A frequency response estimation method to compensate for channel differences in distributed acoustic sensing systems include two compensation algorithms, online and offline, and these two compensation algorithms are presented to generate standardized mel-frequency features, as an input to neural networks. By this scheme, the variance of mel-frequency feature space is decreased and normalized among different channels, which enables to use less training data and smaller architectures for classification and anomalous event detection tasks.

A method and apparatus of locating subsurface geohazards in a geographical area that includes: receiving a plurality of seismic trace signals in the geographical area based on a shot gather from a seismic shot source; isolating and stacking the plurality of seismic trace signals to generate a windowed trace signal associated with refraction traces from the seismic shot source; transforming the windowed trace signal to a frequency domain; calculating a low frequency to high frequency ratio for the transformed trace signal; outputting the calculated ratio to a two-dimensional array representing the geographical area at a source location and at a mean receiver location; repeating the steps for a plurality of other shot gathers in the geographical area; and multiplying each source location ratio with one or more mean receiver location ratios on the two-dimensional array to generate a final frequency ratio map.

Disclosed are methods of marine 3D seismic data acquisition that do not require compensation for winds and currents.

Disclosed are methods of marine 3D seismic data acquisition that do not require compensation for winds and currents.

A mechanical-model based earthquake-induced landslide hazard assessment method in earthquake-prone mountainous area includes: obtaining the cohesion and internal friction angle through a geological map of the study area and a geotechnical physical parameter; obtaining simulated ground motions by combining a pulse-like ground motion effect model and a pulse-like ground motion response model; calculating slope permanent displacement according to the simulated ground motions, the cohesion, the internal friction angle and other parameters; obtaining a statistical relationship between the permanent displacement and a landslide probability according to permanent displacement data derived from historical earthquake-induced landslides and historical strong earthquake records; and predicting earthquake-induced landslide probability according to the slope permanent displacement and the statistical relationship between the permanent displacement and the landslide probability, and quantitatively evaluating earthquake-induced landslide hazard through the earthquake-induced landslide probability.

Seismic data may provide valuable information with regard to the description such as the location and/or change of hydrocarbon deposits within a subsurface region of the Earth. The present disclosure generally discusses techniques that may be used by a computing system to interpolate or deblend data utilizing a projection on convex sets (POCS) interpolation algorithm. The utilized POCS interpolation algorithm operates in parallel for frequency of a set of frequencies of a seismic frequency spectrum.

Systems and methods include a method for deblending signal and noise data. A shot domain for actual sources, a receiver domain for virtual sources, and a receiver domain for actual sources are generated from blended shot data. A dictionary of signal atoms is generated. Each signal atom includes a small patch of seismic signal data gathered during a small time window using multiple neighboring traces. A dictionary of noise atoms is generated. Each noise atom includes a small patch of seismic noise data gathered during a small time window using multiple neighboring traces. A combined signal-and-noise dictionary is generated that contains the signal atoms and the noise atoms. A sparse reconstruction of receiver domain data is created from the combined signal-and-noise dictionary. The sparse reconstruction is split into deblended data and blending noise data based on atom usage to create deblended shot domain gathers for actual sources.

Methods and systems for determining an interbed multiple attenuated pre-stack seismic dataset are disclosed. The methods include forming a post-stack seismic image composed of post-stack traces from the pre-stack seismic dataset and identifying a first, second, and third post-stack horizon on each of the post-stack traces. The methods further include for each pre-stack trace, generating a first, second, and third multiple-generator trace based on the first, second and third post-stack horizon and determining a correlation trace based, at least in part, on a correlation between the first multiple-generator trace and the second multiple-generator trace. The methods still further include predicting an interbed multiple trace by convolving the correlation trace and the third multiple-generator trace, determining an interbed multiple attenuated trace by subtracting the interbed multiple trace from a corresponding pre-stack seismic trace, and determining the interbed multiple attenuated pre-stack seismic dataset by combining the interbed multiple attenuated traces.

Systems and a method are disclosed. The method includes obtaining a plurality of raw seismic datasets for a subterranean region of interest, wherein each raw seismic dataset is generated by a high-speed train traversing a train track at a unique speed. The method further includes determining a plurality of processed seismic datasets by processing each of the plurality of raw seismic datasets and determining a final seismic dataset by combining the plurality of processed seismic datasets. The method still further includes identifying subterranean features within the subterranean region of interest using the final seismic dataset.

Implementations described and claimed herein provide systems and methods for reservoir modeling. In one implementation, an input dataset comprising seismic data is received for a particular subsurface reservoir. Based on the input dataset and utilizing a deep learning computing technique, a plurality of trained reservoir models may be generated based on training data and/or validation information to model the particular subsurface reservoir. From the plurality of trained reservoir models, an optimized reservoir model may be selected based on a comparison of each of the plurality of reservoir models to a dataset of measured subsurface characteristics.