Monoclinic morphotropic phase and grain size-induced polarization rotation in Pb(Mg1∕3Nb2∕3)O3–PbTiO3

2006 ◽  
Vol 89 (25) ◽  
pp. 252906 ◽  
Author(s):  
J. Carreaud ◽  
J. M. Kiat ◽  
B. Dkhil ◽  
M. Algueró ◽  
J. Ricote ◽  
...  
Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1547-1558 ◽  
Author(s):  
L. D. Slater ◽  
D. R. Glaser

Resistivity and induced polarization (IP) measurements (0.1–1000 Hz) were made on clay‐free unconsolidated sediments from a sandy, alluvial aquifer in the Kansas River floodplain. The sensitivity of imaginary conductivity σ″, a fundamental IP measurement, to lithological parameters, fluid conductivity, and degree of saturation was assessed. The previously reported power law dependence of IP on surface area and grain size is clearly observed despite the narrow lithologic range encountered in this unconsolidated sedimentary sequence. The grain‐size σ″ relationship is effectively frequency independent between 0.1 and 100 Hz but depends on the representative grain diameter used. For the sediments examined here, d90, the grain diameter of the coarsest sediments in a sample, is well correlated with σ″. The distribution of the internal surface in the well‐sorted, sandy sediments investigated here is such that most of the sample weight is likely required to account for the majority of the internal surface. We find the predictive capability of the Börner model for hydraulic conductivity (K)estimation from IP measurements is limited when applied to this narrow lithologic range. The relatively weak dependence of σ″ on fluid conductivity (σw) observed for these sediments when saturated with an NaCl solution (0.06–10 S/m) is consistent with competing effects of surface charge density and surface ionic mobility on σ″ as previously inferred for sandstone. Importantly, IP parameters are a function of saturation and exhibit hysteretic behavior over a drainage and imbibition cycle. However, σ″ is less dependent than the real conductivity σ′ on saturation. In the case of evaporative drying, the σ″ saturation exponent is approximately half of the σ′ exponent. Crosshole IP imaging illustrates the potential for lithologic discrimination of unconsolidated sediments. A fining‐upward sequence correlates with an upward increase in normalized chargeability Mn, a field IP parameter proportional to σ″. The hydraulic conductivity distribution obtained from the Börner model discriminates a hydraulically conductive sand–gravel from overlying medium sand.


2014 ◽  
Vol 12 (12) ◽  
pp. 121404-121407 ◽  
Author(s):  
Zheng Tan Zheng Tan ◽  
Xianping Sun Xianping Sun ◽  
Jun Luo Jun Luo ◽  
Yong Cheng Yong Cheng ◽  
Xiuchao Zhao Xiuchao Zhao ◽  
...  

2002 ◽  
Vol 207 (1-6) ◽  
pp. 201-208 ◽  
Author(s):  
V.M. Entin ◽  
I.I. Ryabtsev ◽  
A.E. Boguslavsky ◽  
Yu.V. Brzhazovsky

2018 ◽  
Vol 16 (2) ◽  
pp. 25
Author(s):  
Dicky Ahmad Zaky ◽  
Suparwoto Suparwoto

The spectral induced polarization (SIP) method can provide apparent complex resistivity based on measurements of multi frequency. SIP method also can provide more detail information about physical properties of rocks and minerals because SIP can give spectral parameters or Cole-Cole parameters such as, changeability (m), time constant (τ) and frequency dependence (c). An Experimental study in laboratory has been conducted to knowing the SIP response of some test sample. The measurement system is built with digital oscilloscope Pico ADC-100 as device for sampling the input and output voltage. Amplifier is used to doubled up the signal and input differential. The range frequency of measurement is 10−2 Hz - 103 Hz. Porouspot Cu − CuSO4 is used to minimize the polarization at potential electrode. A Matlab listings is used to calculate the response of impedance and phase. The result from calibration that used the parallel circuit RC indicate that the measurement system was good. SIP response of porous model indicate that the response form an asymptotic resistivity, and the peak of phase is in the range frequency where the dispersion happen. The result also indicate that resistivity of small grain size model is larger than the big grain size model. Result from sample of mineralized rocks did not indicate a perfect SIP response, it is influenced by the contact between mineral and water was minimum.


2021 ◽  
Author(s):  
Lukas Aigner ◽  
Timea Katona ◽  
Hadrien Michel ◽  
Arsalan Ahmed ◽  
Thomas Hermans ◽  
...  

<p>Detailed information on the clay content of the subsurface and its spatial distribution plays a critical role in the interaction between surface- and groundwater. In this study, we investigate a new methodology to integrate data measured with electromagnetic and electrical geophysical methods, namely, the transient electromagnetic (TEM) and the spectral induced polarization (SIP) to quantify subsurface clay content in an imaging framework. The methodology is tested in data sets collected at a quarry close to Vienna and consists of a ca. 10 m thick clay layer below a ca. 8 m thick overburden of sandy silts. Our data set includes SIP data collected along a 315 m long profile with an electrode separation of 5 m in a frequency range from 0.1 to 225 Hz. Along this profile, we measured 26 TEM soundings using a 12.5 m loop with 24 windows recording in a time range between 4 and 140 μs. Ground truth information corresponds to grain size analysis conducted in 25 soil samples collected in a depth from 5 to 28 m. SIP inversion results at a single frequency provided structural a-priori information to improve the inversion of the TEM data. The inverted TEM conductivity model, nearest to the position of soil sample collection, was correlated to the grain size distribution and the resulting positive exponential relationship was used to obtain vertical 1D variations of clay content with depth. All sounding positions were interpolated to obtain a 2D image of subsurface clay content. This clay content variations were then compared to images of the Cole-Cole parameters, describing the frequency dependence of SIP imaging results. To evaluate the uncertainty in our clay estimations, we applied the Bayesian evidential learning 1D imaging (BEL1D). We obtained uncertainties of layer thickness, resistivity, and clay content by integrating the clay-conductivity relationship derived from TEM data into the BEL1D framework.</p>


2007 ◽  
Vol 15 (15) ◽  
pp. 9476 ◽  
Author(s):  
P. S. Davids ◽  
B. A. Block ◽  
M. R. Reshotko ◽  
K. C. Cadien

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