Abstract: A method is described for generating distributed acoustic sensing (DAS) vertical seismic profile (VSP) data including receiving, at one or more processing cores, synthetic DAS acquisition parameters; modeling synthetic pressure-field data; augmenting the synthetic pressure-field data using reciprocity to generate an augmented dataset; sorting the augmented dataset into pressure-field common shot gathers; and converting the pressure-field common shot gathers to strain-rate DAS VSP data. The converting may be done by taking a spatial derivative of pressure along a DAS cable; performing a temporal integral to find particle velocity along the DAS cable; and taking a spatial derivative of the partial velocity along the DAS cable to convert the pressure-field common shot gathers to strain-rate DAS VSP data.

Abstract: A method is described for processing distributed acoustic sensing seismic data including receiving, at one or more processors, DAS seismic data, converting the DAS seismic data into pressure data, and processing the pressure data is disclosed. The converting may be done by performing a spatial integral of the DAS seismic data along a DAS cable to get a particle velocity, and performing a spatial derivative of the particle velocity along the DAS cable and a temporal integral to generate the pressure data. The pressure data may then be processed by methods including but not limited to performing reverse time migration to generate a seismic image.

Abstract: A method for predicting mass of heavy vehicles based on networked operating data and machine learning includes: collecting operating data; extracting speed, engine output torque, satellite elevation, and gear position under a driving condition; determining a transmission ratio of the heavy vehicle based on the gear position; filtering the speed, engine output torque, and the satellite elevation using three of the plurality of filtering parameters; determining a filtered vehicle longitudinal acceleration under the driving condition using the filtered speed, and one of the plurality of filtering parameters; determining a filtered road gradient sine value under the driving condition using the filtered speed, satellite elevation, and one of the plurality of filtering parameters; and inputting the speed, engine output torque, vehicle longitudinal acceleration, road gradient sine value, and transmission ratio into a vehicle mass prediction model to obtain a predicted mass.

Abstract: A method for predicting mass of heavy vehicles based on networked operating data and machine learning includes: collecting operating data; extracting speed, engine output torque, satellite elevation, and gear position under a driving condition; determining a transmission ratio of the heavy vehicle based on the gear position; filtering the speed, engine output torque, and the satellite elevation using three of the plurality of filtering parameters; determining a filtered vehicle longitudinal acceleration under the driving condition using the filtered speed, and one of the plurality of filtering parameters; determining a filtered road gradient sine value under the driving condition using the filtered speed, satellite elevation, and one of the plurality of filtering parameters; and inputting the speed, engine output torque, vehicle longitudinal acceleration, road gradient sine value, and transmission ratio into a vehicle mass prediction model to obtain a predicted mass.

Abstract: A method is described for seismic imaging improved by an estimation of attenuation including receiving a pre-migration seismic dataset D(s, r; t) representative of a subsurface volume of interest wherein s indicates source location, r indicates receiver location, and t is the recorded travel time; calculating a pre-migration attenuated travel time t*(s, r; t); computing a time derivative of the pre-migration attenuated travel time wherein 1/Q(s, r; t)=?t*(s, r; t)/?t; performing a first migration on D(s,r;t) to generate common image point (CIP) gathers G(x, h) wherein x is subsurface image point and h is angle or offset; performing a second migration on D(s, r; t)*1/Q(s, r; t) to generate weighted common image point (CIP) gathers G1/Q(x, h); and calculating a conditioned ratio of the weighted CIP gathers G1/Q(x, h) over the CIP gathers G(x, h) to get CIP gathers of 1/Q(x, h) is disclosed.

Abstract: A method is described for seismic imaging improved by an estimation of attenuation including receiving a pre-migration seismic dataset D(s, r; t) representative of a subsurface volume of interest wherein s indicates source location, r indicates receiver location, and t is the recorded travel time; calculating a pre-migration attenuated travel time t*(s, r; t); computing a time derivative of the pre-migration attenuated travel time wherein 1/Q(s, r; t)=?t*(s, r; t)/?t; performing a first migration on D(s,r;t) to generate common image point (CIP) gathers G(x, h) wherein x is subsurface image point and h is angle or offset; performing a second migration on D(s, r; t)*1/Q(s, r; t) to generate weighted common image point (CIP) gathers G1/Q(x, h); and calculating a conditioned ratio of the weighted CIP gathers G1/Q(x, h) over the CIP gathers G(x, h) to get CIP gathers of 1/Q(x, h) is disclosed.

Abstract: A method is described for seismic data processing including receiving a seismic dataset D(s, r; t) representative of a subsurface volume of interest; calculating a pre-migration attenuated travel time t*(s, r; t); performing a first migration on D(s, r; t) to generate common image point (CIP) gathers G(x, h); performing a second migration on D(s, r; t)*t*(s, r; t) to generate weighted common image point (CIP) gathers Gt*(x, h); and calculating a conditioned ratio of the weighted CIP gathers Gt*(x, h) over the CIP gathers G(x, h) to get CIP gathers of attenuated traveltime t*(x, h). The CIP gathers of attenuated traveltime t*(x, h) may be used to perform seismic tomography to generate an attenuation (Q) model.

Abstract: Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. The systems and methods may: obtain subsurface data and well data corresponding to a subsurface volume of interest; obtain a parameter model; use the subsurface data and the well data to generate multiple production parameter maps; apply the parameter model to the multiple production parameter maps to generate refined production parameter values; generate multiple refined production parameter graphs; display the multiple refined production parameter graphs; generate one or more user input options; receive a defined well design and the one or more user input options selected by a user to generate limited production parameter values; generate a representation of estimated reservoir productivity as a function of position in the subsurface volume of interest using the defined well design and visual effects; and display the representation.

Abstract: Systems, methods, and storage media for detecting a given subsurface event in a subsurface volume of interest are disclosed. Exemplary implementations may: receive a subsurface fiber optic data set; receive a sensor data set using one or more sensors; constrain the subsurface fiber optic data set based on a given parameter value of a given parameter within a certain range to generate a constrained subsurface fiber optic data set; use sets of models to refine the constrained subsurface fiber optic data set to generate a refined subsurface fiber optic data set; estimate an event location of the given subsurface event based on the refined subsurface fiber optic data set; and estimate an origin time based on the event location.