Investigation of Mars seismic noise using modelling techniques developed on Earth

Aggarwal, Kanchan; Mukhopadhyay, Siddhartha; Tangirala, Arun K.

doi:10.1016/j.ifacol.2020.06.105

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Needless to say, the goodness of a prediction-based method depends on the model quality. To this end, the authors of this work, in a separate study, have developed a systematic methodology for building statistically appropriate time-series models for seismic noise [ 29 ]. This work essentially builds on the models developed and exploits the advantages of a model-predictive framework, especially for handling low SNR situations.…”

Section: Literature Reviewmentioning

confidence: 99%

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

2021

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Onset detection of P-wave in seismic signals is of vital importance to seismologists because it is not only crucial to the development of early warning systems but it also aids in estimating the seismic source parameters. All the existing P-wave onset detection methods are based on a combination of statistical signal processing and time-series modeling ideas. However, these methods do not adequately accommodate some advanced ideas that exist in fault detection literature, especially those based on predictive analytics. When combined with a time-frequency (t-f) / temporal-spectral localization method, the effectiveness of such methods is enhanced significantly. This work proposes a novel real-time automatic P-wave detector and picker in the prediction framework with a time-frequency localization feature. The proposed approach brings a diverse set of capabilities in accurately detecting the P-wave onset, especially in low signal-to-noise ratio (SNR) conditions that all the existing methods fail to attain. The core idea is to monitor the difference in squared magnitudes of one-step-ahead predictions and measurements in the time-frequency bands with a statistically determined threshold. The proposed framework essentially accommodates any suitable prediction methodology and time-frequency transformation. We demonstrate the proposed framework by deploying auto-regressive integrated moving average (ARIMA) models for predictions and the well-known maximal overlap discrete wavelet packet transform (MODWPT) for the t-f projection of measurements. The ability and efficacy of the proposed method, especially in detecting P-waves embedded in low SNR measurements, is illustrated on a synthetic data set and 200 real-time data sets spanning four different geographical regions. A comparison with three prominently used detectors, namely, STA/LTA, AIC, and DWT-AIC, shows improved detection rate for low SNR events, better accuracy of detection and picking, decreased false alarm rate, and robustness to outliers in data. Specifically, the proposed method yields a detection rate of 89% and a false alarm rate of 11.11%, which are significantly better than those of existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

2021

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“…However, statistical approaches usually only provide estimation or probabilities of future earthquake events. In addition, stationarity and correlation between data in a linear form have a high impact on the statistic-based prediction results [5]. Unfortunately, earthquake datasets are usually not stationary.…”

Section: Introductionmentioning

confidence: 99%

LUTanh Activation Function to Optimize BI-LSTM in Earthquake Forecasting

2024

IJIES

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Earthquakes, as unpredictable and potentially catastrophic events, have long captured the attention of geophysicists due to their profound impact on communities. The devastating consequences of these events underscore the critical need for early earthquake prediction systems capable of forecasting location, magnitude, and depth. With the rapid advancements in technology, particularly in the fields of data science, earthquake prediction methods have undergone significant evolution. This study introduces a proposed method that modified ReLU (Rectified Linear Unit) called LUTanh (Linear Unit Hyperbolic Tangent) which combine both benefits of ReLU and Tanh activation functions. This study applied and compared the proposed method performance in long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM) algorithms for predicting earthquake disaster. Furthermore, this proposed method was tested in architecture of multiple input multiple output (MIMO) principle in predicting the occurrence of earthquake disaster. The evaluation results decisively reveal that the LUTanh applied in Bi-LSTM model, particularly when optimized with the Adam optimizer, consistently outperforms the LSTM counterpart. Error assessments of LUTanh in Bi-LSTM consistently demonstrate lower average error scores compared to the origin ReLU activation function up to 4% of mean absolute error (MAE) and 3% of mean square error (MSE).

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Investigation of Mars seismic noise using modelling techniques developed on Earth

Cited by 2 publications

References 13 publications

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

LUTanh Activation Function to Optimize BI-LSTM in Earthquake Forecasting

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