 Mt of quartz, 0.88 Mt of feldspars, 0.22 Mt of carbonates, 1.39 Mt of clays, and 0.06 Mt of hematite, which plays a vital role in maintaining the Red Sea nutrient balance.
  • A self-adaptive deep learning algorithm for intelligent natural gas pipeline control

    Zhang, Tao; Bai, Hua; Sun, Shuyu (Energy Reports, Elsevier BV, 2021-06-15) [Article]
    Natural gas has been recognized as a promising energy supply for modern society due to its relatively less air pollution in consumption, while pipeline transportation is preferred especially for long-distance transmissions. A simplified pipeline control scenario is proposed in this paper to deeply accelerate the management and decision process in pipeline dispatch, in which a direct relevance between compressor operations and the inlet flux at certain stations is established as the main dispatch logic. A deep neural network is designed with specific input and output features for this scenario and the hyper-parameters are carefully tuned for a better adaptability of this problem. The realistic operation data of two pipelines have been obtained and prepared for learning and testing. The proposed algorithm with the optimized network structure is proved to be effective and reliable in predicting the pipeline operation status, under both the normal operation conditions and abnormal situations. The successful definition of "ghost compressors" make this algorithm to be the first self-adaptive deep learning algorithm to assist natural gas pipeline intelligent control.
  • Effects of different empirical ground motion models on seismic hazard maps for North Iceland

    Kowsari, Milad; Halldorsson, Benedikt; Snæbjörnsson, Jónas; Jonsson, Sigurjon (Soil Dynamics and Earthquake Engineering, Elsevier BV, 2021-06-12) [Article]
    This study builds on previous site-specific hazard studies for North Iceland, specifically regarding delineation of seismic sources and seismicity parameters. Using a Monte Carlo approach to generate synthetic earthquake catalogues for North Iceland, and multiple ground motion models (GMMs) that have been proposed and used for probabilistic seismic hazard analysis (PSHA) in Iceland in the past, the variability of the resulting hazard estimates is presented in a map-form. The variability in the hazard estimates is quite large, which is a direct result of the inconsistency in the GMMs used in previous studies. We show how this is caused by the inability of these models to capture the characteristic amplitude attenuation of Icelandic earthquake ground motion with distance, thus casting doubt on the validity of the resulting PSHA of past studies. In contrast, we re-evaluated the variability of PSHA for North Iceland based on new empirical Bayesian GMMs that not only satisfy all the conditions required for use in PSHA, but also fully capture the characteristics of the existing Icelandic ground motion dataset and in addition contain elements that account for the saturation of near-fault peak ground motions at large magnitudes. The results quantify how the variability in the GMMs, contribute to the range of spatial distribution of PSHA amplitudes and uncertainties. The results show that the confidence in the PSHA values is significantly increased using the new models vs. the older ones. The confidence of the PSHA values is quantified through the coefficient of variation. The confidence is shown to be largest over distance ranges where data is most abundant. On the other hand, the confidence decreases considerably at near-fault and far-field distances, primarily because of lack of data for those distances. The findings highlight the importance of using appropriate GMMs for PSHA in Iceland and give a spatial sense of the relative levels of confidence of hazard estimates. They moreover highlight the need for a revision of the PSHA using not only the new GMMs, but also physics-based earthquake source models.
  • Simultaneous Bayesian Estimation of Non-Planar Fault Geometry and Spatially-Variable Slip

    Dutta, Rishabh; Jonsson, Sigurjon; Vasyura-Bathke, Hannes (Journal of Geophysical Research: Solid Earth, American Geophysical Union (AGU), 2021-06-06) [Article]
    Large earthquakes are usually modeled with simple planar fault surfaces or a combination of several planar fault segments. However, in general, earthquakes occur on faults that are non-planar and exhibit significant geometrical variations in both the along-strike and down-dip directions at all spatial scales. Mapping of surface fault ruptures and high-resolution geodetic observations are increasingly revealing complex fault geometries near the surface and accurate locations of aftershocks often indicate geometrical complexities at depth. With better geodetic data and observations of fault ruptures, more details of complex fault geometries can be estimated resulting in more realistic fault models of large earthquakes. To address this topic, we here parametrize non-planar fault geometries with a set of polynomial parameters that allow for both along-strike and down-dip variations in the fault geometry. Our methodology uses Bayesian inference to estimate the non-planar fault parameters from geodetic data, yielding an ensemble of plausible models that characterize the uncertainties of the non-planar fault geometry and the fault slip. The method is demonstrated using synthetic tests considering slip spatially distributed on a single continuous finite non-planar fault surface with varying dip and strike angles both in the down-dip and along-strike directions. The results show that fault-slip estimations can be biased when a simple planar fault geometry is assumed in presence of significant non-planar geometrical variations. Our method can help to model earthquake fault sources in a more realistic way and may be extended to include multiple non-planar fault segments or other geometrical fault complexities.
  • Bayesian identification of oil spill source parameters from image contours

    El Mohtar, Samah; Ait-El-Fquih, Boujemaa; Knio, Omar; Lakkis, Issam; Hoteit, Ibrahim (Marine Pollution Bulletin, Elsevier BV, 2021-06-04) [Article]
    Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.
  • PINNeik: Eikonal solution using physics-informed neural networks

    Waheed, Umair bin; Haghighat, Ehsan; Alkhalifah, Tariq Ali; Song, Chao; Hao, Qi (Computers and Geosciences, Elsevier BV, 2021-06-04) [Article]
    The eikonal equation is utilized across a wide spectrum of science and engineering disciplines. In seismology, it regulates seismic wave traveltimes needed for applications like source localization, imaging, and inversion. Several numerical algorithms have been developed over the years to solve the eikonal equation. However, these methods require considerable modifications to incorporate additional physics, such as anisotropy, and may even breakdown for certain complex forms of the eikonal equation, requiring approximation methods. Moreover, they suffer from computational bottleneck when repeated computations are needed for perturbations in the velocity model and/or the source location, particularly in large 3D models. Here, we propose an algorithm to solve the eikonal equation based on the emerging paradigm of physics-informed neural networks (PINNs). By minimizing a loss function formed by imposing the eikonal equation, we train a neural network to output traveltimes that are consistent with the underlying partial differential equation. We observe sufficiently high traveltime accuracy for most applications of interest. We also demonstrate how the proposed algorithm harnesses machine learning techniques like transfer learning and surrogate modeling to speed up traveltime computations for updated velocity models and source locations. Furthermore, we use a locally adaptive activation function and adaptive weighting of the terms in the loss function to improve convergence rate and solution accuracy. We also show the flexibility of the method in incorporating medium anisotropy and free-surface topography compared to conventional methods that require significant algorithmic modifications. These properties of the proposed PINN eikonal solver are highly desirable in obtaining a flexible and efficient forward modeling engine for seismological applications.
  • An exploratory multi-scale framework to reservoir digital twin

    Zhang, Tao; Sun, Shuyu (Advances in Geo-Energy Research, Yandy Scientific Press, 2021-06-04) [Article]
    In order to make full use of the information provided in the physical reservoirs, including the production history and environmental conditions, the whole life cycle of reservoir discovery and recovery should be considered when mapping in the virtual space. A new concept of reservoir digital twin and the exploratory multi-scale framework is proposed in this paper, covering a wide range of engineering processes related with the reservoirs, including the drainage, sorption and phase change in the reservoirs, as well as extended processes like injection, transportation and on-field processing. The mathematical tool package for constructing the numerical description in the digital space for various engineering processes in the physical space is equipped with certain advanced models and algorithms developed by ourselves. For a macroscopic flow problem, we can model it either in the Navier-Stokes scheme, suitable for the injection, transportation and oil-water separation processes, or in the Darcy scheme, suitable for the drainage and sorption processes. Lattice Boltzmann method can also be developed as a special discretization of the Navier-Stokes scheme, which is easy to be coupled with multiple distributions, for example, temperature field, and a rigorous Chapman-Enskog expansion is performed to show the equivalence between the lattice Bhatnagar-Gross-Krook formulation and the corresponding Navier-Stokes equations and other macroscopic models. Based on the mathematical toolpackage, for various practical applications in petroleum engineering related with reservoirs, we can always find the suitable numerical tools to construct a digital twin to simulate the operations, design the facilities and optimize the processes.
  • Investigation of the dynamics of immiscible displacement of a ganglion in capillaries

    Salama, Amgad; Cai, Jianchao; Kou, Jisheng; Sun, Shuyu; El-Amin, Mohamed F.; Wang, Yi (Capillarity, Yandy Scientific Press, 2021-06-03) [Article]
    In this work the problem of displacing a ganglion of a fluid by another immiscible one in capillaries is investigated. A modeling approach is developed to predict the location of the ganglion with time. The model describes two patterns; namely, when the ganglion totally exists inside the tube, and when the advancing interface of the ganglion has broken through the exit of the tube. The model is valid for the case in which the ganglion is wetting as well as when it is nonwetting to the wall of the tube. It also considers the situation in which both the advancing and the receding interfaces assume, generally, different contact angles. For the special case when the displacement process is quasistatic, both the receding and the advancing contact angles may be considered the same. Under these conditions, interfacial tension force plays no role and the ganglion moves as a plug inside the tube with a constant velocity. When the viscosity ratio between the invading fluid and the ganglion is one (i.e., both phases are having the same viscosity) the motion reduces to the Hagen-Poiseuille flow in pipes. Once the advancing interface breaks through the exit of the tube, interfacial tension starts to take part in the displacement process and the ganglion starts to accelerate or decelerate according to the viscosity ratio. When the ganglion is nonwetting, interfacial tension becomes in the direction of the flow and is opposite to the flow otherwise. The model accounts for external forces such as pressure and gravity in addition to capillarity. A computational fluid dynamics analysis of this system is conducted for both types of wettability scenarios and shows very good match with the results of the developed model during both the two modes of flow patterns. This builds confidence in the developed modeling approach. Other cases have also been explored to highlight the effects of other scenarios.

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