Sensor receiver nulls and null steering. One example embodiment is method in which a direction from a sensor position to a noise source is determined. A coordinate rotation is applied to a first set of signal values, wherein each signal value of the first set of signal values is based on an output of a corresponding component of a three-component particle motion sensor at the sensor position. The applying generates a rotated set of signal values. The coordinate rotation comprises a coordinate rotation transforming a first set of coordinate axes to a second set of coordinate axes, wherein the first set of coordinate axes has each coordinate axis aligned with a corresponding component of the three-component particle motion sensor at the sensor position, and the second set of coordinate axes comprises a first axis pointed in a direction opposite the direction from the sensor position to the noise source.

In acoustic well logging, for each inversion depths of a well at which logging of data occurs, acoustic log signals representative of waveforms received at acoustic receivers are processed in a frequency domain to derive field dispersion curve(s). A neural net is operated to generate formation shear slowness value(s) from the curve(s), and resulting signal(s) indicative of shear slowness values are saved, transmitted, plotted, printed or processed. An apparatus for carrying out the method includes a logging tool having at least one activatable acoustic wave source; spaced and acoustically isolated therefrom in the logging tool an array of acoustic detectors that on the detection of acoustic wave energy generate electrical or electronic log signal(s) characteristic of acoustic energy waves detected by the acoustic detector(s); and at least one processing device associated with or forming part of the logging tool for processing the log signal(s).

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 system and method for forming a denoised seismic image of a subterranean region of interest is provided. The method includes obtaining an observed seismic dataset for a subterranean region of interest and forming a plurality of common midpoint gathers having a plurality of traces, each trace having an ordinate series of sample values, a common-midpoint location and a unique value of a secondary sorting parameter. The method further includes, for each of the plurality of common midpoint gathers, selecting a set of spatially adjacent common midpoint gathers using a spatial windowing operator and determining a weighted midpoint gather based on the common midpoint gather and the set of spatially adjacent common midpoint gathers. The method still further includes forming a denoised seismic dataset by combining the weighted midpoint gathers using an inverse spatial windowing operator and forming the denoised seismic image based on the denoised seismic dataset.

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 and a system for implementing the method are disclosed wherein the source wavelet, input parameter models, and seismic 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, suppresses low wavenumber and low frequency noises, by balancing lateral and vertical amplitudes to produce an image of subsurface reflectors located within a survey area having higher lateral resolutions and wavenumbers, as well as higher high-cut frequencies, and lower low-cut frequencies in complex media, than could otherwise not be achieved by other methods commonly known in the art.

The invention notably relates to a computer-implemented method for processing a 4D seismic signal relative to a subsoil, the subsoil including a zone subject to extraction and/or injection, the method comprising: providing the 4D seismic signal; identifying a part of the 4D seismic signal corresponding to a zone of the subsoil distinct from the zone subject to extraction and/or injection; determining a noise model of the 4D seismic signal based on the identified part of the 4D seismic signal; and processing the 4D seismic signal based on the noise model. This improves the field of 4D seismic data processing.

A method for calculating a velocity vp(f, T.sub.opt) of a spatially aliased wave that propagates along a cable includes tensioning the cable, wherein plural sensors are distributed along the cable; measuring with the plural sensors a parameter that is associated with vibrations that propagate along the cable; calculating a phase velocity vp(f) of the spatially aliased wave that propagates along the cable, as a function of a time frequency fin a spatial-temporal frequency domain FK; calculating a model-based velocity vp(f, T) of the spatially aliased wave as a function of the time frequency f and a tension T in the cable; and calculating the velocity vp(f, T.sub.opt) of the spatially aliased wave using a model-guided regression, which is based on the phase velocity vp(f) and the model-based velocity vp(f, T). The velocity vp(f, T.sub.opt) is a function of the temporal frequency f.

The present disclosure discloses a method of obtaining a seismic while drilling signal. The method comprises the following steps: arranging geophones by using a first observation method to obtain a first seismic reference signal and a second seismic reference signal; arranging geophones by using a second observation method to obtain first seismic data; arranging geophones by using a third observation method to obtain second seismic data; comparing the first seismic reference signal with the second seismic reference signal to obtain a first output reference signal, and optimizing the first output signal to obtain a second output reference signal. The present disclosure obtains square matrix and near-wellhead seismic while drilling data through the combination of geophone square matrix combined observation, near-wellhead observation, and survey line observation, the data acquisition efficiency is relatively high, the signal-to-noise ratio is high, and thus, the problem of near-surface noise interference is effectively solved.

A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.