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.

Systems and methods for picking seismic stacking velocity based on structures in a subterranean formation include: receiving seismic data representing a subterranean formation; generating semblance spectrums from the seismic data representing the subterranean formation; smoothing the semblance spectrums; and picking stacking velocities based on the smoothed semblance spectrums.

Systems and methods to estimate a pore pressure of source rock include a pore pressure estimation processor, an executable, or both, and are operable to (i) calculate an estimate pore pressure based on overburden gradient data, a compaction velocity profile, hydrocarbon maturity, and an unloading velocity profile, (ii) determine a total organic content (TOC) estimate of the source rock based on a bulk density at a vertical depth measured using the density logging tool, (iii) determine a correction factor based on (a) the TOC estimate and (b) vitrinite ratio R.sub.o data, and (iv) update the estimated pore pressure in real-time based on the correction factor.

The present invention discloses a system and method for phased array sound wave advanced geological exploration for a shield tunneling machine. The system includes a phased array sound wave emitting and receiving apparatus, a probe automatic telescopic apparatus, an automatic protection and cleaning apparatus, and a signal processing and imaging system. Sonic probes are installed on a side wall of a main spoke, opposite to a rotation direction, of a cutterhead of the shield tunneling machine, on the basis of automatic detection of a telescopic state and a contact state, sonic array probes are enabled to make contact with a tunnel face by a hydraulic push rod, a focus sound wave is emitted by using a phased array emitting technology, and a reflected wave signal with front geological information reflected from the front of the tunnel face is received. A scanning direction of a sound wave beam is controlled and changed continuously through a host system, on the premise of obtaining a suspected abnormal body position, the suspected position is imaged in detail by using a focusing image till scanning of a whole two-dimensional section is completed, then the cutterhead is rotated to change an arrangement direction of an array to continue scanning of a next two-dimensional section, and finally three-dimensional geological exploration in front of the tunnel face is realized.

An example method is for producing a seismic wave velocity model of a subsurface area. The method includes receiving, by a processor of a computing system, from a seismic receiver, seismic data input comprising a recorded seismic wave field. The method includes receiving, by the processor, an initial 1D velocity model of the subsurface area. The method includes determining, by the processor, a Laplace-Fourier transform of the recorded seismic wave field. The method includes regenerating, by the processor, the current 1D velocity model to generate inverted data representing the subsurface area. The method may include performing, by the processor, an upscaling of a plurality of 1D velocity models to produce a 3D velocity model.

A computer-implemented method may include obtaining seismic data acquired in a time-domain for a subterranean region of interest. The method may further include obtaining a property model for the subterranean region of interest. The method may further include determining one or more time shifts using a segment dynamic image warping function based on the seismic data and the property model. The method may further include determining an adjoint source operator using the derived time shift and one-way wave equation. The method may further include updating the property model using a gradient solver in a data-domain reflection traveltime inversion. The method may further include outputting the updated property model for the subterranean region of interest. The method may further include generating a seismic image for the subterranean region of interest using the updated property model.

A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.

A method for weathered layer correction of seismic data includes identifying arrival times in the seismic data corresponding to a weathered layer velocity gradient. A velocity model of the weathered layer is generated using the arrival times. The seismic data are time adjusted using the velocity model.

Methods of and systems for forming an image of a subterranean region of interest are disclosed. The method includes obtaining an observed seismic dataset and a seismic velocity model for the subterranean region of interest and generating a simulated seismic dataset based on the seismic velocity model and the source and receiver geometry of the observed seismic dataset. The method also includes forming a plurality of time-windowed trace pairs from the simulated and the observed seismic datasets, and forming an objective function based on a penalty function and a cross-correlation between the members of each pair. The method further includes determining a seismic velocity increment based on the extremum of the objective function and forming an updated seismic velocity model by combining the seismic velocity increment and the seismic velocity model, and forming the image of the subterranean region of interest based on the updated seismic velocity model.

Methods and systems, including computer programs encoded on a computer storage medium can be used for identifying primary-wave (P-wave) and secondary-wave (S-wave) characteristics of an underground formation by separating P-wave and S-wave modes of seismic data generated by applying a seismic source to a subterranean region of a geological area. Particle motion vectors of a P-wave are parallel to a propagation vector of the P-wave, whereas particle motion vectors of an S-wave are perpendicular to a propagation vector of the S-wave. The parallel and perpendicular relationship between the motion and propagation vectors of the respective P- and S-waves provide a basis for separating P- and S-wave components from a wavefield. The separation methodology extracts P-wave components and S-wave components from the wavefield based on an estimated angle between propagation vectors and wave motion vectors for the wavefield.