Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving seismic data acquired by at least one receiver of a geologic survey system configured to perform a geologic survey of a subterranean formation, wherein the seismic data is associated with reflected acoustic signals generated by at least one source of the geologic survey system; calculating a ground force signal by stacking the acoustic signals generated by the least one source; calculating, using the ground force signal, a time and depth variant quality factor (Q) of the subterranean formation; and compensating, based on the time and depth variant Q, attenuation in the seismic data.

Apparatus and methods are described, including identifying an arrival of a first arriving S-wave emitted from a seismic source at an array (120) of sensors (129, 140) in real-time, by continuously analyzing waveforms received by the sensors (120, 140), and continuously monitoring back-azimuth and slowness data within the detected waveforms. Arrival of a first arriving P-wave emitted from the seismic source at the array (120) of sensors (129, 140) is identified, based upon the back-azimuth and slowness data. Slowness and back azimuth of the first arriving P-wave are determined, by analyzing a waveform of the P-wave, and based upon the determined slowness of the first arriving P-wave, the arrival of the first arriving S-wave at the array (120) of sensors (129, 140) is identified. Other applications are also described.

Aspects of the present disclosure relate to using artificial intelligence for high-resolution earth modeling. Embodiments include receiving training data, comprising: wellbore attributes relating to a plurality of depth points; and adjacent waveform data relating to a first plurality of directions with respect to each depth point of the plurality of depth points. Embodiments include providing at least a subset of the training data as inputs to a machine learning model. Embodiments include receiving outputs from the machine learning model based on the inputs. Embodiments include iteratively adjusting parameters of the machine learning model based on the outputs and the training data.

A seismic switch (SS) that is able to detect and signal when internal faults have occurred within the SS is described. The SS provides safety class functionality to the detection of seismic activity. For example, the SS may detect earthquakes above a specified level, resulting in the disconnection of electrical power to a radioactive waste storage facility, which could result in the ignition of waste materials should the storage facility and/or storage container fail during a seismic event. By reducing the risk of fire under these circumstances, the possibility of offsite releases is significantly reduced.

A seismic warning system comprises: a plurality of sensors, each sensor sensitive to a physical phenomenon associated with seismic events and operative to output an electronic signal representative of the sensed physical phenomenon; a data acquisition unit communicatively coupled to receive the electronic signal from each of the plurality of sensors, the data acquisition unit comprising a processor configured to estimate characteristics of a seismic event based on the electronic signal associated with a P-wave from each of the plurality of sensors; and a local device communicatively coupled to the data acquisition unit. The plurality of sensors, the data acquisition unit and the local device are local to one another.

A seismic warning system comprises: a plurality of sensors, each sensor sensitive to a physical phenomenon associated with seismic events and operative to output an electronic signal representative of the sensed physical phenomenon; a data acquisition unit communicatively coupled to receive the electronic signal from each of the plurality of sensors, the data acquisition unit comprising a processor configured to estimate characteristics of a seismic event based on the electronic signal associated with a P-wave from each of the plurality of sensors; and a local device communicatively coupled to the data acquisition unit. The plurality of sensors, the data acquisition unit and the local device are local to one another.

Systems and methods for visualizing attributes of multiple fault surfaces in real time by calculating the attributes as each respective fault surface is picked.

Techniques for determining a wellbore drilling path includes identifying input seismic data associated with a subterranean zone that includes a wellbore drilling target. The input seismic data includes primary seismic events and multiple seismic events. The input seismic data is processed to remove the multiple seismic events and at least one of the primary seismic events from the input seismic data. An orthogonalization of the processed input seismic data is performed to recover the at least one primary seismic event into a seismic image of the subterranean zone that excludes at least a portion of the multiple seismic events. A wellbore path is determined from a terranean surface toward the wellbore drilling target for a drilling geo-steering system based on the seismic image of the subterranean zone.

The present disclosure is related to absolute strength and absolute sensitivity in seismic data. Seismic data from a marine survey can be received. The seismic data can be calibrated based on an absolute strength of a seismic source and an absolute sensitivity of a seismic receiver. The calibrated seismic data can be processed.

A system and a computer-implemented include the following. A field dataset of seismic waves is received that is obtained by receivers during a drilling period from a drilling operation at a target well. The drilling period includes drilling and non-drilling phases. The field dataset is analyzed to determine locations of seismic waves. A reconstructed wavefield is determined by applying a passive seismic imaging condition over time and based on locations of the receivers. Using the reconstructed wavefield, a time series is computed for the seismic waves, and a time-frequency transform is applied on the time series. Sources and locations of tube waves resulting from acoustic signatures of the drill bit the drilling phases are determined. Sources and locations of the body waves caused by the tube waves are determined. A petrophysical model of the target well is updated in real-time based on the analyzing and the waves.