This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.

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.

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.

Systems and methods include a computer-implemented method for determining normalized apparent power. Drilling acoustic signals corresponding to a time domain and generated during drilling of a well. A fast Fourier transformation (FFT) is performed using the drilling acoustic signals to generate FFT data. Normalized FFT data is generated using normalization parameters and a drill string rotation rate record of a drill string used to drill the well. The drill string rotation rate is received during drilling. Normalized apparent power is determined from data points of a predetermined top percentage of the normalized FFT data within a lithological significant frequency range. The normalized apparent power is a measure of the power of the drilling acoustic signals and it is a function of the amplitude and frequency of the normalized FFT data. The lithological significant frequency range is a frequency range within which the drill sounds are more closely related with lithology.

The present invention provides an intelligent geophysical data acquisition system and acquisition method for shale oil and gas optical fiber. A pipe string is arranged in a metal casing, and an external armored optical cable is fixed outside the metal casing; an, internal armored optical cable is fixed outside the pipe string; the external armored optical cable comprises a downhole acoustic sensing optical cable, two multi-mode optical fibers, a strain optical cable and a pressure sensor array, and further comprises horizontal ground acoustic sensing optical cables arranged in the shallow part of the ground according to an orthogonal grid, and artificial seismic source excitation points arranged on the ground according to the orthogonal grid.

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.

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.

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.

Accordingly, there are disclosed herein geologic modeling methods and systems employing reference-based inversion of seismic image volumes. An illustrative method embodiment includes: (a) obtaining a measured seismic image volume; (b) determining a reference seismic image volume based on a reference model; (c) deriving a synthesized seismic image volume from a geologic model; (d) detecting at least one geologic model region where the synthesized seismic image volume and the measured seismic image volume are mismatched; (e) finding a reference model region where the reference seismic image volume best matches the measured seismic image volume; (f) replacing content of the at least one geologic model region with content of the reference model region to obtain an improved geologic model; and (g) outputting the improved geologic model.

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.