Effects of Digital Filtering in Data Processing of Seismic Acceleration Records

Mollova, G.

doi:10.1155/2007/29502

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…Since there are various sources of noise in the measured accelerogram, processing data without noise filtering will mislead the analysis result, as shown in Figure 3. Details on the noise filtering schemes for seismic accelerometers are given elsewhere [24]. Raw data of acceleration records include noise from various sources such as instrument's installation conditions and environments.…”

Section: Structure Of the Monitoring Systemmentioning

confidence: 99%

Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers

Jeong

Jang

Nam³

et al. 2020

Applied Sciences

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In this study, a structural health monitoring system for cable-stayed bridges is developed. In the system, condition assessment of the structure is performed based on measured records from seismic accelerometers. Response indices are defined to monitor structural safety and serviceability and derived from the measured acceleration data. The derivation process of the indices is structured to follow the transformation from the raw data to the final outcome. The process includes, noise filtering, baseline correction, numerical integration, and calculation of relative differences. The system is packed as a condition assessment program, which consists of four major process of the structural health evaluation: (i) format conversion of the raw data, (ii) noise filtering, (iii) generation of response indices, and (iv) condition evaluation. An example set of limit states is presented to evaluate the structural condition of the test-bed cable-stayed bridge.

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Section: Structure Of the Monitoring Systemmentioning

confidence: 99%

Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers

Jeong

Jang

Nam³

et al. 2020

Applied Sciences

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“…Hence, numerous methods have been presented for this in the past few decades that can be classified into four main categories: (1) infinite impulse response (IIR) filters, known as low-pass, high-pass, band-pass, and band-stop filter (e.g., [5]); (2) Fourier-based filters (e.g., [6][7][8]); (3) EMD-based (empirical mode decomposition) filters (e.g., [9][10][11][12][13][14]); (4) wavelet-based filters (e.g., [15][16][17]). …”

Section: Introductionmentioning

confidence: 99%

A Nonparametric Method for Automatic Denoising of Microseismic Data

Peng

Wang

2018

Shock and Vibration

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Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform. The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets. The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data.

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“…Mollova [6] presented the application of digital filtering using SeismoSignal on an actual earthquake record in Turkey. The effects of various digital filtering models (including Chebyshev, Butterworth, Bessel and Elliptic) were compared and discussed.…”

Section: Introductionmentioning

confidence: 99%

Systematic Approaches for Signal Processing of Soil Shaking Table Test

2017

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Abstract.Selecting an appropriate methodology in signal processing of shaking table test is of fundamental importance for evaluating the soil dynamic parameters. This paper presents the systematic approaches in selecting an appropriate signal processing method for a soil shaking table test. A soil model of 150 mm in diameter and 100 mm in height was tested on a 1g shaking table. The soil model was instrumented with accelerometers to measure the resultant accelerations under different shaking motions. Under normal data processing practices, the acceleration data was adjusted and numerically integrated to obtain the linear displacement data. However, the accuracy and reliability of the computed displacement data and the adopted adjustment technique often raise arguments. In the present study, the acceleration data was subjected to two baseline correction schemes and one method of digital band-pass filtering. The results from the three signal processing methods were compared with the actual displacement data obtained from a direct measurement using laser displacement sensor. Bandpass filtering method was found to be a more reliable data processing method in this particular study. The use of laser displacement sensor can serve as a useful reference when choosing an appropriate adjustment scheme in data processing.

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Effects of Digital Filtering in Data Processing of Seismic Acceleration Records

Cited by 13 publications

References 8 publications

Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers

Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers

A Nonparametric Method for Automatic Denoising of Microseismic Data

Systematic Approaches for Signal Processing of Soil Shaking Table Test

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