Local seismic attributes

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. A29-A33 ◽  
Author(s):  
Sergey Fomel
Keyword(s):  
Image Registration ◽  
Seismic Signal ◽  
Seismic Attributes ◽  
Data Sets ◽  
Local Similarity ◽  
Seismic Signals ◽  
Practical Applications ◽  
Regularized Inversion ◽  
Signal Characteristics ◽  
Land Survey

Local seismic attributes measure seismic signal characteristics not instantaneously, at each signal point, and not globally, across a data window, but locally in the neighborhood of each point. I define local attributes with the help of regularized inversion and demonstrate their usefulness for measuring local frequencies of seismic signals and local similarity between different data sets. I use shaping regularization for controlling the locality and smoothness of local attributes. A multicomponent-image-registration example from a nine-component land survey illustrates practical applications of local attributes for measuring differences between registered images.

NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

2018 ◽  
pp. 73-78
Author(s):  
Yu. V. Morozov ◽  
M. A. Rajfeld ◽  
A. A. Spektor
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Simulation Procedure ◽  
Proposed Model ◽  
Seismic Location ◽  
Passive Seismic ◽  
System Characteristics ◽  
Primary Pulse

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

Seismic signal characteristics and interpretation of the 2020 “6.17” Danba landslide dam failure hazard chain process

Landslides ◽  
2021 ◽  
Author(s):  
Yan Yan ◽  
Yifei Cui ◽  
Dingzhu Liu ◽  
Hui Tang ◽  
Yongjian Li ◽  
...  
Keyword(s):  
Landslide Dam ◽  
Seismic Signal ◽  
Dam Failure ◽  
Chain Process ◽  
Signal Characteristics

Bayesian feature learning for seismic compressive sensing and denoising

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. O91-O104 ◽  
Author(s):  
Georgios Pilikos ◽  
A. C. Faul
Keyword(s):  
Compressive Sensing ◽  
State Of The Art ◽  
Feature Learning ◽  
Seismic Signal ◽  
Basis Functions ◽  
Sparse Signals ◽  
Seismic Signals ◽  
Process Factor ◽  
Learned Features ◽  
Fixed Basis

Extracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations. Compressive sensing (CS) is a framework that allows reconstruction of sparse signals from fewer measurements than conventional sampling rates. In seismic CS, the use of sparse transforms has some success; however, defining fixed basis functions is not trivial given the plethora of possibilities. Furthermore, the assumption that every instance of a seismic signal is sparse in any acquisition domain under the same transformation is limiting. We use beta process factor analysis (BPFA) to learn sparse transforms for seismic signals in the time slice and shot record domains from available data, and we use them as dictionaries for CS and denoising. Algorithms that use predefined basis functions are compared against BPFA, with BPFA obtaining state-of-the-art reconstructions, illustrating the importance of decomposing seismic signals into learned features.

scholarly journals Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Short Term Memory ◽  
The Body ◽  
Data Sets ◽  
Additional Information ◽  
Machine Learning Model ◽  
Signal Characteristics ◽  
Wide Range ◽  
Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

Spectral Algorithms for Signal Generation as Learning-Methodical Tool for Engineer Preparation

2019 ◽  
pp. 254-272
Author(s):  
Vladimir V. Syuzev ◽  
Elena V. Smirnova ◽  
Kirill Kucherov ◽  
Vladimir Gurenko ◽  
Gurgen Khachatrian
Keyword(s):  
Systems Development ◽  
Signal Generation ◽  
Decision Task ◽  
Real Time Control ◽  
Spectral Simulation ◽  
Practical Applications ◽  
Time Control ◽  
Signal Characteristics ◽  
Basic Functions ◽  
Simulation Algorithms

In this chapter, a spectral method of deterministic signals simulation is proposed. The target is to solve the teaching methodological problem of individual tasks creation for students: future engineers in the field of real-time control systems' development and research. The tasks are related to the correlation theory. The method of simulation algorithms tuning with the help of specified spectrally correlation of signal characteristics and their parameters in the harmonic Fourier and Hartley basis is presented. To expand the set of task's variants for the independent student self-preparation the task of signals simulation has been formulated mathematically and solved in arbitrary complex and real systems of orthogonal basic functions. The requirements have been underlined on how teacher can teach basic functions for the practical applications. During such kind of decision task, the student will be able to counts the multiplicity, precision, and computational complexity of the resulting spectral simulation algorithms.

Cluster Based Medical Image Registration Using Optimized Neural Network

2017 ◽  
pp. 1437-1467
Author(s):  
Joydev Hazra ◽  
Aditi Roy Chowdhury ◽  
Paramartha Dutta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Registration ◽  
Convergence Rate ◽  
Optimization Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Learning Method ◽  
Local Minima ◽  
The Neural Network

Registration of medical images like CT-MR, MR-MR etc. are challenging area for researchers. This chapter introduces a new cluster based registration technique with help of the supervised optimized neural network. Features are extracted from different cluster of an image obtained from clustering algorithms. To overcome the drawback regarding convergence rate of neural network, an optimized neural network is proposed in this chapter. The weights are optimized to increase the convergence rate as well as to avoid stuck in local minima. Different clustering algorithms are explored to minimize the clustering error of an image and extract features from suitable one. The supervised learning method applied to train the neural network. During this training process an optimization algorithm named Genetic Algorithm (GA) is used to update the weights of a neural network. To demonstrate the effectiveness of the proposed method, investigation is carried out on MR T1, T2 data sets. The proposed method shows convincing results in comparison with other existing techniques.

scholarly journals Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

2019 ◽  
Vol 5 (11) ◽  
pp. 85 ◽  
Author(s):  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Clustering Algorithm ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Scene Reconstruction ◽  
Data Sets ◽  
Practical Applications ◽  
Input Dataset ◽  
Better Than

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

scholarly journals Applicataion of ormsby wavelet for generation of synthetic seismic signals

2018 ◽  
Vol 7 (2.7) ◽  
pp. 794
Author(s):  
E Sai Sumanth ◽  
V Joseph ◽  
Dr K S Ramesh ◽  
Dr S Koteswara Rao
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Transforms ◽  
Seismic Signal ◽  
Core Structure ◽  
Seismic Signals ◽  
Seismic Reflections ◽  
Input Wave

Investigation of signals reflected from earth’s surface and its crust helps in understanding its core structure. Wavelet transforms is one of the sophisticated tools for analyzing the seismic reflections. In the present work a synthetic seismic signal contaminated with noise is synthesized  and analyzed using Ormsby wavelet[1]. The wavelet transform has efficiently extracted the spectra of the synthetic seismic signal as it smoothens the noise present in the data and upgrades the flag quality of the seismic data due to termers. Ormsby wavelet gives the most redefined spectrum of the input wave so it could be used for the analysis of the seismic reflections. 

scholarly journals Seismic monitoring of torrential and fluvial processes

2016 ◽  
Vol 4 (2) ◽  
pp. 285-307 ◽  
Author(s):  
Arnaud Burtin ◽  
Niels Hovius ◽  
Jens M. Turowski
Keyword(s):  
Seismic Noise ◽  
Seismic Waves ◽  
Seismic Signal ◽  
Seismic Monitoring ◽  
Surface Processes ◽  
Seismic Signals ◽  
Seismic Arrays ◽  
Time Frequency ◽  
Continuous Survey ◽  
Long Time

Abstract. In seismology, the signal is usually analysed for earthquake data, but earthquakes represent less than 1 % of continuous recording. The remaining data are considered as seismic noise and were for a long time ignored. Over the past decades, the analysis of seismic noise has constantly increased in popularity, and this has led to the development of new approaches and applications in geophysics. The study of continuous seismic records is now open to other disciplines, like geomorphology. The motion of mass at the Earth's surface generates seismic waves that are recorded by nearby seismometers and can be used to monitor mass transfer throughout the landscape. Surface processes vary in nature, mechanism, magnitude, space and time, and this variability can be observed in the seismic signals. This contribution gives an overview of the development and current opportunities for the seismic monitoring of geomorphic processes. We first describe the common principles of seismic signal monitoring and introduce time–frequency analysis for the purpose of identification and differentiation of surface processes. Second, we present techniques to detect, locate and quantify geomorphic events. Third, we review the diverse layout of seismic arrays and highlight their advantages and limitations for specific processes, like slope or channel activity. Finally, we illustrate all these characteristics with the analysis of seismic data acquired in a small debris-flow catchment where geomorphic events show interactions and feedbacks. Further developments must aim to fully understand the richness of the continuous seismic signals, to better quantify the geomorphic activity and to improve the performance of warning systems. Seismic monitoring may ultimately allow the continuous survey of erosion and transfer of sediments in the landscape on the scales of external forcing.

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