SIMULATING SPATIAL CONTEXT OF A DATASET

1 . A method comprising:

receiving an input dataset that represents partial spatial information of an area of interest;

providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest; and

using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, wherein each output dataset comprises simulated contextual spatial information for the area of interest.

2 . The method of claim 1 , wherein the machine learning model is a conditional Generative Adversarial Network (cGAN).

3 . The method of claim 1 , wherein the input dataset is a seismic dataset that represents the partial spatial information of the area of interest.

4 . The method of claim 1 , wherein the input dataset is an input seismic cube that has a first dimension, and wherein each output dataset is an output seismic cube that has a second dimension larger than the first dimension.

5 . The method of claim 1 , wherein the input dataset is a photographic image dataset that represents the partial spatial information of the area of interest.

6 . The method of claim 1 , further comprising:

training the machine learning model to generate, based on the partial spatial information, the contextual spatial information for the area of interest.

7 . The method of claim 6 , wherein the machine learning model is a conditional Generative Adversarial Network (cGAN), and wherein training the machine learning model comprises:

training a generator network of the cGAN to generate the contextual spatial information for the area of interest based on the partial spatial information, wherein the generator network is trained based on feedback received from a discriminator network of the cGAN, and wherein the discriminator network is configured to distinguish between real data and simulated data generated by the generator network.

8 . The method of claim 7 , wherein the real data and the simulated data are conditioned on training partial spatial information.

9 . The method of claim 1 , further comprising:

generating a model of the area of interest based on the at least one second seismic dataset.

10 . The method of claim 1 , wherein the area of interest is at least one of a surface or subsurface.

11 . A system comprising:

one or more processors configured to perform operations comprising:

receiving an input dataset that represents partial spatial information of an area of interest;

providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest; and

using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, wherein each output dataset comprises simulated contextual spatial information for the area of interest.

12 . The system of claim 11 , wherein the machine learning model is a conditional Generative Adversarial Network (cGAN).

13 . The system of claim 11 , wherein the input dataset is a seismic dataset that represents the partial spatial information of the area of interest.

14 . The system of claim 11 , wherein the input dataset is an input seismic cube that has a first dimension, and wherein each output dataset is an output seismic cube that has a second dimension larger than the first dimension.

15 . The system of claim 11 , wherein the input dataset is a photographic image dataset that represents the partial spatial information of the area of interest.

16 . The system of claim 11 , the operations further comprising:

training the machine learning model to generate, based on the partial spatial information, the contextual spatial information for the area of interest.

17 . The system of claim 16 , wherein the machine learning model is a conditional Generative Adversarial Network (cGAN), and wherein training the machine learning model comprises:

training a generator network of the cGAN to generate the contextual spatial information for the area of interest based on the partial spatial information, wherein the generator network is trained based on feedback received from a discriminator network of the cGAN, and wherein the discriminator network is configured to distinguish between real data and simulated data generated by the generator network.

18 . A non-transitory computer-readable storage medium storing instructions executable by a computer system, the instructions when executed by the computer system cause the computer system to perform operations comprising:

receiving an input dataset that represents partial spatial information of an area of interest;

providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest; and

using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, wherein each output dataset comprises simulated contextual spatial information for the area of interest.

19 . The non-transitory computer-readable storage medium of claim 18 , wherein the machine learning model is a conditional Generative Adversarial Network (cGAN).

20 . The non-transitory computer-readable storage medium of claim 18 , wherein the input dataset is a seismic dataset that represents the partial spatial information of the area of interest.