Earthquake source characterization by machine learning algorithms applied to acoustic signals

Gomez, Bernabe; Kadri, Usama

doi:10.1038/s41598-021-02483-w

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…RFC is an ensemble technique based on decision trees that can be used for both classification and regression tasks, such as support vector regressor (SVR) [46]. The RFC algorithm operates by dividing the dataset into smaller subsets and has been shown to be effective in a variety of contexts [47,48].…”

Section: Machine Learningmentioning

confidence: 99%

Infragravity Wave Oscillation Forecasting in a Shallow Estuary

Gomez,

Giddings,

Gallien

2024

JMSE

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Infragravity (IG) waves are low-frequency water waves, which can propagate into harbors and estuaries, affecting currents and sediment transport processes. Understanding and predicting IG oscillations inside harbors and estuaries is critical to coastal management and estimating future resilience to climate change impacts. High-resolution water level and flow velocity observations collected within Seal Beach Wildlife Refuge in Southern California are analyzed for IG energy related to atmospheric parameters, water levels, and offshore wave conditions. A proof of concept approach for predicting infragravity oscillations within an estuary using machine learning (ML) is presented.

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Section: Machine Learningmentioning

confidence: 99%

Infragravity Wave Oscillation Forecasting in a Shallow Estuary

Gomez,

Giddings,

Gallien

2024

JMSE

Self Cite

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“…For seismic air guns, Kyhn et al [47] used traditional energy sum detectors. For automatic earthquake detection, numerous methods have been used, including data processing [48][49][50][51] and statistical techniques [3,52,53]. Although we have not found extensive use of HMMs for hydroacoustic seismic events, they have been widely used for land-based seismicity, such as those linked specifically to volcanic activity [54][55][56] and tectonic-related earthquakes [57][58][59].…”

Section: Introductionmentioning

confidence: 99%

An HMM-DNN-Based System for the Detection and Classification of Low-Frequency Acoustic Signals from Baleen Whales, Earthquakes, and Air Guns off Chile

Buchan

Duran

Rojas³

et al. 2023

Remote Sensing

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Marine passive acoustic monitoring can be used to study biological, geophysical, and anthropogenic phenomena in the ocean. The wide range of characteristics from geophysical, biological, and anthropogenic sounds sources makes the simultaneous automatic detection and classification of these sounds a significant challenge. Here, we propose a single Hidden Markov Model-based system with a Deep Neural Network (HMM-DNN) for the detection and classification of low-frequency biological (baleen whales), geophysical (earthquakes), and anthropogenic (air guns) sounds. Acoustic data were obtained from the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization station off Juan Fernandez, Chile (station HA03) and annotated by an analyst (498 h of audio data containing 30,873 events from 19 different classes), and then divided into training (60%), testing (20%), and tuning (20%) subsets. Each audio frame was represented as an observation vector obtained through a filterbank-based spectral feature extraction procedure. The HMM-DNN training procedure was carried out discriminatively by setting HMM states as targets. A model with Gaussian Mixtures Models and HMM (HMM-GMM) was trained to obtain an initial set of HMM target states. Feature transformation based on Linear Discriminant Analysis and Maximum Likelihood Linear Transform was also incorporated. The HMM-DNN system displayed good capacity for correctly detecting and classifying events, with high event-level accuracy (84.46%), high weighted average sensitivity (84.46%), and high weighted average precision (89.54%). Event-level accuracy increased with higher event signal-to-noise ratios. Event-level metrics per class also showed that our HMM-DNN system generalized well for most classes but performances were best for classes that either had a high number of training exemplars (e.g., generally above 50) and/or were for classes of signals that had low variability in spectral features, duration, and energy levels. Fin whale and Antarctic blue whale song and air guns performed particularly well.

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“…Employing digital signal processing techniques (DPS), we can analyze sound recordings of underwater earthquakes, that train artificial intelligence (AI) algorithms to classify the type of earthquake (i.e. horizontal or vertical) and its moment magnitude (strength) [5]. This is a significant step for a reliable early tsunami warning system since the type of earthquake can dictate if a tsunami will be generated at all.…”

Section: Introdcutionmentioning

confidence: 99%

An Efficient Tsunami Arrival Time Estimator Coupled to Acoustic Gravity Wave Multi-Fault Rupture solution

Abdolali

Kadri

Williams

et al. 2022

Preprint

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The Dijkstra's algorithm is employed to find the shortest paths between the epicenter and all the wet nodes on a global triangular unstructured mesh using the phase speed of surface gravity waves and progressive acoustic modes. The phase speed estimator takes into account the simultaneous effects of the slight compressibility of water, sea-bed elasticity, and static compression of the ocean under gravity, leading to precise calculation of the arrival time [1]. Coupled to a mathematical solution for multi-fault rupture along the transect extracted from the shortest path between the source and destination is then employed to characterize the genesis of the tsunami [2]. The effectiveness of the aforementioned algorithms and distinct arrival time of different the tsunami and its precursor components (acoustic modes) at DART and deep ocean acoustic observations can be used for tsunami early warning systems. [1]

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Earthquake source characterization by machine learning algorithms applied to acoustic signals

Cited by 6 publications

References 45 publications

Infragravity Wave Oscillation Forecasting in a Shallow Estuary

Infragravity Wave Oscillation Forecasting in a Shallow Estuary

An HMM-DNN-Based System for the Detection and Classification of Low-Frequency Acoustic Signals from Baleen Whales, Earthquakes, and Air Guns off Chile

An Efficient Tsunami Arrival Time Estimator Coupled to Acoustic Gravity Wave Multi-Fault Rupture solution

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