scholarly journals The behaviour of near-surface soils through ultrasonic near-surface inundation testing

2021 ◽  
Author(s):  
Oliver-Denzil Taylor ◽  
Amy Cunningham, ◽  
Robert Walker ◽  
Mihan McKenna ◽  
Kathryn Martin ◽  
...  
Keyword(s):  
Seismic Wave ◽  
Signal Propagation ◽  
Flow Chamber ◽  
Controlled Environment ◽  
Laboratory Equipment ◽  
Near Surface ◽  
Seismic Waveforms ◽  
Soil Fabric ◽  
Rain Events ◽  
Wave Properties

Seismometers installed within the upper metre of the subsurface can experience significant variability in signal propagation and attenuation properties of observed arrivals due to meteorological events. For example, during rain events, both the time and frequency representations of observed seismic waveforms can be significantly altered, complicating potential automatic signal processing efforts. Historically, a lack of laboratory equipment to explicitly investigate the effects of active inundation on seismic wave properties in the near surface prevented recreation of the observed phenomena in a controlled environment. Presented herein is a new flow chamber designed specifically for near-surface seismic wave/fluid flow interaction phenomenology research, the ultrasonic near-surface inundation testing device and new vp-saturation and vs-saturation relationships due to the effects of matric suction on the soil fabric.

Unpacking storm damages on a developed shoreline: Relating dune erosion and urban runoff

Shore & Beach ◽  
2019 ◽  
pp. 35-45
Author(s):  
Patrick Barrineau ◽  
Timothy Kana
Keyword(s):  
South Carolina ◽  
Storm Events ◽  
Near Surface ◽  
Dune Erosion ◽  
Groundwater Flux ◽  
Impact Scale ◽  
Cape Hatteras ◽  
Myrtle Beach ◽  
Rain Events ◽  
Elevation Model

Hurricane Matthew (2016) caused significant beach and dune erosion from Cape Hatteras, North Carolina, USA, to Cape Canaveral, Florida, USA. At Myrtle Beach, South Carolina, the storm caused beach recession, and much of the southern half of the city’s beaches appeared to be overwashed in post-storm surveys. Around half of the city’s beaches appeared overwashed following the storm; however, the Storm Impact Scale (SIS; Sallenger 2000) applied to a pre-storm elevation model suggests less than 10% of the city’s beaches should have experienced overwash. Spatial analysis of elevation and land cover data reveals dunes that were “overwashed” during Matthew drain from watersheds that are >35% impervious, where those showing only dune recession are <5% impervious. The densely developed downtown of Myrtle Beach sits on a low seaward-sloping terrace. Additionally, indurated strata beneath the downtown area can prevent groundwater from draining during excessive rain events. As a result, the most continuous impervious surface cover and near-surface strata lie within a half-kilometer of the beach and drain directly to the backshore. Along the U.S. Southeast coast, this is somewhat rare; many coastal systems feature a lagoon or low-lying bottomland along their landward border, which facilitates drainage of upland impervious surfaces following storm passage. At Myrtle Beach, all of the stormwater runoff is drained directly to the beach through a series of outfall pipes. Many of the outfall pipes are located along the backshore, near the elevation of storm surge during Matthew. Runoff from Matthew’s heavy rains was observed causing ponding on the landward side of the foredune and scouring around beach access walkways. Based on these observations, the severe dune erosion experienced near downtown Myrtle Beach during Hurricane Matthew may have been caused by runoff and/or groundwater flux rather than overwash. These results highlight an unexpected relationship between upland conditions and dune erosion on a developed shoreline. That is, dune erosion can be caused by mechanisms beside overwash during storm events.

scholarly journals Seismic wave properties in time-dependent porosity homogeneous media

2007 ◽  
Vol 170 (3) ◽  
pp. 1227-1242 ◽  
Author(s):  
G. Quiroga-Goode ◽  
R. Padilla-Hernández ◽  
S. Jiménez-Hernández
Keyword(s):  
Seismic Wave ◽  
Time Dependent ◽  
Homogeneous Media ◽  
Wave Properties

An approach to construct a Netherlands-wide ground-motion amplification model

2021 ◽  
Author(s):  
Janneke van Ginkel ◽  
Elmer Ruigrok ◽  
Rien Herber
Keyword(s):  
The Netherlands ◽  
Ground Motion ◽  
Seismic Wave ◽  
Transfer Functions ◽  
Site Response ◽  
Data Availability ◽  
Earthquake Ground Motion ◽  
Sediment Layer ◽  
Small Magnitude ◽  
Near Surface

&lt;p&gt;Local site conditions can strongly influence the level of amplification of ground-motion at the surface during an earthquake. Especially near-surface low velocity sediments overlying stiffer seismic bedrock modify earthquake ground motions in terms of amplitudes and frequency content, the so-called site response. Earthquake ground-motion site response is of great concern because it can lead to amplified surface shaking resulting in significant damage on structures despite small magnitude events. The Netherlands has tectonically related seismic activity in the southern region with magnitudes up to 5.8 measured so far. In addition, gas extraction in the Groningen field in the northern part of the Netherlands, is regularly causing shallow (3 km), low magnitude (Mw max= 3.6), induced earthquakes. The shallow geology of the Netherlands consists of a very heterogeneous soft sediment cover, which has a strong effect on seismic wave propagation and in particular on the amplitude of ground shaking.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The ambient seismic field and local earthquakes recorded over 69 borehole stations in Groningen are used to define relationships between the subsurface lithological composition, measured shear-wave velocity profiles, horizontal-to-vertical spectral ratios (HVSR) and empirical transfer functions (ETF). For the Groningen region we show that the HVSR matches the ETF well and conclude that the HVSR can be used as a first proxy for earthquake site-response. In addition, based on the ETFs we observe that most of the seismic wave amplification occurs in the top 50 m of the much thicker sediment layer. Here, a velocity contrast is present between the very soft Holocene clays and peat on top of the stiffer Pleistocene sands.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Based on the learnings from Groningen we first constructed sediment type classes for the Dutch subsurface, each class representing a level of expected amplification. Secondly, the HVSR curves are estimated for all surface seismometers in the Netherlands seismic network and a sediment class is assigned to each location. Highest HVSR peak amplitudes are measured at sites with the highest level of amplification of the sediment classification. Based on this correlation and the presence of a detailed shallow geological model at most sites in the Netherlands, a simplistic approach is presented to predict amplification at any location with sufficient lithologic information. With this approach based on the shallow sediment composition, we can obtain constraints on the seismic hazard in areas that have limited data availability but have potential risk of seismicity, for example due to geothermal energy extraction.&lt;/p&gt;

Small-scale physical modeling of seismic-wave propagation using unconsolidated granular media

Geophysics ◽  
2014 ◽  
Vol 79 (6) ◽  
pp. T323-T339 ◽  
Author(s):  
Ludovic Bodet ◽  
Amine Dhemaied ◽  
Roland Martin ◽  
Régis Mourgues ◽  
Fayçal Rejiba ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Seismic Wave ◽  
Physical Modeling ◽  
Granular Media ◽  
Laser Doppler Vibrometer ◽  
Seismic Wave Propagation ◽  
Acoustic Mode ◽  
Physical Models ◽  
Small Scale ◽  
Near Surface

Laboratory physical modeling and laser-based experiments are frequently proposed to tackle theoretical and methodological issues related to seismic prospecting, e.g., when experimental validations of processing or inversion techniques are required. Lasers are mainly used to simulate typical field acquisition setups on homogeneous and consolidated materials assembled into laboratory-scale physical models (PMs) of various earth structures. We suggested the use of granular materials to study seismic-wave propagation in unconsolidated and porous media and target near-surface exploration and hydrogeologic applications. We designed and tested the reproducibility of an experimental procedure to build and probe PMs consisting of micrometric glass beads (GBs). A mechanical source and a laser-Doppler vibrometer were used to record small-scale seismic lines at the surface of three GBs models. When guided surface acoustic mode theory should prevail in such unconsolidated granular packed structure under gravity, we only considered elastic-wave propagation in stratified media to interpret recorded data. Thanks to basic seismic processing and inversion methods (first arrivals and dispersion analyses), we were able to correctly retrieve the gradients of pressure- and shear-wave velocities in our models. A 3D elastic finite difference simulation of the experiment offered, despite significant differences in terms of amplitudes, a supplementary validation of our approximation, as far as elastic properties of the medium were concerned.

scholarly journals Regional Thermodynamic Characteristics Distinguishing Long- and Short-Duration Freezing Rain Events over North America

2020 ◽  
Vol 35 (2) ◽  
pp. 657-671
Author(s):  
Christopher D. McCray ◽  
John R. Gyakum ◽  
Eyad H. Atallah
Keyword(s):  
United States ◽  
Short Duration ◽  
Thermodynamic Characteristics ◽  
The United States ◽  
Freezing Rain ◽  
Near Surface ◽  
Cold Air ◽  
South Central ◽  
Surface Temperatures ◽  
Rain Events

Abstract Freezing rain is an especially hazardous winter weather phenomenon that remains particularly challenging to forecast. Here, we identify the salient thermodynamic characteristics distinguishing long-duration (six or more hours) freezing rain events from short-duration (2–4 h) events in three regions of the United States and Canada from 1979 to 2016. In the northeastern United States and southeastern Canada, strong surface cold-air advection is not common during freezing rain events. Colder onset temperatures at the surface and in the near-surface cold layer support longer-duration events there, allowing heating mechanisms (e.g., the release of latent heat of fusion when rain freezes at the surface) to act for longer periods before the surface reaches 0°C and precipitation transitions to rain. In the south-central United States, cold air at the surface is replenished via continuous cold-air advection, reducing the necessity of cold onset surface temperatures for event persistence. Instead, longer-duration events are associated with warmer and deeper &gt;0°C warm layers aloft and stronger advection of warm and moist air into this layer, delaying its erosion via cooling mechanisms such as melting. Finally, in the southeastern United States, colder and especially drier onset conditions in the cold layer are associated with longer-duration events, with evaporative cooling crucial to maintaining the subfreezing surface temperatures necessary for freezing rain. Through an improved understanding of the regional conditions supporting freezing rain event persistence, we hope to provide useful information to forecasters in their attempt to predict these potentially damaging events.

Integration of Machine Learning on Distributed Acoustic Sensing Surveys

2020 ◽  
Author(s):  
Camille Jestin ◽  
Clément Hibert ◽  
Gaëtan Calbris ◽  
Vincent Lanticq
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Fibre Optic ◽  
Rock Falls ◽  
Near Surface ◽  
Acoustic Sensing ◽  
Seismic Waveforms ◽  
Fibre Optic Cable ◽  
Distributed Acoustic Sensing

&lt;p&gt;Distributed Acoustic Sensing (DAS) is an innovative technique which has been recently employed for near-surface geophysics purposes. It involves the use of fibre-optic cable as a sensor. The fibre is analysed by sending a laser pulse from an interrogator unit. The phase of the backscattered signal contains the information on the strain on the cable, enabling the detection of a passing acoustic wave with enough energy for the cable excitation. Allowing the interrogation of long profiles and the generation of a dense spatial sampling, uneasy to obtain with classic geophysical techniques, DAS instrumentation then proved its relevance for seismic applications but also for infrastructure monitoring.&lt;/p&gt;&lt;p&gt;During DAS acquisition, and more precisely when closely looking at infrastructures integrity, it is necessary to clearly identify the source of the acoustic vibrations at the structure neighbourhood. Indeed, in the context of pipeline monitoring for example, it appears important to be able to classify events which generate seismic signals recorded by DAS systems and which can be related to a potential threat for the structure. In order to launch an alarm if necessary, the source identification must be fast, accurate and robust. Moreover, because DAS acquisition can generate traces every few meters along fibres of tens of kilometres, the used machine-learning algorithm must demonstrate its ability to handle a big amount of data.&lt;/p&gt;&lt;p&gt;In this study, we analyse the efficiency of the Random Forests (RF) machine-learning algorithm applied to data acquired with DAS system for the discrimination of event sources. RF algorithm has been selected because of its ability to handle large numbers of attributes related to signal characteristics and to enable a good reliability for the discrimination of sources. This algorithm has already proved its efficiency for automated classification of seismic waveforms (e.g. earthquakes, volcanic tremors, rock falls, avalanches, etc.).&lt;/p&gt;&lt;p&gt;We focus our study on tests lead along a gas pipeline instrumented with fibre-optic cable. Different third-party works have been conducted: excavation, saw sections, drill, jackhammer, etc. We work on the discrimination of six classes of seismic source. After running a detection phase based on a threshold on signal energy, we obtain several hundred of exploitable seismic traces to inject to the RF algorithm. We demonstrate the efficiency of the application of machine learning on DAS data to discriminate seismic waveforms from the correct class, with an overall precision on our test set of 99%.&lt;/p&gt;

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