Body: <p>I am currently doing research work on an inversion of geophysical data using Machine Learning. I have come across some research work where a Convolutional Neural Network (CNN) has been used effectively for this purpose (for example, <a href="https://academic.oup.com/gji/article/218/2/817/5484841" rel="nofollow noreferrer">his</a>).</p>
  <p>I am particularly interested in how to prepare my input and output labelled data for this machine learning application, since the input will be the observed geophysical signal, and the label output will be the causative density or susceptibility distribution (for gravity and magnetic, respectively).</p>
  <p>I need some assistance and insight as to how to prepare the data for this CNN application.</p>
  <p><strong>Additional Explanation</strong></p>
  <p><em>Experimental setup:</em> Measurements are taking above the ground surface. These measurements are signals that reflect the distribution of a physical property (e.g., density) in the ground beneath. For modelling, the subsurface is discretised into squares or cubes each having a constant but unknown physical property (e.g., density).</p>
  <p>How it applies to CNN: I want my input data to be the Measurements taken above ground. The output should then be the causative density distribution (that is, the value of the density in each cube/squares)</p>
  <p><em><strong>See attached picture</strong></em> (flat top is the &quot;above ground&quot;, all other prisms represent the discretisation of the subsurface. I want to train the CNN to give out a density value for each cube in the subsurface, given the above ground measurements)</p>
  <p><a href="https://i.sstatic.net/vLcvu.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/vLcvu.png" alt="enter image description here" /></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-06T12:54:28.113Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-06T13:12:40.447Z
Comments.Text: No, not images. @norbertk the input data will be gravity signals measured in a grid (or line) setting above the surface, whilst the output would be the corresponding density distribution
Comments.UserDisplayName: W. Oni
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-06T14:00:12.183Z
Comments.Text: You mean of the density values? Yes
Comments.UserDisplayName: W. Oni
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-06T14:19:51.717Z
Comments.Text: I have added a picture for clarity
Comments.UserDisplayName: W. Oni
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-06T18:10:08.380Z
Comments.Text: Thanks for the answer. My main stumbling block now is how to prepare my data. How do I actually handle this aspect. Each input (the data signal) is an n-by-n matrix, and the output is an n-by-m matrix containing the density of each block. I would have thousands of such pairs of matrices. How do I store such data in a way that it's conformable to the CNN set up? This is my major confusion
Comments.UserDisplayName: W. Oni
Tags: <convolutional-neural-networks><ai-design><labeled-datasets>
Title: How do I prepare my data for a CNN to be applied to a geophysical-related problem?
UserDisplayName: W. Oni
ThreadText: # How do I prepare my data for a CNN to be applied to a geophysical-related problem?
  
  (Asked by: username_0 on 2021-09-06)
  
  I am currently doing research work on an inversion of geophysical data using Machine Learning. I have come across some research work where a Convolutional Neural Network (CNN) has been used effectively for this purpose (for example, [his](https://academic.oup.com/gji/article/218/2/817/5484841)).
  
  I am particularly interested in how to prepare my input and output labelled data for this machine learning application, since the input will be the observed geophysical signal, and the label output will be the causative density or susceptibility distribution (for gravity and magnetic, respectively).
  
  I need some assistance and insight as to how to prepare the data for this CNN application.
  
  **Additional Explanation**
  
  *Experimental setup:* Measurements are taking above the ground surface. These measurements are signals that reflect the distribution of a physical property (e.g., density) in the ground beneath. For modelling, the subsurface is discretised into squares or cubes each having a constant but unknown physical property (e.g., density).
  
  How it applies to CNN: I want my input data to be the Measurements taken above ground. The output should then be the causative density distribution (that is, the value of the density in each cube/squares)
  
  ***See attached picture*** (flat top is the "above ground", all other prisms represent the discretisation of the subsurface. I want to train the CNN to give out a density value for each cube in the subsurface, given the above ground measurements)
  
  [![enter image description here](https://i.sstatic.net/vLcvu.png)](https://i.sstatic.net/vLcvu.png)
  
  **Tags:** convolutional-neural-networks, ai-design, labeled-datasets
  > **Comments:**
  > **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  > **username_0**: No, not images. @norbertk the input data will be gravity signals measured in a grid (or line) setting above the surface, whilst the output would be the corresponding density distribution  
  > **username_0**: You mean of the density values? Yes  
  > **username_0**: I have added a picture for clarity  
  > **username_0**: Thanks for the answer. My main stumbling block now is how to prepare my data. How do I actually handle this aspect. Each input (the data signal) is an n-by-n matrix, and the output is an n-by-m matrix containing the density of each block. I would have thousands of such pairs of matrices. How do I store such data in a way that it's conformable to the CNN set up? This is my major confusion  
  
  ---
  
  ## 0 answers
  
  