Crossline wavefield reconstruction from multicomponent streamer data: Part 1 — Multichannel interpolation by matching pursuit (MIMAP) using pressure and its crossline gradient

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB53-WB67 ◽  
Author(s):  
Massimiliano Vassallo ◽  
Ali Özbek ◽  
Kemal Özdemir ◽  
Kurt Eggenberger
Keyword(s):  
Matching Pursuit ◽  
Synthetic Data ◽  
Basis Functions ◽  
Irregular Sampling ◽  
Seismic Events ◽  
Optimal Basis ◽  
Motion Measurements ◽  
Streamer Data ◽  
Seismic Wavefield ◽  
Seismic Measurements

We introduce a technique that uses multicomponent seismic measurements to reconstruct the seismic wavefield at any desired crossline position between towed streamers. This method, called multichannel interpolation by matching pursuit (MIMAP), operates on pressure and crossline particle-motion measurements. It is based on the matching-pursuit technique and iteratively reconstructs the signal as a combination of optimal basis functions. Being a data-dependent technique, MIMAP can interpolate severely aliased data without assumptions about seismic events such as linearity or the model related to the seismic wavefield. MIMAP has the capability to perform well in the presence of irregular sampling and is robust when only a small number of samples are available. Using synthetic data examples, we show that the new method has the potential to interpolate signals that are sampled at realistic crossline streamer spacing and in the presence of noise.

Crossline wavefield reconstruction from multicomponent streamer data: Part 2 — Joint interpolation and 3D up/down separation by generalized matching pursuit

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB69-WB85 ◽  
Author(s):  
Ali Özbek ◽  
Massimiliano Vassallo ◽  
Kemal Özdemir ◽  
Dirk-Jan van Manen ◽  
Kurt Eggenberger
Keyword(s):  
Particle Motion ◽  
Matching Pursuit ◽  
Synthetic Data ◽  
Basis Functions ◽  
Seismic Events ◽  
Data Sets ◽  
Optimal Basis ◽  
A New Technique ◽  
Simultaneous Interpolation ◽  
Streamer Data

Computation of the 3D upgoing/downgoing separated wavefield at any desired position within a marine streamer spread is enabled by multicomponent streamers that can measure the crossline and vertical components of water-particle motion in addition to the pressure. We introduce the concept of simultaneous interpolation and deghosting and describe a new technique, generalized matching pursuit (GMP), to achieve this. This method is based on the matching-pursuit technique and iteratively reconstructs the signal as a combination of optimal basis functions. In the GMP method, the basis functions describing the unknown 3D upgoing wavefield are filtered by appropriate forward ghost operators before being matched to the multicomponent measurements. As a data-dependent method, GMP can operate on data samples that are highly aliased in the crossline direction without relying on assumptions about seismic events such as linearity. The technique is naturally suitable for data with only a small number of samples that may be irregularly spaced. We demonstrate the efficacy and robustness of the GMP method on several synthetic data sets of increasing complexity and in the presence of noise.

Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. U67-U76 ◽  
Author(s):  
Robert J. Ferguson
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Real Data ◽  
Structural Data ◽  
Irregular Sampling ◽  
Data Set ◽  
Near Surface ◽  
Damped Least Squares ◽  
Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

Performance of a rotational sensor at Etna, Italy focusing on back-azimuth estimations of volcano-seismic events

2021 ◽  
Author(s):  
Martina Rosskopf ◽  
Eva P. S. Eibl ◽  
Gilda Currenti ◽  
Philippe Jousset ◽  
Joachim Wassermann ◽  
...  
Keyword(s):  
Rotation Rate ◽  
Mount Etna ◽  
Seismic Events ◽  
Volcano Seismology ◽  
Rotational Seismology ◽  
Long Period ◽  
Tectonic Events ◽  
Back Azimuth ◽  
Seismic Wavefield

<p>The field of rotational seismology has only recently emerged. Portable 3 component rotational sensors are commercially available since a few years which opens the pathway for a first use in volcano-seismology. The combination of rotational and translational components of the wavefield allows identifying and filtering for specific seismic wave types, estimating the back azimuth of an earthquake, and calculating local seismic phase velocities.</p><p>Our work focuses on back-azimuth calculations of volcano-tectonic and long-period events detected at Etna volcano in Italy. Therefore, a continuous full seismic wavefield of 30 days was recorded by a BlueSeis-3A, the first portable rotational sensor, and a broadband Trillium Compact seismometer located next to each other at Mount Etna in August and September of 2019. In this study, we applied two methods for back-azimuth calculations. The first one is based on the similarity of the vertical rotation rate to the horizontal acceleration and the second one uses a polarization analysis from the two horizontal components of the rotation rate. The estimated back-azimuths for volcano-tectonic events were compared to theoretical back-azimuths based on the INGV event catalog and the long-period event back-azimuths were analyzed for their dominant directions. We discuss the quality of our back azimuths with respect to event locations and evaluate the sensitivity and benefits of the rotational sensor focusing on volcano-seismic events on Etna regarding the signal to noise ratios, locations, distances, and magnitudes.</p>

Synthetic Data Generation to Support Irregular Sampling in Sensor Networks

2004 ◽  
pp. 211-234 ◽  
Author(s):  
Lewis Girod ◽  
Ramesh Govindan ◽  
Deepak Ganesan ◽  
Deborah Estrin ◽  
Yan Yu
Keyword(s):  
Sensor Networks ◽  
Synthetic Data ◽  
Irregular Sampling ◽  
Data Generation ◽  
Synthetic Data Generation

scholarly journals RobOMP: Robust variants of Orthogonal Matching Pursuit for sparse representations

2019 ◽  
Author(s):  
Carlos A Loza
Keyword(s):  
Mean Squared Error ◽  
Matching Pursuit ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Weight Vector ◽  
Orthogonal Matching Pursuit ◽  
Main Mode ◽  
Observation Matrix ◽  
And Performance

Sparse coding aims to find a parsimonious representation of an example given an observation matrix or dictionary. In this regard, Orthogonal Matching Pursuit (OMP) provides an intuitive, simple and fast approximation of the optimal solution. However, its main building block is anchored on the minimization of the Mean Squared Error cost function (MSE). This approach is only optimal if the errors are distributed according to a Gaussian distribution without samples that strongly deviate from the main mode, i.e. outliers. If such assumption is violated, the sparse code will likely be biased and performance will degrade accordingly. In this paper, we introduce five robust variants of OMP (RobOMP) fully based on the theory of M-Estimators under a linear model. The proposed framework exploits efficient Iteratively Reweighted Least Squares (IRLS) techniques to mitigate the effect of outliers and emphasize the samples corresponding to the main mode of the data. This is done adaptively via a learned weight vector that models the distribution of the data in a robust manner. Experiments on synthetic data under several noise distributions and image recognition under different combinations of occlusion and missing pixels thoroughly detail the superiority of RobOMP over MSE-based approaches and similar robust alternatives. We also introduce a denoising framework based on robust, sparse and redundant representations that open the door to potential further applications of the proposed techniques. The five different variants of RobOMP do not require parameter tuning from the user and, hence, constitute principled alternatives to OMP.

scholarly journals Nonlinear multidimensional scaling and visualization of earthquake clusters over space, time and feature space

2005 ◽  
Vol 12 (1) ◽  
pp. 117-128 ◽  
Author(s):  
W. Dzwinel ◽  
D. A. Yuen ◽  
K. Boryczko ◽  
Y. Ben-Zion ◽  
S. Yoshioka ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Synthetic Data ◽  
Feature Space ◽  
Seismic Events ◽  
Agglomerative Clustering ◽  
Local Cluster ◽  
Small Magnitude ◽  
Earthquake Parameters ◽  
Dimensional Scaling ◽  
Multi Dimensional Scaling

Abstract. We present a novel technique based on a multi-resolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs. The observed data represent seismic activities around the Japanese islands during 1997-2003. The synthetic data were generated by numerical simulations for various cases of a heterogeneous fault governed by 3-D elastic dislocation and power-law creep. At the highest resolution, we analyze the local cluster structures in the data space of seismic events for the two types of catalogs by using an agglomerative clustering algorithm. We demonstrate that small magnitude events produce local spatio-temporal patches delineating neighboring large events. Seismic events, quantized in space and time, generate the multi-dimensional feature space characterized by the earthquake parameters. Using a non-hierarchical clustering algorithm and nonlinear multi-dimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of the multivariate clusters. At the spatial resolutions characteristic of the earthquake parameters, all of the ongoing seismicity both before and after the largest events accumulates to a global structure consisting of a few separate clusters in the feature space. We show that by combining the results of clustering in both low and high resolution spaces, we can recognize precursory events more precisely and unravel vital information that cannot be discerned at a single resolution.

Joint 3D reconstruction and deghosting of multisensor streamer data by generalized matching pursuit

2012 ◽  
Author(s):  
Ali Özbek ◽  
Massimiliano Vassallo ◽  
Kurt Eggenberger ◽  
Dirk-Jan van Manen ◽  
Kemal Özdemir ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Matching Pursuit ◽  
Streamer Data

scholarly journals RobOMP: Robust variants of Orthogonal Matching Pursuit for sparse representations

2019 ◽  
Author(s):  
Carlos A Loza
Keyword(s):  
Mean Squared Error ◽  
Matching Pursuit ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Weight Vector ◽  
Orthogonal Matching Pursuit ◽  
Main Mode ◽  
Observation Matrix ◽  
And Performance

Sparse coding aims to find a parsimonious representation of an example given an observation matrix or dictionary. In this regard, Orthogonal Matching Pursuit (OMP) provides an intuitive, simple and fast approximation of the optimal solution. However, its main building block is anchored on the minimization of the Mean Squared Error cost function (MSE). This approach is only optimal if the errors are distributed according to a Gaussian distribution without samples that strongly deviate from the main mode, i.e. outliers. If such assumption is violated, the sparse code will likely be biased and performance will degrade accordingly. In this paper, we introduce five robust variants of OMP (RobOMP) fully based on the theory of M-Estimators under a linear model. The proposed framework exploits efficient Iteratively Reweighted Least Squares (IRLS) techniques to mitigate the effect of outliers and emphasize the samples corresponding to the main mode of the data. This is done adaptively via a learned weight vector that models the distribution of the data in a robust manner. Experiments on synthetic data under several noise distributions and image recognition under different combinations of occlusion and missing pixels thoroughly detail the superiority of RobOMP over MSE-based approaches and similar robust alternatives. We also introduce a denoising framework based on robust, sparse and redundant representations that open the door to potential further applications of the proposed techniques. The five different variants of RobOMP do not require parameter tuning from the user and, hence, constitute principled alternatives to OMP.

Sign in / Sign up

Export Citation Format

Share Document