Ioannis Vlachos

Unstable rock blocks and cliffs pose a widespread geohazard, and their mechanical state can be tracked through their ambient-vibration resonance frequencies, whose decrease anticipates progressive failure. Such monitoring is usually performed with expensive broadband instrumentation, limiting spatial and temporal coverage. Here we assess whether a low-cost, IoT-enabled node—built around a Raspberry Pi single-board computer, a 24-bit sigma-delta digitiser and a force- balance accelerometer (Geobit FBA-200)—can identify the resonance of an unstable coastal rock block at the celebrated “moving rock” of Kounopetra (Paliki peninsula, Kefalonia, Greece), a site historically renowned for visually perceptible rocking boulders. We stress that the low-amplitude 7.7 Hz structural eigenvibration characterised here is a distinct phenomenon from the historically documented ∼0.3 Hz macroscopic, quasi-rigid rocking of the boulder: the former is the ambient–vibration resonance of the fractured rock mass, the latter a large-amplitude rigid-body oscillation. Two identical nodes recorded ground acceleration simultaneously for nine hours: one on the fractured Kounopetra rock mass and one on stable ground 25 m away, used as a reference. The rock station exhibits a clear, temporally stable fundamental resonance at f₀ = 7.7 Hz (Q ≈ 50, damping ζ ≈ 1%), amplified by up to an order of magnitude relative to the reference and entirely absent from it, whereas a narrow 20.5 Hz line present on both nodes is identified as instrument-related and discarded. A simultaneous two-station analysis further shows that the ambient sources are extremely local (only 0.4% of transient activity is common to the two nodes 25 m apart), quantifying a design constraint for differential schemes. The results demonstrate that a low-cost force-balance node is sufficient to establish a resonance baseline for an unstable rock block, opening the way to dense, affordable early-warning networks; the main limitations are the single vertical component and the short record, which preclude polarisation analysis and long-term tracking of f₀.

Xylella fastidiosa is a devastating pathogen that has significantly impacted olive cultivation, particularly in Southern Italy, since 2013. Monitoring and early identification are crucial for managing the spread of this disease and minimising associated economic losses. This study presents a low-cost system employing electrical conductivity (EC) probes to monitor the water status of olive tree stems, enabling the early detection of X. fastidiosa. The system provides real-time data by detecting changes in EC, which are possibly correlated with the presence of the pathogen. In contrast to traditional detection methods like PCR, which are expensive and require laboratory facilities, this system offers a practical, field-deployable, and sustainable solution. In a testbed, EC probes were installed on an olive tree in Southern Italy, with data transmitted via a GSM network to a central server for analysis. Powered by solar energy, the system successfully detected changes in the stem’s water content, which were subsequently confirmed to be associated with pathogen presence. This low-cost EC probe system initially demonstrates promising potential for the early detection of X. fastidiosa, enabling proactive management strategies. Future research will focus on improving the system’s sensitivity and conducting extensive field trials across diverse environments and applications.

The use of a dense network of commercial high-cost seismographs for earthquake monitoring is often financially unfeasible. A viable alternative to address this limitation is the development of a network of low-cost seismographs capable of monitoring local seismic events with a precision comparable to that of high-cost instruments within a specified distance from the epicenter. The primary aim of this study is to compare the performance of an advanced, contemporary low-cost seismograph with that of a commercial, high-cost seismograph. The proposed system is enhanced through the integration of a 24-bit analog-to-digital converter board and an optimized architecture for a low-noise signal amplifier employing active components for seismic signal detection. To calibrate and assess the performance of the low-cost seismograph, an installation was deployed in a region of high seismic activity in Evgiros, Lefkada Island, Greece. The low-cost system was co-located with a high-resolution 24-bit commercial digitizer, equipped with a broadband (30 s—50 Hz) seismometer. An uninterrupted dataset was collected from the low-cost system over a period of more than two years, encompassing 60 local events with magnitudes ranging from 0.9 to 3.2, epicentral distances from 5.71 km to 23.45 km, and focal depths from 1.83 km to 19.69 km. Preliminary findings demonstrate a significant improvement in the accuracy of earthquake magnitude estimation compared to the initial configuration of the low-cost seismograph. Specifically, the proposed system achieved a mean error of ±0.087 when benchmarked against the data collected by the high-cost commercial seismograph. These results underscore the potential of low-cost seismographs to serve as an effective and financially accessible solution for local seismic monitoring.

The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that—when the proposed methodology is applied—are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency.

The low cost seismograph under study consists of low cost open source hardware and software microprocessor boards (Arduino Uno R3  and Raspberry Pi 3 B+ ), customized low noise design signal amplifiers, low power dissipation sophisticated power supply, two (2) kinds of earth ground shaking sensors a) Ceramic Accelerometer with cutoff frequency fc=0.15Hz and b) moving coil geophone with cutoff frequency fc=4.5Hz. The signals from the two sensors are amplified independently, while an active second order low-pass anti-alias filter and an 8th order active low-pass anti-alias filter have been used. Finally, a low-cost microprocessor board is responsible for digitizing the analog data from the amplified signal of the sensors with a frequency sampling rate of 345Hz. The aim of the present work is to design and test a systemic protocol in order to evaluate the performance of the proposed low cost seismograph for monitoring local to regional seismicity and micro seismicity. The proposed low-cost system was installed in an area of high seismic activity (Lefkada Island – Village Evgiros) and the recordings are transmitted to the database continuously from the day of its installation up today. Thus have create an amount of data for more than 280 days and all of those data have been stored to our database. Collocated with a high resolution 24 bits digitizer equipped with a broad band seismometer give us the opportunity to compare the recordings. To this end, a testing list of 15 local events has been created with different epicenters and magnitudes. For each event the recording signals have been analyzed in terms of a) power spectrum analysis, b) estimation of first arrival times of both P and S waves, c) signal amplitudes and d) earthquake duration. The choice of those specific measures was done in order to evaluate the performance of the low-cost seismograph in terms of certain seismic parameters such as magnitude, epicenter and source properties. Initial results in terms of the proposed protocol are also presented showing an adequate performance of the propose low cost seismograph. Keywords:low-cost instruments, Ionian islands, performance protocolAcknowledgements«Telemachus – Innovative Seismic Risk Management Operational System of the Ionian Islands» which is part of the Operational Program «Ionian Islands 2014-2020» and is co-financed by the European Regional Development Fund (ERDF) (National Strategic Reference Framework - NSRF 2014-20).