Nonlinear Processes in Geophysics

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Combined list of the recent articles of the journal Nonlinear Processes in Geophysics and the recent discussion forum Nonlinear Processes in Geophysics Discussions
Updated: 3 days 20 hours ago

Negentropy anomaly analysis of the borehole strain associated with the Ms 8.0 Wenchuan earthquake

Tue, 05/14/2019 - 12:30
Negentropy anomaly analysis of the borehole strain associated with the Ms 8.0 Wenchuan earthquake
Kaiguang Zhu, Zining Yu, Chengquan Chi, Mengxuan Fan, and Kaiyan Li
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-22,2019
Manuscript under review for NPG (discussion: open, 0 comments)
The borehole strain data of the Guza station are used to study the negentropy anomalies before the Wenchuan earthquake. We observed the distribution of anomalies in the skewness-kurtosis domain and accumulated the anomaly frequency over time. Our results show: 1) The earthquake moment is proved to be a critical time during the whole earthquake process. 2) Two cumulative acceleration phases are corresponding to the two crustal stress releases, which may be the precursors to the earthquake.

CNOP based on ACPW for Identifying Sensitive Regions of Typhoon Target Observations with WRF Model

Thu, 05/09/2019 - 12:30
CNOP based on ACPW for Identifying Sensitive Regions of Typhoon Target Observations with WRF Model
Bin Mu, Linlin Zhang, Shijin Yuan, and Wansuo Duan
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-24,2019
Manuscript under review for NPG (discussion: open, 0 comments)
In this paper, we rewrite the adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm based on principal component analysis (ACPW) and applied it to solve conditional nonlinear optimal perturbation (CNOP) in the WRF-ARW for identifying sensitive areas of typhoon target observations. The experimental results show that the ACPW is meaningful, feasible and effective.

Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model

Tue, 05/07/2019 - 12:30
Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model
Mallory Carlu, Francesco Ginelli, Valerio Lucarini, and Antonio Politi
Nonlin. Processes Geophys., 26, 73-89, https://doi.org/10.5194/npg-26-73-2019, 2019
We explore the nature of instabilities in a well-known meteorological toy model, the Lorenz 96, to unravel key mechanisms of interaction between scales of different resolutions and time scales. To do so, we use a mathematical machinery known as Lyapunov analysis, allowing us to capture the degrees of chaoticity associated with fundamental directions of instability. We find a non-trivial group of such directions projecting significantly on slow variables, associated with long term dynamics.

On the nonlinear and Solar-forced nature of the Chandler wobble in the Earth's pole motion

Fri, 04/26/2019 - 12:30
On the nonlinear and Solar-forced nature of the Chandler wobble in the Earth's pole motion
Dmitry M. Sonechkin
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-12,2019
Manuscript under review for NPG (discussion: open, 2 comments)
I look for a combination of some external periodicities, which period coincides with Chandler's period. My predecessors considered a model of the linear oscillator with a viscosity and several periodic external forces. Necessary and sufficient condition for emergence of a peak in power spectrum at Chandler's period is nonlinearity of the oscillator being considered. The main achievement of my work is the proof that it is necessary to consider the raw nonlinear equations of L. Euler.

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

Tue, 04/23/2019 - 12:30
Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data
Fei Lu, Nils Weitzel, and Adam H. Monahan
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-16,2019
Manuscript under review for NPG (discussion: open, 0 comments)
ll-posedness of the inverse problem and sparse noisy data are two major challenges in the modeling of high-dimensional spatiotemporal processes. We present a Bayesian inference method with a strongly regularized posterior to overcome these challenges, enabling joint state-parameter estimation and quantifying uncertainty in the estimation. We demonstrate the method on a physically motivated nonlinear stochastic partial differential equation arising from paleoclimate construction.

Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks

Wed, 04/17/2019 - 12:30
Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks
Xianqiong Cheng, Qihe Liu, Pingping Li, and Yuan Liu
Nonlin. Processes Geophys., 26, 61-71, https://doi.org/10.5194/npg-26-61-2019, 2019
This paper is based on a deep learning neural network to invert the Rayleigh surface wave velocity of the crustal thickness, which is a new geophysical inversion solution that proved to be effective and practical. Through comparative experiments, we found that deep learning neural networks can more accurately reveal the non-linear relationship between phase velocity and crustal thickness than traditional shallow networks. Deep learning neural networks are more efficient than Monte Carlo methods.

Competition between chaotic advection and diffusion: stirring and mixing in a 3-D eddy model

Fri, 04/05/2019 - 12:30
Competition between chaotic advection and diffusion: stirring and mixing in a 3-D eddy model
Genevieve Jay Brett, Larry Pratt, Irina Rypina, and Peng Wang
Nonlin. Processes Geophys., 26, 37-60, https://doi.org/10.5194/npg-26-37-2019, 2019
The relative importance of chaotic stirring and smaller-scale turbulent mixing for the distribution of dye in an idealized ocean flow feature is quantified using three different methods. We find that stirring is the dominant process in large areas with fast stirring, while mixing dominates in small fast-stirring regions and all slow-stirring regions. This quantification of process dominance can help oceanographers think about when to model stirring accurately, which can be costly.

Technique for solving for microseismic source location parameters based on adaptive particle swarm optimization

Tue, 04/02/2019 - 12:30
Technique for solving for microseismic source location parameters based on adaptive particle swarm optimization
Hong-Mei Sun, Jian-Zhi Yu, Xing-Li Zhang, Bing-Guo Wang, and Rui-Sheng Jia
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-11,2019
Manuscript under review for NPG (discussion: open, 2 comments)
An intelligent method is presented for locating microseismic source based on particle swarm optimization (PSO) concept. It eliminates microseismic source locating errors caused by inaccurate velocity model of the earth medium. The method uses as the target of PSO a global minimum of the sum of squared discrepancies between modeled arrival times and measured arrival times. The discrepancies are calculated for all pairs of detectors of a seismic monitoring system, Then, the adaptive PSO algorithm is applied to locate the microseismic source and obtain optimal value of the P-wave velocity. The PSO algorithm adjusts inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters to avoid the PSO algorithm trapping by local optima during the solution process. The origin time of the microseismic event is estimated by minimizing the sum of squared discrepancies between the modeled arrival times and the measured arrival times. This Sum is calculated using the obtained estimates of the microseismic source coordinates and P-wave velocity. The effectiveness of the PSO algorithm was verified through inversion of a theoretical model and two analyses of actual data from mine blasts in different locations. Compared with the classic least squares method, the PSO algorithm displays faster convergence and higher accuracy of microseismic source positioning. Moreover, there is no need to measure the microseismic wave velocity in advance: the PSO algorithm eliminates the adverse effects caused by error in the P-wave velocity when locating a microseismic source using traditional methods.

Explosive instability due to flow over a rippled bottom

Tue, 04/02/2019 - 12:30
Explosive instability due to flow over a rippled bottom
Raunak Raj and Anirban Guha
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-13,2019
Manuscript under review for NPG (discussion: open, 3 comments)

In this paper, we study Bragg resonance, i.e. the triad interaction between surface and/or interfacial waves with bottom ripple, in the presence of background velocity. We show that when one of the constituent waves of the triad has negative energy, the amplitudes of all the waves grow exponentially. This is very different from classic Bragg resonance in which one wave decays to cause the growth of the other. The instabilities we observe are explosive and are different from normal mode shear instabilities since our velocity profiles are linearly stable. Our work may explain the existence of large amplitude internal waves over periodic bottom ripples in the presence of tidal flow observed in oceans and estuaries.

Characterization of the South Atlantic Anomaly

Fri, 03/29/2019 - 13:30
Characterization of the South Atlantic Anomaly
Khairul Afifi Nasuddin, Mardina Abdullah, and Nurul Shazana Abdul Hamid
Nonlin. Processes Geophys., 26, 25-35, https://doi.org/10.5194/npg-26-25-2019, 2019
This research intends to characterize the South Atlantic Anomaly (SAA) by applying the power spectrum analysis approach. The outcomes of the research revealed that the SAA region had a tendency to be persistent during active periods and normal periods. It can be said that it experiences this characteristic because of the Earth’s magnetic field strength. It is very important for spacecraft entering the SAA to take safety precautions in order to minimize the damage.

Application of fractal models to delineate mineralized zones in the Pulang porphyry copper deposit, Yunnan, Southwest China

Wed, 03/27/2019 - 13:30
Application of fractal models to delineate mineralized zones in the Pulang porphyry copper deposit, Yunnan, Southwest China
Xiaochen Wang, Qinglin Xia, Tongfei Li, Shuai Leng, Yanling Li, Li Kang, Zhijun Chen, and Lianrong Wu
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-8,2019
Manuscript under review for NPG (discussion: open, 1 comment)
We utilized concentration-volume and power spectrum-volume fractal models to delineate different grade mineralization of Pulang deposit. The high grade mineralization determined by S-V model give better relations with potassic zones of 3D geological model based on the relationship between results of fractal methods and geological logging of drillholes.There is a better correlation between moderate and weak grade mineralization obtained from C-V model and phyllic zones of Pulang deposit.

Revising the stochastic iterative ensemble smoother

Fri, 03/15/2019 - 13:30
Revising the stochastic iterative ensemble smoother
Patrick N. Raanes, Andreas S. Stordal, and Geir Evensen
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-10,2019
Manuscript under review for NPG (discussion: final response, 3 comments)
A popular (variational ensemble smoother) method of data assimilation is simplified. An exact relationship between ensemble linearizations (linear regression) and adjoints (analytic derivatives) is established.

Particle Clustering and Subclustering as a Proxy for Mixing in Geophysical Flows

Thu, 03/14/2019 - 13:30
Particle Clustering and Subclustering as a Proxy for Mixing in Geophysical Flows
Rishiraj Chakraborty, Aaron Coutino, and Marek Stastna
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-6,2019
Manuscript under review for NPG (discussion: open, 2 comments)
In this paper, we highlight a specific example of large scale flows. We talk about technical developments of how to extract regions of dense mixing in the flow using graph theoretic tools from discrete Mathematics.

Data assimilation using adaptive, non-conservative, moving mesh models

Mon, 03/11/2019 - 13:30
Data assimilation using adaptive, non-conservative, moving mesh models
Ali Aydoğdu, Alberto Carrassi, Colin T. Guider, Chris K. R. T. Jones, and Pierre Rampal
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-9,2019
Manuscript under review for NPG (discussion: final response, 3 comments)
Computational models involving adaptive meshes can both evolve dynamically and be remeshed. The remeshing means that the state vector dimension changes in time and across ensemble members, making EnKF unsuitable for the assimilation of observational data. We develop a modification in which the analysis is performed on a fixed uniform grid onto which the ensemble is mapped, with resolution related to the remeshing criteria. The approach is tested on two 1D models proving success.

Denoising stacked autoencoders for transient electromagnetic signal denoising

Fri, 03/01/2019 - 13:30
Denoising stacked autoencoders for transient electromagnetic signal denoising
Fanqiang Lin, Kecheng Chen, Xuben Wang, Hui Cao, Danlei Chen, and Fanzeng Chen
Nonlin. Processes Geophys., 26, 13-23, https://doi.org/10.5194/npg-26-13-2019, 2019
The deep-seated information is reflected in the late-stage data of the second field. By introducing the deep learning algorithm integrated with the characteristics of the secondary field data, we can map the contaminated data in late track data to a high-probability position. By comparing several filtering algorithms, the SFSDSA method has better performance and the denoising signal is conducive to further improving the effective detection depth.

Data assimilation as a deep learning tool to infer ODE representations of dynamical models

Thu, 02/28/2019 - 13:30
Data assimilation as a deep learning tool to infer ODE representations of dynamical models
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-7,2019
Manuscript under review for NPG (discussion: final response, 3 comments)
This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, Kuramoto-Sivashinski models and a two-scale Lorenz model.

Exploring the sensitivity of Northern Hemisphere atmospheric circulation to different surface temperature forcing using a statistical–dynamical atmospheric model

Mon, 02/18/2019 - 13:30
Exploring the sensitivity of Northern Hemisphere atmospheric circulation to different surface temperature forcing using a statistical–dynamical atmospheric model
Sonja Totz, Stefan Petri, Jascha Lehmann, Erik Peukert, and Dim Coumou
Nonlin. Processes Geophys., 26, 1-12, https://doi.org/10.5194/npg-26-1-2019, 2019

Climate and weather conditions in the mid-latitudes are strongly driven by the large-scale atmosphere circulation. Observational data indicate that important components of the large-scale circulation have changed in recent decades, including the strength and the width of the Hadley cell, jets, storm tracks and planetary waves.

Here, we use a new statistical–dynamical atmosphere model (SDAM) to test the individual sensitivities of the large-scale atmospheric circulation to changes in the zonal temperature gradient, meridional temperature gradient and global-mean temperature. We analyze the Northern Hemisphere Hadley circulation, jet streams, storm tracks and planetary waves by systematically altering the zonal temperature asymmetry, the meridional temperature gradient and the global-mean temperature. Our results show that the strength of the Hadley cell, storm tracks and jet streams depend, in terms of relative changes, almost linearly on both the global-mean temperature and the meridional temperature gradient, whereas the zonal temperature asymmetry has little or no influence. The magnitude of planetary waves is affected by all three temperature components, as expected from theoretical dynamical considerations. The width of the Hadley cell behaves nonlinearly with respect to all three temperature components in the SDAM. Moreover, some of these observed large-scale atmospheric changes are expected from dynamical equations and are therefore an important part of model validation.

Precision Annealing Monte Carlo Methods for Statistical Data Assimilation: Metropolis-Hastings Procedures

Fri, 02/08/2019 - 13:30
Precision Annealing Monte Carlo Methods for Statistical Data Assimilation: Metropolis-Hastings Procedures
Adrian S. Wong, Kangbo Hao, Zheng Fang, and Henry D. I. Abarbanel
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-1,2019
Manuscript under review for NPG (discussion: final response, 4 comments)
Our paper deals with data assimilation methods for chaotic systems and how one can make predictions from incomplete data of such systems. The method that we chose to explore in detail is a Monte Carlo method with an annealing heuristic. Our results show that Monte Carlo methods are a viable alternatives to the standard set of derivative-based methods. We verify the method using the Lorenz 96 system due to the simplicity of that system.

Mahalanobis distance based recognition of changes in the dynamics of seismic process

Tue, 02/05/2019 - 13:30
Mahalanobis distance based recognition of changes in the dynamics of seismic process
Teimuraz Matcharashvili, Zbigniew Czechowski, and Natalia Zhukova
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2018-57,2019
Manuscript under review for NPG (discussion: final response, 3 comments)

In present work we aimed to analyze regularity of seismic process based on all its spatial, temporal and energetic characteristics. Increments of cumulative times, increments of cumulative distances and increments of cumulative seismic energies, have been calculated from southern California earthquake catalogue, 1975 to 2017.

Used method of analysis represented combination of multivariate Mahalanobis distance calculation with the surrogate data testing. Prior to proceed to the analysis of dynamical features of seismic process we have tested used approach for two different 3 dimensional models in which dynamical features were changed from more regular to the more randomized conditions by adding some extent of noises.

Analysis of variability in the extent of regularity of seismic process have been accomplished for different representative threshold values.

According to results of our analysis about third part of considered 50 data windows, the original seismic process is indistinguishable from random process by its features of temporal, spatial and energetic variability. It was shown that prior to strong earthquake occurrences, in periods of relatively small earthquakes generation, percentage of windows in which seismic process is indistinguishable from random process essentially increases (to 60–80 %). At the same time, in periods of aftershock activity in all considered windows the process of small earthquake generation become regular and thus is strongly different from randomized catalogues.

In some periods of catalogue time span seismic process looks closer to randomness while in other cases it becomes closer to regular behavior. Exactly, in periods of relatively decreased earthquake generation activity (at smaller energy release), seismic process looks random-like while in periods of occurrence of strong events, followed by series of aftershocks, it reveal significant deviation from randomness – the extent of regularity essentially increases. The period, for which such deviation from the random behavior can last, depends on the amount of seismic energy released by the strong earthquake.

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