Mapping Groundwater Fluctuations in the Coastal Los Angeles Basin by Seismic Interferometry

2021 ◽  
Author(s):  
Shujuan Mao ◽  
Michel Campillo ◽  
Robert van der Hilst ◽  
Albanne Lecointre
Keyword(s):  
Los Angeles ◽  
Spatial Patterns ◽  
Surface Deformation ◽  
Seismic Velocity ◽  
Real Data ◽  
Coda Wave ◽  
Central Basin ◽  
Imaging Protocol ◽  
Groundwater Fluctuations ◽  
Spatio Temporal

<p>Changes in crustal seismic velocity (d<em>v/v</em>) can be monitored continuously in time by interferometry of seismic ambient noise. This approach has been successfully employed to study temporal perturbations in stress fields, rock fractures, and fluids. Here we go one step further with this monitoring technique, by not only detecting the temporal changes but also imaging the inhomogeneous spatial distributions of d<em>v/v</em>. We implement the spatial imaging by leveraging travel-time shifts at successive lag times and solving inverse problems based on coda-wave sensitivity kernels. We then use these space-time observations of d<em>v/v </em>to investigate the groundwater fluctuations in the Coastal Los Angeles (LA) Basin during 2000-2020. Imaging of d<em>v/v</em> demonstrates the spatial patterns of groundwater variations: Seasonal changes are most pronounced within confined zones in Santa Ana Basin and LA Central Basin, whereas the long-term changes extend to a broader area including the unconfined forebay. We further compare d<em>v/v</em> observations with InSAR measurements and find strongly consistent spatial patterns. Compared with surface deformation measurements, d<em>v/v</em> additionally help to characterize the depths of several aquifers in the study area. This real-data application substantiates the validity of our d<em>v/v</em> imaging protocol, and shows the promise of using spatio-temporal d<em>v/v</em> observations to monitor surficial hydrological processes.</p>

scholarly journals Spatially Heterogeneous Land Surface Deformation Data Fusion Method Based on an Enhanced Spatio-Temporal Random Effect Model

2019 ◽  
Vol 11 (9) ◽  
pp. 1084 ◽  
Author(s):  
Qiang Shi ◽  
Wujiao Dai ◽  
Rock Santerre ◽  
Zhiwei Li ◽  
Ning Liu
Keyword(s):  
Los Angeles ◽  
Spatial Heterogeneity ◽  
Land Surface ◽  
Surface Deformation ◽  
Random Effect ◽  
Random Effect Model ◽  
Fusion Method ◽  
Fusion Model ◽  
Spatio Temporal ◽  
Monitoring Area

The spatio-temporal random effect (STRE) model, a type of spatio-temporal Kalman filter model, can be used for the fusion of the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate high spatio-temporal resolution deformation series, assuming that the land deformation is spatially homogeneous in the monitoring area. However, when there are multiple deformation sources in the monitoring area, complex spatial heterogeneity will appear. To improve the fusion accuracy, we propose an enhanced STRE fusion method (eSTRE) by taking spatial heterogeneity into consideration. This new method integrates the spatial heterogeneity constraints in the STRE model by constructing extra-constrained spatial bases for the heterogeneous area. The effectiveness of this method is verified by using simulated data and real land surface deformation data. The results show that eSTRE can reduce the root mean square (RMS) of InSAR interpolation results by 14% and 23% on average for a simulation experiment and Los Angeles experiment, respectively, indicating that the new proposed method (eSTRE) is substantially better than the previous STRE fusion model.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Experiments on generation of surface waves by an underwater moving bottom

2015 ◽  
Vol 471 (2178) ◽  
pp. 20150069 ◽  
Author(s):  
Timothée Jamin ◽  
Leonardo Gordillo ◽  
Gerardo Ruiz-Chavarría ◽  
Michael Berhanu ◽  
Eric Falcon
Keyword(s):  
Free Surface ◽  
Surface Waves ◽  
Surface Deformation ◽  
Fluid Layer ◽  
Fluid Velocity ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Spatio Temporal ◽  
Experimental Findings

We report laboratory experiments on surface waves generated in a uniform fluid layer whose bottom undergoes an upward motion. Simultaneous measurements of the free-surface deformation and the fluid velocity field are focused on the role of the bottom kinematics (i.e. its spatio-temporal features) in wave generation. We observe that the fluid layer transfers bottom motion to the free surface as a temporal high-pass filter coupled with a spatial low-pass filter. Both filter effects are often neglected in tsunami warning systems, particularly in real-time forecast. Our results display good agreement with a prevailing linear theory without any parameter fitting. Based on our experimental findings, we provide a simple theoretical approach for modelling the rapid kinematics limit that is applicable even for initially non-flat bottoms: this may be a key step for more realistic varying bathymetry in tsunami scenarios.

Single-station seismic monitoring of permafrost on Mt. Zugspitze (Germany) over the past 15 years

2021 ◽  
Author(s):  
Fabian Lindner ◽  
Joachim Wassermann
Keyword(s):  
Travel Time ◽  
Seismic Velocity ◽  
Temperature Record ◽  
Lapse Time ◽  
Resistivity Tomography ◽  
Single Station ◽  
Velocity Variations ◽  
Spatio Temporal ◽  
Velocity Changes ◽  
Permafrost Thawing

<p>Permafrost thawing affects mountain slope stability and can trigger hazardous rock falls. As rising temperatures promote permafrost thawing, spatio-temporal monitoring of long-term and seasonal variations in the perennially frozen rock is therefore crucial in regions with high hazard potential. With various infrastructure in the summit area and population in the close vicinity, Mt. Zugspitze in the German/Austrian Alps is such a site and permafrost has been monitored with temperature logging in boreholes and lapse-time electrical resistivity tomography. Yet, these methods are expensive and laborious, and are limited in their spatial and/or temporal resolution.</p><p>Here, we analyze continuous seismic data from a single station deployed at an altitude of 2700 m a.s.l. in a research station, which is separated by roughly 250 m from the permafrost affected ridge of Mt. Zugspitze. Data are available since 2006 (with some gaps) and reveal high-frequency (>1 Hz) anthropogenic noise likely generated by the cable car stations at the summit. We calculate single-station cross-correlations between the different sensor components and investigate temporal coda wave changes by applying the recently introduced wavelet-based cross-spectrum method. This approach provides time series of the travel time relative to the reference stack as a function of frequency and lag time in the correlation functions. In the frequency and lag range of 1-10 Hz and 0.5-5 s respectively, we find various parts in the coda that show clear annual variations and an increasing trend in travel time over the past 15 years of consideration. Converting the travel time variations to seismic velocity variations (assuming homogeneous velocity changes affecting the whole mountain) results in seasonal velocity changes of up to a few percent and on the order of 0.1% decrease per year. Yet, estimated velocity variations do not scale linearly with lag time, which indicates that the medium changes are localized rather than uniform and that the absolute numbers need to be taken with caution. The annual velocity variations are anti-correlated with the temperature record from the summit but delayed by roughly one month.</p><p>The phasing of the annual seismic velocity change (relative to the temperature record) is in agreement with a previous study employing lapse-time electrical resistivity tomography. Furthermore, the decreasing trend in seismic velocity happens concurrently with an increasing trend in temperature. The results therefore suggest that the velocity changes are related to seasonal thaw and refreeze and permafrost degradation and thus highlight the potential of seismology for permafrost monitoring. By adding additional receivers and/or a fiber-optic cable for distributed acoustic sensing, hence increasing the spatial resolution, the presented method holds promise for lapse-time imaging of permafrost bodies with high spatio-temporal resolution from passive measurements.</p>

scholarly journals A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

2021 ◽  
Vol 18 (6) ◽  
pp. 7685-7710
Author(s):  
Yukun Tan ◽  
◽  
Durward Cator III ◽  
Martial Ndeffo-Mbah ◽  
Ulisses Braga-Neto ◽  
...  
Keyword(s):  
State Space ◽  
State Space Model ◽  
Synthetic Data ◽  
Public Health Intervention ◽  
Disease Outbreaks ◽  
Real Data ◽  
Epidemiological Data ◽  
Transmission Parameters ◽  
Intervention Measures ◽  
Spatio Temporal

<abstract><p>Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.</p></abstract>

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach

2017 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Driving Mechanism ◽  
Distributed Hydrological Model ◽  
Eof Analysis ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

scholarly journals Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement

2021 ◽  
Vol 25 (2) ◽  
pp. 957-982 ◽  
Author(s):  
Petra Hulsman ◽  
Hubert H. G. Savenije ◽  
Markus Hrachowitz
Keyword(s):  
Time Series ◽  
Spatial Patterns ◽  
Temporal Dynamics ◽  
Water Storage ◽  
Remotely Sensed ◽  
Satellite Observations ◽  
Model Structure ◽  
Total Water ◽  
Groundwater Reservoir ◽  
Spatio Temporal

Abstract. Satellite observations can provide valuable information for a better understanding of hydrological processes and thus serve as valuable tools for model structure development and improvement. While model calibration and evaluation have in recent years started to make increasing use of spatial, mostly remotely sensed information, model structural development largely remains to rely on discharge observations at basin outlets only. Due to the ill-posed inverse nature and the related equifinality issues in the modelling process, this frequently results in poor representations of the spatio-temporal heterogeneity of system-internal processes, in particular for large river basins. The objective of this study is thus to explore the value of remotely sensed, gridded data to improve our understanding of the processes underlying this heterogeneity and, as a consequence, their quantitative representation in models through a stepwise adaptation of model structures and parameters. For this purpose, a distributed, process-based hydrological model was developed for the study region, the poorly gauged Luangwa River basin. As a first step, this benchmark model was calibrated to discharge data only and, in a post-calibration evaluation procedure, tested for its ability to simultaneously reproduce (1) the basin-average temporal dynamics of remotely sensed evaporation and total water storage anomalies and (2) their temporally averaged spatial patterns. This allowed for the diagnosis of model structural deficiencies in reproducing these temporal dynamics and spatial patterns. Subsequently, the model structure was adapted in a stepwise procedure, testing five additional alternative process hypotheses that could potentially better describe the observed dynamics and pattern. These included, on the one hand, the addition and testing of alternative formulations of groundwater upwelling into wetlands as a function of the water storage and, on the other hand, alternative spatial discretizations of the groundwater reservoir. Similar to the benchmark, each alternative model hypothesis was, in a next step, calibrated to discharge only and tested against its ability to reproduce the observed spatio-temporal pattern in evaporation and water storage anomalies. In a final step, all models were re-calibrated to discharge, evaporation and water storage anomalies simultaneously. The results indicated that (1) the benchmark model (Model A) could reproduce the time series of observed discharge, basin-average evaporation and total water storage reasonably well. In contrast, it poorly represented time series of evaporation in wetland-dominated areas as well as the spatial pattern of evaporation and total water storage. (2) Stepwise adjustment of the model structure (Models B–F) suggested that Model F, allowing for upwelling groundwater from a distributed representation of the groundwater reservoir and (3) simultaneously calibrating the model with respect to multiple variables, i.e. discharge, evaporation and total water storage anomalies, provided the best representation of all these variables with respect to their temporal dynamics and spatial patterns, except for the basin-average temporal dynamics in the total water storage anomalies. It was shown that satellite-based evaporation and total water storage anomaly data are not only valuable for multi-criteria calibration, but can also play an important role in improving our understanding of hydrological processes through the diagnosis of model deficiencies and stepwise model structural improvement.

Investigation of ambient noise seismological methods on a bridge model

2021 ◽  
Author(s):  
Chun-Man Liao ◽  
Franziska Mehrkens ◽  
Celine Hadziioannou ◽  
Ernst Niederleithinger
Keyword(s):  
Wave Propagation ◽  
Large Scale ◽  
Seismic Velocity ◽  
Measurement Data ◽  
Correlation Coefficients ◽  
Coda Wave ◽  
Velocity Change ◽  
Promising Tool ◽  
Bridge Model ◽  
Noise Measurements

&lt;p&gt;The aim of this work is to investigate the application of seismological noise-based monitoring for bridge structures. A large-scale two-span concrete bridge model with a build-in post-tensioning system, which is exposed to environmental conditions, is chosen as our experimental test structure. Ambient seismic noise measurements were carried out under different pre-stressed conditions. Using the seismic interferometry technique, which is applied to the measurement data in the frequency domain, we reconstruct waveforms that relate to wave propagation in the structure. The coda wave interferometry technique is then implemented by comparing two waveforms recorded in two pre-stress states. Any relative seismic velocity changes are identified by determining the correlation coefficients and reveal the influence of the pre-stressing force. The decrease of the wave propagation velocity indicates the loss of the pre-stress and weakening stiffness due to opening or event extension of cracks. We conclude that the seismological methods used to estimate velocity change can be a promising tool for structural health monitoring of civil structures.&lt;/p&gt;

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