Strain to ground motion conversion of distributed acoustic sensing data for earthquake magnitude and stress drop determination

Abstract. The use of distributed acoustic sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves' apparent phase velocity, which varies for different seismic phases ranging from fast body waves to slow surface and scattered waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body waves compared to slow scattered waves and ambient noise and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P and S wave records. The demonstrated ability to resolve source parameters using P waves on horizontal ocean-bottom fibers is key for the implementation of DAS-based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore earthquakes, including those capable of generating tsunami.

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Strain to Ground Motion Conversion of DAS Data for Earthquake Magnitude and Stress Drop Determination

10.5194/egusphere-egu21-7601 ◽

2021 ◽

Author(s):

Itzhak Lior ◽

Anthony Sladen ◽

Diego Mercerat ◽

Jean-Paul Ampuero ◽

Diane Rivet ◽

...

Keyword(s):

Early Warning ◽

Stress Drop ◽

Source Parameters ◽

Simulated Data ◽

Earthquake Early Warning ◽

Body Waves ◽

Ocean Bottom ◽

Correlated Noise ◽

S Wave ◽

Earthquake Source Parameters

The use of Distributed Acoustic Sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop, is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves&#8217; apparent phase velocity, which varies for different seismic phases ranging from fast body-waves to slow surface- and scattered-waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time-series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body-waves compared to slow scattered-waves and ambient noise, and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P- and S-wave records. The demonstrated ability to resolve source parameters using P-waves on horizontal ocean-bottom fibers is key for the implementation of DAS based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore and tsunami earthquakes.

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Strain to Ground Motion Conversion of DAS Data for Earthquake Magnitude and Stress Drop Determination

10.5194/se-2020-219 ◽

2021 ◽

Author(s):

Itzhak Lior ◽

Anthony Sladen ◽

Diego Mercerat ◽

Jean-Paul Ampuero ◽

Diane Rivet ◽

...

Keyword(s):

Early Warning ◽

Stress Drop ◽

Source Parameters ◽

Simulated Data ◽

Earthquake Early Warning ◽

Body Waves ◽

Ocean Bottom ◽

Correlated Noise ◽

S Wave ◽

Earthquake Source Parameters

Abstract. The use of Distributed Acoustic Sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop, is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves' apparent phase velocity, which varies for different seismic phases ranging from fast body-waves to slow surface- and scattered-waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time-series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body-waves compared to slow scattered-waves and ambient noise, and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P- and S-wave records. The demonstrated ability to resolve source parameters using P-waves on horizontal ocean-bottom fibers is key for the implementation of DAS based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore and tsunami earthquakes.

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Predicting approximate seismic responses in multistory buildings from real-time earthquake source information, for earthquake early warning applications

Bulletin of Earthquake Engineering ◽

10.1007/s10518-021-01088-y ◽

2021 ◽

Author(s):

Gemma Cremen ◽

Omar Velazquez ◽

Benazir Orihuela ◽

Carmine Galasso

Keyword(s):

Real Time ◽

Early Warning ◽

Source Parameters ◽

Earthquake Source ◽

Earthquake Early Warning ◽

Multistory Buildings ◽

Building Model ◽

Beam Combination ◽

Earthquake Source Parameters ◽

Wide Range

AbstractRegional earthquake early warning (EEW) alerts and related risk-mitigation actions are often triggered when the expected value of a ground-motion intensity measure (IM), computed from real-time magnitude and source location estimates, exceeds a predefined critical IM threshold. However, the shaking experienced in mid- to high-rise buildings may be significantly different from that on the ground, which could lead to sub-optimal decision-making (i.e., increased occurrences of false and missed EEW alarms) with the aforementioned strategy. This study facilitates an important advancement in EEW decision-support, by developing empirical models that directly relate earthquake source parameters to resulting approximate responses in multistory buildings. The proposed models can leverage real-time earthquake information provided by a regional EEW system, to provide rapid predictions of structure-specific engineering demand parameters that can be used to more accurately determine whether or not an alert is triggered. We use a simplified continuum building model consisting of a flexural/shear beam combination and vary its parameters to capture a wide range of deformation modes in different building types. We analyse the approximate responses for the building model variations, using Italian accelerometric data and corresponding source parameter information from 54 earthquakes. The resulting empirical prediction equations are incorporated in a real-time Bayesian framework that can be used for building-specific EEW applications, such as (1) early warning of floor-shaking sensed by occupants; and (2) elevator control. Finally, we demonstrate the improvement in EEW alert accuracy that can be achieved using the proposed models.

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Insights into deep learning for earthquake magnitude and location estimation

10.5194/egusphere-egu21-4718 ◽

2021 ◽

Author(s):

Jannes Münchmeyer ◽

Dino Bindi ◽

Ulf Leser ◽

Frederik Tilmann

Keyword(s):

Deep Learning ◽

Real Time ◽

Early Warning ◽

Source Parameters ◽

Spectral Composition ◽

Learning Model ◽

Location Estimation ◽

Training Set ◽

Earthquake Source Parameters ◽

Deep Learning Model

The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and can learn features and their relation from data.A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks (station spacing down to 10 km) of strong motion sensors, and North Chile with a sparser network (station spacing around 40 km) of broadband stations. We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Third, the model fails for events with few similar training examples. For magnitude, this means that the largest events are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.

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NEAR REAL-TIME DETERMINATION OF EARTHQUAKE SOURCE PARAMETERS FOR TSUNAMI EARLY WARNING FROM GEODETIC OBSERVATIONS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-117-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 117-120

Author(s):

Sunanda Manneela ◽

T. Srinivasa Kumar ◽

Shailesh R. Nayak

Keyword(s):

Real Time ◽

Early Warning ◽

Rapid Determination ◽

Moment Tensor ◽

Source Parameters ◽

Critical Role ◽

Tsunami Source ◽

Tsunami Early Warning ◽

Earthquake Source Parameters

Exemplifying the tsunami source immediately after an earthquake is the most critical component of tsunami early warning, as not every earthquake generates a tsunami. After a major under sea earthquake, it is very important to determine whether or not it has actually triggered the deadly wave. The near real-time observations from near field networks such as strong motion and Global Positioning System (GPS) allows rapid determination of fault geometry. Here we present a complete processing chain of Indian Tsunami Early Warning System (ITEWS), starting from acquisition of geodetic raw data, processing, inversion and simulating the situation as it would be at warning center during any major earthquake. We determine the earthquake moment magnitude and generate the centroid moment tensor solution using a novel approach which are the key elements for tsunami early warning. Though the well established seismic monitoring network, numerical modeling and dissemination system are currently capable to provide tsunami warnings to most of the countries in and around the Indian Ocean, the study highlights the critical role of geodetic observations in determination of tsunami source for high-quality forecasting.

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Automatic Earthquake Detection and De-noising for Distributed Acoustic Sensing: Examples from On-land and Underwater Fibers

10.5194/egusphere-egu2020-8484 ◽

2020 ◽

Author(s):

Itzhak Lior ◽

Daniel Mata ◽

Gauthier Guerin ◽

Diane Rivet ◽

Anthony Sladen ◽

...

Keyword(s):

Optical Fibers ◽

Cross Correlation ◽

Early Warning Systems ◽

Earthquake Catalog ◽

Ocean Bottom ◽

Detection Scheme ◽

Acoustic Sensing ◽

Earthquake Detection ◽

Waveform Similarity ◽

Distributed Acoustic Sensing

The use of underwater optical fibers, such as those currently traversing most of the world's oceans, for distributed acoustic sensing (DAS) holds great potential for seismic monitoring by complementing on-land seismic observations, especially near underwater faults. The analysis of underwater DAS records presents special challenges due to the noisy environment and the uneven cable-seafloor coupling. To fully exploit the potential of these records, automatically detecting and extracting seismic signals is imperative. To this end, a new automatic earthquake detection scheme is presented, based on waveform-similarity. Cross correlations between nearby records along the fiber are continuously calculated in short overlapping intervals. Earthquakes are detected as abrupt increases in cross correlation values over large segments of the cable. This procedure is applied to records of four existing fibers: one on land (Near Teil, south of France) and three underwater (one in Toulon, south of France, and two in Pylos, south-west Greece). Detected earthquakes are compared to earthquake catalogs and detection thresholds are obtained. That several of the detected earthquakes do not appear in any earthquake catalog demonstrates the proposed method's robustness. The cross correlation time shifts are then used to perform moveout corrections to the time series and phase weighted stacking (PWS) is applied to groups of neighboring traces. Unlike simple stacking approaches, PWS significantly enhances signal to noise ratios, allowing for more precise earthquake analysis and characterization. Further developing and applying such automatic techniques to ocean bottom fibers will enhance the performance of earthquake early warning systems, improving alert times for earthquakes occurring on underwater faults.

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Preliminary Results of an Earthquake Early Warning System in Costa Rica

Frontiers in Earth Science ◽

10.3389/feart.2021.700843 ◽

2021 ◽

Vol 9 ◽

Author(s):

Juan Porras ◽

Frédérick Massin ◽

Mario Arroyo-Solórzano ◽

Ivonne Arroyo ◽

Lepolt Linkimer ◽

...

Keyword(s):

Costa Rica ◽

Early Warning ◽

Early Warning System ◽

Source Parameters ◽

Warning System ◽

Earthquake Early Warning ◽

Capital City ◽

San Jose ◽

Earthquake Early Warning System ◽

Finite Fault

We analyze the performance of a prototype earthquake early warning system deployed at the National Seismological Network of Costa Rica in collaboration with the Swiss Seismological Service by presenting the real-time performance during six earthquakes (Mw 5.1-6.4) that took place during 2018 and 2019. We observe that, despite only limited efforts to optimize the existing network of 158 stations, for EEW purposes, the network density allows fast determination of source parameters using both the Virtual Seismologist and the Finite Fault Rupture Detector algorithms. Shallow earthquakes on or near-shore are routinely identified within 11–20 s of their occurrence. The warning times for the capital city of San Jose are of 43 s for epicenters located at 220 km, like for the Mw 6.4 Armuelles earthquake. On the other hand, during the time analyzed, the EEW system did not provide positive warning times for earthquakes at distances less than 40 km from San Jose. Even though large (Mw > 7) distant historical earthquakes have not caused heavy damage in San Jose, there is potential for developing an EEW system for Costa Rica, especially for the purposes of rapid earthquake notifications, disaster response management, and seismic risk mitigation.

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Subsurface Imaging with Ocean‐Bottom Distributed Acoustic Sensing and Water Phases Reverberations

Geophysical Research Letters ◽

10.1029/2021gl095287 ◽

2022 ◽

Author(s):

Zack J. Spica ◽

Jorge C. Castellanos ◽

Loïc Viens ◽

Kiwamu Nishida ◽

Takeshi Akuhara ◽

...

Keyword(s):

Ocean Bottom ◽

Subsurface Imaging ◽

Acoustic Sensing ◽

Distributed Acoustic Sensing

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Utilizing Distributed Acoustic Sensing and Ocean Bottom Fiber Optic Cables for Submarine Structural Characterization

10.31223/x5sp4z ◽

2020 ◽

Author(s):

Feng Cheng ◽

Jonathan Ajo-Franklin ◽

Benxin Chi ◽

Nathaniel J. Lindsey ◽

Craig Dawe

Keyword(s):

Structural Characterization ◽

Fiber Optic ◽

Ocean Bottom ◽

Acoustic Sensing ◽

Distributed Acoustic Sensing

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A Discussion on the measurement and interpretation of changes of strain in the Earth - Derivation of rupture area and stress-drop from body wave displacement spectra and the relative material strength in deep seismic zones

Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences ◽

10.1098/rsta.1973.0064 ◽

1973 ◽

Vol 274 (1239) ◽

pp. 361-368

Keyword(s):

Stress Drop ◽

Body Wave ◽

Source Parameters ◽

Source Area ◽

Great Depth ◽

Body Waves ◽

Material Strength ◽

Seismic Zone ◽

Rupture Area ◽

The Moment

The seismic moment and source area of an earthquake can be determined by fitting theoretical displacement amplitude spectra to observed ones. From these basic parameters the dislocation at the source and the stress-drop can be estimated. This method was tested in the case of four earthquakes for which the source parameters were known from observed surface ruptures. The uncertainty in the moment and area determinations was found to be approximately a factor of 2; for the displacement and stress-drop it was approximately a factor of 3 and 5 respectively. The application of spectral analysis of body waves to earthquakes in the deep seismic zone of Tonga-Kermadec indicate that stress-drop as well as apparent stress increase with depth and decrease again at great depth. This observation is interpreted as reflecting increasing material strength in the deep seismic zone near 450 km, with a reduction of strength at still greater depths. It is proposed that the temperature distribution in the downgoing slab of lithosphere causes this pattern.

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