Unsolved Problems in Near Surface Geophysics

  • #1
AryaKimiaghalam
82
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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FAQ: Unsolved Problems in Near Surface Geophysics

What are the primary unsolved problems in near surface geophysics?

Primary unsolved problems in near surface geophysics include accurately characterizing subsurface heterogeneities, improving the resolution of geophysical imaging techniques, understanding the impact of environmental factors on geophysical measurements, developing better inversion algorithms, and integrating multi-disciplinary data to create comprehensive subsurface models.

How can we improve the resolution of geophysical imaging techniques?

Improving the resolution of geophysical imaging techniques can be achieved through advancements in sensor technology, the development of higher-frequency sources, enhanced data processing algorithms, and the use of machine learning to better interpret complex data sets. Additionally, combining multiple geophysical methods can help to provide a more detailed picture of the subsurface.

What are the challenges in characterizing subsurface heterogeneities?

Challenges in characterizing subsurface heterogeneities include the inherent complexity and variability of geological formations, limitations in current measurement technologies, the presence of noise in data, and the difficulty in accurately modeling the subsurface. Addressing these challenges requires improved data acquisition techniques, advanced modeling approaches, and better integration of different geophysical methods.

How do environmental factors impact geophysical measurements?

Environmental factors such as temperature, moisture content, and soil composition can significantly impact geophysical measurements by altering the physical properties of the subsurface materials. These changes can affect the propagation of seismic waves, the electrical conductivity, and the magnetic susceptibility of the ground, leading to variations in the data collected. Understanding and mitigating these effects is crucial for accurate subsurface characterization.

What are the current limitations of inversion algorithms in near surface geophysics?

Current limitations of inversion algorithms in near surface geophysics include the non-uniqueness of solutions, computational intensity, sensitivity to initial conditions, and the difficulty in incorporating complex geological structures. Improving inversion algorithms involves developing more robust mathematical models, leveraging high-performance computing, and integrating prior geological knowledge to constrain the solutions.

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