Systems and methods for picking seismic stacking velocity based on structures in a subterranean formation include: receiving seismic data representing a subterranean formation; generating semblance spectrums from the seismic data representing the subterranean formation; smoothing the semblance spectrums; and picking stacking velocities based on the smoothed semblance spectrums.

Techniques to reduce noise in seismic data by receiving a set of seismic data comprising a plurality of input volumes each inclusive of positional data and at least one additional attribute related to the seismic data, selecting a first input volume of the plurality of input volumes having a first additional attribute related to the seismic data, and generating a pilot volume by selecting a range of input volumes of the plurality of input volumes and stacking input volumes of the range of input volumes with the first input volume. Additionally, generating a trained dictionary based upon transformation of the pilot volume, transforming the first input volume into transformed data, imposing a sparse condition on the transformed data utilizing the trained dictionary to generate sparsified data, and inverse transforming the sparsified data to generate an output data volume as a portion of a set of modified seismic data.

In the field of seismic data collection and analysis, the problem of data compression is considered. It is desired to reduce the storage and transmission requirements of seismic data, such as associated with microseismic monitoring and processing, VSP (vertical seismic profile) surveys, and the like, for instance using a distributed acoustic sensor (DAS), which can generate in excess of 50 GB of data per hour on a survey that lasts days. A method and system for data compression that separates the data collected into additive signal and noise components, and compresses the estimated signal component for transmission, storage, and analysis, is described. The idea is that the signal component, which exhibits clear structure across the traces, may be accurately described with relatively few parameters, and therefore may be significantly compressed without loss of important information.

Methods and apparatus are described for using ambient acoustic energy as a passive source in marine seismic surveys. An example embodiment includes (a) accessing signals that were recorded by sensors in the presence of acoustic energy that was emitted by a passive source during a marine seismic survey; (b) identifying a point to represent a location from which the acoustic energy was emitted; (c) isolating, from the recorded signals, a direct wavefield arriving at the sensors from a direction of the identified point; and (d) generating an estimated passive source wavefield at the identified point by backpropagating the isolated direct wavefield to the identified point. The estimated passive source wavefield may be used, together with signals recorded by the sensors, to generate an image of a subsurface earth volume without the use of active seismic sources.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for evaluating the filling characteristics of a deep paleokarst reservoir through well-to-seismic integration, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

Seismic exploration of an underground formation uses seismic excitations to probe the formation's properties such as reflectivity that can be imaged using reverse time migration. Using an equal area spherical binning at reflection points improves and simplifies RTM imaging together with adaptability to the data acquisition geometry, while overcoming drawbacks of conventional cylindrical binning.

A device may include a processor that may separate or deblend signals acquired with simultaneous source shooting, in an environment with background noise or other types of noises. The processor may expand a receiver gather before the time of source excitation. The processor may use the expanded time window (e.g., negative time window) to allocate the background noise or other types of noises after removal. The processor may use signal recovery operations to reallocate leaked or misplaced signals created during the separation iterations, including the signals inside the expanded time window, to a correct source excitation and timing. Expanding a receiver gather time window and reallocating leaked or misplaced signals may improve a deblended output used in generating a seismic image.

A hydroponic growing apparatus, system, and method is provided. The hydroponic grower includes one or more seed beds each having a length and a width operably supported by a framework. A seed belt is rotatable around each of the one or more seed beds between loading and offloading positions to and from a seed growing position. Seed is discharged onto the seed belt for hydroponically growing a seed atop of the one or more seed beds. Seed growth can be offloaded through the path of a stream of liquid exiting a liquid nozzle for cutting through offloaded seed growth in at least one direction.

Various aspects described herein relate to a machine learning based signal recovery. In one example, a computer-implemented method of noise contaminated signal recovery includes receiving, at a server, a first signal including a first portion and a second portion, the first portion indicative of data collected by a plurality of sensors, the second portion representing noise; performing a first denoising process on the first signal to filter out the noise to yield a first denoised signal; applying a machine learning model to determine a residual signal indicative of a difference between the first signal and the first denoised signal; and determining a second signal by adding the residual signal to the first denoised signal, the second signal comprising (i) signals of the first portion with higher magnitudes than the noise in the second portion, and (ii) signals of the first portion having lower magnitudes than the noise in the second portion.

An object of the present invention is to provide a method of a practical level for performing sensor fusion of multiple vibration sensors such as a seismometer, and in an example, to extend a dynamic range of a high-sensitivity geophone by sensor fusion of the high-sensitivity geophone and a low-sensitivity acceleration geophone. A state related to the high-sensitivity geophone is estimated by capturing, in a Kalman filter, an acceleration record from the low-sensitivity acceleration geophone and a velocity record, or a displacement record, and an acceleration record of the high-sensitivity geophone, and estimating and calculating them as the linear Kalman filter problem with a control input. The high-sensitivity geophone is an actual device, but the state can be estimated using a sensor value of the low-sensitivity acceleration geophone even when the record is saturated. This extends the dynamic range of the high-sensitivity geophone.