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 Hyperspectral Library of Foliar Diseases of Wheat

2021 ◽  
Author(s):  
David Bohnenkamp ◽  
Jan Behmann ◽  
Stefan Paulus ◽  
Ulrike Steiner ◽  
Anne-Katrin Mahlein
Keyword(s):  
Developmental Stages ◽  
Leaf Tissue ◽  
Plant Diseases ◽  
Identification System ◽  
Spectral Library ◽  
Foliar Diseases ◽  
Robust Detection ◽  
Analysis Methods ◽  
Detection And Identification ◽  
Biotrophic Fungi

This work established a hyperspectral library of important foliar diseases of wheat in time series to detect spectral changes from infection to symptom appearance induced by different pathogens. The data was generated under controlled conditions at the leaf-scale. The transition from healthy to diseased leaf tissue was assessed, spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that are indicative of a certain developmental stage during pathogenesis - defined as turning points - were combined into a spectral library. Different machine learning analysis methods were applied and compared to test the potential of this library for the detection and quantification of foliar diseases in hyperspectral images. All evaluated classifiers provided a high accuracy for the detection and identification for both the biotrophic fungi and the necrotrophic fungi of up to 99%. The potential of applying spectral analysis methods, in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques of plant diseases under field conditions.

scholarly journals Application of Mathematical Morphological Filtering to Improve the Resolution of Chang'e-3 Lunar Penetrating Radar Data

2019 ◽  
Vol 11 (5) ◽  
pp. 524 ◽  
Author(s):  
Jianmin Zhang ◽  
Zhaofa Zeng ◽  
Ling Zhang ◽  
Qi Lu ◽  
Kun Wang
Keyword(s):  
High Frequency ◽  
Noise Suppression ◽  
Low Frequency ◽  
Radar Data ◽  
Structural Elements ◽  
Geological Structures ◽  
Mode Decomposition ◽  
Morphological Filtering ◽  
Scale Range ◽  
Band Pass

As one of the important scientific instruments of lunar exploration, the Lunar Penetrating Radar (LPR) onboard China’s Chang'E-3 (CE-3) provides a unique opportunity to image the lunar subsurface structure. Due to the low-frequency and high-frequency noises of the data, only a few geological structures are visible. In order to better improve the resolution of the data, band-pass filtering and empirical mode decomposition filtering (EMD) methods are usually used, but in this paper, we present a mathematical morphological filtering (MMF) method to reduce the noise. The MMF method uses two structural elements with different scales to extract certain scale-range information from the original signal, at the same time, the noise beyond the scale range of the two different structural elements is suppressed. The application on synthetic signals demonstrates that the morphological filtering method has a better performance in noise suppression compared with band-pass filtering and EMD methods. Then, we apply band-pass filtering, EMD, and MMF methods to the LPR data, and the MMF method also achieves a better result. Furthermore, according to the result by MMF method, three stratigraphic zones are revealed along the rover's route.

DETECTION OF P‐WAVES FROM WEAK SOURCES AT GREAT DISTANCES

Geophysics ◽  
1964 ◽  
Vol 29 (2) ◽  
pp. 197-211 ◽  
Author(s):  
Jon F. Claerbout
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
P Wave ◽  
Pass Band ◽  
Multiple Time ◽  
Multiple Time Series ◽  
P Waves ◽  
Short Period ◽  
Band Pass ◽  
Detection Of Signals

Optimum (Wiener sense) filters for suppression of noise in multiple time series are computed by a new method due to E. A. Robinson. Filters for prediction error and interpolation error have been used to detect P‐wave signals from three teleseismic events. These filters facilitate detection of signals in noise with low signal‐to‐noise ratios. The instrumentation consists of short‐period Benioff seismometers, both three‐component stations and surface arrays of verticals. It was found that microseismic noise in the pass band of these instruments is more accurately termed “Brownian motion of a surface” than “random waveforms with characteristic direction(s) of propagation.” Thus, single time‐series filters work almost as well as multiple time‐series matrix filters. Prediction‐error filters gave results substantially more satisfactory than simple band‐pass filters.

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.

scholarly journals MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 27
Author(s):  
Jianxiao Zhu ◽  
Xu Li ◽  
Peng Jin ◽  
Qimin Xu ◽  
Zhengliang Sun ◽  
...  
Keyword(s):  
Network Architecture ◽  
Visual Cues ◽  
Effective Means ◽  
Vehicle Detection ◽  
Feature Maps ◽  
Convolutional Network ◽  
Robust Detection ◽  
Tightly Coupled ◽  
Multi Level ◽  
Composite Module

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

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