scholarly journals Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

2021 ◽  
Author(s):  
Rigobert Tibi ◽  
Patrick Hammond ◽  
Ronald Brogan ◽  
Christopher J. Young ◽  
Keith Koper
Keyword(s):  
Input Signal ◽  
Noise Suppression ◽  
Fundamental Problem ◽  
Real Data ◽  
Denoising Method ◽  
Similar Frequency ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Band Pass ◽  
Music Information

ABSTRACT Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ∼5  dB, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ∼0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ∼2–5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ∼15  dB.

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

scholarly journals Improved Faraday Rotation Estimation in Spaceborne PolSAR Data Using Total Variation Denoising

2019 ◽  
Vol 11 (24) ◽  
pp. 2943
Author(s):  
Wulong Guo ◽  
Lu Liu ◽  
Bo Liu ◽  
Liang Chen ◽  
Haisheng Zhao ◽  
...  
Keyword(s):  
Total Variation ◽  
Spatial Resolution ◽  
Faraday Rotation ◽  
Noise Suppression ◽  
High Spatial Resolution ◽  
Window Size ◽  
Real Data ◽  
Denoising Method ◽  
Estimation System ◽  
Ionospheric Sounding

Faraday rotation (FR) is a serious problem for spaceborne polarization SAR (PolSAR) systems at L and P bands. One way to solve the problem is to estimate the FR from PolSAR data for further compensation. Therefore, precise estimation of FR from PolSAR data not only determines the compensation effect of polarimetric systems but also benefits the ionospheric sounding with high spatial resolution. Among the factors that affect the FR estimation, system noise is a non-neglectable factor. Although average filtering (AF) has been widely used in previous works for noise removing it depends on large window size, and therefore reduces the spatial resolution of FR estimation. In order to realize optimal noise suppression with minimized resolution loss, the total variation (TV) denoising method is applied in this paper. By testing the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) full-pol datasets, TV and AF are compared and validated. Results using synthetic and real data show that, after TV denoising, the FR can be recovered with high spatial resolution and the noise level in estimated FR is reduced more effectively than that after AF.

scholarly journals Building Tag-Aware Groups for Music High-Order Ranking and Topic Discovery

2010 ◽  
Vol 1 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Dimitrios Rafailidis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Real Data ◽  
Social Tagging ◽  
Topic Discovery ◽  
Retrieval Systems ◽  
The Social ◽  
Information Retrieval Systems ◽  
Aware Group ◽  
User Groups ◽  
Music Information ◽  
Ranking Analysis

In popular music information retrieval systems, users have the opportunity to tag musical objects to express their personal preferences, thus providing valuable insights about the formulation of user groups/communities. In this article, the authors focus on the analysis of social tagging data to reveal coherent groups characterized by their users, tags and music objects (e.g., songs and artists), which allows for the expression of discovered groups in a multi-aspect way. For each group, this study reveals the most prominent users, tags, and music objects using a generalization of the popular web-ranking concept in the social data domain. Experimenting with real data, the authors’ results show that each Tag-Aware group corresponds to a specific music topic, and additionally, a three way ranking analysis is performed inside each group. Building Tag-Aware groups is crucial to offer ways to add structure in the unstructured nature of tags.

scholarly journals 3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

2021 ◽  
Vol 13 (11) ◽  
pp. 2145
Author(s):  
Yawen Liu ◽  
Bingxuan Guo ◽  
Xiongwu Xiao ◽  
Wei Qiu
Keyword(s):  
Real Data ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Denoising Method ◽  
3D Mesh ◽  
Mesh Denoising ◽  
Scene Structure ◽  
Sharp Edges ◽  
Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

scholarly journals Batch seismic inversion using the iterative ensemble Kalman smoother

2021 ◽  
Author(s):  
Michael Gineste ◽  
Jo Eidsvik
Keyword(s):  
Seismic Data ◽  
Time Windows ◽  
Seismic Inversion ◽  
Efficient Estimation ◽  
Solution Strategy ◽  
Adaptive Procedure ◽  
Waveform Data ◽  
Kalman Smoother ◽  
Seismic Waveform ◽  
Single Batch

AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data.

scholarly journals A new approach for three component seismic array processing

1994 ◽  
Vol 37 (3) ◽  
Author(s):  
O. K. Kedrov ◽  
V. E. Permyakova
Keyword(s):  
Noise Suppression ◽  
Effective Means ◽  
Seismic Arrays ◽  
Robust Detection ◽  
Detection And Identification ◽  
Seismic Vibrations ◽  
Band Pass ◽  
Polarization Filtering ◽  
Detection Of Signals ◽  
New Procedures

The new concept and methodology of regional seismic arrays (RSA) equipped by three component (3 C) sensors (Z, NS, EH9, are proposed. Such system could be more perfect tool of Earth interior investigations. This aim can be achieved by introduction of polarization filtering of 3 C seismic vibrations as an effective means of noise suppression and robust detection and identification of secondary body phases of the signals. The proposed algorithm is based on: 1) linear phase band pass frequency filtering of N 3 C records in M bands; 2) polarization filtering of all 3 C records in all L directions where array beams are routinely oriented; 3) calculation of L beams in M bands using polarized P, SV and SH traces of individual sensors; 4) detection of signals on the L*M P, SV and SH traces; 5) location of the event. The main new procedures are 2) and 3). Due to these new approaches the procedures 4) and 5) will be improved in comparison with,those routinely used today at RSA's. This work includes the theoretical consideration of proposed method efficiency and preliminary experimental results.

A hybrid correlation denoising method based on complex resistivity and application on spread spectrum induced polarization data

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
Siming He ◽  
Jian Guan ◽  
Yi Wang ◽  
Xiu Ji ◽  
Hui Wang
Keyword(s):  
Hybrid Method ◽  
Spread Spectrum ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Induced Polarization ◽  
Data Simulation ◽  
Denoising Method ◽  
Complex Resistivity ◽  
Geophysical Surveys

In electrical exploration techniques, an effective suppression method for Gaussian and impulsive random noise in spread spectrum induced polarization (SSIP) continues to be challenging for conventional denoising methods. Remnant noise influences the complex resistivity spectrum and damages the subsequent interpretation of geophysical surveys. We present a hybrid method based on a correlation function and complex resistivity, which introduces the correlation analyses between the transmitting source, the measured potential, and the injected current signal. According to the analyses, reliable results for complex resistivity spectra can be calculated, which can be further used for noise suppression. We apply the hybrid method to both numerical and field experiments to process measured SSIP data. Simulation tests show that the hybrid method not only suppresses the two types of noise but also improves the relative error of the complex resistivity spectrum. Field data processing shows that the hybrid method can minimize the standard deviation of the data and possess a greater ability to distinguish adjacent objects, which can improve the reliability of the data in subsequent processing and interpretation.

Certain and Uncertain Triangulation in Multiple Camera Vision Systems via LMIs

2013 ◽  
pp. 112-124
Author(s):  
Graziano Chesi ◽  
Yeung Sam Hung
Keyword(s):  
Fundamental Problem ◽  
Real Data ◽  
Computational Time ◽  
Linear Matrix ◽  
Vision Systems ◽  
Standard Case ◽  
Multiple Camera ◽  
L2 Norm ◽  
Reprojection Error ◽  
Image Projections

Triangulation is a fundamental problem in computer vision that consists of estimating the 3D position of a point of the scene from the estimates of its image projections on some cameras and from the estimates of the projection matrices of these cameras. This chapter addresses multiple view L2 triangulation, i.e. triangulation for vision systems with a generic number of cameras where the sought 3D point is searched by minimizing the L2 norm of the image reprojection error. The authors consider the standard case where estimates of all the image points are available (referring to such a case as certain triangulation), and consider also the case where some of such estimates are not available for example due to occlusions (referring to such a case as uncertain triangulation). In the latter case, it is supposed that the unknown image points belong to known regions such as line segments or ellipses. For these problems, the authors propose a unified methodology that exploits the fundamental matrices among the views and provides a candidate 3D point through the solution of a convex optimization problem based on linear matrix inequalities (LMIs). Moreover, the chapter provides a simple condition that allows one to immediately establish whether the found 3D point is optimal. Various examples with synthetic and real data illustrate the proposed technique, showing in particular that the obtained 3D point is almost always optimal in practice, and that its computational time is indeed small.

scholarly journals The InSight Blind Test: An Opportunity to Bring a Research Dataset into Teaching Programs

2020 ◽  
Vol 91 (2A) ◽  
pp. 1064-1073
Author(s):  
Julien Balestra ◽  
Jean-Luc Berenguer ◽  
Florence Bigot-Cormier ◽  
Françoise Courboulex ◽  
Lucie Rolland ◽  
...  
Keyword(s):  
High School Students ◽  
Real Data ◽  
School Students ◽  
Blind Test ◽  
Broadband Seismometer ◽  
Waveform Data ◽  
Scientific Challenge ◽  
Teaching Programs ◽  
Short Time ◽  
Insight Mission

Abstract On 26 November 2019, SEIS, the first broadband seismometer designed for the Martian environment (Lognonné et al., 2019) landed on Mars, thanks to National Aeronautics and Space Administration’s (NASA’s) InSight mission. On 6 April 2019 (sol 128), the InSight Science team detected the first historical “marsquake” (NASA news release). Before it was recorded, the InSight Science team developed the InSight blind test (hereafter, IBT), which consists of a 12-month period of continuous waveform data combining realistic estimates of Martian background seismic noise, 204 tectonic, and 35 impact events (Clinton et al., 2017). This project was originally designed to prepare scientists for the arrival of real data from the upcoming InSight mission. This article presents the work carried out by middle and high school students during this challenge. This project offered schools the opportunity to participate in and strengthen the link between secondary schools and universities. The IBT organizers accepted the approach to enable 14 schools to take part in this scientific challenge. After a training process, each school analyzed the IBT dataset to contribute to the collaborative School Team catalog. The schools relied on a manual procedure combining analyses in time and frequency domains. At the end, a combined catalog was submitted as one of the IBT entries. The IBT organizers then assessed the catalog submitted by the consortium of schools together with the results from science teams (Van Driel et al., 2019). The schools achieved a total of 15 correct detections over a short time period. Although this number may seem modest compared with the 239 synthetic marsquakes included in the IBT waveform data, these correct detections were entirely made during class time. All in all, the students seemed to be fully engaged, and this exercise seemed to increase their scientific inquiry skills to fulfill their task as a team.

Fast Locking Time Biquadratic Band-Pass Filter Utilizing Nonlinear Sliding-Mode Controller

2020 ◽  
pp. 2150154
Author(s):  
Darko Mitić ◽  
Goran Jovanović ◽  
Mile Stojčev ◽  
Dragan Antić
Keyword(s):  
Input Signal ◽  
Sliding Mode ◽  
Design Procedure ◽  
Signal Frequency ◽  
Sliding Mode Controller ◽  
Band Pass Filter ◽  
Central Frequency ◽  
Pass Filter ◽  
Bicmos Technology ◽  
Band Pass

This paper considers design procedure of fast locking time self-tuning [Formula: see text] biquadratic band-pass filter with nonlinear sliding mode control. A sliding mode controller is building block of the phase control loop (PCL) involved to push central frequency to reach input signal frequency very fast, approximately 100–200[Formula: see text]ns. The sliding mode controller is realized by using a tunable delay line, enabling optimal filter locking time for different input signal frequencies. The filter possesses low sensitivity to component discrepancy and is applied as a selective amplifier. The 0.13[Formula: see text][Formula: see text]m SiGe BiCMOS technology has been utilized for design and verification of the presented filter. This filter has central frequency up to 220[Formula: see text]MHz, quality factor [Formula: see text] and 25[Formula: see text]dB gain.

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