A non-blended dataset related to a same surveyed area as a blended dataset is used to deblend the blended dataset. The non-blended dataset may be used to calculate a model dataset emulating the blended dataset, or may be transformed in a model domain and used to derive sparseness weights, model domain masking, scaling or shaping functions used to deblend the blended dataset.

Techniques for determining a wellbore drilling path includes identifying input seismic data associated with a subterranean zone that includes a wellbore drilling target. The input seismic data includes primary seismic events and multiple seismic events. The input seismic data is processed to remove the multiple seismic events and at least one of the primary seismic events from the input seismic data. An orthogonalization of the processed input seismic data is performed to recover the at least one primary seismic event into a seismic image of the subterranean zone that excludes at least a portion of the multiple seismic events. A wellbore path is determined from a terranean surface toward the wellbore drilling target for a drilling geo-steering system based on the seismic image of the subterranean zone.

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.

A method and apparatus for seismic analysis include obtaining an initial geophysical model and seismic data for a subsurface region; producing a subsurface image of the subsurface region with the seismic data and the geophysical model; generating a map of one or more geologic features of the subsurface region by automatically interpreting the subsurface image; and iteratively updating the geophysical model, subsurface image, and map of geologic features by: building an updated geophysical model based on the geophysical model of a prior iteration constrained by one or more geologic features from the prior iteration; imaging the seismic data with the updated geophysical model to produce an updated subsurface image; and automatically interpreting the updated subsurface image to generate an updated map of geologic features. The method and apparatus may also include post-stack migration, pre-stack time migration, pre-stack depth migration, reverse-time migration, gradient-based tomography, and/or gradient-based inversion methods.

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 are provided for generating a model for detection of seismic events. In this regard, one or more processors may receive from one or more stations located along an underwater optical route, one or more time series of polarization states of a detected light signal during a time period. The one or more processors may transform the one or more time series of polarization states into one or more spectrums in a frequency domain. Seismic activity data for the time period may be received by the one or more processors, where the seismic activity data include one or more seismic events detected in a region at least partially overlapping the underwater optical route. The one or more processors then generate a model for detecting seismic events based on the one or more spectrums and the seismic activity data.

Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

A method is claimed for automatically transforming sonic well logs from a depth domain to a seismic two-way travel-time domain. The method includes obtaining a training well with a measured sonic well log in the depth domain and a borehole seismic dataset in the depth domain and obtaining an application well with only a measured sonic well log in the depth domain. The method further includes training a network to predict a transformed sonic well log for the training well based, at least in part, on the measured sonic well log and the borehole seismic dataset in the training well, and predicting with the network, the transformed sonic well log in the application well.

A mechanical-model based earthquake-induced landslide hazard assessment method in earthquake-prone mountainous area includes: obtaining the cohesion and internal friction angle through a geological map of the study area and a geotechnical physical parameter; obtaining simulated ground motions by combining a pulse-like ground motion effect model and a pulse-like ground motion response model; calculating slope permanent displacement according to the simulated ground motions, the cohesion, the internal friction angle and other parameters; obtaining a statistical relationship between the permanent displacement and a landslide probability according to permanent displacement data derived from historical earthquake-induced landslides and historical strong earthquake records; and predicting earthquake-induced landslide probability according to the slope permanent displacement and the statistical relationship between the permanent displacement and the landslide probability, and quantitatively evaluating earthquake-induced landslide hazard through the earthquake-induced landslide probability.

A computer implemented method for analyzing a reservoir grid modeling a reservoir geological formation is provided in which the reservoir grid corresponds to a 3D grid of cells associated to respective values of at least one geological property. The method includes obtaining a 4D seismic image of the reservoir geological formation. A skeleton of the 4D seismic image is calculated, and the skeleton extends between at least one origin and a plurality of extremities. Each point of the skeleton is associated to a value of the at least one geological property of the reservoir grid. Flow time values are calculated for a fluid flowing from the origin to the extremities along the skeleton, based on the at least one geological property values associated to the points of the skeleton. The reservoir grid is calculated based on the flow time values.