Abstract: A machine-implemented method, at least one non-transitory computer-readable medium storing instructions, and a computing system are provided for attenuating noise. A computing system receives a seismic image and generates a first image using a first neural network configured to identify low-frequency ground roll in a seismic image, and a second image using a second neural network configured to identify reflections in the seismic image. A combined image is generated by combining the first image and the second image. The first neural network and the second neural network are adjusted to reduce a difference between the combined image and the seismic image using frequency constraint to guide separation of the seismic image into the first image and the second image.

Abstract: A method includes receiving seismic data and an attribute, the seismic data and the attribute representing a subsurface volume, receiving training labels for the seismic data, training a machine learning model to identify features in the subsurface volume and to reconstruct the attribute, based at least in part on the seismic data and the training labels, generating an interpretation by predicting features in the subsurface volume using the machine learning model, generating a reconstructed attribute using the machine learning model, comparing the reconstructed attribute with the attribute that was received to identify one or more sections of the subsurface volume, and generating a recommendation to acquire additional training labels in the one or more sections that were identified.

Abstract: Certain aspects are directed to a method for estimating subsurface properties. The method may generally include receiving seismic data associated with a subsurface region; incorporating noise into the seismic data to create modified seismic data; inputting the modified seismic data into a machine-learning model configured to output a predicted subsurface property; obtaining from the machine-learning model, the predicted subsurface property, wherein the predicted subsurface property is based on the modified seismic data; and deriving an associated measure of uncertainty for the predicted subsurface property, wherein the predicted subsurface property is associated with the subsurface region.

Abstract: A method includes receiving a seismic survey that includes a plurality of seismic slices. The method also includes converting the seismic slices into an embedding. The embedding includes one or more vectors. Each of the vectors includes more than 3 dimensions. The method also includes generating a plot based at least partially upon the embedding.

Abstract: Systems, computer-readable media, and methods are provided. Blended baseline data is generated by numerically blending unblended baseline data according to a simultaneous shooting schedule scheme. Pseudo-deblended baseline seismic data is generated by applying a pseudo-deblending procedure to the blended baseline data. Machine learning labels are generated from common gathers of the pseudo-deblended baseline data and the unblended baseline data. A neural network is trained using the labels, the common gathers of the pseudo-deblended baseline data, and the unblended baseline data to produce common gathers of deblended baseline seismic data from the common gathers of the pseudo-deblended baseline seismic data.

Abstract: A method includes receiving a seismic survey that includes a plurality of seismic slices. The method also includes converting the seismic slices into an embedding. The embedding includes one or more vectors. Each of the vectors includes more than 3 dimensions. The method also includes generating a plot based at least partially upon the embedding.

Abstract: A method for seismic surveying includes receiving a baseline dataset and a plurality of sparse monitoring datasets, generating a decimated baseline dataset by removing one or more sources, receivers, or both from the baseline dataset, generating a reconstructed baseline dataset by inputting the decimated baseline dataset into a machine learning model, generating reconstructed monitoring datasets by inputting the plurality of sparse monitoring datasets to the machine learning model, the machine learning model having been trained based on a comparison of the reconstructed baseline dataset to the baseline seismic dataset, determining accuracies for the plurality of sparse monitoring datasets by comparing the reconstructed monitoring datasets to the baseline dataset, and selecting one or more survey geometries for arranging physical sources and physical receivers in a seismic survey based at least in part on the accuracies of the plurality of sparse monitoring datasets.

Abstract: A method for seismic processing includes extracting, using a first machine learning model, one or more seismic features from seismic data representing a subsurface domain, receiving one or more well logs representing one or more subsurface properties in the subsurface domain, and predicting, using a second machine learning model, the one or more subsurface properties in the subsurface domain at a location that does not correspond to an existing well based on the seismic data, the one or more well logs, and the one or more seismic features that were extracted from the seismic data.

Abstract: Systems, computer-readable media, and methods are provided. Blended baseline data is generated by numerically blending unblended baseline data according to a simultaneous shooting schedule scheme. Pseudo-deblended baseline seismic data is generated by applying a pseudo-deblending procedure to the blended baseline data. Machine learning labels are generated from common gathers of the pseudo-deblended baseline data and the unblended baseline data. A neural network is trained using the labels, the common gathers of the pseudo-deblended baseline data, and the unblended baseline data to produce common gathers of deblended baseline seismic data from the common gathers of the pseudo-deblended baseline seismic data.

Abstract: A method includes obtaining a synthetic seismogram representing a seismic well tie, a shifted synthetic seismogram representing the seismic well tie, and a shift input including domain shift data for converting well log data from a depth domain to a time domain, generating a shift label based on the synthetic seismogram and the shifted synthetic seismogram using a machine learning model, determining that an accuracy of the shift label is less than a threshold based on a comparison of the shift input and the shift label, adjusting the machine learning model in response to determining that the accuracy of the shift label is less than the threshold, predicting a second shift for a second seismic well tie from a second seismogram using the machine learning model, and generating a seismic image based on the second seismic well tie, the second seismogram, and the second shift.

Abstract: A method for modeling a subsurface property for a subterranean volume of interest includes receiving input measurement data representing a subterranean volume of interest, predicting a subsurface property based at least in part on the input measurement data using a first machine learning model, predicting a subsurface property model based at least in part on the subsurface property, the input measurement data, or both, using a second machine learning model, predicting synthetic measurement data based at least in part on the subsurface property model using a third machine learning model, a physics-based model, or both, comparing the synthetic measurement data and the input measurement data, and training the first machine learning model, the second machine learning model, or both based at least in part on the comparing.

Abstract: A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.

Abstract: A machine-implemented method, at least one non-transitory computer-readable medium storing instructions, and a computing system are provided for attenuating noise. A computing system receives a seismic image and generates a first image using a first neural network configured to identify low-frequency ground roll in a seismic image, and a second image using a second neural network configured to identify reflections in the seismic image. A combined image is generated by combining the first image and the second image. The first neural network and the second neural network are adjusted to reduce a difference between the combined image and the seismic image using frequency constraint to guide separation of the seismic image into the first image and the second image.

Abstract: A method for seismic processing includes extracting, using a first machine learning model, one or more seismic features from seismic data representing a subsurface domain, receiving one or more well logs representing one or more subsurface properties in the subsurface domain, and predicting, using a second machine learning model, the one or more subsurface properties in the subsurface domain at a location that does not correspond to an existing well based on the seismic data, the one or more well logs, and the one or more seismic features that were extracted from the seismic data.

Abstract: Various embodiments of the present disclosure are directed to systems and methods for data collection using fiber-optic cable in a well, and analysis of the data to determine instantaneous frequency, instantaneous phase, instantaneous amplitude, and/or dominant frequency. These measures can be used to determine parameters associated with the operation of the well. The parameters can be used to control the operation of the well and/or the fracturing process.

Abstract: A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.

Abstract: A method can include receiving a first trained machine model trained via unsupervised learning using unlabeled seismic image data; receiving labeled seismic image data acquired via an interactive interpretation process; and building a second trained machine model, as initialized from the first trained machine model, via supervised learning using the received labels, where the second trained machine model predicts stratigraphy of a geologic region from seismic image data of the geologic region.

Abstract: Various embodiments of the present disclosure are directed to systems and methods for data collection using fiber-optic cable in a well, and analysis of the data to determine instantaneous frequency, instantaneous phase, instantaneous amplitude, and/or dominant frequency. These measures can be used to determine parameters associated with the operation of the well. The parameters can be used to control the operation of the well and/or the fracturing process.