Use of Geophysics for Levee Investigation Levee - - PowerPoint PPT Presentation

Use of Geophysics for Levee Investigation

Levee State-of-the-Practice Symposium

April 22, 2016

Horacio Ferriz

California State University Stanislaus With the collaboration of

Koichi Hayashi, Geometrics Resham Sandhu, CSU East Bay Ashley Loogman, Fremont Gold

Use of Geophysics for Levee Investigation

Why geophysics?

Types of levees

Fauchard, C., Mériaux, P., 2007

Anatomy of a levee

 Airborne electromagnetic induction  Galvanic electric resistivity  Capacitively coupled resistivity  Traditional electromagnetic induction  Ground penetrating radar  Spontaneous potential  Seismic refraction  Multi-channel analysis of surface waves (R- and L-wave

surveys)

What has been tried?

Airborne electromagnetic induction

(Hickey, 2012)

Galvanic electric resistivity

Electromagnetic induction Slingram E-34

Fauchard, C., Mériaux, P., 2007, Geophysical and geotechnical methods for diagnosing flood protection dikes: Editions Quae, ISBN 978-27592-00313, 128 pp.

The Sacramento-San Joaquin estuary

Capacitively-coupled resistivity

Dipole-dipole CC resistivity surveys

Multi-channel analysis of surface waves

Schematic of the process of multi-channel analysis of R- surface waves (reproduced with permission from Park Seismic webpage www.parkseismic.com).

Sherman Island – A case study

Sandhu, 2017 Hayashi et al, 2013

Areas of future research

The Levee Project

The Levee Project is a collaborative educational project between CSU Stanislaus, Merced College, and Delta College. Its goal is to have students from the different institutions meet, work collaborative in the data acquisition, and develop interest in graduating from college with a STEM degree. Our “graduates” have used the data to prepare posters (AEG, GSA, WRPI, COAST, NASA, and JPL) and to apply for graduate school. Besides, there is nothing like a sunny day in the estuary! Resistivity mapping

Questions? hferriz@csustan.edu

Capacitively-coupled resistivity is a technique that arose from pioneering work in the US and Russia [Kuras et al. (2006) provide a good historical perspective]. The technique was further developed and popularized by Geometrics, through their OhmMapper instrument, which exploits the capacitor properties of shielded coaxial cables. The method relies on one transmitter and several receivers that are dragged behind a person

• r vehicle. In our case we used one transmitter and five receivers.

The method is comparable to the dipole-dipole method of resistivity surveying, where a current is injected using two current electrodes separated by a distance a, and the drop in the voltage of the potential field is measured by a different pair of potential electrodes, also separated by a distance a, which are placed at varying distances na from the current electrodes (where n is a number that varies from 0.5, 1.0, 1.5, 2.0, …). The known current and the voltage drop are used to calculate the resistance of the ground in

• hms, and using a suitable geometric factor, to calculate the resistivity of the ground in
• hms·m. In the case of the Geometrics OhmMapper, the “electrodes” are the woven

metallic shields of coaxial cables on both sides of the transmitter, the geometric function is a complicated expression (Geometrics, 2009), and the voltage “electrodes” are the shields of coaxial cables on both sides of each receiver.

CAPACITIVELY-COUPLED RESISTIVITY

OHM-MAPPER - THE DEVIL IS IN THE DETAILS

When the transmitter applies a current to the current “electrodes” one of them develops a positive charge and the other develops a negative charge. The current cannot flow through the ground because of the cable insulation. Instead, each cable behaves like a capacitor, where one plate is the woven metallic shield of the coaxial cable and the other is the ground. The cable with the negative charge repels electrons in the ground, while the cable with the positive charge attracts electrons, thus creating an electrical current in the ground and the instantaneous development of a potential field. At the same time all this is happening, the transmitter sends a radio signal to each of the potential receivers, imprinting on them the timing and intensity of the current. Just like in the case of the current “electrodes”, the potential “electrodes” measure the negative and positive charges induced in their capacitors to calculate the voltage drop and the corresponding apparent resistivity. The calculated value is an apparent resistivity in that a very simple assumption is made about the attribution of the resistivity value in the subsurface; the assumption is that it is the resistivity of a point at depth located at 45º down from the center of the current and potential electrodes, a point that we will call “attribution node”

Since there are five receivers, separated from each other by distance a, each electric pulse of the transmitter generates 5 data points at different depths. The array is dragged along the ground at a slow speed (say 3.6 km/hr or 60 m/min, or 1 m/sec) and a new pulse is delivered every 0.5 sec, so apparent resistivities are gathered every half meter (the precise number is determined by GPS), so in a very short time a dense swath of five data points at different depths can be acquired over a distance of several hundreds

• f meters. Our survey lines had typical lengths or 500 to 1,000 m.

To increase the depth of data collection, the initial distance between the transmitter and the first receiver (which must be of the magnitude na) can be increased by changing the spacing to a different value of na. In our case, we used a values of 2.5 m, and n values

• f 1, 2, 2.5, and 3.

The last step is to invert the data (Loke, 2013; Loke et al., 2013). We used a least- squares inversion with a “fast” Jacobian matrix (Loke et al., 2013) implemented by the program RES2DINV (Loke, 2004). In layman terms, a model of the resistivity distribution with depth is “guessed”, the apparent resistivity values such model would create at each

• f the attribution nodes is calculated, and is then compared with the field value for the

same attribution nodes. The values would of course be different, so the initial “guess” is refined, with the purpose of minimizing the square of the difference between the field attributed values and the model calculated values. After several iterations a best fit is achieved, and the resulting model is presented as the best possible model of resistivity distributions in a tomogram form (as we know such solutions are not unique, and it is good practice to produce two or three models by changing the inversion parameters).

Multichannel analysis of surface waves is a technique popularized by Park et al. (1999) and Miller et al. (2000) for the estimation of seismic velocities at shallow depths. Very readable explanations of the technique can be found in Park et al. (2007) and the website www.MASWA.com As explained by Park et al. (2007), the multichannel analysis

• f surface waves (MASW) method uses surface waves in the lower frequencies (e.g., 4-

100 Hz), which propagate through a depth of several tens meters, to estimate shear- wave velocity (Vs) variations with depth. Shear-wave velocity is directly proportional to the square root of the shear modulus μ and inversely proportional to the square root of the bulk density ρ [Vs = SQRT(μ/ρ)], which in turn are linked to the stiffness and compaction of the soil materials. The method was developed for use with Raleigh waves (R-waves), but in our case we used Love waves (L-waves) generated by hitting the end of a heavy railroad tie with a 10 lb sledge hammer (our selection was based on the pure shear nature of L-waves. Lane (2009) compared the phase velocity spectra of repeat surveys using R- and L-waves on the flood plain of the Tennessee River, where a few meters of soil cover limestone bedrock, and found that at this particular site the records from the Love wave data analysis produced a superior phase velocity spectrum in comparison to the spectrum

• btained from Rayleigh wave data.

MULTI-CHANNEL ANALYSIS OF SEISMIC SURFACE WAVES

For every location, or set, we stacked three “blows” of the hammer to cancel random noise and enhance the signal, collected the data using 10 horizontal field geophones with a natural frequency of 4.5 Hz, with a distance of 2.5 m between the source and the first geophone, and a constant separation of 2.5 m between geophones. For the sake of ease of data acquisition the geophones were mounted on heavy docking stations on a landstreamer, both manufactured by Geo Stuff, and the array was advanced by pulling it with a truck at 5 m intervals. The records of every five sets were gathered by using the common-depth point technique of seismic reflection. The sampling depth of a particular frequency component of surface waves is in direct proportion to its wavelength, so the measured surface wave velocity is both frequency- and depth-dependent. In other words, the surface waves disperse themselves at different depths as a function of their wavelengths/frequencies. The multichannel analysis of surface waves method uses this dispersion property of surface waves for the purpose of Vs profiling in 1D (depth) under each midpoint of the array. Since the array is being dragged along and every five sets are gathered, each gather (every 5 m) generates a 1D velocity profile. By contouring the 1D profiles along the entire length of the survey line (typically 500 to 1,000 m), a 2D tomogram of shear wave velocities is created. To facilitate identification and picking of the surface wave dispersion curve, a wavefield transform is applied to the seismic record to convert data from the offset-time dimension (x-t) to the frequency-wavenumber (f-k), frequency-slowness (f-p) or frequency-velocity (f-v) dimensions. Common schemes for applying this transform include the intercept time-slowness (tau-p) transform, also referred to as the slant stack transform (Thorson and Claerbout, 1985), f-k transform (Nolet and Panza, 1976), frequency domain beamformer (Johnson and Dudgeon, 1993), or phase shift transform (Park et al, 1998).

Dispersive phases show a distinct pattern of normal modes in low-velocity surface layers: sloping down from high phase velocities at low frequencies, to lower phase velocities at higher frequencies. The distinctive slope of dispersive waves is a real advantage of the 1/p-f analysis; other arrivals, such as body waves and air waves, cannot have such a slope, so the analyst can “ignore” them by picking only the points along the sloping maxima. In our case we used a Geometrics Geode exploration seismograph to collect the data, and the SeisImager/SW software package developed by Hayashi (2009) to process the data and develop the 2D tomograms. Unlike the normal dispersive pattern, as shown in Figure 5, in which phase velocity decreases as frequency increases, the data obtained in this study shows reversed dispersive character. Phase velocity increases with frequency, probably due to the seismic velocity inversion in which the high-velocity layer corresponding to levee embankment overlays the low-velocity layers corresponding to peat or clay. Inversion based on Love waves is still in the research and development stage, and there is no commercially available processing software for it. Shear-wave velocity tomograms were created by a simple wavelength transformation (Xia et al., 1999), in which wavelength calculated from phase velocity and frequency is divided by three and plotted at depth. Uncertainty is generally large in the surface wave data processing, including higher modes, even if the inversion is applied. It should be noted that accuracy and reliability of shear wave tomograms shown in this paper is limited, particularly in deeper layers beneath embankment. However, the S-wave velocity contrasts are large so we believe our interpretation of the results is robust.

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