Abstract:
Translational data in a first direction is measured by particle motion sensors contained in an elongated housing of a sensor device provided at an earth surface. The particle motion sensors are spaced apart along a second, different direction along a longitudinal axis of the elongated housing. Rotation data around a third direction is computed based at least in part on computing a gradient of the translational data with respect to the second direction.
Abstract:
To perform noise attenuation for seismic surveying, a sensor assembly is deployed on a ground surface, where the sensor assembly has a seismic sensor to measure seismic waves propagated through a subterranean structure, and a divergence sensor comprising a pressure sensor to measure noise. First data is received from the seismic sensor, and second data is received from the divergence sensor. The first data and the second data are combined to attenuate noise in the first data.
Abstract:
Translational data acquired by at least one seismic sensor is received. Gradient sensor data acquired by at least one gradient sensor is received. Estimated translational data at a position away from at least one position of the at least one seismic sensor is computed, where the computing is based on the gradient sensor data and the translational data.
Abstract:
A seismic sensor device includes an elongated housing for placement at least partially into an earth surface. A plurality of particle motion sensors are contained in the elongated housing to measure translational data in a first direction, where plural pairs of the particle motion sensors are spaced apart along a second, different direction along a longitudinal axis of the elongated housing. A communication interface communicates the measured translational data to a computer system configured to compute a gradient based on respective differences of the measured translational data of the corresponding plural pairs of the particle motion sensors, and compute one or more of rotation data and divergence data using the gradient.