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.

Abstract: A system for monitoring a pipe network for a fire protection system having an evaluation unit with a communication device, and at least one sensor which is arranged on or within at least one pipe of a plurality of pipes of the pipe network and is in communicative signal connection with the communication device. The at least one sensor is configured to collect measurement data indicative of changes in a hydraulic condition of the at least one pipe and to transmit this measurement data to the communication device of the evaluation unit. The evaluation unit is configured to evaluate the measurement data based on assigning the at least one pipe to the specific pipe category in order to generate an evaluation data record indicative of the hydraulic condition of the at least one pipe.

Abstract: This disclosure presents a fault prediction system using a deep learning neural network, such as a convolutional neural network. The fault prediction system utilizes as input seismic data, and then derives various seismic attributes from the seismic data. In various aspects, the seismic attributes can be normalized and have importance coefficients determined. A sub-set of seismic attributes can be selected to reduce computing resources and processing time. The deep learning neural network can utilize the seismic data and seismic attributes to determine parameterized results representing fault probabilities. The fault prediction system can utilize the fault probabilities to determine fault predictions which can be represented as a predicted new seismic data, such as using a three-dimensional image.

Abstract: System and methods are disclosed herein for creating interaction path records using time thresholds between interaction paths. The system retrieves the time threshold and a plurality of user interaction records that each include a timestamp. The system sorts the plurality of user interaction records based on the timestamp. The system then identifies the path-start record as being separated from a prior interaction by an amount of time that exceeds the time threshold. The system identifies the path-end record as being separated from a subsequent interaction by an amount of time that exceeds the time threshold. The system creates an interaction path record, stores that records, and generates a summary with information about the path.

Abstract: A method for picking first breaks on a synthetic seismic dataset for a full wavefield travel-time inversion is provided. This method includes obtaining a synthetic seismic dataset and for each synthetic trace, determining a plurality of synthetic trace amplitude maxima and minima, determining a modified energy ratio trace, and forming a trace mask based at least in part, on an amplitude of the synthetic trace and on an amplitude of the modified energy ratio trace. The method further includes determining for each synthetic trace, a positive estimated first break based on the plurality of synthetic trace amplitude maxima and the trace mask and a negative estimated first bread based on the plurality of synthetic trace amplitude minima and the trace mask. The method concludes with determining a predicted first break for each synthetic trace from the positive estimate first break and the negative estimate first break.

Abstract: The systems, methods, and apparatuses described herein provide integrated weather forecast products designed to assist operations managers with operational decision-making related to a designated event or set of events. The present disclosure provides a way to process weather data from various sources and in diverse data formats containing varying spatial resolutions and temporal resolutions, in order to generate an integrated and cohesive weather projection product such that the weather projection product is continuous in both spatial and temporal domains, subject to data availability, relative to a designated event or set of events.

Abstract: A seismic observation device includes a data acquisition unit that acquires measurement data from each of a plurality of sensors that measure different types of state quantities related to movement of a ground, and an event determination unit that determines whether or not a predetermined event related to the movement of the ground has occurred on the basis of the measurement data from the plurality of sensors.

Abstract: A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

Abstract: Systems and methods of tracking attributes of objects, including a sensor configured to detect data related to one or more attributes of the object, a tracking device attachable to the object to transmit data detected by the sensor, a software application modifiable by a user, the software application being configured to receive transmitted data from the tracking device, firmware within a chip on the tracking device configured to determine a frequency of data transmissions from the tracking device to the software application based on transmission rules related to characteristics of transmitted data, and a display configured to show the frequency of data transmissions from the tracking device to the software application.

Abstract: A multi-scale unsupervised seismic velocity inversion method based on an autoencoder for observation data. Large-scale information is extracted by the autoencoder, which is used for guiding an inversion network to complete the recovery of different-scale features in a velocity model, thereby reducing the non-linearity degree of inversion. A trained encoder part is embedded into the network to complete the extraction of seismic observation data information at the front end, so it can better analyze the information contained in seismic data, the mapping relationship between the data and velocity model is established better, then the inversion method is unsupervised, and location codes are added to the observation data to assist the network in perceiving the layout form of an observation system, which facilitates practical engineering application. Thus a relatively accurate inversion result of the seismic velocity model when no real geological model serves as a network training label can be achieved.

Abstract: An example method for monitoring operations of a business organization electronic computing device includes executing a checklist on a monitoring electronic computing device to obtain an operational status of an application being implemented on the business organization electronic computing device. Data is received from the business organization electronic computing device regarding the operational status of the application being implemented on the business organization electronic computing device. When a determination is made that the data identifies an operational threat for the business organization electronic computing device, an action is proposed or implemented to remediate the operational threat. An implementation of the checklist is adjusted based on an effectiveness of the action to remediate the operational threat.

Abstract: Data relating to an amount of contaminant in a fluid is obtained from a contaminant detection device. Aeration detection raw voltage data output and particle detection raw voltage data output from the contaminate detection device are received at an external data processing system separate and independent from the contaminate detection device. The aeration detection raw voltage data output and particle detection raw voltage data output are analyzed and cleaned to determine real time information on the amount of air bubbles and solid contaminate particles in the fluid, and then supplied to an onboard monitoring and controlling module of a machine for controlling one or more operations onboard the machine based on the determined amount of solid contaminate particles.

Abstract: A method of predicting parameters of an earthquake uses an array of ionosondes to scan an observed volume of an ionosphere located above a seismically active zone. The method includes monitoring ionograms provided by the array of ionosondes; detecting the presence of at least one seismic-induced irregularity (SII); determining a first predicted parameter corresponding to an epicenter location; and determining one or more predicted parameters selected from a group consisting of a magnitude, a time of occurrence, and a hypocenter depth. Algorithms for calculating the predicted parameters are presented in detail.