Cascaded deep-learning techniques for generating high resolution horizon data

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 unfiltered seismic data corresponding to a subsurface region of interest (ROI);
• filtering the unfiltered seismic data to generate filtered seismic data;
• retrieving a first horizon label as a two-dimensional (2D) mask;
• training a first horizon deep learning model utilizing the filtered seismic data and the first horizon label as 2D mask, wherein the trained first horizon deep learning model has a first output that is a 2D probability map having a first resolution, wherein the first output provides an approximate location of one or more horizon picks, and wherein the first horizon deep learning model is an image segmentation convolutional neural network;
• and
• applying a second horizon deep learning model to a second horizon label as a one-dimensional (1D) mask and a portion of the unfiltered seismic data, wherein the first and second horizon labels represent the same data, wherein the same data in the first and second horizontal labels is formatted differently, wherein the portion of the unfiltered seismic data is within a threshold distance of the approximate location of the one or more horizon picks and further wherein the second horizon deep learning model has a second output that is one or more specific horizon locations having a second resolution.

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 unfiltered seismic data corresponding to a subsurface region of interest (ROI);
• filtering the unfiltered seismic data to generate filtered seismic data, wherein the filtered seismic data is a vertically resampled version of the unfiltered seismic data for the ROI;
• retrieving a first horizon label as a two-dimensional (2D) mask from a user or a data storage component;
• training a first horizon deep learning model utilizing the filtered seismic data and the first horizon label as 2D mask, wherein the trained first horizon deep learning model has a first output that is a 2D probability map having a first resolution, wherein the 2D probability map provides an approximate location of one or more horizon picks, wherein the first horizon deep learning model is an image segmentation convolutional neural network;
• and
• retrieving a second horizon label as a one-dimensional (1D) mask from the user or data storage component, wherein the first and second horizon labels represent the same data, and wherein the same data in the first and second horizontal labels is formatted differently;
• applying a second horizon deep learning model to the second horizon label as 1D mask and a portion of the unfiltered seismic data that is within a threshold distance of the approximate location of the one or more horizon picks, wherein the second horizon deep learning model has a second output that is one or more specific horizon locations having a second resolution, wherein the second resolution is higher resolution than the first resolution, wherein the second horizon deep learning model is one of a segmentation convolutional neural network and a regression convolutional neural network, wherein the second resolution is identical to an unfiltered seismic data resolution, and wherein the second output comprises a plurality of one-dimensional vector outputs representing horizon locations;
• displaying one or both of the first output and the second output;
• and
• performing a worksite action in response to one or both of the first output and the second output, wherein the worksite action comprises generating and transmitting a signal that causes a physical action to occur at the worksite that includes the subsurface ROI, and wherein the physical action includes selecting a specific worksite.