A method and a system for implementing the method are disclosed wherein the seismic input data and land acquisition input data may be obtained from a non-flat surface, sometimes mild or foothill topography as well as the shot and receiver lines might not necessarily be straight, and often curve to avoid obstacles on the land surface. In particular, the method and system disclosed, decomposes the cross-spread data into sparse common spread beams, then maps those sparse beams into common-spread depth domain, in order to finally stack them to construct the subsurface depth images. The common spread beam migration and processing have higher signal to noise ratio, as well as faster turn-around processing time, for the cross-spread land acquisition over the common-shot or common offset beam migration/processing. The common spread beam migration method and system disclosed, will eventually help illuminate and interpret the hydro-carbonate targets for the seismic processing.

The disclosed technology provides solutions for identifying noise in seismic profile data sets. In some aspects, a process of the disclosed technology includes steps for receiving wellbore data including seismic measurements, processing the wellbore data to generate a seismic input image including visual representations of the one or more seismic measurements, and processing the seismic input image to identify a noise region in the seismic input image. Systems and machine-readable media are also provided.

A computer system manages model information for defining a U-Net configured to execute, on the input time-series data, an encoding operation for extracting a feature map relating to the target wave by using downsampling blocks and a decoding operation for outputting data for predicting the first motion time of the target wave by using upsampling blocks, executes the encoding operation and the decoding operation on the input time-series data by using the model information. The downsampling blocks and the upsampling blocks each includes a residual block. The residual block includes a time attention block calculates a time attention for emphasizing a specific time domain in the feature map. The time attention block includes an arithmetic operation for calculating attentions different in time width, and calculates a feature map to which the time attention is added by using the attentions.

The present disclosure relates to a method of processing seismic signals comprising: receiving a set of seismic signals, applying a wavelet transformation to the set of signals and generating transformed signals across a plurality of scales. Then for each scale determining coherence information indicative of the transformed signals and generating a comparison matrix comparing the transformed signals, then outputting seismic attribute information based on combined coherence information.

Computing device, computer instructions and method for processing energy at a free-surface reflection relating to an air-water interface. The method includes receiving input seismic data recorded with seismic sensors; receiving wave-height data that describes an actual shape of a top surface of a body of water; processing up-going energy at a receiver and down-going energy following a reflection at the sea-surface, using the input seismic data and a linear operator modified to take into account the wave-height data; and generating an image of the subsurface based on the up-going energy or the down-going energy or a combination of the input seismic data and one of the up-going or down-going energy.

Seismic data of a subterranean region is received by data processing apparatus. The seismic data includes multiple seismic data points. For each seismic data point, gradients are computed based on the received seismic data and a dip angle is computed based on the gradients for the each seismic data point. The dip angle is smoothed using anisotropic diffusion.

An optical communication system that enables any deployed fiber-optic cable to function as an earthquake-detection sensor. In an example embodiment, a WDM optical transmitter of one network node operates to transmit a CW optical signal together with legacy data-carrying optical signals. At another network node, a low-complexity, low-latency coherent optical receiver is used to obtain time-resolved measurements of the Stokes parameters of the CW optical signal. The signal-processing chain of the optical receiver employs digital filtering to select frequency components of the measurements streams corresponding to seismic disturbances of the fiber-optical cable connecting the nodes. The selected frequency components are then used to compute values of an earthquake indicator, which are reported to a network controller. Based on such reports from three or more nodes, the network controller can determine the epicenter and magnitude of the earthquake and, if warranted, may generate a tsunami forecast.

A simultaneous sources seismic acquisition method is described that introduces notch diversity to improve separating the unknown contributions of one or more sources from a commonly acquired set of wavefield signals while still allowing for optimal reconstruction properties in certain diamond-shaped regions. In particular, notch diversity is obtained by heteroscale encoding.

The present disclosure describes methods and systems for estimating dispersion spectra for full waveform sonic (FWS) logging. One computer-implemented method includes receiving FWS data, performing frequency-spatial (FX) transform on the FWS data, using a nonstationary predictive error filtering (PEF) inversion on the transformed FWS data to estimate local matrix L and matrix P, calculating an inverse covariance matrix based on the estimated local matrix L and matrix P, and obtaining a nonstationary maximum likelihood method (MLM) spectra based on the inverse covariance matrix.

A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.