Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics

Megathrust faults host the largest earthquakes on Earth which can trigger cascading hazards such as devastating tsunamis.Determining characteristics that control subduction zone earthquake and tsunami dynamics is critical to mitigate megathrust hazards, but is impeded by structural complexity, large spatio-temporal scales, and scarce or asymmetric instrumental coverage.Here we show that tsunamigenesis and earthquake dynamics are controlled by along-arc variability in regional tectonic stresses together with depth-dependent variations in rigidity and yield strength of near-fault sediments. We aim to identify dominant regional factors controlling megathrust hazards. To this end, we demonstrate how to unify and verify the required initial conditions for geometrically complex, multi-physics earthquake-tsunami modeling from interdisciplinary geophysical observations. We present large-scale computational models of the 2004 Sumatra-Andaman earthquake and Indian Ocean tsunami that reconcile near- and far-field seismic, geodetic, geological, and tsunami observations and reveal tsunamigenic trade-offs between slip to the trench, splay faulting, and bulk yielding of the accretionary wedge.Our computational capabilities render possible the incorporation of present and emerging high-resolution observations into dynamic-rupture-tsunami models. Our findings highlight the importance of regional-scale structural heterogeneity to decipher megathrust hazards.

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3D linked megathrust, dynamic rupture and tsunami propagation and inundation modeling: Dynamic effects of supershear and tsunami earthquakes

10.5194/egusphere-egu21-15257 ◽

2021 ◽

Author(s):

Sara Aniko Wirp ◽

Alice-Agnes Gabriel ◽

Elizabeth H. Madden ◽

Maximilian Schmeller ◽

Iris van Zelst ◽

...

Keyword(s):

Subduction Zones ◽

Large Scale ◽

Dynamic Models ◽

Initial Conditions ◽

Poisson Ratio ◽

Fault Slip ◽

Moment Magnitude ◽

Dynamic Rupture ◽

S Wave ◽

Tsunami Propagation

Earthquake rupture dynamic models capture the variability of slip in space and time while accounting for the structural complexity which is characteristic for subduction zones. The use of a geodynamic subduction and seismic cycling (SC) model to initialize dynamic rupture (DR) ensures that initial conditions are self-consistent and reflect long-term behavior. We extend the 2D geodynamical subduction and SC model of van Zelst et al. (2019) and use it as input for realistic 3-dimensional DR megathrust earthquake models. We follow the subduction to tsunami run-up linking approach described in Madden et al. (2020), including a complex subduction setup along with their resulting tsunamis. The distinct variation of shear traction and friction coefficients with depth lead to realistic average rupture speeds and dynamic stress drop as well as efficient tsunami generation.&#160;We here analyze a total of 14 subduction-initialized 3D dynamic rupture-tsunami scenarios. By varying the hypocentral location along arc and depth, we generate 12 distinct unilateral and bilateral earthquakes with depth-variable slip distribution and directivity, bimaterial, and geometrical effects in the dynamic slip evolutions. While depth variations of the hypocenters barely influence the tsunami behavior, lateral varying nucleation locations lead to a shift in the on-fault slip which causes time delays of the wave arrival at the coast. The fault geometry of our DR model that arises during the SC model is non-planar and includes large-scale roughness. These features (topographic highs) trigger supershear rupture propagation in up-dip or down-dip direction, depending on the hypocentral depth.In two additional scenarios, we analyze variations in the energy budget of the DR scenarios. In the SC model, an incompressible medium is assumed (&#957;=0.5) which is valid only for small changes in pressure and temperature. Unlike in the DR model where the material is compressible and a different Poisson&#8217;s ratio (&#957;=0.25) has to be assigned. Poisson&#8217;s ratios between 0.1 and 0.4 stand for various compressible materials. To achieve a lower shear strength of all material on and off the megathrust fault and to facilitate slip, we increase the Poisson ratio in the DR model to &#957;=0.3 which is consistent with basaltic rocks. As a result, larger fault slip is concentrated at shallower depths and generates higher vertical seafloor displacement and earthquake moment magnitude respectively. Even though the tsunami amplitudes are much higher, the same dynamic behavior as in the twelve hypocenter-variable models can be observed. Lastly, we increase fracture energy by changing the critical slip distance in the linear slip-weakening frictional parameterization. This generates a tsunami earthquake (Kanamori, 1972) characterized by low rupture velocity (on average half the amount of s-wave speed) and low peak slip rate, but at the same time large shallow fault slip and moment magnitude. The shallow fault slip of this event causes the highest vertical seafloor uplift compared to all other simulations. This leads to the highest tsunami amplitude and inundation area while the wavefront hits the coast delayed compared to the other scenarios.

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MULTIPLE OSCILLATORY STATES IN MODELS OF COLLECTIVE NEURONAL DYNAMICS

International Journal of Neural Systems ◽

10.1142/s0129065714500208 ◽

2014 ◽

Vol 24 (06) ◽

pp. 1450020 ◽

Cited By ~ 23

Author(s):

STILIYAN KALITZIN ◽

MARCUS KOPPERT ◽

GEORGE PETKOV ◽

FERNANDO LOPES DA SILVA

Keyword(s):

Analytical Model ◽

Large Scale ◽

Computational Models ◽

Phase Synchronization ◽

Initial Conditions ◽

State Transitions ◽

Model Parameters ◽

Reactive Control ◽

Stable States ◽

Optimal Method

In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102–S109) and does not correspond directly to realistic neuronal processes (excitatory–inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.

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Typological bottlenecks: How large-scale regional language typologies help us interpret global prehistory

Linguistic Typology ◽

10.1515/lingty-2016-0030 ◽

2016 ◽

Vol 20 (3) ◽

Author(s):

Roger Blench

Keyword(s):

Southeast Asia ◽

Large Scale ◽

Initial Conditions ◽

Regional Scale ◽

Modern Humans ◽

Regional Patterns ◽

Human Culture ◽

Regional Language ◽

Mainland Southeast Asia

AbstractIt is unlikely that local or highly specific typological characteristics of language correlate with other aspects of human culture and history. However, at regional scale, the broad typology of languages does reflect bottlenecks. The paper argues that these regions of high typological similarity are due neither to chance nor long-term convergence, but reflect the initial conditions of settlement. This suggests that regions can be characterised by negative typology, i.e., the absence of globally common traits. Conversely, typological uniformity occurs in mainland Southeast Asia, a region notable for the similarities between language structures. An expansion of the remit of typology can uncover large regional patterns which can be tied to the archaeological narrative of the early expansion of modern humans.

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Validating a high-throughput tracking system: ATLAS as a regional-scale alternative to GPS

10.1101/2021.02.09.430514 ◽

2021 ◽

Cited By ~ 1

Author(s):

Christine E. Beardsworth ◽

Evy Gobbens ◽

Frank van Maarseveen ◽

Bas Denissen ◽

Anne Dekinga ◽

...

Keyword(s):

Large Scale ◽

Animal Movement ◽

Tracking System ◽

Regional Scale ◽

Gps Tracking ◽

List Type ◽

Large Species ◽

Trade Offs ◽

Stationary Test ◽

Scale Alternative

AbstractFine-scale tracking of animal movement is important to understand the proximate mechanisms of animal behaviour. While GPS tracking is an excellent tool for measuring animal movement, trade-offs between tag weight, cost and lifespan limit its application to relatively large species, a small number of individuals or short tracking durations, respectively. The reverse-GPS system – ATLAS – uses lighter, cheaper tags compared to GPS tags, that can also last long periods of time at high sampling frequencies. Six systems are now operational worldwide and have successfully tracked over 50 species in various landscape types. This growing use of ATLAS to track animal movement motivates further refinement of best-practice application and an assessment of its accuracy.Here, we test the accuracy and precision of the largest ATLAS system, located in the Dutch Wadden Sea using concurrent GPS measurements as a reference. This large-scale ATLAS system consists of 26 receivers and covers 1326 km2 of intertidal region, with almost no physical obstacles for radio signals, providing a useful baseline for other systems. To measure accuracy, we calculated the distance between ATLAS and GPS location estimates for a route (mobile test) and 16 fixed locations (stationary test) on the Griend mudflat.ATLAS-derived location estimates differed on average 4.2 m from GPS-estimated stationary test sites and 5.7 m from GPS tracks taken whilst moving between them. Signals that were collected by more receiver stations were more accurate, although even 3-receiver localisations were comparable with GPS localisations (∼10 m difference). Higher receiver stations detected the tag at longer distances.Future ATLAS users should consider the height of receivers, their spatial arrangement, density and the movement mode of the study species (e.g., ground-dwelling or flying). In conclusion, ATLAS provides an accurate, regional-scale alternative to global GPS-based tracking with which hundreds of relatively small-bodied species can be tracked simultaneously for long periods of time. Our study shows that ATLAS is a valid alternative, providing comparable location estimates to GPS.

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Verification of an ADER-DG method for complex dynamic rupture problems

Geoscientific Model Development ◽

10.5194/gmd-7-847-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 847-866 ◽

Cited By ~ 17

Author(s):

C. Pelties ◽

A.-A. Gabriel ◽

J.-P. Ampuero

Keyword(s):

Initial Conditions ◽

High Order ◽

High Order Accuracy ◽

Dynamic Rupture ◽

Earthquake Dynamics ◽

High Order Derivative ◽

Friction Laws ◽

Bimaterial Interfaces ◽

Rate And State Friction ◽

Dg Method

Abstract. We present results of thorough benchmarking of an arbitrary high-order derivative discontinuous Galerkin (ADER-DG) method on unstructured meshes for advanced earthquake dynamic rupture problems. We verify the method by comparison to well-established numerical methods in a series of verification exercises, including dipping and branching fault geometries, heterogeneous initial conditions, bimaterial interfaces and several rate-and-state friction laws. We show that the combination of meshing flexibility and high-order accuracy of the ADER-DG method makes it a competitive tool to study earthquake dynamics in geometrically complicated setups.

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Structural heterogeneity and old-growthness: A first regional-scale assessment of Sardinian forests

Annals of Forest Research ◽

10.15287/afr.2020.1968 ◽

2020 ◽

Vol 63 (2) ◽

pp. 103-120

Author(s):

Sergio Fantini ◽

◽

Mauro Fois ◽

Paolo Casula ◽

Giuseppe Fenu ◽

...

Keyword(s):

Species Composition ◽

Regional Scale ◽

Structural Complexity ◽

Canopy Cover ◽

Structural Heterogeneity ◽

Mediterranean Forests ◽

Forest Stands ◽

Old Growth ◽

Tree Species Composition ◽

Growth Features

Mediterranean forests have been altered by several human activities. Consequently, relatively intact forests that have been unmodified by humans for a relatively long time (i.e., old-growth forests) are often reduced to isolated and fragmented stands. However, despite their high conservation value, little is known about their features and even presence several Mediterranean areas. First steps of their investigation are based on the identification of old-growth features such as amount of large‐size and old trees, tree species composition, canopy heterogeneity, occurrence and amount of deadwood. The Structural Heterogeneity Index (SHI) is commonly used to summarise features of old-growthness in one single value. Here, the SHI was derived for 68 plots included in 45 forest stands within the 4,297 km2 of territory that is covered by forests in Sardinia. SHI values were affected by variables that are likely to be related to forest age and structural complexity, such as presence of cerambycids, canopy cover, forest layers, location and three old-growthness classes. Results confirm a high structural variability among forests with old-growth features, determined by the presence, or lack, of given living and deadwood features. Our findings identified, for the first time, most of the forest stands that need special protection in Sardinia for the presence of old-growth features. In this sense, the SHI was confirmed useful for improving their management and conservation, although more specific and deeper studies are necessary for better understanding their species composition and dynamics.

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Verification of an ADER-DG method for complex dynamic rupture problems

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-5981-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 5981-6034 ◽

Cited By ~ 8

Author(s):

C. Pelties ◽

A.-A. Gabriel ◽

J.-P. Ampuero

Keyword(s):

Initial Conditions ◽

High Order ◽

High Order Accuracy ◽

Dynamic Rupture ◽

Earthquake Dynamics ◽

Complex Dynamic ◽

Order Accuracy ◽

High Order Derivative ◽

Rate And State Friction ◽

Dg Method

Abstract. We present thorough benchmarking of an arbitrary high-order derivative Discontinuous Galerkin (ADER-DG) method on unstructured meshes for advanced earthquake dynamic rupture problems. We validate the method in comparison to well-established numerical methods in a series of verification exercises, including dipping and branching fault geometries, heterogeneous initial conditions, bi-material cases and several rate-and-state friction constitutive laws. We show that the combination of meshing flexibility and high-order accuracy of the ADER-DG method makes it a competitive tool to study earthquake dynamics in complicated setups.

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DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

Protein and Peptide Letters ◽

10.2174/0929866527666201202103411 ◽

2020 ◽

Vol 27 ◽

Author(s):

Zaheer Ullah Khan ◽

Dechang Pi

Keyword(s):

Large Scale ◽

Computational Models ◽

Research Work ◽

Training Data ◽

Training Dataset ◽

Validation Dataset ◽

Cytokine Signaling ◽

Minority Class ◽

Independent Dataset ◽

Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

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Mapping the adaptive landscape of a major agricultural pathogen reveals evolutionary constraints across heterogeneous environments

The ISME Journal ◽

10.1038/s41396-020-00859-w ◽

2021 ◽

Author(s):

Anik Dutta ◽

Fanny E. Hartmann ◽

Carolina Sardinha Francisco ◽

Bruce A. McDonald ◽

Daniel Croll

Keyword(s):

Life History ◽

Large Scale ◽

Life History Traits ◽

Genetic Correlations ◽

Stress Factors ◽

Life History Trait ◽

Adaptive Landscape ◽

Heterogeneous Environments ◽

Growth Responses ◽

Trade Offs

AbstractThe adaptive potential of pathogens in novel or heterogeneous environments underpins the risk of disease epidemics. Antagonistic pleiotropy or differential resource allocation among life-history traits can constrain pathogen adaptation. However, we lack understanding of how the genetic architecture of individual traits can generate trade-offs. Here, we report a large-scale study based on 145 global strains of the fungal wheat pathogen Zymoseptoria tritici from four continents. We measured 50 life-history traits, including virulence and reproduction on 12 different wheat hosts and growth responses to several abiotic stressors. To elucidate the genetic basis of adaptation, we used genome-wide association mapping coupled with genetic correlation analyses. We show that most traits are governed by polygenic architectures and are highly heritable suggesting that adaptation proceeds mainly through allele frequency shifts at many loci. We identified negative genetic correlations among traits related to host colonization and survival in stressful environments. Such genetic constraints indicate that pleiotropic effects could limit the pathogen’s ability to cause host damage. In contrast, adaptation to abiotic stress factors was likely facilitated by synergistic pleiotropy. Our study illustrates how comprehensive mapping of life-history trait architectures across diverse environments allows to predict evolutionary trajectories of pathogens confronted with environmental perturbations.

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A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽

10.3390/cancers13092111 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2111

Author(s):

Bo-Wei Zhao ◽

Zhu-Hong You ◽

Lun Hu ◽

Zhen-Hao Guo ◽

Lei Wang ◽

...

Keyword(s):

Drug Target ◽

Large Scale ◽

Computational Models ◽

Structural Information ◽

Characteristic Curve ◽

Representation Learning ◽

Graph Representation ◽

Convolutional Network ◽

Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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