Reduce Self-Noise of Velocity Broadband Seismometer in Low Frequency Using Internal Position Output

This paper proposes a method for enhancing seismometer detection capabilities in the low-frequency range (on the order of mHz) by utilizing the seismometer’s Position output(PosOut) replacing the traditional broadband velocity output(VelOut). Theoretical analysis of Equivalent Input Noise(EIN) and noise transfer functions for two output configurations (VelOut and PosOut) reveals that while forward-path noise remains unaffected by the output change, the integrator and amplifier driver noises are significantly influenced. As a result, PosOut exhibits lower EIN at low frequencies but higher noise at higher frequencies. This is primarily due to the integrator shifting from the feedback path to the forward path, along with changes in the closed-loop system’s frequency response. Based on these findings, several noise optimization strategies are proposed. To validate the theoretical predictions, simulations based on the parameters of a commercial seismometer were conducted, confirming the noise shaping behavior under both output configurations. Experimental validation using four 3T seismometer further supports the theory, showing that the EIN of the PosOut is lower than that of the VelOut below 0.02 Hz, with PSD reductions of 7.5 dB to 10 dB at 1 mHz. The results indicate that the PosOut provides a significant advantage in detecting weak low-frequency signals, and offers a promising approach for both future seismic sensor designs and signal processing applications focused on low-noise, low-frequency detection.

Authors

Institutions

Publication Details

Journal
IEEE Sensors Journal
Published
2025-04-07
DOI
https://doi.org/10.1109/jsen.2025.3556705
Primary Topic
Geophysics and Sensor Technology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reduce Self-Noise of Velocity Broadband Seismometer in Low Frequency Using Internal Position Output

Kaichen Song, Lingyun Ye, Zhongkun Zhang, Xinglin Sun et al.
1 links
IEEE Sensors Journal
Geophysics and Sensor Technology
article

Reduce Self-Noise of Velocity Broadband Seismometer in Low Frequency Using Internal Position Output

Kaichen Song, Lingyun Ye, Zhongkun Zhang, Xinglin Sun, Yini Sun, Bin Chen
article en

Abstract

This paper proposes a method for enhancing seismometer detection capabilities in the low-frequency range (on the order of mHz) by utilizing the seismometer’s Position output(PosOut) replacing the traditional broadband velocity output(VelOut). Theoretical analysis of Equivalent Input Noise(EIN) and noise transfer functions for two output configurations (VelOut and PosOut) reveals that while forward-path noise remains unaffected by the output change, the integrator and amplifier driver noises are significantly influenced. As a result, PosOut exhibits lower EIN at low frequencies but higher noise at higher frequencies. This is primarily due to the integrator shifting from the feedback path to the forward path, along with changes in the closed-loop system’s frequency response. Based on these findings, several noise optimization strategies are proposed. To validate the theoretical predictions, simulations based on the parameters of a commercial seismometer were conducted, confirming the noise shaping behavior under both output configurations. Experimental validation using four 3T seismometer further supports the theory, showing that the EIN of the PosOut is lower than that of the VelOut below 0.02 Hz, with PSD reductions of 7.5 dB to 10 dB at 1 mHz. The results indicate that the PosOut provides a significant advantage in detecting weak low-frequency signals, and offers a promising approach for both future seismic sensor designs and signal processing applications focused on low-noise, low-frequency detection.

IEEE Sensors JournalVol. 25(10)
Hangzhou Normal University (CN), Zhejiang University (CN)
Affordable and clean energy
Openalex Percentile: Top 91%
Geophysics and Sensor Technology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.