Vortex formation and dissolution in sheared sands

Using digital image correlation, we track the displacement fluctuations within a persistent shear band in a dense sand specimen bounded by glass walls undergoing plane strain compression. The data evidences a clear, systematic, temporally recurring pattern of vortex formation, dissolution, and reformation throughout macroscopic softening and critical state regimes. During softening, locally affine deformation zones are observed at various locations along the shear band, which we argue to be kinematic signatures of semi-stable force chains. Force chain collapse then occurs, inducing vortex formation. Local jamming at the conflux of opposing displacements between adjacent vortices arrests the vortices, providing an avenue for potential new force chains to form amidst these jammed regions. The process repeats itself temporally throughout the critical state. The pattern further correlates with fluctuations in macroscopic shear stress. We characterize the nature of the observed vortices, as they are different in our sands comprised of irregular shaped particles, as compared to previous observations from experiments and numerical simulations which involved circular or rounded particles. The results provide an interesting benchmark for behavior of non-circular/non-spherical particles undergoing shear.National Science Foundation (U.S.) (grant CMMI-0748284)University of Southern CaliforniaUniversity of Southern California Women in Science and Engineering (WiSE) Progra

Prognostic factors for esophageal squamous cell Carcinoma-A Population-Based study in Golestan province, Iran, a high incidence area

Golestan Province in northern Iran is an area with a high incidence of esophageal squamous cell carcinoma (ESCC). We aimed to investigate prognostic factors for ESCC and survival of cases in Golestan, on which little data were available. We followed-up 426 ESCC cases participating in a population-based case-control study. Data were analyzed using the Kaplan-Meier method and the Cox proportional hazard models. Median survival was 7 months. Age at diagnosis was inversely associated with survival, but the association was disappeared with adjustment for treatment. Residing in urban areas (hazard ratio, HR = 0.70; 95 CI 0.54-0.90) and being of non-Turkmen ethnic groups (HR = 0.76; 95 CI 0.61-0.96) were associated with better prognosis. In contrast to other types of tobacco use, nass (a smokeless tobacco product) chewing was associated with a slightly poorer prognosis even in models adjusted for other factors including stage of disease and treatment (HR = 1.38; 95 CI 0.99-1.92). Opium use was associated with poorer prognosis in crude analyses but not in adjusted models. Almost all of potentially curative treatments were associated with longer survival. Prognosis of ESCC in Golestan is very poor. Easier access to treatment facilities may improve the prognosis of ESCC in Golestan. The observed association between nass chewing and poorer prognosis needs further investigations; this association may suggest a possible role for ingestion of nass constituents in prognosis of ESCC. © 2011 Aghcheli et al

Association of tooth loss and oral hygiene with risk of gastric adenocarcinoma

Poor oral health and tooth loss have been proposed as possible risk factors for some chronic diseases, including gastric cancer. However, a small number of studies have tested these associations. We conducted a case-control study in Golestan Province, Iran, that enrolled 309 cases diagnosed with gastric adenocarcinoma (118 noncardia, 161 cardia, and 30 mixed-locations) and 613 sex, age, and neighborhood matched controls. Data on oral health were obtained through physical examination and questionnaire including tooth loss, the number of decayed, missing, and filled teeth, and frequency of tooth brushing. ORs and 95% confidence intervals (95% CI) were obtained using conditional logistic regression models adjusted for potential confounders. Standard one degree-of-freedom linear trend test and a multiple degree-of-freedom global test of the effect of adding oral hygiene variables to the model were also calculated. Our results showed apparent associations between tooth loss and decayed, missing, filled teeth (DMFT) score with risk of gastric cancer, overall and at each anatomic subsite. However, these associations were not monotonic and were strongly confounded by age. The results also showed that subjects who brushed their teeth less than daily were at significantly higher risk for gastric cardia adenocarcinoma ORs (95% CI) of 5.6 (1.6-19.3). We found evidence for an association between oral health and gastric cancer, but the nonmonotonic association, the relatively strong effect of confounder adjustment, and inconsistent results across studies must temper the strength of any conclusions. © 2013 AACR

A MULTIDIRECTIONAL DEEP NEURAL NETWORK FOR SELF-SUPERVISED RECONSTRUCTION OF SEISMIC DATA

Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then applies it to reconstruct the missing parts of the same dataset. We propose two improvements to the task: a rearrangement of the data, and a new deep-learning approach. We rearrange the traces of a 2D acquisition line as 3D data cubes, sorting the traces by the source and receiver coordinates. This 3D representation offers more information about the structure of the seismic events and allows a coherent reconstruction of them. However, learning the structure of events in 3D cubes is more complicated than in 2D images while the size of the training dataset is limited. Thus, we propose a specific architecture and training strategy to take advantage of 3D data samples, while benefiting from the simplicity of 2D reconstructions. Our proposed multidirectional convolutional neural network has two parallel branches trained to perform 2D reconstructions along the vertical and horizontal directions and a small 3D part that combines their results. We use our method to reconstruct data gaps resulting from several missing shots in a benchmark synthetic and a real land dataset. Compared to a conventional 3D U-net, our network learns to reconstruct the events more accurately. Compared to 2D U-nets, our method avoids the discontinuities that arise from the 2D reconstruction of each trace of the missing shot gathers. Key words: Interpolation, Seismic, Deep learning

A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data

Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then applies it to reconstruct the missing parts of the same dataset. We propose two improvements to the task: a rearrangement of the data, and a new deep-learning approach. We rearrange the traces of a 2D acquisition line as 3D data cubes, sorting the traces by the source and receiver coordinates. This 3D representation offers more information about the structure of the seismic events and allows a coherent reconstruction of them. However, learning the structure of events in 3D cubes is more complicated than in 2D images while the size of the training dataset is limited. Thus, we propose a specific architecture and training strategy to take advantage of 3D data samples, while benefiting from the simplicity of 2D reconstructions. Our proposed multidirectional convolutional neural network has two parallel branches trained to perform 2D reconstructions along the vertical and horizontal directions and a small 3D part that combines their results. We use our method to reconstruct data gaps resulting from several missing shots in a benchmark synthetic and a real land dataset. Compared to a conventional 3D U-net, our network learns to reconstruct the events more accurately. Compared to 2D U-nets, our method avoids the discontinuities that arise from the 2D reconstruction of each trace of the missing shot gathers

Large-offset P-wave traveltime in layered transversely isotropic media

Large-offset seismic data processing, imaging, and velocity estimation require an accurate traveltime approximation over a wide range of offsets. In layered transversely isotropic media with a vertical symmetry axis, the accuracy of traditional traveltime approximations is limited to near offsets. We have developed a new traveltime approximation that maintains the accuracy of classic equations around the zero offset and exhibits the correct curvilinear asymptote at infinitely large offsets. Our approximation is based on the conventional acoustic assumption. Its equation incorporates six parameters. To define them, we use the Taylor series expansion of the exact traveltime around the zero offset and a new asymptotic series for the infinite offset. Our asymptotic equation indicates that the traveltime behavior at infinitely large offsets is dominated by the properties of the layer with the maximum horizontal velocity in the sequence. The parameters of our approximation depend on the effective zero-offset traveltime, the normal moveout velocity, the anellipticity, a new large-offset heterogeneity parameter, and the properties of the layer with the maximum horizontal velocity in the sequence. We have applied our traveltime approximation (1) to directly calculate traveltime and ray parameter at given offsets, as analytical forward modeling, and (2) to estimate the first four of the aforementioned parameters for the layers beneath a known high-velocity layer. Our large-offset heterogeneity parameter includes the layering effect on the reflections’ traveltime.POCTEFA 2014-2020 Project PIXIL (EFA362/19) Artificial Intelligence in BCAM number EXP. 2019/0043

Nonhyperbolic normal moveout stretch correction with deep learning automation

Normal-moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching as an undesired by-product. We have addressed the NMO stretching problem with two methods: (1) an exact stretch-free NMO correction that prevents the stretching of primary reflections and (2) an approximate post-NMO stretch correction. Our stretch-free NMO produces parallel moveout trajectories for primary reflections. Our post-NMO stretch correction calculates the moveout of stretched wavelets as a function of offset. Both methods are based on the generalized moveout approximation and are suitable for application in complex anisotropic or heterogeneous environments. We use new moveout equations, modify the original parameter functions to be a constant over the primary reflections, and then interpolate the seismogram amplitudes at the calculated traveltimes. For fast and automatic modification of the parameter functions, we use deep learning. We design a deep neural network (DNN) using convolutional layers and residual blocks. To train the DNN, we generate a set of 40,000 synthetic NMO-corrected common-midpoint gathers and the corresponding desired outputs of the DNN. The data set is generated using different velocity profiles, wavelets, and offset vectors, and it includes multiples, ground roll, and band-limited random noise. The simplicity of the DNN task - a 1D identification of primary reflections - improves the generalization in practice. We use the trained DNN and find successful applications of our stretch-correction method on synthetic and different real data sets

Ensemble Deep Learning for Enhanced Seismic Data Reconstruction

Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep-learning techniques offer promising solutions for reconstructing missing data parts by utilizing existing data. Nonetheless, self-supervised methods frequently struggle with capturing under-represented features such as weaker events, crossing dips, and higher frequencies. To address these challenges, we propose a novel ensemble deep model (EDM) along with a tailored self-supervised training approach for reconstructing seismic data with consecutive missing traces. Our model comprises two branches of U-nets, each fed from distinct data transformation modules aimed at amplifying under-represented features and promoting diversity among learners. Our loss function minimizes relative errors at the outputs of individual branches and the entire model, ensuring accurate reconstruction of various features while maintaining overall data integrity. Additionally, we employ masking while training to enhance sample diversity and memory efficiency. Applications on two benchmark synthetic datasets and two real datasets demonstrate improved accuracy compared to a conventional U-net, successfully reconstructing weak events, diffractions, higher frequencies, and reflections covered by groundroll. Despite these advancements, our method does incur three times the training cost compared to a simple U-net

Semi-blind-trace algorithm for self-supervised attenuation of trace-wise coherent noise

Trace-wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self-supervised deep learning to attenuate this type of noise, the conventional blind-trace deep learning trains a network to blindly reconstruct each trace in the data from its surrounding traces; it attenuates isolated trace-wise noise but causes signal leakage in clean and noisy traces and reconstruction errors next to each noisy trace. To reduce signal leakage and improve denoising, we propose a new loss function and masking procedure in a semi-blind-trace deep learning framework. Our hybrid loss function has weighted active zones that cover masked and non-masked traces. Therefore, the network is not blinded to clean traces during their reconstruction. During training, we dynamically change the masks' characteristics. The goal is to train the network to learn the characteristics of the signal instead of noise. The proposed algorithm enables the designed U-net to detect and attenuate trace-wise noise without having prior information about the noise. A new hyperparameter of our method is the relative weight between the masked and non-masked traces' contribution to the loss function. Numerical experiments show that selecting a small value for this parameter is enough to significantly decrease signal leakage. The proposed algorithm is tested on synthetic and real off-shore and land data sets with different noises. The results show the superb ability of the method to attenuate trace-wise noise while preserving other events. An implementation of the proposed algorithm as a Python code is also made available

Approximations for traveltime, slope, curvature, and geometrical spreading of elastic waves in layered transversely isotropic media

Each seismic body wave, including quasi compressional, shear, and converted wave modes, carries useful subsurface information. For processing, imaging, amplitude analysis, and forward modeling of each wave mode, we need approximate equations of traveltime, slope (ray-parameter), and curvature as a function of offset. Considering the large offset coverage of modern seismic acquisitions, we propose new approximations designed to be accurate at zero and infinitely large offsets over layered transversely isotropic media with vertical symmetry axis (VTI). The proposed approximation for traveltime is a modified version of the extended generalized moveout approximation that comprises six parameters. The proposed direct approximations for ray-parameter and curvature use new, algebraically simple, equations with three parameters. We define these parameters for each wave mode without ray tracing so that we have similar approximate equations for all wave modes that only change based on the parameter definitions. However, our approximations are unable to reproduce S-wave triplications that may occur in some strongly anisotropic models. Using our direct approximation of traveltime derivatives, we also obtain a new expression for the relative geometrical spreading. We demonstrate the high accuracy of our approximations using numerical tests on a set of randomly generated multilayer models. Using synthetic data, we present simple applications of our approximations for normal moveout correction and relative geometrical spreading compensation of different wave modes.POCTEFA 2014-2020 Project PIXIL (EFA362/19