Disclosed are methods, systems, and computer-readable medium to perform operations including: generating, using a source wavelet and a current velocity model, modeled seismic data of the subterranean formation; applying a pre-condition to a seismic data residual calculated using the modeled seismic data and acquired seismic data from the subterranean formation; generating a velocity update using the source wavelet and the pre-conditioned seismic data residual; updating, using the velocity update, the current velocity model to generate an updated velocity model; determining that the current velocity model satisfies a predetermined condition; and responsively determining that the updated velocity model is the velocity model of the subterranean formation.

A method for waveform inversion, the method including receiving observed data d, wherein the observed data d is recorded with sensors and is indicative of a subsurface of the earth; calculating estimated data p, based on a model m of the subsurface; calculating, using a trained neural network, a misfit function J.sub.ML; and calculating an updated model m.sub.t+1 of the subsurface, based on an application of the misfit function J.sub.ML to the observed data d and the estimated data p.

Methods, systems, and computer-readable medium to perform operations including: clipping an average velocity grid of a seismic reference surface (SRSAV), in an oil and gas field, to remove average velocity data of a region containing high-angle, horizontal (HA/HZ) boreholes, wherein the seismic reference surface approximates a geological reference surface; based on (i) a depth grid of the geological reference surface (GRSD) generated using HA/HZ borehole data, and (ii) a time grid of the seismic reference surface (SRST), generating borehole average velocity grid (BAV) along the HA/HZ boreholes; gridding the BAV with the clipped SRSAV to generate a hybrid seismic borehole average velocity grid (HSBAV) of the oil and gas field; and based on the HSBAV and the SRST, generating a hybrid seismic geological depth grid (HSGD) of the oil and gas field.

Methods and systems for identifying near-surface heterogeneities in a subterranean formation using surface seismic arrays can include: recording raw seismic data using sensors at ground surface; applying a band bass filter to the raw seismic data using a central frequency; picking a phase arrival time for the filtered data; generating an initial starting phase velocity model for tomographic inversion from the raw seismic data; applying tomographic inversion to the filtered data to generate a dispersion map associated at the central frequency; repeating the applying a band bass filter, picking a phase arrival time, generating an initial starting velocity model, and applying tomographic inversion steps for each of a set of central frequencies; and generating a three-dimensional dispersion volume representing near-surface conditions in the subterranean formation by combining the dispersion maps.

Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.

An apparatus, method, and system for determining body wave slowness from guided borehole waves. The method includes selecting a target axial resolution based on the size of a receiver array, obtaining a plurality of waveform data sets corresponding to a target formation zone and each acquired at a different shot position, computing a slowness-frequency 2D dispersion semblance map for each waveform data set, stacking the slowness-frequency 2D dispersion semblance maps to generate a stacked 2D semblance map, and determining a body wave slowness from the extracted dispersion curve. The method may also include generating a self-adaptive weighting function based on a dispersion model and the extracted dispersion curve, fitting the weighted dispersion curve and the dispersion model to determine a body wave slowness that minimizes the misfit between the weighted dispersion curve and the dispersion model. The method can be applied to both frequency-domain and time-domain processing.

A method and system for forming a seismic image of a subterranean region are disclosed. The method includes determining an initial plan for a seismic survey with a value for each member of a set of acquisition parameters and acquiring a first seismic dataset from a first portion of the seismic survey based on the initial plan. The method further includes transmitting the first seismic dataset to a seismic processor, determining a first seismic image from the first seismic dataset by performing expedited seismic processing and determining a first updated plan for the seismic survey based on the first seismic image and acquiring a second seismic dataset from a second portion of the seismic survey based on the first updated plan. The method still further includes transmitting the second seismic dataset to the seismic processor and determining the seismic based on the first seismic dataset and the second seismic dataset.

Systems and methods are provided for determining a formation body wave slowness from an acoustic wave. Waveform data is determined by logging tool measuring the acoustic wave. Wave features are determined from the waveform data and a model is applied to the wave features to determine data-driven scale factors The data-driven scale factors can be used to determine a body wave slowness within a surrounding borehole environment and the body wave slowness can be used to determine formation characteristics of the borehole environment.

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.

A method may include obtaining a P-wave velocity model and velocity ratio data regarding a geological region of interest. The method may further include generating, based on the P-wave velocity model and the velocity ratio data, an initial S-wave velocity model regarding the geological region of interest. The method may further include determining various velocity boundaries within the initial S-wave velocity model using a trained model. The method may further include updating the initial S-wave velocity model using the velocity boundaries, an automatically-selected cross-correlation lag value based on various seismic migration gathers, and a migration-velocity analysis to produce an updated S-wave velocity model. The method further includes generating a combined velocity model for the geological region of interest using the updated S-wave velocity model and the P-wave velocity model.