A method includes receiving modelled seismic data that is to be recognized by the at least one classification and/or segmentation processor. The modelled seismic data can be represented within a transform domain. The method includes generating an output via the at least one processor based on the received modelled seismic data. The method also includes comparing the output of the at least one processor with a desired output. The method also includes modifying the at least one processor so that the output of the processor corresponds to the desired output.

This disclosure relates to systems and methods for collecting, integrating, processing, distributing, and analyzing spatial and/or spatio-temporal information associated with a variety of data sources and/or locations. In some embodiments, systems and methods described herein allow for collection and integration of information included in one or more spatial and/or spatio-temporal data streams and/or other related information that may be utilized in connection with one or more analytical processes. In certain embodiments, the disclosed embodiments may allow a user to, among other things, interact with spatio-temporal information associated with a variety of diverse data sources, generate visualizations using such data, and/or perform desired analytical queries based on the data.

The present invention provides non-transitory computer-readable media and systems are suitable for evaluating a dynamic wellbore azimuth and inclination measurement based on measurements acquired by a downhole tool capable of acquiring accelerometer (gravity) and magnetic field measurements representative of the earth's gravitational and magnetic fields. These non-transitory computer-readable media and systems can also be used for evaluating static inclination and azimuth measurements. These non-transitory computer-readable media and systems comprising the present invention provide an improvement over the prior art for their function and address many shortcomings of prior art.

The present invention provides methods and systems are presented suitable for evaluating a dynamic wellbore azimuth and inclination measurement based on measurements acquired by a downhole tool capable of acquiring accelerometer (gravity) and magnetic field measurements representative of the earth's gravitational and magnetic fields. These methods and systems can also be used for evaluating static inclination and azimuth measurements. These methods and systems comprising the present invention provide an improvement over the prior art for their function and address many shortcomings of prior art.

A method can include receiving a digital operational plan that specifies computational tasks for seismic workflows, that specifies computational resources and that specifies execution information; dispatching instructions that provision the computational resources for one of the computational tasks for one of the seismic workflows; issuing a request for the execution information; receiving the requested execution information during execution of the one of the computational tasks using the provisioned computational resources; and, based on the received execution information indicating that the execution of the one of the computational tasks deviates from the digital operational plan, dispatching at least one additional instruction that provisions at least one additional computational resource for the one of the computational tasks for the one of the seismic workflows.

The present invention discloses a low signal-to-noise ratio (SNR) seismic event detection model based on a deep residual shrinkage network (DRSN). Considering the different noises contained in seismic records acquired by different seismic sensors, a DRSN based on a residual network was constructed to detect seismic events from low SNR seismic records. In the constructed network, a soft thresholding function (a shrinkage function) was inserted into the deep network structure as a nonlinear transform layer to effectively filter the impact of noise-related features, and an attention mechanism, together with an adaptive soft thresholding block, was also incorporated to automatically obtain the optimal denoising threshold for seismic signals, which ensures different signals are processed to the best effect. After training, the DRSN adaptively determines the denoising threshold so that each seismic signal has its own threshold set and seismic events can be accurately detected under strong background noise.