Pitfalls in near-surface geophysical interpretation: Challenging paradigms and misconceptions

2018 ◽  
Vol 6 (4) ◽  
pp. SL1-SL9 ◽  
Author(s):  
David C. Nobes ◽  
Estella Atekwana

Too often, ideas become so well-established that they take on the roles of paradigms, and challenging those paradigms can be difficult, even if they are flawed. Similarly, misconceptions can take root and become firmly entrenched and again are difficult to dislodge. Both of these situations are fundamentally unscientific. Science makes progress when established theories are shown to be incorrect or at least incomplete. To do that, we have to let the data that we collect tell their stories. We should not impose models upon the data, but rather allow the data to yield those models that best represent those features that are absolutely necessary to fit the data, an approach often called “Occam’s inversion.” We also should not impose nonphysical and unscientific limits on our interpretation models. We evaluate several examples from our own experiences: the electrical properties of faults, nonuniqueness in potential fields, the influence of nonaqueous phase liquids and water on ground-penetrating radar and electrical resistivity, and the geophysical response of seafloor mineralization. In each case, a reviewer or another scientist questioned the conclusions using unscientific or incorrect arguments or assumptions. We must let the data speak.

2021 ◽  
pp. 1-53
Author(s):  
Lei Fu ◽  
Lanbo Liu

Ground-penetrating radar (GPR) is a geophysical technique widely used in near-surface non-invasive detecting. It has the ability to obtaining a high-resolution internal structure of living trunks. Full wave inversion (FWI) has been widely used to reconstruct the dielectric constant and conductivity distribution for cross-well application. However, in some cases, the amplitude information is not reliable due to the antenna coupling, radiation pattern and other effects. We present a multiscale phase inversion (MPI) method, which largely matches the phase information by normalizing the magnitude spectrum; in addition, a natural multiscale approach by integrating the input data with different times is implemented to partly mitigate the local minimal problem. Two synthetic GPR datasets generated from a healthy oak tree trunk and from a decayed trunk are tested by MPI and FWI. Field GPR dataset consisting of 30 common shot GPR data are acquired on a standing white oak tree (Quercus alba); the MPI and FWI methods are used to reconstruct the dielectric constant distribution of the tree cross-section. Results indicate that MPI has more tolerance to the starting model, noise level and source wavelet. It can provide a more accurate image of the dielectric constant distribution compared to the conventional FWI.


2018 ◽  
Vol 25 (4) ◽  
pp. 285-300 ◽  
Author(s):  
Çağlayan Balkaya ◽  
Ümit Yalçın Kalyoncuoğlu ◽  
Mehmet Özhanlı ◽  
Gözde Merter ◽  
Olcay Çakmak ◽  
...  

2009 ◽  
Vol 40 (1) ◽  
pp. 33-44 ◽  
Author(s):  
Nils Granlund ◽  
Angela Lundberg ◽  
James Feiccabrino ◽  
David Gustafsson

Ground penetrating radar operated from helicopters or snowmobiles is used to determine snow water equivalent (SWE) for annual snowpacks from radar wave two-way travel time. However, presence of liquid water in a snowpack is known to decrease the radar wave velocity, which for a typical snowpack with 5% (by volume) liquid water can lead to an overestimation of SWE by about 20%. It would therefore be beneficial if radar measurements could also be used to determine snow wetness. Our approach is to use radar wave attenuation in the snowpack, which depends on electrical properties of snow (permittivity and conductivity) which in turn depend on snow wetness. The relationship between radar wave attenuation and these electrical properties can be derived theoretically, while the relationship between electrical permittivity and snow wetness follows a known empirical formula, which also includes snow density. Snow wetness can therefore be determined from radar wave attenuation if the relationship between electrical conductivity and snow wetness is also known. In a laboratory test, three sets of measurements were made on initially dry 1 m thick snowpacks. Snow wetness was controlled by stepwise addition of water between radar measurements, and a linear relationship between electrical conductivity and snow wetness was established.


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