The current disclosure is directed to methods and systems to determine properties of a subterranean formation located below a body of water. The methods and systems compute synthetic pressure and velocity vector wavefields that represent acoustic energy interactions within a model environment that comprises a model body of water located above a model subterranean formation. The model environment is separated into a stationary region and a time-varying region. The methods and systems include determining properties of the subterranean formation by iteratively adjusting the model environment to approximate the actual subterranean formation. The model environment is iteratively adjusted until a minimum difference between the synthetic pressure and velocity vector wavefields computed for each change to the model environment and actual pressure and velocity wave fields obtained from a marine seismic survey of the subterranean formation is achieved.

Disclosed herein are various embodiments of a method and apparatus to enhance spatial sampling in all nominally horizontal directions for Dual-Sensor seismic data at the bottom of a body of water such as the ocean. The sensor apparatus on the water bottom is comprised of sensing elements for linear particle motion, for rotational motion, for pressure measurement, for pressure gradients, and for static orientation. Stress and wavefield conditions known at the water bottom allow numerical calculations that yield enhanced spatial sampling of pressure and nominally vertical linear particle motion, up to double the conventional (based on physical sensor locations) Nyquist spatial frequency in two nominally horizontal independent directions. The method and apparatus have a wide range of application in Ocean Bottom Seismic 3D, 4D, and Permanent Reservoir Monitoring surveys, and other marine seismic surveys, in oil and gas exploration and production.

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.

Systems and methods for generating seismic images of subterranean features including: receiving raw seismic data of a subterranean formation; selecting a portion of the raw seismic data; transforming the selected portion of the raw seismic data from a first domain to a second domain; generating soft constraint data corresponding to the selected portion of the raw seismic data; calculating at least one weight using the generated soft constraint data; generating a weighted transformed data set by applying at least one weight to the transformed selected portion of the raw seismic data; selecting at least one data point of the generated weighted transformed data set; and removing the selected at least one data point from the weighted transformed data set to generate revised seismic data.

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.

Various implementations directed to quality control and preconditioning of seismic data are provided. In one implementation, a method may include receiving particle motion data from particle motion sensors disposed on seismic streamers. The method may also include performing quality control (QC) processing on the particle motion data. The method may further include performing preconditioning processing on the QC-processed particle motion data. The method may additionally include attenuating noise in the preconditioning-processed particle motion data.

A method includes receiving seismic data of a seismic survey; defining a two-dimensional domain in dimensions x and y; identifying a target trace (S, R) of the seismic survey where S represents a source at (x.sub.s, y.sub.s) and where R represents a receiver at (X.sub.R, y.sub.R); defining with respect to the two-dimensional domain, a source trace (S, X.sub.1) as a primary trace, a receiver trace (R, X.sub.2) as a primary trace, and a generator trace (X.sub.1, X.sub.2) as associated with an interbed multiple generator; convolving the primary traces and crosscorrelating with the generator trace for a plurality of different (X.sub.1, X.sub.2) pairs where each of the plurality of (X.sub.1, X.sub.2) pairs defines a line segment where the line segments are substantially parallel to one another; and, based at least in part on the convolving the primary traces and crosscorrelating with the generator trace, generating seismic data with attenuated multiple energy.

The present invention relates to a method and system of warning of estimated time of arrival and expected intensity in a given area resulting from a seismic movement, which comprises a plurality of measurement elements or monitoring stations configuring a network of measurement elements, said method comprising the steps of: arranging the plurality of measurement elements in a specific area; communicating each of the measuring elements with at least one common point or control center; storing in each measuring element an identifier that will uniquely identify the same within the network of monitoring stations; transforming by means of the measuring element the measurement of the movement to a scalar or set of scalars representing the intensity of the movement; transmitting periodically and in real time the measurement and the unique identifier thereof to the control center for the duration of the movement; recording through the control center the individualized measurements from each of the monitoring stations; verifying through the control center if the received measurement corresponds to an actual earthquake or a mechanical noise; designating a destination point; determining the expected intensity and expected arrival time; automatically dispatching an earthquake early warning to the destination point.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a seismic data de-blending model. In one aspect, a method comprises: obtaining a plurality of de-blending training examples, wherein each de-blending training example defines: (i) one or more blended seismic traces, and (ii) for each blended seismic trace, a corresponding plurality of target unblended seismic traces; using the de-blending training examples to train a de-blending model having a plurality of de-blending model parameters, comprising, for each de-blending training example: processing the one or more blended seismic traces of the training example using the de-blending model to generate an output which defines, for each of the one or more blended seismic traces of the training example, a corresponding plurality of estimated unblended seismic traces; and adjusting values of the plurality of de-blending model parameters.

Disclosed are methods, systems, and computer-readable medium for a full seismic wavefield de-aliasing workflow. To achieve the de-aliasing, the workflow employs a four-dimension (4D) anti-leakage anti-aliasing regularization algorithm. The workflow involves application of successive de-aliasing steps while restricting computations only to the significant spatial dimensions. In areas of strong elastic property variation in the near-surface, the benefit of de-aliasing the full wavefield is both significant and demonstrable. In addition to achieving de-aliased sampling of the full wavefield, the workflow reduces the complexity of both the computational and geophysical aspects of the problem of de-aliasing full wavefields.