3D Transdimensional Seismic Tomography of the Inner Core

2021 ◽  
Author(s):  
Henry Brett ◽  
Rhys Hawkins ◽  
Karen Lythgoe ◽  
Lauren Waszek ◽  
Arwen Deuss
Keyword(s):  
Seismic Tomography ◽  
Inner Core ◽  
Probability Distributions ◽  
Model Space ◽  
Core Structure ◽  
P Wave ◽  
Quality Data ◽  
Seismic Structure ◽  
Data Set ◽  
Detailed Interpretation

<p>The inner core contains strong seismic heterogeneity, both laterally and from the surface to the centre. Accurately resolving the seismic structure of the inner core is key to unravelling the evolution of the core. Seismic models of inner core structure are often limited by their parameterization, which means it is difficult to interpret which features of the inner core are real (e.g. hemispheres or the inner most inner core). To overcome this we conduct seismic tomography using transdimensional inversion on a high quality data set of 5296 differential and 2344 absolute P-wave travel times. By taking a transdimensional approach we allow the data to define how the model space is parameterized and this provides us with both the mean structure of the inner core but also the probability distributions of each model parameter. This allows us to identify which regions of the model space are well constrained and likewise which regions are poorly constrained. We compare results from a static MCMC model and a transdimensional MCMC model, this provides confidence in our results as both models show clear similarities in structure. From no prior assumptions on inner core structure we recover many first order observations: such as anisotropic hemispheres and an isotropic outer inner core (OIC) along with potential observations of an inner most inner core. With higher resolution than previous inner core tomography we can provide more detailed interpretation of inner core structure and draw conclusions with greater confidence. We also conduct transdimensional inversions on a subset of our data which does not contain South Sandwich Islands (SSI) events which are considered by many to be unreliable or contaminated with mantle structure. The overall inner core structure remains largely the same however, showing that the SSI data does not significantly alter our final interpretations.</p>

scholarly journals Inner core anisotropy measured using new ultra-polar PKIKP paths

2020 ◽  
Vol 223 (2) ◽  
pp. 1230-1246
Author(s):  
Henry Brett ◽  
Arwen Deuss
Keyword(s):  
Seismic Anisotropy ◽  
Inner Core ◽  
Rotation Axis ◽  
Body Waves ◽  
P Wave ◽  
Mantle Structure ◽  
Data Set ◽  
Seismic Stations ◽  
Ray Path ◽  
Ray Paths

SUMMARY We measure the seismic anisotropy of the inner core using PKPbc-PKPdf and PKPab-PKPdf differential traveltimes, as a function of the angle ζ between the Earth’s rotation axis and the ray path in the inner core. Previous research relied heavily on body waves originating in the South Sandwich Islands (SSI) and travelling to seismic stations in Alaska to sample inner core velocities with low ζ (polar paths). These SSI polar paths are problematic because they have anomalous travel time anomalies, there are no ultra-polar SSI paths with ζ < 20° and they only cover a small part of the inner core. Here we improve constraints on inner core anisotropy using recently installed seismic stations at high latitudes, especially in the Antarctic, allowing us to measure ultra-polar paths with ζ ranging from 20°–5°. Our new data show that the SSI’s polar events are fast but still within the range of velocities measured from ray paths originating elsewhere. We further investigate the effect of mantle structure on our data set finding that the SSI data are particularly affected by fast velocities underneath the SSI originating from the subducted South Georgia slab, which is currently located just above the core mantle boundary. This fast velocity region results in mantle structure being misinterpreted as inner core structure and we correct for this using a P-wave tomographic model. We also analyse the effect of velocity changes on the ray paths within the inner core and find that faster velocities significantly change the ray path resulting in the ray travelling deeper into the inner core and spending more time in the inner core. To remove this effect, we propose a simple but effective method to correct each event-station pair for the velocity-dependent ray path changes in the inner core, producing a more reliable fractional traveltime measurement. Combining the new ultra-polar data with mantle and ray path corrections results in a more reliable inner core anisotropy measurement and an overall measured anisotropy of 1.9–2.3 per cent for the whole inner core. This is lower than previous body wave studies (3 per cent anisotropy) and in better agreement with the value of inner core anisotropy measured by normal modes (2 per cent anisotropy). We also identify regional variation of anisotropic structure in the top 500 km of the inner core, which appears to be more complex than simple hemispherical variations. These regional variations are independent of the SSI data and are still present when these data are excluded. We also find a potential innermost inner core with a radius of 690 km and stronger anisotropy.

Observing the signature of the magnetic field's behaviour in the radial variation of inner core anisotropy

2020 ◽  
Author(s):  
Janneke de Jong ◽  
Lennart de Groot ◽  
Arwen Deuss
Keyword(s):  
Magnetic Field ◽  
Heat Flux ◽  
Seismic Anisotropy ◽  
Inner Core ◽  
Body Wave ◽  
Outer Core ◽  
Core Structure ◽  
Reversal Rate ◽  
Seismic Structure ◽  
The Magnetic Field

<p>The release of latent heat and lighter materials during inner core solidification is the driving force of the liquid iron flow in the outer core which generates the Earth's magnetic field. It is well known that the behaviour of the magnetic field varies over long time scales. Two clearly identifiable regimes are recognized, (i) superchrons and (ii) periods of hyperactivity (Biggin et al. 2012). Superchrons are characterized by an exceptionally low reversal rate of the magnetic pole and are associated with a low core mantle boundary (CMB) heat flux. Hyperactive periods are defined by a high reversal rate and have a high CMB heat flux.</p><p>Here we investigate whether the occurrence of these two regimes is related to radial variations in inner core seismic structure. Using seismic body-wave observations of compressional PKIKP-waves (Irving & Deuss 2011, Waszek & Deuss 2011, Lythgoe et al. 2013)., we construct a model of inner core anisotropy by comparing the difference between travel times for polar and equatorial rays. Anisotropy is the directional dependence of wave velocity and is determined by the structure of iron crystals in the inner core, hence changes in seismic anisotropy are due to changes in inner core crystal texture. We invert for radial changes in anisotropy while allowing for lateral variations and find that a model of the inner core containing five layers best fits our data. The model contains an isotropic uppermost inner core and four deeper layers with varying degrees of anisotropy.</p><p>Texture differences of the inner core iron crystals have been linked to changes in the solidification process of the inner core (Bergman et al. 2005), i.e. the motor of outer core flow. Therefore, the observed anisotropy variation, caused by variations of inner core solidification, might be related to changes in the behaviour of the magnetic field. Using an inner core growth model (Buffett et al. 1996) we convert depth to time for a range of inner core nucleation ages between 3.0 and 0.5 Ga (Olsen 2016). We find a remarkable correlation between the solidification time of the seismically observed layers and the occurrence of the magnetic regimes for two inner core ages; one with a nucleation at 1.4 Ga and one at 0.6 Ga, corresponding to an average CMB heat flux of 7.6 TW and 16.7 TW respectively.</p><p>Although we currently cannot differentiate between these two inner core ages considering our results alone, they do show that a relation between inner core structure and the behaviour of the magnetic field is possible, and suggest that seismic observations of inner core structure might provide new and independent insights into the magnetic field and its history.</p>

scholarly journals P-Wave-Only Inversion of Challenging Walkaway VSP Data for Detailed Estimation of Local Anisotropy and Reservoir Parameters: A Case Study of Seismic Processing in Northern Poland

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2061
Author(s):  
Mateusz Zaręba ◽  
Tomasz Danek ◽  
Michał Stefaniuk
Keyword(s):  
Seismic Signal ◽  
Field Conditions ◽  
P Wave ◽  
Quality Data ◽  
Local Anisotropy ◽  
Processing Scheme ◽  
Vsp Data ◽  
Walkaway Vsp ◽  
Anisotropy Parameters ◽  
Detailed Interpretation

In this paper, we present a detailed analysis of walkaway vertical seismic profiling (VSP) data, which can be used to obtain Thomsen parameters using P-wave-only inversion. Data acquisition took place in difficult field conditions, which influenced the quality of the data. Therefore, this paper also shows a seismic data processing scheme that allows the estimation of correct polarization angles despite poor input data quality. Moreover, we showed that it is possible to obtain reliable and detailed values of Thomsen’s anisotropy parameters for data that are challenging due to extremely difficult field conditions during acquisition and the presence of an overburden of salt and anhydrite (Zechstein formation). This complex is known for its strong seismic signal-attenuating properties. We designed a special processing workflow with a signal-matching procedure that allows reliable estimation of polarization angles for low-quality data. Additionally, we showed that P-wave-only inversion for the estimation of local anisotropy parameters can be used as valuable additional input for detailed interpretation of geological media, even if anisotropy is relatively low.

High-resolution passive seismic tomography for 3D velocity, Poisson’s ratio ν, and P-wave quality QP in the Delvina hydrocarbon field, southern Albania

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. B89-B112 ◽  
Author(s):  
G-Akis Tselentis ◽  
Nikolaos Martakis ◽  
Paraskevas Paraskevopoulos ◽  
Athanasios Lois
Keyword(s):  
Wave Velocity ◽  
Seismic Tomography ◽  
Oil Field ◽  
P Wave ◽  
S Wave ◽  
Gas Field ◽  
Data Set ◽  
Geophysical Surveys ◽  
Passive Seismic ◽  
Hydrocarbon Field

We have studied using traveltimes of P- and S-waves and initial seismic-pulse rise-time measurements from natural microearthquakes to derive 3D P-wave velocity VP information (mostly structural) as well as P- and S-wave velocity VP/VS and P-wave quality factor QP information (mostly lithologic) in a known hydrocarbon field in southern Albania. During a 12-month monitoring period, 1860 microearthquakes were located at a 50-station seismic network and were used to obtain the above parameters. The data set included earthquakes with magnitudes ranging from –0.1 to 3.0 R (Richter scale) and focal depths typically occurring between 2 and 10 km. Kohonen neural networks were implemented to facilitate the lithological classification of the passive seismic tomography (PST) results. The obtained results, which agreed with data from nearby wells, helped delineate the structure of the reservoir. Two subregions of the investigated area, one corresponding to an oil field and one to a gas field, were correlated with the PST results. This experiment showed that PST is a powerful new geophysical technique for exploring regions that present seismic penetration problems, difficult topographies, and complicated geologies, such as thrust-belt regions. The method is economical and environmentally friendly, and it can be used to investigate very large regions for the optimal design of planned 2D or 3D conventional geophysical surveys.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

2020 ◽  
Vol 499 (4) ◽  
pp. 5641-5652
Author(s):  
Georgios Vernardos ◽  
Grigorios Tsagkatakis ◽  
Yannis Pantazis
Keyword(s):  
Confidence Intervals ◽  
Galaxy Evolution ◽  
Gravitational Lensing ◽  
Probability Distributions ◽  
Mass Density ◽  
Ground Truth ◽  
Gaussian Random Fields ◽  
Training Data ◽  
Gravitational Lens ◽  
Data Set

ABSTRACT Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.

scholarly journals Geophysical and analytical determination of overstressed zones in exploited coal seam: a case study

2021 ◽  
Author(s):  
Dariusz Chlebowski ◽  
Zbigniew Burtan
Keyword(s):  
Coal Seam ◽  
Wave Velocity ◽  
Seismic Tomography ◽  
Analytical Modeling ◽  
Coal Mines ◽  
Vertical Stress ◽  
P Wave ◽  
Rockburst Hazard ◽  
State Of Stress ◽  
P Wave Velocity

AbstractA variety of geophysical methods and analytical modeling are applied to determine the rockburst hazard in Polish coal mines. In particularly unfavorable local conditions, seismic profiling, active/passive seismic tomography, as well as analytical state of stress calculating methods are recommended. They are helpful in verifying the reliability of rockburst hazard forecasts. In the article, the combined analysis of the state of stress determined by active seismic tomography and analytical modeling was conducted taking into account the relationship between the location of stress concentration zones and the level of rockburst hazard. A longwall panel in the coal seam 501 at a depth of ca.700 m in one of the hard coal mines operating in the Upper Silesian Coal Basin was a subject of the analysis. The seismic tomography was applied for the reconstruction of P-wave velocity fields. The analytical modeling was used to calculate the vertical stress states basing on classical solutions offered by rock mechanics. The variability of the P-wave velocity field and location of seismic anomaly in the coal seam in relation to the calculated vertical stress field arising in the mined coal seam served to assess of rockburst hazard. The applied methods partially proved their adequacy in practical applications, providing valuable information on the design and performance of mining operations.

Two-dimensional Bayesian inversion of magnetotelluric data using trans-dimensional Gaussian processes

2021 ◽  
Author(s):  
Daniel Blatter ◽  
Anandaroop Ray ◽  
Kerry Key
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Field Data ◽  
Probability Distributions ◽  
Bulk Composition ◽  
Computational Cost ◽  
Model Space ◽  
Bayesian Inversion ◽  
Adaptive Finite Element ◽  
Magnetotelluric Data

Summary Bayesian inversion of electromagnetic data produces crucial uncertainty information on inferred subsurface resistivity. Due to their high computational cost, however, Bayesian inverse methods have largely been restricted to computationally expedient 1D resistivity models. In this study, we successfully demonstrate, for the first time, a fully 2D, trans-dimensional Bayesian inversion of magnetotelluric data. We render this problem tractable from a computational standpoint by using a stochastic interpolation algorithm known as a Gaussian process to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modeling codes. The Gaussian process links a trans-dimensional, parallel tempered Markov chain Monte Carlo sampler, which explores the parsimonious model space, to MARE2DEM, an adaptive finite element forward solver. MARE2DEM computes the model response using a dense parameter mesh with resistivity assigned via the Gaussian process model. We demonstrate the new trans-dimensional Gaussian process sampler by inverting both synthetic and field magnetotelluric data for 2D models of electrical resistivity, with the field data example converging within 10 days on 148 cores, a non-negligible but tractable computational cost. For a field data inversion, our algorithm achieves a parameter reduction of over 32x compared to the fixed parameter grid used for the MARE2DEM regularized inversion. Resistivity probability distributions computed from the ensemble of models produced by the inversion yield credible intervals and interquartile plots that quantitatively show the non-linear 2D uncertainty in model structure. This uncertainty could then be propagated to other physical properties that impact resistivity including bulk composition, porosity and pore-fluid content.

scholarly journals A Benchmark and Evaluation of Non-Rigid Structure from Motion

2020 ◽  
Author(s):  
Sebastian Hoppe Nesgaard Jensen ◽  
Mads Emil Brix Doest ◽  
Henrik Aanæs ◽  
Alessio Del Bue
Keyword(s):  
Computer Vision ◽  
Structure From Motion ◽  
State Of The Art ◽  
The State ◽  
Quality Data ◽  
Data Set ◽  
Rigid Structure ◽  
Public Data ◽  
3D Information ◽  
Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

scholarly journals Seismic Structure of Local Crustal Earthquakes beneath the Zipingpu Reservoir of Longmenshan Fault Zone

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Haiou Li ◽  
Xiwei Xu ◽  
Wentao Ma ◽  
Ronghua Xie ◽  
Jingli Yuan ◽  
...  
Keyword(s):  
Fault Zone ◽  
Three Dimensional ◽  
P Wave ◽  
The Tibetan Plateau ◽  
Seismic Structure ◽  
Infiltration Depth ◽  
Low Velocity ◽  
Longmenshan Fault Zone ◽  
Velocity Models ◽  
Longmenshan Fault

Three-dimensional P wave velocity models under the Zipingpu reservoir in Longmenshan fault zone are obtained with a resolution of 2 km in the horizontal direction and 1 km in depth. We used a total of 8589 P wave arrival times from 1014 local earthquakes recorded by both the Zipingpu reservoir network and temporary stations deployed in the area. The 3-D velocity images at shallow depth show the low-velocity regions have strong correlation with the surface trace of the Zipingpu reservoir. According to the extension of those low-velocity regions, the infiltration depth directly from the Zipingpu reservoir itself is limited to 3.5 km depth, while the infiltration depth downwards along the Beichuan-Yingxiu fault in the study area is about 5.5 km depth. Results show the low-velocity region in the east part of the study area is related to the Proterozoic sedimentary rocks. The Guanxian-Anxian fault is well delineated by obvious velocity contrast and may mark the border between the Tibetan Plateau in the west and the Sichuan basin in the east.

Sign in / Sign up

Export Citation Format

Share Document