Abstract: Disclosed are a method for correcting a text, an electronic device and a storage medium. The method includes: acquiring a text to be corrected; acquiring a phonetic symbol sequence of the text to be corrected; and obtaining a corrected text by inputting the text to be corrected and the phonetic symbol sequence into a text correction model, in which, the text correction model obtains the corrected text by: detecting an error word in the text to be corrected, determining a phonetic symbol corresponding to the error word in the phonetic symbol sequence, and adding the phonetic feature corresponding to the phonetic symbol behind the error word to obtain a phonetic symbol text, and correcting the error word and the phonetic feature in the phonetic symbol text to obtain the corrected text.

Abstract: A translation method, a model training method, apparatuses, electronic devices and storage mediums, which relate to the field of artificial intelligence technologies, such as machine learning technologies, information processing technologies, are disclosed. In an implementation, a weight for each translation model in at least two pre-trained translation models translating a to-be-translated specified sentence is acquired based on the specified sentence and a pre-trained weighting model; and the specified sentence is translating using the at least two translation models based on the weight for each translation model translating the specified sentence.

Abstract: A method for training a speech translation model includes: obtaining a trained first text translation model and a speech recognition model, and constructing a candidate speech translation model to be trained based on the first text translation model and the speech recognition model; obtaining at least one of a first sample source language speech or a first sample source language text to obtain a training sample of the candidate speech translation model; and training the candidate speech translation model based on the training sample until the training is completed, and obtaining a trained target speech translation model.

Abstract: The present disclosure provides a translation method and apparatus, an electronic device, and a non-transitory storage medium. An implementation includes: determining an encoded feature of a sentence to be translated by an encoding module; determining, by a graph network module, a knowledge fusion feature of the sentence to be translated based on a preset graph network, wherein the preset graph network is constructed based on a polysemous word in a source language corresponding to the sentence to be translated and a plurality of translated words corresponding to the polysemous word in a target language; determining, by a decoding network, a translated sentence corresponding to the sentence to be translated based on the encoded feature and the knowledge fusion feature.

Abstract: A method includes seismic wave field continuation, imaging and data analysis steps that are applied in a near well region.

Abstract: A vector sensor system includes an optical fiber and a sensor array having a plurality of sensor levels and a plurality of optical fiber vector sensors, each sensor level having at least one of the optical fiber vector sensors. The sensor system further includes circuitry configured to provide optical input signals into the optical fiber and to receive optical output signals from the optical fiber. Each optical fiber vector sensor includes a vector mandrel and a first length of the optical fiber wound around the mandrel. The sensor levels are connected to one another by a second length of the optical fiber. Circuitry is configured to extract from the optical return signals backscattered light information from the first lengths of the optical fiber and to determine phase change information between the optical input signals and the optical output signals based on the backscattered light information.

Abstract: A computer-implemented method includes providing a first velocity model obtained from a vertical seismic profile survey representative of an upper region of a subterranean formation. Wavefields from the first velocity model are datumed using wave equations to a datum line between the upper region and a target area beneath the upper region to obtain datumed wavefields. The method further includes obtaining interferometric common shot data and interferometric common midpoint data from the datumed wavefield using wave equations at the datum line. The first velocity model, the datumed wavefield, wavefield equations, and the interferometric common midpoint data are then used to generate a second velocity model representative of velocities in the target area.

Abstract: A method includes seismic wave field continuation, imaging and data analysis steps that are applied in a near well region.

Abstract: A method includes providing a first velocity model obtained from a VSP survey representative of an upper region of a subterranean formation. Wavefield equations from the first velocity model are datumed to a datum line between the upper region and a target area beneath the upper region to obtain datumed wavefield equations. The method further includes obtaining interferometric common shot data and interferometric common midpoint data from the datumed wavefield equations at the datum line. The first velocity model, the datumed wavefield equations, and the interferometric common midpoint data are then used to generate a second velocity model representative of velocities in the target area.