Extrapolating green's function estimated using multidimensional deconvolution beyond receiver grid through deep learning

A computing system, comprising:

• one or more processors;
• and
• a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
• receiving input data, wherein the input data comprises an original upward-downward diffusion (UDD) seismic image and an original multi-dimensional deconvolution (MDD) seismic image;
• training a generator and a discriminator based upon the input data to produce a trained generator and a trained discriminator, wherein training comprises:
• training the generator to transform the original UDD seismic image to the original MDD seismic image, which includes generating a fake MDD seismic image based upon the original UDD seismic image;
• training the discriminator to distinguish the original MDD seismic image from the fake MDD seismic image;
• training the generator to transform the original MDD seismic image to the original UDD seismic image, which includes generating a fake UDD seismic image based upon the original MDD seismic image;
• and
• training the discriminator to distinguish the real UDD seismic image from the fake UDD seismic image;
• receiving new input data, wherein the new input data comprises a new original UDD seismic image and/or a new original MDD seismic image;
• and
• transforming the new original UDD seismic image into a first transformed seismic image using the trained generator, wherein the first transformed seismic image is the same as or more similar to the new original MDD seismic image;
• or
• transforming the new MDD seismic image into a second transformed seismic image using the trained generator, wherein the second transformed seismic image is the same as or more similar to the new original UDD seismic image.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:

• receiving input data, wherein the input data comprises an original upward-downward diffusion (UDD) seismic image and an original multi-dimensional deconvolution (MDD) seismic image;
• normalizing the input data to produce normalized input data, wherein normalizing the input data modifies amplitudes of the original UDD seismic image and the original MDD seismic image to be in a range from −1 to +1;
• iteratively training a generator and a discriminator based upon the normalized input data to produce a trained generator and a trained discriminator, wherein iteratively training comprises:
• (A) training the generator to transform the original UDD seismic image to the original MDD seismic image, wherein training the generator comprises:
• generating a fake MDD seismic image based upon the original UDD seismic image;
• determining an adversarial loss that occurs in response to generating the fake MDD seismic image, wherein the adversarial loss is determined using the discriminator;
• generating a reconstructed UDD seismic image using the generator based upon the fake MDD seismic image;
• determining a difference between the original UDD seismic image and the reconstructed UDD seismic image, which represents a forward cycle consistency loss;
• and
• adjusting weights of the generator based upon the adversarial loss and the forward cycle consistency loss, which causes a new original MDD seismic image to be more difficult for the discriminator to distinguish from a new fake MDD seismic image that is generated by the generator;
• (B) training the discriminator to distinguish the original MDD seismic image from the fake MDD seismic image, wherein training the discriminator comprises:
• determining a first loss to classify the original MDD seismic image as real;
• determining a second loss to classify the fake MDD seismic image as fake;
• and
• adjusting weights of the discriminator based upon the first and second losses to more accurately distinguish the new original MDD seismic image from the new fake MDD seismic image;
• (C) training the generator to transform the original MDD seismic image to the original UDD seismic image, wherein training the generator comprises:
• generating a fake UDD seismic image based upon the original MDD seismic image;
• determining an adversarial loss that occurs in response to generating the fake UDD seismic image, wherein the adversarial loss is determined using the discriminator;
• and
• adjusting weights of the generator based upon the adversarial loss, which causes a new original UDD seismic image to be more difficult for the discriminator to distinguish from a new fake UDD seismic image that is generated by the generator;
• (D) training the discriminator to distinguish the real UDD seismic image from the fake UDD seismic image, wherein training the discriminator comprises:
• determining a third loss to classify the original UDD seismic image as real;
• determining a fourth loss to classify the fake UDD seismic image as fake;
• and
• adjusting weights of the discriminator based upon the third and fourth losses to more accurately distinguish the new original UDD seismic image from the new fake UDD seismic image;
• receiving new input data, wherein the new input data comprises the new UDD seismic image and/or the new MDD seismic image;
• transforming the new UDD seismic image into a first transformed seismic image using the trained generator, wherein the first transformed seismic image is the same as the new MDD seismic image;
• and
• transforming the new MDD seismic image into a second transformed seismic image using the trained generator, wherein the second transformed seismic image is the same as the new UDD seismic image.