Adaptive STA–LTA with Outlier Statistics

Jones, J. A. A.; Baan, Mirko van der

doi:10.1785/0120140203

Cited by 17 publications

(4 citation statements)

References 44 publications

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“…They have been verified with respect to Vrancea seismicity and are currently used for climate change impact analysis. Essentially, the time series representing the gas emissions (radon, CO 2 ) are integrated after the extraction of the mean, then an algorithm for the detection of STA/LTA (Short-Term Averages/Long-Term Averages) of Allen type ( [36][37][38]) or 2SD (two standard deviations) is applied [33,39]. Signal integration is performed with a function from the LabVIEW library that performs numerical integration using the trapezoidal rule.…”

Section: Analysis Methods and Case Studiesmentioning

confidence: 99%

The Results and Developments of the Radon Monitoring Network in Seismic Areas

et al. 2023

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The analysis of the relationship between radon and seismicity was previously carried out in the seismic zone of Vrancea (Romania), positioning the measuring stations on tectonic faults. This article analyzed the evolution of radon under conditions of deep and surface seismicity and the presence of mud volcanoes, as well as fires caused by gasses emanating from the ground. The monitoring area was extended to the Black Sea and the area of the Făgăraș-Câmpulung fault, where a special radon detection system was established and proposed for patenting. The case study was the impact of the earthquakes in Turkey (7.8 R and 7.5 R on 6 February 2023) on the seismically active areas in Romania in terms of gas emissions (radon, CO2). The main analysis methods for radon (we also included CO2) were applied to integrated time series and the use of anomaly detection algorithms. Data analysis showed that the effects of global warming led to variations in seasonal gas emissions compared to previous years. This made it difficult to analyze the data and correlate it with seismicity. Several of the cases presented require more in-depth analysis to determine the cause of the unusually high radon levels. The primary purpose of establishing the monitoring network is to use the gas emissions as seismic precursors, but the measurements are affected by the conditions under which the monitoring is conducted. In some cases, we are dealing with the effects of pollution, and in other cases, more extensive studies are required. One solution we plan to use is to expand the measurement points to locate the source of the anomalies and use weather data to determine the impact of global warming on the measurements. The main conclusions related to the development of a radon monitoring network and, in general, to the emission of gasses in earthquake-prone areas relate to the importance of the choice of equipment, monitoring location, and installation method.

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Section: Analysis Methods and Case Studiesmentioning

confidence: 99%

The Results and Developments of the Radon Monitoring Network in Seismic Areas

et al. 2023

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“…As indicated above, we should expect that previous to an eruptive episode the Shannon entropy must evolve toward zero, or reaching a minimum. In order to quantify the decay of this feature we used a widely accepted algorithm such as Short Time Average (STA)/Long Time Average (LTA) (Jones & van der Baan, 2015; Trnkoczy, 2009). We estimated the mean value of the Shannon entropy for each volcano during resting periods (SE 0 ) and implemented small windows of analysis to calculate how the Shannon entropy was evolving (SE(i)) according to this resting value, using Equation 2.…”

Section: Feature Extraction and Model Developmentmentioning

confidence: 99%

Volcanic Early Warning Using Shannon Entropy: Multiple Cases of Study

Rey‐Devesa,

Benítez,

Prudencio

et al. 2023

JGR Solid Earth

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The search for pre‐eruptive observables that can be used for short‐term volcanic forecast remains a scientific challenge. Pre‐eruptive patterns in seismic data are usually identified by analyzing seismic catalogs (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the volcanic medium as a consequence of changes in the volume of the volcano. However, although successful volcanic predictions have been achieved, there is still no generally valid model suitable for a large range of eruptive scenarios. In this study, we evaluate the potential use of Shannon entropy as short‐term volcanic eruption forecasting extracted from seismic signals at five well studied volcanoes (Etna, Mount St. Helens, Kilauea, Augustine, and Bezymianny). We identified temporal patterns that can be monitored as short‐term eruptive precursors. We quantified the decay of Shannon entropy prior to eruptions, noting that changes appear between 4 days and 12 hr before. When Shannon entropy is combined with the temporal evolution of other features (i.e., energy, kurtosis, and the frequency index), we can elaborate physical models according to the occurring volcanic processes. Our results show that pre‐eruptive variation in Shannon entropy is a confident short‐term volcanic eruption monitoring tool.

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“…In this study, we propose a detection method using the "short-time-average through long-time-average" (STA/LTA) algorithm [17]. This algorithm computes the threshold in an adaptive manner to maintain a constant false alarm rate (CFAR).…”

Section: Detection Algorithmmentioning

confidence: 99%

Real-Time Detection of Intruders Using an Acoustic Sensor and Internet-of-Things Computing

Al-Khalli,

Alateeq,

Almansour

et al. 2023

Sensors

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Modern home automation systems include features that enhance security, such as cameras and radars. This paper proposes an innovative home security system that can detect burglars by analyzing acoustic signals and instantly notifying the authorized person(s). The system architecture incorporates the concept of the Internet of Things (IoT), resulting in a network and a user-friendly system. The proposed system uses an adaptive detection algorithm, namely the “short-time-average through long-time-average” algorithm. The proposed algorithm is implemented by an IoT device (Arduino Duo) to detect people’s acoustical activities for the purpose of home/office security. The performance of the proposed system is evaluated using 10 acoustic signals representing actual events and background noise. The acoustic signals were generated by the sounds of keys shaking, the falling of a small object, the shrinking of a plastic bag, speaking, footsteps, etc. The effects of different algorithms’ parameters on the performance of the proposed system have been thoroughly investigated.

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Adaptive STA–LTA with Outlier Statistics

Cited by 17 publications

References 44 publications

The Results and Developments of the Radon Monitoring Network in Seismic Areas

The Results and Developments of the Radon Monitoring Network in Seismic Areas

Volcanic Early Warning Using Shannon Entropy: Multiple Cases of Study

Real-Time Detection of Intruders Using an Acoustic Sensor and Internet-of-Things Computing

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