On-site Sensor Noise Evaluation and Detectability

in Low Cost Accelerometers

Marco Manso

and Mourad Bezzeghoud

Instituto de Ciências da Terra, Universidade de Évora, Évora, Portugal

Departamento de Física (ECT), Instituto de Ciências da Terra (IIFA), Universidade de Évora, Portugal

Keywords: Accelerometers, Seismology, Environmental Monitoring, Noise.

Abstract: Seismic networks help understanding the phenomena related with seismic events. These networks are

employing low-cost accelerometers in order to achieve high-density deployments enabling accurate

characterisation (high resolution) of strong earthquake motion and early warning capabilities. In order to

assess the applicability of low-cost accelerometers in seismology, it is essential to evaluate their noise

characteristics and identify their detectability thresholds. In this paper, a method is proposed that provides an

indication of sensor noise, being demonstrated on different sensors. The method is designed to adapt to a

sensor’s characteristics while on-site and in-operation, thus removing potentially related logistical and

maintenance bottlenecks.

1 INTRODUCTION

Seismic events can be extreme and severe threats to

humanity, causing a heavy death toll, serious

destruction and damage. Helping to understand these

phenomena, seismic networks have been deployed in

increasing number, filling in gaps in the global

coverage and improving our understanding of the

physical processes that cause earthquakes.

For example, Portugal has made a significant

effort to develop the Broadband Portuguese seismic

network integrating seismological stations supporting

real-time monitoring of the earthquake activity

(Caldeira et al., 2007). The Portuguese national

network (Instituto Português do Mar e da Atmosfera

- IPMA) is the seismic monitoring of all the

Portuguese territory, from the Azores and Madeira

archipelagos to the mainland territory, covering the

extensive Azores-Gibraltar plate boundary segment.

This national network also contributes to global

monitoring efforts.

EMSO-PT (http://emso-pt.pt/), the Portuguese

counterpart of the European Multidisciplinary

Seafloor and water column Observatory (EMSO), is

an infrastructure jointly funded by the Portuguese

government and the European Commission that aims

https://orcid.org/0000-0003-0953-049X

https://orcid.org/0000-0002-4908-0422

to create and develop infrastructures for scientific and

technological research within the scope of Marine

Sciences. One the goals of EMSO-PT is to improve

the national seismic monitoring network, thus

allowing for the development of an Earthquake Early

Warning System (EEWS), including those generated

in the Atlantic region in and adjacent to the

Portuguese territory. Considering the seismogenic

Eurasia-Nubia plate boundary located south of

mainland Portugal, current efforts by the Instituto de

Ciências da Terra (ICT), University of Évora (UE)

and IPMA aim to densify the seismic network in the

extreme west of the Algarve.

A paradigm change occurred in the United States

by deploying high density seismic networks with the

capability to record the propagation of seismic

activity in high resolution: The California Institute of

Technology (CalTech) that established the

Community Seismic Network (CSN), an earthquake

monitoring system based on a dense array of low-cost

acceleration sensors (more than 1000) aiming to

produce block-by-block strong shaking

measurements during an earthquake (see

http://csn.caltech.edu/, last accessed 2020/08/14);

The University of Southern California's (USC)

Quake-Catcher Network (QCN) began rolling out

100

Manso, M. and Bezzeghoud, M.

On-site Sensor Noise Evaluation and Detectability in Low Cost Accelerometers.

DOI: 10.5220/0010319001000106

In Proceedings of the 10th International Conference on Sensor Networks (SENSORNETS 2021), pages 100-106

ISBN: 978-989-758-489-3

6000 tiny sensors in the San Francisco Bay Area,

being part of the densest networks of seismic sensors

ever devoted to study earthquakes in real time (see

https://quakecatcher.net/, last accessed 2020/08/14).

Following this trend, the ICT and UE are

developing the Seismic Sensor Network Alentejo

(SSN-Alentejo) that brings the most dense seismic

sensor network ever deployed in Portugal. This novel

network plans to deploy 60 low-cost sensors

distributed in a mesh configuration spaced on average

10 km and covering an area of about 5000 square

kilometres (Manso et. al, 2020).

A high dense network-enabled seismic network

operating in the principle of “live” data brings the

opportunity to explore new applications in

seismology, including real-time earthquake detection,

more accurate characterisation (high resolution) of

strong earthquake motion and the generation of

Shakesmaps in near real-time.

The remainder of this paper is organised as

follows. Section 2 presents the background for this

work, describing the relevant characteristics of low-

cost accelerometers. Section 3 presents an analysis of

sensor noise based on measurements collected from

accelerometers, describing a suitable method for on-

site and while in-operation. The method is used to

determine the sensor detectability threshold related

with seismic activity. Section 4 concludes this paper.

2 BACKGROUND

In the last years, sensors and sensing network

technology evolved at a fast pace, resulting in

improved performance (resolution, sensibility and

processing capacity), operation (energy efficiency,

operation time) and connectivity (broadband

communications), at significant cost reduction. Low-

cost Micro-Electro Mechanical Systems (MEMS)

accelerometers, in particular, demonstrated the

capability to generate relevant data for seismic

analysis in dense deployment contexts (Lainé and

Mougenot, 2014).

MEMS technology has enabled the mass

production of small size accelerometers. Capacitive

accelerometers, in particular, are highly popular due

to reduced cost, their simple structure, and the ability

to integrate the sensor close to the readout electronics.

When subjected to an acceleration, the inertial mass

shifts cause a proportional change in capacitance. By

measuring the capacitance change, the acceleration

can be calculated.

In order to properly exploit its data, it is important

to take into account MEMS benefits and limitations,

(Farine et al., 2003; Evans et al., 2014; Manso et al.,

2017) including: adequate sensitivity, noise level,

and range (measured in g) to be applicable to

earthquake strong-motion acquisition (M>3),

however, limited by the high level of instrumental

self-noise especially affecting measurement of low

frequency weak-motion forces; well fit to measure

high frequency (>40Hz) ground motion since their

resonant frequency (typically above 1 kHz) is far

above the seismic band pass; measure the gravity

acceleration component thus providing a useful

reference for sensitivity calibration and tilt

measurement; have high acceleration ranges (several

g) and can sustain high acceleration (several hundred

g); complement broadband seismometers by

detecting weak high frequency signals.

There is a wide range of low-cost accelerometers

built for different purposes and exhibiting different

characteristics. Concerning seismological

applications, the following parameters should be

taken into account: Range: Specifies the minimum

and maximum acceleration values it can measure. It

is often represented relative to g (e.g., ±2g);

Resolution: Specifies both (i) the degree to which a

change can be detected and (ii) the maximum possible

value that can be measured. For example, a digital

sensor with 16-bits resolution is able to quantify

65536 possible values. If the scale is set to ±2g

(hence, a 4g range) the minimum possible change that

can be detected is about 61µg; Noise density:

Accelerometers are subject to noise produced by

electronic and mechanical sources. Since they have a

small inertial mass, noise increases at low

frequencies. The noise density is often represented in

terms of power spectral density (PSD) and is

expressed as g/√Hz. It varies with the measurement

bandwidth: when multiplied by it, the resulting value

represents the minimum acceleration values that can

be resolved; Bandwidth: Specifies the frequency

range that the sensor operates in. It is limited to the

natural resonance frequency of the mechanical

structure of the accelerometer itself, which is

typically very high (>kHz); Sample rate: Specifies

the number of measurements (samples) per second.

This paper main focus is to observe the presence

of sensor noise among several accelerometers. The

most relevant parameter is therefore “Noise density”.

Next, an analysis of sensor noise measured from

different accelerometers is provided.

On-site Sensor Noise Evaluation and Detectability in Low Cost Accelerometers

101

3 NOISE ANALYSIS OF

LOW-COST

ACCELEROMETERS

The main limiting characteristic of consumer-based

MEMS accelerometers in seismological applications

is the presence of sensor noise that is originated from

the sensor’s electrical and mechanical components.

Ultimately, the sensor noise determines the minimum

resolution of the sensor. Typically, accelerometers’

manufacturers provide in the respective datasheets an

indication of sensor noise via the parameter “power

spectral density” (PSD) that is measured in g/√Hz.

Multiplying the PSD value by the square root of the

measurement bandwidth gives the root mean square

(RMS) acceleration noise, which is the minimal

resolvable value for acceleration (NXP, 2007). It is

noted that noise increases with bandwidth.

In this chapter, an indication of sensor noise is

measured by deploying and collecting acceleration

data from several accelerometers while at rest

position. The sensor noise assessment is made by

calculating the standard deviation (eq. 1) of the signal

(calculated using a “moving window” of 100

samples), after removing the DC value. The lower the

standard deviation the lower the sensor noise.

𝜎



∑















(1)

Where: i is the sample number, x

is the measurement

related with sample i, µ is the mean value and N is the

sample size.

The environment where accelerometers are

installed might be affected by external factors (e.g.,

traffic or seismic activity), which can be registered by

accelerometers and should be excluded from the

sensor noise analysis. In order to exclude these

“signals” from “noise”, a threshold logic is defined

and implemented as follows:

let 𝜎𝑛 be the standard deviation related

with sample window n

let

𝜎



be the registered minimum

standard deviation for the running

period

if (

𝜎𝑛 > 𝜎



. 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 ) then

is signal

else

is noise

endif

The first part of the analysis uses dedicated

accelerometers operating at different bandwidth,

while the second part compares the sensor noise in

dedicated accelerometers and consumer smartphones.

Note that this analysis assumes a “quiet”

environment, thus the presence of background

environmental noise is not taken into account.

3.1 Sensor Noise in Dedicated

Accelerometers

In this subchapter, an indication of sensor noise is

measured in two dedicated accelerometers, namely:

 Analog ADXL355, a 3-axis digital sensor with

20-bit resolution, noise density (as PSD) of

25µg/√Hz. (source: https://analog.com)

 Invensense MPU-6050 with 16-bit resolution,

noise density (as PSD) of 400µg/√Hz. (source:

https://www.invensense.com)

Based on the specifications, the ADXL355 sensor

noise is substantially lower (16x less) than the MPU-

6050. Moreover, sensors are setup to work at different

bandwidth in order to observe its effect in sensor

noise.

The results are presented next.

3.1.1 ADXL355 Measurements

The ADXL355 is setup to operate in three different

sampling frequencies: 15Hz, 100Hz and 1KHz. The

measured magnitude acceleration values subtracted

by the average (in g) are presented in Figure 1. As it

can be seen, the magnitude of the acceleration

increases with the sampling frequency.

Figure 1: ADXL355 Measured Acceleration Magnitude for

different sampling frequencies.

The measured standard deviation for ADXL-355 is

presented in Figure 2 and Table 1. Two types are

0 200040006000800010000

-0.004 -0.002 0.000 0.002 0.004

Sample Number

Acceleration (g)

ccele

ation magnitude (in g) with

DXL355 senso

est

f=1kHz

f=100Hz

f=15Hz

SENSORNETS 2021 - 10th International Conference on Sensor Networks

102

considered for analysis: 𝜎



that represents the

“sample window” with lowest sensor noise, and

𝜎



that provides an indication of the average

value of all included 𝜎.

Figure 2: ADXL355 Measured Standard Deviation for

different sampling frequencies.

Table 1: ADXL355 Measured Standard Deviation:

minimum recorded value and mean value.

ADXL355



MIN

(mg)



MEAN

(mg)

∆

(mg)

1000 Hz 0.4143 0.4394 0.0252

100 Hz 0.1734 0.1950 0.0217

15 Hz 0.0555 0.0563 0.0008

As expected, increasing the sample frequency

increases sensor noise, resulting in higher dispersion in

measurements and thus in a higher standard deviation.

The lowest standard deviation value (0.0555mg) was

recorded at 15Hz (the lowest sample frequency used)

and the highest standard deviation value (0.4143 mg)

was recorded at 1KHz). This trend is also present in the

difference between 𝜎



and 𝜎



3.1.2 MPU-6050 Measurements

Figure 3: MPU-6050 Measured Acceleration Magnitude for

different sampling frequencies

The MPU-6050 is setup to operate in three different

sampling frequencies: 5Hz, 10Hz and 100Hz. The

measured magnitude acceleration values subtracted

by the average (in g) are presented in Figure 3. Once

again, the magnitude of the acceleration increases

with the sampling frequency.

The measured standard deviation for MPU-6050

is presented in Figure 2 and Table 1. As previously,

the analysis considers 𝜎



and 𝜎



Figure 4: MPU-6050 Measured Standard Deviation for

different sampling frequencies

Table 2: MPU-6050 Measured Standard Deviation:

minimum recorded value and mean value.

MPU-

6050



MIN

(mg)



MEAN

(mg)

∆

(mg)

100 Hz

3.4253 3.7606 0.3354

50 Hz

2.5713 2.6515 0.0802

10 Hz

1.3122 1.3472 0.0350

Again, sensor noise increases with the sample

frequency: the lowest standard deviation value

(1.3122 mg) was recorded at 10Hz (the lowest sample

frequency used) and the highest standard deviation

value (3.4253 mg) was recorded at 100Hz). This trend

is also present in the difference between 𝜎



and

𝜎



. Moreover, the standard deviation value can

also be used to compare sensor noise between

different accelerometers: Table 1 and Table shows

that, at a sampling frequency of 100Hz, the MPU-

6050 standard deviation value is higher (about 20x

higher) than ADXL-355, as expected from their

respective datasheets.

A comparison between different accelerometers

sensor noise is given next.

3.2 Sensor Noise in Smartphones and

Dedicated Sensors

In this subchapter, an indication of sensor noise is

measured for different accelerometers, including

those present in consumer smartphones, operating at

the same sampling frequency (100Hz) for purposes of

comparing the associated sensor noise. The following

devices were analysed:

0 2000 4000 6000 8000

-0.005 0.000 0.005

Sample Number

Acceleration (g)

ccele

ation magnitude (in g) with MPU-6050 senso

est

f=100Hz

f=50Hz

f=10Hz

On-site Sensor Noise Evaluation and Detectability in Low Cost Accelerometers

103

 A TCL mobile phone

 A Xiaomi mobile phone

 A CAT mobile phone

 Invensense MPU-6050 (used in 3.1.2)

 ST LIS3DHH dedicated accelerometer

 Analog ADXL-355 (used in 3.1.1)

The results are presented next.

Figure 5: Measured Standard Deviation for several

accelerometers operating at a sampling frequency of

100Hz.

Table 3: Measured Standard Deviation for several devices:

minimum recorded value and mean value.

Accelerometers



MIN

(mg)



MEAN

(mg)

TCL phone 3.0115 4.1707

XIAOMI phone 1.8716 2.1893

CAT phone 0.5595 0.6563

MPU-6050 3.4253 3.7606

LIS 3DHH 0.5270 0.5634

ADXL-355 0.1734 0.1950

The developed method yields an indication of sensor

noise, which is sensor specific. As shown in Figure 5

and Table , the dedicated accelerometer ADXL-355

yields the lowest minimum standard deviation

(0.1734 mg), followed by the LIS 3DHH (0.5270

mg), the CAT phone (0.5595 mg). The TCL phone

and the MPU-6050 yield the highest values, with

3.0115 mg and 3.4253 mg respectively. It is also

pertinent to note the disparity between the mean and

the minimum value of standard deviation for the TCL

phone, indicating that the minimum value for

standard deviation alone is not sufficiently robust to

assess sensor noise in actual deployments.

3.3 Detectability Threshold Analysis

A potential application of accelerometers consists in

measuring ground motion for seismological purposes.

In this regard, accelerometers need to have the

necessary sensitivity to detect and measure seismic

events, which can have different magnitudes.

Introduced in Manso et. al (2020), herein it is

presented in equation (2) an estimation of the

detectability threshold (DetecT) of accelerometers,

considering their noise level, as measured in 3.1 and

3.2, multiplied by C, a constant that is used to increase

the assurance that measurements are above noise

level:

𝐷𝑒𝑡𝑒𝑐𝑇  𝜎



.𝐶 (2)

Considering a typical Ground Motion Prediction

Equation (GMPE) proposed by Atkinson (2015) and

resulting Peak Ground Acceleration (PGA), the

accelerometers detectability threshold, depending on

the earthquake magnitude and epicentral distance, is

presented in Figure 6.

Figure 6: Accelerometers detectability threshold for

accelerometers, depending on the earthquake magnitude

and epicentral distance.

Using C=5 in (2), in a best case scenario, the ADLX-

355 is the sensor with the lowest DetecT, being

capable to detect earthquakes with M=3 and M=5 at

a distance larger than 10 km and 100 km respectively.

Both the MPU-6050 and TCL phone exhibit similar

performance and should be able to detect earthquakes

with M=3 and M=5 at a distance of about 2 km and

20 km respectively.

The ADXL-355 accelerometer exhibited the best

performance based on the measured sensor noise,

thus further analysis is presented. ADXL-355

detectability threshold changes with the chosen

sampling frequency, as illustrated in Figure 7. For a

M=3 event, the ADXL-355 would be able to detect it

at a distance of about 30 Km if operating at a 15Hz

frequency, or about 10 Km if operating at a 1000Hz

frequency. For a M=5 event, the ADXL-355 at 15Hz

would be able to detect it at a distance of about 300

1 5 10 50 100 500 1000

1e-06 1e-04 1e-02 1e+00 1e+02

Distance (km)

PGA (g)

ADXL355

LIS3DHH | CAT phone

XIAOMI phone

MPU6050 | TCL phone

Detectability

M=2

M=3

M=4

M=5

M=6

SENSORNETS 2021 - 10th International Conference on Sensor Networks

104

Km. Therefore, applications where the sampling

frequency can be lowered will benefit with increased

detectability.

Figure 7: ADXL-355 accelerometer detectability threshold

when using different sampling frequencies, depending on

the earthquake magnitude and epicentral distance.

Although promising, these findings are preliminary

for a more thorough analysis, considering the

frequency domain, is required in order to properly

assess the sensors detectability threshold.

4 CONCLUSION

Low-cost accelerometers have found numerous real-

world applications, including in seismology and risk

hazard assessment of buildings and human heritage.

Being low-cost, it facilitates their widespread

adoption enabling the deployment of high-density

networking providing high resolution observation

and massive amount of data that may feed intensive

processing techniques like big data and artificial

intelligence, applying machine learning techniques

and pattern matching-based processing that are much

more sensitive than the power detectors used in

current seismic systems (Addair et al., 2014), making

them especially relevant in the presence of noise and

weak signals.

This work conducted a preliminary analysis of

sensor noise observed in different types of

accelerometers, successfully developing a method to

measure noise on-site and in-operation. The method

produces an indication of sensor noise based on the

measured standard deviation. It yields results

consistent with sensors specifications (i.e., ADXL-

355, LIS 3DHH and MPU-6050) or, when not

available, with the observations. Importantly, the

method adapts to the sensor’s characteristics (e.g.,

sensor noise), allowing to identify the occurrence of

relevant events (i.e., presence of signal), without

necessarily knowing a-priori the sensor specification

(noise is calculated with the sensor in-operation). In

addition, this method also adapts to changing

circumstances, such as “noise” alterations caused by

subtle changes in sensor characteristics (resulting

from e.g., small displacements or temperature

change). When considering a high-density

deployment, logistic and maintenance aspects can

represent serious bottlenecks unless the system

supports adaptive capabilities, as those here

described.

Next steps in this work involve a thorough

analysis of the sensor noise characteristics including

the frequency domain and against a reference sensor,

thus understanding in more depth the applicability of

low-cost accelerometers in real-work applications

related with seismology, as well as their limitations.

ACKNOWLEDGEMENTS

The SSN-Alentejo project is funded by the Science

Foundation of Portugal (FCT) under grant number

ALT20-03-0145- FEDER-031260.

REFERENCES

Addair T, Dodge D, Walter W, and Ruppert S (2014)

Large-scale seismic signal analysis with Hadoop.

Computers & Geosciences 66, 145–154

Atkinson, G. (2015). Ground-Motion Prediction Equation

for Small-to-Moderate Events at Short Hypocentral

Distances, with Application to Induced-Seismicity

Hazards. Bulletin of the Seismological Society of

America. March 2015. DOI: 10.1785/0120140142

Caldeira B, Carrilho F, Miranda M, Bezzeghoud M, Alves

P, Silveira G, Villalonga F, Pena J, Matias L, Borges J,

Vales D, Corela C and Madureira G (2007) Recent

improvements in the Broadband seismic networks in

Portugal. EMSC Newsletters, special issue, 22, 18-19

Evans J, Allen R, Chung A, Cochran E, Guy R, Hellweg M,

Lawrence J (2014) Performance of Several Low-Cost

Accelerometers. Seismological Research Letters

Volume 85, Number 1 January/February 2014.

Farine M, Thorburn N, and Mougenot D (2003) General

Application of MEMS Sensors for Land Seismic

Acquisition – Is it Time? Accessible at:

http://cseg.ca/assets/files/resources/abstracts/2003/218

S0130.pdf

1 5 10 50 100 500 1000

1e-06 1e-04 1e-02 1e+00 1e+02

Distance (km)

PGA (g)

ADXL355@1000Hz

ADXL355@100Hz

ADXL355@15Hz

Detectability

M=2

M=3

M=4

M=5

M=6

On-site Sensor Noise Evaluation and Detectability in Low Cost Accelerometers

105

Lainé J. and Mougenot D. (2014) A high-sensitivity MEMS-

based accelerometer. The Leading Edge. November

2014. Special Section: Sensor technology and

nanotechnology.

http://dx.doi.org/10.1190/tle33111234

Manso M., Bezzeghoud M., Caldeira B. (2017) Design and

Evaluation of a High Throughput Seismic Sensor

Network. Tools for Planning, Deployment and

Assessment. 6th International Conference on Sensor

Networks SENSORNETS, Porto, Portugal, 19 to 21

February.

Manso, M., Bezzeghoud, M., Borges, J., Caldeira, B.,

Abdelhakim A. High-density seismic network for

monitoring Alentejo region (Portugal) and Mitidja

basin region (Algeria). Arab J Geosci 13, 976 (2020).

https://doi.org/10.1007/s12517-020-05972-w

(Published: 17 September 2020).

NXP (2007) Accelerometer Terminology Guide - Quick

Reference Guide. Freescale Semiconductor. Rev 0,

05/2007.

SENSORNETS 2021 - 10th International Conference on Sensor Networks

106