scholarly journals Application of Waveform Stacking Methods for Seismic Location at Multiple Scales

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4729
Author(s):  
Lei Li ◽  
Yujiang Xie ◽  
Jingqiang Tan
Keyword(s):  
Travel Time ◽  
Multiple Scales ◽  
Regional Scale ◽  
Seismic Source ◽  
Small Scale ◽  
Grid Spacing ◽  
Scale Size ◽  
Seismic Location ◽  
Imaging Resolution ◽  
Seismic Source Location

Seismic source location specifies the spatial and temporal coordinates of seismic sources and lays the foundation for advanced seismic monitoring at all scales. In this work, we firstly introduce the principles of diffraction stacking (DS) and cross-correlation stacking (CCS) for seismic location. The DS method utilizes the travel time from the source to receivers, while the CCS method considers the differential travel time from pairwise receivers to the source. Then, applications with three field datasets ranging from small-scale microseismicity to regional-scale induced seismicity are presented to investigate the feasibility, imaging resolution, and location reliability of the two stacking operators. Both of the two methods can focus the source energy by stacking the waveforms of the selected events. Multiscale examples demonstrate that the imaging resolution is not only determined by the inherent property of the stacking operator but also highly dependent on the acquisition geometry. By comparing to location results from other methods, we show that the location bias is consistent with the scale size, as well as the frequency contents of the seismograms and grid spacing values.

Geomechanical and Fracture Network Interpretation of a Devonian Outcrop

2021 ◽  
pp. 1-16
Author(s):  
Scott McKean ◽  
Simon Poirier ◽  
Henry Galvis-Portilla ◽  
Marco Venieri ◽  
Jeffrey A. Priest ◽  
...  
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Fracture Network ◽  
Natural Fracture ◽  
Small Scale ◽  
Nugget Effect ◽  
Schmidt Hammer ◽  
Unconventional Reservoir ◽  
Fracture Anisotropy ◽  
Fracture Intensity

Summary The Duvernay Formation is an unconventional reservoir characterized by induced seismicity and fluid migration, with natural fractures likely contributing to both cases. An alpine outcrop of the Perdrix and Flume formations, correlative with the subsurface Duvernay and Waterways formations, was investigated to characterize natural fracture networks. A semiautomated image-segmentation and fracture analysis was applied to orthomosaics generated from a photogrammetric survey to assess small- and large-scale fracture intensity and rock mass heterogeneity. The study also included manual scanlines, fracture windows, and Schmidt hammer measurements. The Perdrix section transitions from brittle fractures to en echelon fractures and shear-damage zones. Multiple scales of fractures were observed, including unconfined, bedbound fractures, and fold-relatedbed-parallel partings (BPPs). Variograms indicate a significant nugget effect along with fracture anisotropy. Schmidt hammer results lack correlation with fracture intensity. The Flume pavements exhibit a regionally extensive perpendicular joint set, tectonically driven fracturing, and multiple fault-damage zones with subvertical fractures dominating. Similar to the Perdrix, variograms show a significant nugget effect, highlighting fracture anisotropy. The results from this study suggest that small-scale fractures are inherently stochastic and that fractures observed at core scale should not be extrapolated to represent large-scale fracture systems; instead, the effects of small-scale fractures are best represented using an effective continuum approach. In contrast, large-scale fractures are more predictable according to structural setting and should be characterized robustly using geological principles. This study is especially applicable for operators and regulators in the Duvernay and similar formations where unconventional reservoir units abut carbonate formations.

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

scholarly journals RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2815
Author(s):  
Shih-Hung Yang ◽  
Yao-Mao Cheng ◽  
Jyun-We Huang ◽  
Yon-Ping Chen
Keyword(s):  
Receptive Field ◽  
American Sign Language ◽  
Multiple Scales ◽  
Receptive Fields ◽  
Depth Image ◽  
Small Scale ◽  
Feature Maps ◽  
Depth Sensor ◽  
Depth Images ◽  
Effective Transfer

Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the discriminative details of these fingers, RFaNet reweights the low-level features of the hand depth image with those of the non-forearm image and improves finger localization, even when the wrist is occluded. RFaNet captures neighboring and inter-region dependencies between fingers in high-level features. An atrous convolution procedure enlarges the RFs at multiple scales and a non-local operation computes the interactions between multi-scale feature maps, thereby facilitating the building of inter-finger relations. Thus, the representation of a sign is invariant to viewpoint changes, which are primarily responsible for intra-class variability. On an American Sign Language fingerspelling dataset, RFaNet achieved 1.77% higher classification accuracy than state-of-the-art methods. RFaNet achieved effective transfer learning when the number of labeled depth images was insufficient. The fingerspelling representation of a depth image can be effectively transferred from large- to small-scale datasets via highlighting the finger regions and building inter-finger relations, thereby reducing the requirement for expensive fingerspelling annotations.

scholarly journals Are Distribution Patterns Correlated with Plant Traits?

2021 ◽  
Author(s):  
◽  
Benjamin Magana-Rodriguez
Keyword(s):  
Life History ◽  
Leaf Area ◽  
Spatial Patterns ◽  
Life History Traits ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Regional Scale ◽  
Distribution Patterns ◽  
Spatial Arrangement ◽  
Scaling Relationship

<p>The current crisis in loss of biodiversity requires rapid action. Knowledge of species' distribution patterns across scales is of high importance in determining their current status. However, species display many different distribution patterns on multiple scales. A positive relationship between regional (broad-scale) distribution and local abundance (fine-scale) of species is almost a constant pattern in macroecology. Nevertheless interspecific relationships typically contain much scatter. For example, species that possess high local abundance and narrow ranges, or species that are widespread, but locally rare. One way to describe these spatial features of distribution patterns is by analysing the scaling properties of occupancy (e.g., aggregation) in combination with knowledge of the processes that are generating the specific spatial pattern (e.g., reproduction, dispersal, and colonisation). The main goal of my research was to investigate if distribution patterns correlate with plant life-history traits across multiple scales. First, I compared the performance of five empirical models for their ability to describe the scaling relationship of occupancy in two datasets from Molesworth Station, New Zealand. Secondly, I analysed the association between spatial patterns and life history traits at two spatial scales in an assemblage of 46 grassland species in Molesworth Station. The spatial arrangement was quantified using the parameter k from the Negative Binomial Distribution (NBD). Finally, I investigated the same association between spatial patterns and life-history traits across local, regional and national scales, focusing in one of the most diverse families of plant species in New Zealand, the Veronica sect. Hebe (Plantaginaceae). The spatial arrangement was investigated using the mass fractal dimension. Cross-species correlations and phylogenetically independent contrasts were used to investigate the relationships between plant life-history traits and spatial patterns on both data bases. There was no superior occupancy-area model overall for describing the scaling relationship, however the results showed that a variety of occupancy-area models can be fit to different data sets at diverse spatial scales using nonlinear regression. Additionally, here I showed that it is possible to deduce and extrapolate information on occupancy at fine scales from coarse-scale data. For the 46 plantassemblage in Molesworth Station, Specific leaf area (SLA) exhibits a positive association with aggregation in cross-species analysis, while leaf area showed a negative association, and dispersule mass a positive correlation with degree of aggregation in phylogenetic contrast analysis at a local-scale (20 × 20 m resolution). Plant height was the only life-history trait that was associated with degree of aggregation at a regional-scale (100 × 60 mresolution). For the Veronica sect. Hebe dataset, leaf area showed a positive correlation with aggregation while specific leaf area showed a negative correlation with aggregation at a fine local-scale (2.5-60 m resolution). Inflorescence length, breeding system and leaf area showed a negative correlation with degree of aggregation at a regional-scale (2.5-20 km resolution). Height was positively associated with aggregation at national-scale (20-100 km resolution). Although life-history traits showed low predictive ability in explaining aggregation throughout this thesis, there was a general pattern about which processes and traits were important at different scales. At local scales traits related to dispersal and completion such as SLA , leaf area, dispersule mass and the presence of structures in seeds for dispersal, were important; while at regional scales traits related to reproduction such as breeding system, inflorescence length and traits related to dispersal (seed mass) were significant. At national scales only plant height was important in predicting aggregation. Here, it was illustrated how the parameters of these scaling models capture an important aspect of spatial pattern that can be related to other macroecological relationships and the life-history traits of species. This study shows that when several scales of analysis are considered, we can improve our understanding about the factors that are related to species' distribution patterns.</p>

scholarly journals TERENO: German network of terrestrial environmental observatories

2016 ◽  
Vol 2 ◽  
Author(s):  
Heye Reemt Bogena
Keyword(s):  
Regional Scale ◽  
Scale Up ◽  
Climate Change Impacts ◽  
Integrated Modelling ◽  
Small Scale ◽  
Catchment Scale ◽  
Terrestrial Environment ◽  
Regional Analyses ◽  
Research Facilities

Central elements of the TERENO network are “terrestrial observatories” at the catchment scale which were selected in climate sensitive regions of Germany for the regional analyses of climate change impacts. Within these observatories small scale research facilities and test areas are placed in order to accomplish energy, water, carbon and nutrient process studies across the different compartments of the terrestrial environment. Following a hierarchical scaling approach (point-plot-field) these detailed information and the gained knowledge will be transferred to the regional scale using integrated modelling approaches. Furthermore, existing research stations are enhanced and embedded within the observatories. In addition, mobile measurement platforms enable monitoring of dynamic processes at the local scale up to the determination of spatial pattern at the regional scale are applied within TERENO.

scholarly journals Distinguishing between Deep-Water Sediment Facies: Turbidites, Contourites and Hemipelagites

Geosciences ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 68 ◽  
Author(s):  
Dorrik Stow ◽  
Zeinab Smillie
Keyword(s):  
Deep Water ◽  
Large Scale ◽  
Tectonic Setting ◽  
Regional Scale ◽  
Small Scale ◽  
Physical Parameters ◽  
Sedimentary Characteristics ◽  
Natural Systems ◽  
Facies Types ◽  
Water Facies

The distinction between turbidites, contourites and hemipelagites in modern and ancient deep-water systems has long been a matter of controversy. This is partly because the processes themselves show a degree of overlap as part of a continuum, so that the deposit characteristics also overlap. In addition, the three facies types commonly occur within interbedded sequences of continental margin deposits. The nature of these end-member processes and their physical parameters are becoming much better known and are summarised here briefly. Good progress has also been made over the past decade in recognising differences between end-member facies in terms of their sedimentary structures, facies sequences, ichnofacies, sediment textures, composition and microfabric. These characteristics are summarised here in terms of standard facies models and the variations from these models that are typically encountered in natural systems. Nevertheless, it must be acknowledged that clear distinction is not always possible on the basis of sedimentary characteristics alone, and that uncertainties should be highlighted in any interpretation. A three-scale approach to distinction for all deep-water facies types should be attempted wherever possible, including large-scale (oceanographic and tectonic setting), regional-scale (architecture and association) and small-scale (sediment facies) observations.

scholarly journals Contextual design choices and partnerships for scaling early child development programmes

2019 ◽  
Vol 104 (Suppl 1) ◽  
pp. S3-S12 ◽  
Author(s):  
Kate M Milner ◽  
Raquel Bernal Salazar ◽  
Sunil Bhopal ◽  
Alexandra Brentani ◽  
Pia Rebello Britto ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Child Development ◽  
Regional Scale ◽  
Policy Support ◽  
Theory Of Change ◽  
Small Scale ◽  
Early Child Development ◽  
Situational Analysis ◽  
Early Child ◽  
Government Services

Translating the Nurturing Care Framework and unprecedented global policy support for early child development (ECD) into action requires evidence-informed guidance about how to implement ECD programmes at national and regional scale. We completed a literature review and participatory mixed-method evaluation of projects in Saving Brains®, Grand Challenges Canada® funded ECD portfolio across 23 low- and middle-income countries (LMIC). Using an adapted programme cycle, findings from evaluation related to partnerships and leadership, situational analyses, and design for scaling ECD were considered. 39 projects (5 ‘Transition to Scale’ and 34 ‘Seed’) were evaluated. 63% were delivered through health and 84% focused on Responsive Caregiving and Early Learning (RCEL). Multilevel partnerships, leadership and targeted situational analysis were crucial to design and adaptation. A theory of change approach to consider pathways to impact was useful for design, but practical situational analysis tools and local data to guide these processes were lacking. Several RCEL programmes, implemented within government services, had positive impacts on ECD outcomes and created more enabling caregiving environments. Engagement of informal and private sectors provided an alternative approach for reaching children where government services were sparse. Cost-effectiveness was infrequently measured. At small-scale RCEL interventions can be successfully adapted and implemented across diverse settings through processes which are responsive to situational analysis within a partnership model. Accelerating progress will require longitudinal evaluation of ECD interventions at much larger scale, including programmes targeting children with disabilities and humanitarian settings with further exploration of cost-effectiveness, critical content and human resources.

scholarly journals Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity

2020 ◽  
Vol 117 (16) ◽  
pp. 8757-8763 ◽  
Author(s):  
Ji Nie ◽  
Panxi Dai ◽  
Adam H. Sobel
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Climate Model ◽  
Regional Scale ◽  
Vertical Motion ◽  
Precipitable Water ◽  
Small Scale ◽  
Climate Projections ◽  
Regional Patterns

Responses of extreme precipitation to global warming are of great importance to society and ecosystems. Although observations and climate projections indicate a general intensification of extreme precipitation with warming on global scale, there are significant variations on the regional scale, mainly due to changes in the vertical motion associated with extreme precipitation. Here, we apply quasigeostrophic diagnostics on climate-model simulations to understand the changes in vertical motion, quantifying the roles of dry (large-scale adiabatic flow) and moist (small-scale convection) dynamics in shaping the regional patterns of extreme precipitation sensitivity (EPS). The dry component weakens in the subtropics but strengthens in the middle and high latitudes; the moist component accounts for the positive centers of EPS in the low latitudes and also contributes to the negative centers in the subtropics. A theoretical model depicts a nonlinear relationship between the diabatic heating feedback (α) and precipitable water, indicating high sensitivity of α (thus, EPS) over climatological moist regions. The model also captures the change of α due to competing effects of increases in precipitable water and dry static stability under global warming. Thus, the dry/moist decomposition provides a quantitive and intuitive explanation of the main regional features of EPS.

scholarly journals WS-AM: Weakly Supervised Attention Map for Scene Recognition

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1072 ◽  
Author(s):  
Shifeng Xia ◽  
Jiexian Zeng ◽  
Lu Leng ◽  
Xiang Fu
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Scene Recognition ◽  
Small Scale ◽  
Great Success ◽  
Global Feature ◽  
Benchmark Datasets ◽  
Local Mean ◽  
Weakly Supervised ◽  
Fully Connected

Recently, convolutional neural networks (CNNs) have achieved great success in scene recognition. Compared with traditional hand-crafted features, CNN can be used to extract more robust and generalized features for scene recognition. However, the existing scene recognition methods based on CNN do not sufficiently take into account the relationship between image regions and categories when choosing local regions, which results in many redundant local regions and degrades recognition accuracy. In this paper, we propose an effective method for exploring discriminative regions of the scene image. Our method utilizes the gradient-weighted class activation mapping (Grad-CAM) technique and weakly supervised information to generate the attention map (AM) of scene images, dubbed WS-AM—weakly supervised attention map. The regions, where the local mean and the local center value are both large in the AM, correspond to the discriminative regions helpful for scene recognition. We sampled discriminative regions on multiple scales and extracted the features of large-scale and small-scale regions with two different pre-trained CNNs, respectively. The features from two different scales were aggregated by the improved vector of locally aggregated descriptor (VLAD) coding and max pooling, respectively. Finally, the pre-trained CNN was used to extract the global feature of the image in the fully- connected (fc) layer, and the local features were combined with the global feature to obtain the image representation. We validated the effectiveness of our method on three benchmark datasets: MIT Indoor 67, Scene 15, and UIUC Sports, and obtained 85.67%, 94.80%, and 95.12% accuracy, respectively. Compared with some state-of-the-art methods, the WS-AM method requires fewer local regions, so it has a better real-time performance.

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