Unsolved Problems in Near Surface Geophysics

  • #1
AryaKimiaghalam
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6
Hi everyone,

In the past when I was doing my undergrad in physics, I sometimes looked at this page for inspiration in research. However, I was unable to find a similar one for applied geophysics.

Currently, my area of research is exploration non-seismic geophysics, where the focus is on near surface phenomena. I know that in the past century, physics-based methods such as gravimetry, magnetics, time and frequency domain electromagnetics, self potential (SP) and Induced Polarization (IP) have been developed and successfully used for mineral exploration.However when I speak to people in the field, they argue that there is "no juice to be squeezed" and that the probability of a new method emerging is next to none. Apparently the current focus is rather on data integration from the existing methods, with most using AI for that purpose. It is hard to agree with this, since it could imply that applied geophysics is a dead-end subfield of physics.

My question to the community, particularly those who worked with or studied geophysics is the following: What are some unsolved problems in applied geophysics in your opinion? Is there potential for the emergence of a new physics-based method (e.g., IP and SP)? Or rather, is there potential for applying new physics to develop novel geophysical measurement techniques ?(e.g., building better magnetometers).

Very curious to know your thoughts!
Thanks.
 
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  • #3
AryaKimiaghalam said:
What are some unsolved problems in applied geophysics in your opinion?
How have the tools changed, and what will, or has now, become possible?

Numerical computation certainly supports seismic. But what other wave analysis is possible?

Autonomous drones can gather more data, faster, at lower cost.
What could they measure?
What about IR at night?
 
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What are the primary challenges in achieving higher resolution in near-surface geophysical imaging?

The primary challenges include limitations in sensor technology, environmental noise, and the inherent physical properties of the subsurface materials. Higher resolution requires advanced sensors that can detect smaller changes and sophisticated data processing techniques to accurately interpret these signals amidst various noise sources.

How do variations in material properties affect the accuracy of near-surface geophysical methods?

Variations in material properties such as moisture content, porosity, and mineral composition can significantly affect the propagation of geophysical signals (e.g., seismic waves, electromagnetic fields) through the subsurface. These variations can lead to ambiguities in data interpretation, making it difficult to accurately map subsurface features.

What are the current limitations in electromagnetic imaging techniques for near-surface applications?

Current limitations include the depth of penetration, sensitivity to small contrasts in conductivity, and susceptibility to cultural noise (noise from surrounding infrastructure and human activity). These factors can restrict the effectiveness of electromagnetic methods in detailed imaging of the subsurface, particularly in urban or developed areas.

How can we improve the integration of different geophysical methods to solve complex subsurface problems?

Improving integration involves enhancing data processing algorithms to effectively combine datasets from different geophysical methods, such as seismic, resistivity, and ground-penetrating radar. This requires developing robust mathematical models that can accommodate the different scales and types of data, as well as investing in multi-disciplinary collaboration among experts.

What are the emerging technologies that could revolutionize near-surface geophysics?

Emerging technologies include drone-based geophysical sensors, machine learning algorithms for data interpretation, and advanced computational methods like full waveform inversion for higher resolution imaging. These technologies promise to increase the efficiency, resolution, and depth of geophysical surveys, potentially overcoming many of the current limitations.

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