Abstract: The systems and methods described in this specification relate to generating a low frequency model of a subterranean formation for performing a seismic inversion. The systems and methods receive seismic data for a first region of the subterranean formation and well log data of one or more wells located at the first region. The systems and methods determine one or more relative layer attributes of the first region, one or more first input values for a machine learning model, and one or more second input values for the machine learning model. The systems and methods generate, a first relative low frequency model for the first region, and extrapolate, by executing the machine learning model by the processor, the first relative low frequency model to a second region of the subterranean formation.

Abstract: A surface analyzer is provided with a measuring unit, a scatter diagram generation unit, a cluster analysis unit, and a cluster region detection unit. The measuring unit acquires a signal reflecting a quantity of each of a plurality of components or elements that are analysis targets at a plurality of positions on a sample. The scatter diagram generation unit generates a scatter diagram based on a measurement result by the measuring unit. The cluster analysis unit performs the clustering of points in the scatter diagram. The cluster region detection unit acquires, based on clustering information given to each point in the scatter diagram by the cluster analysis unit, for each cluster, cluster region boundary information on a polygon having a predetermined number or less of vertices.

Abstract: A motion capture system and method are provided. In a motion capture method, a plurality of motion datasets are accessed. Each motion dataset is associated with a motion sensing unit at which timestamped motion data points of that motion dataset are generated, each motion sensing unit is configured to be in physical contact with a different part of a body of interest. Each timestamped motion data point is timestamped at the motion sensing unit at which it is generated using a clock time that is synchronized across the plurality of motion sensing units. The timestamped motion data points are processed to generate a kinematic model which describes motion of the respective parts of the body of interest.

Abstract: A method is presented wherein inversion for formation anisotropic constants is achieved using borehole sonic data.

Abstract: The present invention discloses a method for constructing a surface stress distribution cloud map based on critical refraction longitudinal wave detection. The method comprises: firstly, meshing a surface of a detected article; secondly, The mean transverse stress on different grid lines and the mean longitudinal stress on different grid lines were obtained by the critical refraction longitudinal wave detection method; next, calculating the equivalent stress of each mesh node according to the mean transverse stress on different mesh transverse lines and the mean longitudinal stress on different mesh longitudinal lines on the surface of the detected article; and finally, drawing a stress distribution cloud map of the surface of the detected article according to the equivalent stress. The present invention can obtain the stress situations at different points on the surface of the detected article.

Abstract: Examples described herein provide a computer-implemented method that includes analyzing a first dataset by applying the first dataset to a first model to generate a first result. The method further includes analyzing a second dataset by applying the second dataset to a second model to generate a second result. The method further includes performing validation on the first model and the second model by comparing the first result to the second result. The method further includes, responsive to determining that the first result and the second result match, modifying an operational action of a surface assembly based on at least one of the first result or the second result. The method further includes, responsive to determining that the first result and the second result do not match, updating at least one of the first model or the second model.

Abstract: A system can be provided for applying a dynamic filter to a velocity model for converting the domain of seismic data. The system can receive a velocity model for a geological area of interest. The system can apply a dynamic filter to the velocity model for smoothing an anomaly included in the velocity model. The system can apply the velocity model with the smoothed anomaly to seismic data associated with the geological area of interest for converting the domain of the seismic data.

Abstract: Limitations in accuracy and computing power requirements impeding conventional Kirchhoff migration and reverse time migration are overcome by using the wave-equation Kirchhoff, WEK, technique with Kirchhoff migration. WEK technique includes forward-propagating a low-frequency wavefield from a shot location among pre-defined source locations, calculating an arrival traveltime of a maximum amplitude of the low-frequency wavefield, and applying Kirchhoff migration using the arrival traveltime and the maximum amplitude.

Abstract: A compound property prediction method is provided for an electronic device. The method includes obtaining chemical structure information of a target compound, the chemical structure information including an atom and a chemical bond, modeling a chemical structure graph according to the chemical structure information, the chemical structure graph including a first node corresponding to the atom and a first edge corresponding to the chemical bond, constructing an original node feature of the first node and an original edge feature of the first edge, performing a message propagation on the first edge according to the original node feature of the first node and the original edge feature of the first edge to obtain propagation state information of the first edge, and predicting properties of the target compound according to the propagation state information of the first edge.

Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak source. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

Abstract: Embodiments herein relate to a computer-implemented technique that includes generating, in a first portion of a graphical user interface (GUI), a first graphical element related to reflection seismic data of an area of interest. The technique further includes generating, in a second portion of the GUI, a second graphical element related to well structural data of the area of interest. The technique further includes generating, in a third portion of the GUI, a third graphical element that is based on the reflection seismic data and the well structural data. In embodiments, an alteration of the first graphical element or the second graphical element results in a concurrent alteration of the third graphical element. Other embodiments may be described or claimed.

Abstract: A diagnostic disc includes a disc-shaped body having raised walls that encircle the interior of the disc-shaped body and at least one protrusion extending outwardly from the disc-shaped body. The raised walls of the disc-shaped body define a cavity of the disc-shaped body. A non-contact sensor is attached to each of the at least one protrusion. A a printed circuit board (PCB) is positioned within the cavity formed on the disc-shaped body. A vacuum and high temperature tolerant power source is disposed on the PCB along with a wireless charger and circuitry that is coupled to each non-contact sensor and includes at least a wireless communication circuit and a memory. A cover is positioned over the cavity of the disc-shaped body and shields at least a portion of the PCB, circuitry, power source, and wireless charger within the cavity from an external environment.

Abstract: Sonic logging data including a sonic waveform associated with a plurality of shot gathers is accessed. A transformation operator is applied to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator. A machine learning process is performed using the transformed sonic image to determine a sonic slowness associated with the sonic logging data. The sonic slowness is provided as an output.

Abstract: A range finding device includes a light-emitting unit including light-emitting regions; an optical element to guide light emitted from the light-emitting unit; a light-receiving unit including light receiving regions to receive light reflected from an object; and circuitry to control light emission amount of the light-emitting regions; measure an amount of light received at the light receiving regions; measure a distance to the object by measuring a time difference between a start time when the light is emitted from the light-emitting unit and a time when the light reflected from the object is received by the light receiving regions; cause the light-emitting regions to emit the same light amount as a preliminary light emission stage; control the light emission amount of the light-emitting regions for a main light emission stage based on the amount of light measured at the preliminary light emission stage; and measure the distance to the object.

Abstract: A process for generating a velocity model for a sediment-basement interface of a subsurface region includes receiving seismic data representing acoustic signals that are reflected from regions of the subsurface. The process includes receiving potential fields data comprising potential field values that are mapped to locations in the subsurface. The process includes generating weighted time-depth data pairs. The process includes selecting a velocity model that relates a velocity value to a depth value in a time-depth relationship. The process includes optimizing velocity coefficients of the velocity model by determining, for each velocity model of a set, a set of depth estimates for corresponding time values and comparing the set of depth estimates to depth values of the weighted time-depth data pairs. The process includes adjusting the velocity coefficients of the velocity model. The process includes generating a seismic image of the sediment-basement interface.

Abstract: Estimation of velocity models inclusive of receiving seismic data inclusive of data that corresponds to a seismic image, adding a velocity perturbation to a current velocity model that represents a portion of the subsurface responsible for a distortion in the seismic image to generate a perturbed velocity model, generating an image via seismic migration of the seismic data and the perturbed velocity model, generating and assigning a measure of quality to the image, determining whether the measure of quality assigned to the image is an optimal measure of quality at a particular location of the current velocity model, and updating the current velocity model to generate a revised velocity model utilizing the measure of quality assigned to the image when the measure of quality assigned to the image is determined to be the optimal measure of quality at the particular location of the current velocity model.

Abstract: A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.

Abstract: A seismic observation device includes: a waveform acquisition unit that acquires waveform data for a predetermined period including an observation start time of a P wave; a delay time specifying unit that inputs the waveform data to a trained model and acquires, from the trained model, a delay time from the observation start time of the P wave to an observation start time of an S wave; and an observation time estimation unit that estimates the observation start time of the S wave based on the observation start time of the P wave and the delay time.

Abstract: Provided are a fall detection apparatus and method. The fall detection method may include measuring first to third accelerations corresponding to a user, measuring first to third angular velocities corresponding to the user, determining an acceleration value for the user based on at least one of the first to third accelerations, determining an angular velocity value for the user based on at least one of the first and second angular velocities, determining an angle value for the user based on at least one of the first to third accelerations, calculating a triangle feature using the acceleration value, and detecting a fall of the user based on at least one of the acceleration value, the angular velocity value, the angle value, and the triangle feature.

Abstract: The present invention belongs to the field of geological exploration and specifically relates to the fault characterization method and system for precise navigation of deep oil and gas based on image fusion, aiming to solve the problem that faults in deep formations are difficult to characterize with conventional seismic interpretation methods. The present invention includes: obtaining amplitude gradient images by calculating amplitude gradient vectors; calculating dip attribute images based on the enhanced seismic data; fusing gradient amplitude attribute fault confidence region with dip angle attribute data volume that defines the fault position through a hierarchical wavelet transform method to obtain a superimposed fault attribute map; dividing bead-like structures based on superimposed fault attribute maps; calculating the score of branch fault data points based on the center point position of bead-like structures, and dividing and analyzing the dominant fault areas.