Source Characterization for Two Small Earthquakes in Dartmouth, Nova Scotia, Canada: Pushing the Limit of Single Station

2021 ◽  
Author(s):  
Miao Zhang ◽  
Min Liu ◽  
Alexandre Plourde ◽  
Feng Bao ◽  
Ruijia Wang ◽  
...  
Keyword(s):  
Nova Scotia ◽  
Model Building ◽  
Source Parameters ◽  
Velocity Model ◽  
Surface Velocity ◽  
Commercial Development ◽  
Near Surface ◽  
Waveform Modeling ◽  
Earthquake Source Parameters ◽  
Single Station

Abstract A pair of small earthquakes (MN 2.4 and 2.6, Earthquakes Canada) hit the city of Dartmouth, Nova Scotia, Canada, in early March 2020. The events were recorded by three seismic stations within 200 km, but only one station (HAL, <10  km) is close enough to offer high-quality broadband signals. In this study, we explore their source parameters using the nearest station through waveform modeling. A nearby quarry blast (MN 2.0) with known Global Positioning System coordinates is adopted as a reference for regional velocity model building and location calibration. We first build a half-space velocity model by estimating the P-S travel-time difference of the blast and determine the near-surface velocity through full-waveform modeling (i.e., comparing a set of synthetic waveforms with the observed blast). The velocity model is then used to evaluate the pair of earthquakes, in which waveform fitting and Rg/S amplitude ratios suggest source depths of ∼0.7  km. The epicenters of these two earthquakes are situated in a recently constructed commercial development. Lastly, single-station template matching finds no similar earthquakes near the hypocenters of the two events in the past decade and only three aftershocks in the following four months. Taking advantage of a ground-truth blast and waveform modeling, our study demonstrates the potential to construct a detailed regional velocity model and determine accurate earthquake source parameters in regions where only a single station is available.

Full waveform inversion: early start of model building process to resolve the complex near surface model in the Exmouth sub-basin, North West Shelf Australia

2018 ◽  
Vol 58 (2) ◽  
pp. 884
Author(s):  
Lianping Zhang ◽  
Haryo Trihutomo ◽  
Yuelian Gong ◽  
Bee Jik Lim ◽  
Alexander Karvelas
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Image Point ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
North West ◽  
Full Waveform

The Schlumberger Multiclient Exmouth 3D survey was acquired over the Exmouth sub-basin, North West Shelf Australia and covers 12 600 km2. One of the primary objectives of this survey was to produce a wide coverage of high quality imaging with advanced processing technology within an agreed turnaround time. The complexity of the overburden was one of the imaging challenges that impacted the structuration and image quality at the reservoir level. Unlike traditional full-waveform inversion (FWI) workflow, here, FWI was introduced early in the workflow in parallel with acquisition and preprocessing to produce a reliable near surface velocity model from a smooth starting model. FWI derived an accurate and detailed near surface model, which subsequently benefitted the common image point (CIP) tomography model updates through to the deeper intervals. The objective was to complete the FWI model update for the overburden concurrently with the demultiple stages hence reflection time CIP tomography could start with a reasonably good velocity model upon completion of the demultiple process.

Model–parameterization scaling for improving accuracy of seismic tomography in reconstructing near–surface velocity model

2020 ◽  
Author(s):  
Gleb S. Chernyshov ◽  
◽  
Anton A. Duchkov ◽  
Aleksander A. Nikitin ◽  
Ivan Yu. Kulakov ◽  
...  
Keyword(s):  
Model Building ◽  
Regularization Parameter ◽  
Velocity Model ◽  
Surface Velocity ◽  
Surface Model ◽  
Tomographic Inversion ◽  
Near Surface ◽  
Model Parameterization ◽  
Regularization Parameters ◽  
Improving Accuracy

The problem of tomographic inversion is non–unique and requires regularization to solve it in a stable manner. It is highly non–trivial to choose between various regularization approaches or tune the regularization parameters themselves. We study the influence of one particular regularization parameter on the resolution and accuracy the tomographic inversion for the near–surface model building. We propose another regularization parameter, which allows to increase the accuracy of model building.

A robust first-arrival picking workflow using convolutional and recurrent neural networks

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. U109-U119
Author(s):  
Pengyu Yuan ◽  
Shirui Wang ◽  
Wenyi Hu ◽  
Xuqing Wu ◽  
Jiefu Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Building ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Velocity Model ◽  
Surface Velocity ◽  
Data Sets ◽  
Near Surface ◽  
Segmentation Boundary ◽  
First Arrival

A deep-learning-based workflow is proposed in this paper to solve the first-arrival picking problem for near-surface velocity model building. Traditional methods, such as the short-term average/long-term average method, perform poorly when the signal-to-noise ratio is low or near-surface geologic structures are complex. This challenging task is formulated as a segmentation problem accompanied by a novel postprocessing approach to identify pickings along the segmentation boundary. The workflow includes three parts: a deep U-net for segmentation, a recurrent neural network (RNN) for picking, and a weight adaptation approach to be generalized for new data sets. In particular, we have evaluated the importance of selecting a proper loss function for training the network. Instead of taking an end-to-end approach to solve the picking problem, we emphasize the performance gain obtained by using an RNN to optimize the picking. Finally, we adopt a simple transfer learning scheme and test its robustness via a weight adaptation approach to maintain the picking performance on new data sets. Our tests on synthetic data sets reveal the advantage of our workflow compared with existing deep-learning methods that focus only on segmentation performance. Our tests on field data sets illustrate that a good postprocessing picking step is essential for correcting the segmentation errors and that the overall workflow is efficient in minimizing human interventions for the first-arrival picking task.

Multi-Wave Inversion for Near-Surface Velocity Model Building on a Broadband Land Survey in North Kuwait

2020 ◽  
Author(s):  
O. Bouhdiche ◽  
L. Vivin ◽  
P. Plasterie ◽  
T. Rebert ◽  
M. Retailleau ◽  
...  
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Wave Inversion ◽  
Velocity Model Building ◽  
Land Survey

scholarly journals Characterization of seasonal glacial seismicity from a single-station on-ice record at Holtedahlfonna, Svalbard

2019 ◽  
Vol 60 (79) ◽  
pp. 23-36 ◽  
Author(s):  
Andreas Köhler ◽  
Valerie Maupin ◽  
Christopher Nuth ◽  
Ward van Pelt
Keyword(s):  
Seismic Velocity ◽  
Temporal Distribution ◽  
Velocity Model ◽  
Ice Dynamics ◽  
Near Surface ◽  
Waveform Modeling ◽  
Single Station ◽  
Glacier Runoff ◽  
Fast Flow ◽  
Seismic Background

ABSTRACTGlacial seismicity provides important insights into glacier dynamic processes. We study the temporal distribution of cryogenic seismic signals (icequakes) at Holtedahlfonna, Svalbard, between April and August 2016 using a single three-component sensor. We investigate sources of observed icequakes using polarization analysis and waveform modeling. Processes responsible for five icequake categories are suggested, incorporating observations of previous studies into our interpretation. We infer that the most dominant icequake type is generated by surface crevasse opening through hydrofracturing. Secondly, bursts of high-frequency signals are presumably caused by repeated near-surface crevassing due to high strain rates during glacier fast-flow episodes. Furthermore, signals related to resonance in water-filled cracks, fracturing or settling events in dry firn or snow before the melt season, and processes at the glacier bed are observed. Amplitude of seismic background noise is clearly related to glacier runoff. We process ambient seismic noise to invert horizontal-to-vertical spectral ratios for a sub-surface seismic velocity model used to model icequake signals. Our study shows that a single seismic sensor provides useful information about seasonal ice dynamics in case deployment of a network is not feasible.

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