scholarly journals Fault Network Reconstruction using Agglomerative Clustering: Applications to South Californian Seismicity

2020 ◽  
Author(s):  
Yavor Kamer ◽  
Guy Ouillon ◽  
Didier Sornette
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Network Reconstruction ◽  
Small Scale ◽  
Agglomerative Clustering ◽  
Computation Efficiency ◽  
Location Uncertainty ◽  
Spatio Temporal ◽  
Initial Sampling ◽  
Fault Network

Abstract. In this paper we introduce a method for fault network reconstruction based on the 3D spatial distribution of seismicity. One of the major drawbacks of statistical earthquake models is their inability to account for the highly anisotropic distribution of seismicity. Fault reconstruction has been proposed as a pattern recognition method aiming to extract this structural information from seismicity catalogs. Current methods start from simple large scale models and gradually increase the complexity trying to explain the small scale features. In contrast the method introduced here uses a bottom-up approach, that relies on initial sampling of the small scale features and reduction of this complexity by optimal local merging of substructures. First, we describe the implementation of the method through illustrative synthetic examples. We then apply the method to the probabilistic absolute hypocenter catalog KaKiOS-16, which contains three decades of South Californian seismicity. To reduce data size and increase computation efficiency, the new approach builds upon the previously introduced catalog condensation method that exploits the heterogeneity of the hypocenter uncertainties. We validate the obtained fault network through a pseudo prospective spatial forecast test and discuss possible improvements for future studies. The performance of the presented methodology attests the importance of the non-linear techniques used to quantify location uncertainty information, which is a crucial input for the large scale application of the method. We envision that the results of this study can be used to construct improved models for the spatio-temporal evolution of seismicity.

scholarly journals Fault network reconstruction using agglomerative clustering: applications to southern Californian seismicity

2020 ◽  
Vol 20 (12) ◽  
pp. 3611-3625
Author(s):  
Yavor Kamer ◽  
Guy Ouillon ◽  
Didier Sornette
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Network Reconstruction ◽  
Small Scale ◽  
Agglomerative Clustering ◽  
Computation Efficiency ◽  
Location Uncertainty ◽  
Fault Reconstruction ◽  
Initial Sampling ◽  
Fault Network

Abstract. In this paper we introduce a method for fault network reconstruction based on the 3D spatial distribution of seismicity. One of the major drawbacks of statistical earthquake models is their inability to account for the highly anisotropic distribution of seismicity. Fault reconstruction has been proposed as a pattern recognition method aiming to extract this structural information from seismicity catalogs. Current methods start from simple large-scale models and gradually increase the complexity trying to explain the small-scale features. In contrast the method introduced here uses a bottom-up approach that relies on initial sampling of the small-scale features and reduction of this complexity by optimal local merging of substructures. First, we describe the implementation of the method through illustrative synthetic examples. We then apply the method to the probabilistic absolute hypocenter catalog KaKiOS-16, which contains three decades of southern Californian seismicity. To reduce data size and increase computation efficiency, the new approach builds upon the previously introduced catalog condensation method that exploits the heterogeneity of the hypocenter uncertainties. We validate the obtained fault network through a pseudo prospective spatial forecast test and discuss possible improvements for future studies. The performance of the presented methodology attests to the importance of the non-linear techniques used to quantify location uncertainty information, which is a crucial input for the large-scale application of the method. We envision that the results of this study can be used to construct improved models for the spatiotemporal evolution of seismicity.

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

Biogc: A novel framework for biological network classification via machine learning

2021 ◽  
Vol 25 (5) ◽  
pp. 1153-1168
Author(s):  
Bentian Li ◽  
Dechang Pi ◽  
Yunxia Lin ◽  
Izhar Ahmed Khan
Keyword(s):  
Large Scale ◽  
Biological Network ◽  
Structural Information ◽  
Kernel Method ◽  
Small Scale ◽  
Network Data ◽  
Graph Classification ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Complex Structural

Biological network classification is an eminently challenging task in the domain of data mining since the networks contain complex structural information. Conventional biochemical experimental methods and the existing intelligent algorithms still suffer from some limitations such as immense experimental cost and inferior accuracy rate. To solve these problems, in this paper, we propose a novel framework for Biological graph classification named Biogc, which is specifically developed to predict the label of both small-scale and large-scale biological network data flexibly and efficiently. Our framework firstly presents a simplified graph kernel method to capture the structural information of each graph. Then, the obtained informative features are adopted to train different scale biological network data-oriented classifiers to construct the prediction model. Extensive experiments on five benchmark biological network datasets on graph classification task show that the proposed model Biogc outperforms the state-of-the-art methods with an accuracy rate of 98.90% on a larger dataset and 99.32% on a smaller dataset.

scholarly journals Higher order schemes in time for the surface quasi-geostrophic system under location uncertainty

2021 ◽  
Author(s):  
Camilla Fiorini ◽  
Long Li ◽  
Étienne Mémin
Keyword(s):  
Fluid Dynamics ◽  
Large Scale ◽  
Higher Order ◽  
Small Scale ◽  
Milstein Scheme ◽  
Location Uncertainty ◽  
Stochastic Transport ◽  
Smooth Component ◽  
Lagrangian Velocity ◽  
Optimal Polynomial

<p>In this work we consider the surface quasi-geostrophic (SQG) system under location uncertainty (LU) and propose a Milstein-type scheme for these equations. The LU framework, first introduced in [1], is based on the decomposition of the Lagrangian velocity into two components: a large-scale smooth component and a small-scale stochastic one. This decomposition leads to a stochastic transport operator, and one can, in turn, derive the stochastic LU version of every classical fluid-dynamics system.<span> </span></p><p>    SQG is a simple 2D oceanic model with one partial differential equation, which models the stochastic transport of the buoyancy, and an operator which relies the velocity and the buoyancy.</p><p><span>    </span>For this kinds of equations, the Euler-Maruyama scheme converges with weak order 1 and strong order 0.5. Our aim is to develop higher order schemes in time: the first step is to consider Milstein scheme, which improves the strong convergence to the order 1. To do this, it is necessary to simulate or estimate the Lévy area [2].</p><p><span>    </span>We show with some numerical results how the Milstein scheme is able to capture some of the smaller structures of the dynamic even at a poor resolution.<span> </span></p><p><strong>References</strong></p><p>[1] E. Mémin. Fluid flow dynamics under location uncertainty. <em>Geophysical & Astrophysical Fluid Dynamics</em>, 108.2 (2014): 119-146.<span> </span></p><p>[2] J. Foster, T. Lyons and H. Oberhauser. An optimal polynomial approximation of Brownian motion. <em>SIAM Journal on Numerical Analysis</em> 58.3 (2020): 1393-1421.</p>

Inertia–gravity waves in a liquid-filled, differentially heated, rotating annulus

2015 ◽  
Vol 782 ◽  
pp. 144-177 ◽  
Author(s):  
Anthony Randriamampianina ◽  
Emilia Crespo del Arco
Keyword(s):  
Prandtl Number ◽  
Gravity Waves ◽  
Large Scale ◽  
Baroclinic Instability ◽  
Zonal Flow ◽  
Small Scale ◽  
Mean Flow ◽  
Baroclinic Waves ◽  
Fluid Properties ◽  
Spatio Temporal

Direct numerical simulations based on high-resolution pseudospectral methods are carried out for detailed investigation into the instabilities arising in a differentially heated, rotating annulus, the baroclinic cavity. Following previous works using air (Randriamampianina et al., J. Fluid Mech., vol. 561, 2006, pp. 359–389), a liquid defined by Prandtl number $Pr=16$ is considered in order to better understand, via the Prandtl number, the effects of fluid properties on the onset of gravity waves. The computations are particularly aimed at identifying and characterizing the spontaneously emitted small-scale fluctuations occurring simultaneously with the baroclinic waves. These features have been observed as soon as the baroclinic instability sets in. A three-term decomposition is introduced to isolate the fluctuation field from the large-scale baroclinic waves and the time-averaged mean flow. Even though these fluctuations are found to propagate as packets, they remain attached to the background baroclinic waves, locally triggering spatio-temporal chaos, a behaviour not observed with the air-filled cavity. The properties of these features are analysed and discussed in the context of linear theory. Based on the Richardson number criterion, the characteristics of the generation mechanism are consistent with a localized instability of the shear zonal flow, invoking resonant over-reflection.

Challenges in geophysical mapping of glaciotectonic structures

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. B287-B303 ◽  
Author(s):  
Anne-Sophie Høyer ◽  
Ingelise Møller ◽  
Flemming Jørgensen
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Geophysical Methods ◽  
Small Scale ◽  
Reflection Seismic ◽  
Electromagnetic Data ◽  
Transient Electromagnetic ◽  
Scale Structures ◽  
Geologic Setting ◽  
Types Of Information

Glaciotectonic complexes have been recognized worldwide — traditionally described on the basis of outcrops or geomorphological observations. In the past few decades, geophysics has become an integral part of geologic mapping, which enables the mapping of buried glaciotectonic complexes. The geophysical methods provide different types of information and degrees of resolution and thus, a different ability to resolve the glaciotectonic structures. We evaluated these abilities on the basis of an integrated application of four commonly used geophysical methods: airborne transient electromagnetics, high-resolution reflection seismic, geoelectrical, and ground-penetrating radar (GPR). We covered an area of [Formula: see text] in a formerly glaciated region in the western part of Denmark. The geologic setting was highly heterogeneous with glaciotectonic deformation observed in the form of large-scale structures in the seismic and airborne transient electromagnetic data to small-scale structures seen in the GPR and geoelectrical data. The seismic and GPR data provided detailed structural information, whereas the geoelectrical and electromagnetic data provided indirect lithological information through resistivities. A combination of methods with a wide span in resolution capabilities can therefore be recommendable to characterize and understand the geologic setting. The sequence of application of the different methods is primarily determined by the gross expenditure required for acquisition and processing, e.g., per kilometer of the surveys. Our experience suggested that airborne electromagnetic data should be acquired initially to obtain a 3D impression of the geologic setting. Based on these data, areas can be selected for further investigation with the more detailed but also more expensive and time-consuming methods.

HERMAS

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Yiwei Song ◽  
Dongzhe Jiang ◽  
Yunhuai Liu ◽  
Zhou Qin ◽  
Chang Tan ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Representation Learning ◽  
User Profiling ◽  
Similarity Measurement ◽  
Learning Framework ◽  
Computation Efficiency ◽  
Trajectory Similarity ◽  
Spatio Temporal ◽  
Embedding Performance

Efficient representations for spatio-temporal cellular Signaling Data (SD) are essential for many human mobility applications. Traditional representation methods are mainly designed for GPS data with high spatio-temporal continuity, and thus will suffer from poor embedding performance due to the unique Ping Pong Effect in SD. To address this issue, we explore the opportunity offered by a large number of human mobility traces and mine the inherent neighboring tower connection patterns. More specifically, we design HERMAS, a novel representation learning framework for large-scale cellular SD with three steps: (1) extract rich context information in each trajectory, adding neighboring tower information as extra knowledge in each mobility observation; (2) design a sequence encoding model to aggregate the embedding of each observation; (3) obtain the embedding for a trajectory. We evaluate the performance of HERMAS based on two human mobility applications, i.e. trajectory similarity measurement and user profiling. We conduct evaluations based on a 30-day SD dataset with 130,612 users and 2,369,267 moving trajectories. Experimental results show that (1) for the trajectory similarity measurement application, HERMAS improves the Hitting Rate (HR@10) from 15.2% to 39.2%; (2) for the user profiling application, HERMAS improves the F1-score for around 9%. More importantly, HERMAS significantly improves the computation efficiency by over 30x.

scholarly journals Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

2021 ◽  
Vol 17 (2) ◽  
pp. e1008689
Author(s):  
Viktor Sip ◽  
Meysam Hashemi ◽  
Anirudh N. Vattikonda ◽  
Marmaduke M. Woodman ◽  
Huifang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Epileptic Seizures ◽  
Brain Network ◽  
Temporal Organization ◽  
Data Driven ◽  
Patient Specific ◽  
Epileptogenic Zone ◽  
Surgery Outcome ◽  
Spatio Temporal

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

scholarly journals Sampling Design Method of Fast Optimal Latin Hypercube

2019 ◽  
Vol 37 (4) ◽  
pp. 714-723 ◽  
Author(s):  
Pengcheng Ye ◽  
Guang Pan ◽  
Shan Gao
Keyword(s):  
Large Scale ◽  
Sampling Design ◽  
Design Method ◽  
Design Methods ◽  
Small Scale ◽  
Optimal Sampling ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Computation Efficiency ◽  
Optimal Sampling Design

In engineering design optimization, the optimal sampling design method is usually used to solve large-scale and complex system problems. A sampling design (FOLHD) method of fast optimal Latin hypercube is proposed in order to overcome the time-consuming and poor efficiency of the traditional optimal sampling design methods. FOLHD algorithm is based on the inspiration that a near optimal large-scale Latin hypercube design can be established by a small-scale initial sample generated by using Successive Local Enumeration method and Translational Propagation algorithm. Moreover, a sampling resizing strategy is presented to generate samples with arbitrary size and owing good space-filling and projective properties. Comparing with the several existing sampling design methods, FOLHD is much more efficient in terms of the computation efficiency and sampling properties.

Sign in / Sign up

Export Citation Format

Share Document