Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

A performance-level seismic motion hazard analysis method includes: (1) extracting seismic motion data and denoising the data; (2) extracting feature parameters from the data, and carrying out initialization; (3) generating a training set, an interval set and a test set; (4) training a multi-layer neural network based on the training set; (5) training output values of the neural network based on the interval set, and calculating a mean and a standard deviation of relative errors of the output values; (6) training the neural network based on the test set to determine output values, and calculating a magnitude interval based on an interval confidence; (7) carrying out probabilistic seismic hazard analysis to determine an annual exceeding probability and a return period of a performance-level seismic motion; and (8) determining a magnitude and an epicentral distance that reach the performance-level seismic motion based on the performance-level seismic motion and consistent probability.

Systems and methods are provided for generating a model for detection of seismic events. In this regard, one or more processors may receive from one or more stations located along an underwater optical route, one or more time series of polarization states of a detected light signal during a time period. The one or more processors may transform the one or more time series of polarization states into one or more spectrums in a frequency domain. Seismic activity data for the time period may be received by the one or more processors, where the seismic activity data include one or more seismic events detected in a region at least partially overlapping the underwater optical route. The one or more processors then generate a model for detecting seismic events based on the one or more spectrums and the seismic activity data.

Sound signal when a wave generated by a geo-acoustic event source reaches any monitoring point (S1), constructing a theoretical propagation difference model and an observed propagation difference model of the waveform characterization quantity between monitoring points, to calculate a waveform characterization quantity difference value between two monitoring points (S2); and constructing an objective function based on the theoretical propagation difference model and the observed propagation difference model, and obtaining the location of the geo-acoustic event by means of inversion based on the objective function (S3). According to the geo-acoustic event location method, the arrival time, time domain parameters, spectral information, and waveform shape of the geo-acoustic signal when the wave generated by the geo-acoustic event source reaches any monitoring point are considered, then the non-uniformity of a propagation medium is comprehensively reflected, and the inversion precision of geo-acoustic event location is finally improved.

A method including: obtaining intercept and gradient stacks and an effective stress volume that correspond to seismic data for a subsurface region; determining Chi angles as a function of effective stress; and generating a seismic volume with the Chi angles that vary as a function of effective stress.

A seismic sensor 10 comprises an acceleration acquisition unit 21, an acceleration waveform generation unit 22, a frequency sensing unit 24, and an earthquake determination unit 25. The acceleration acquisition unit 21 detects vibration and measures the acceleration of the vibration. The acceleration waveform generation unit 22 generates an acceleration waveform that indicates the relation between the elapsed time and the acceleration measured by the acceleration acquisition unit 21. The frequency sensing unit 24 senses the frequency of the acceleration waveform generated by the acceleration waveform generation unit 22 using a zero-crossing method. The earthquake determination unit 25 determines whether or not there is an earthquake on the basis of the frequency sensed by the frequency sensing unit 24.

A device for monitoring and identifying a mountain torrent and debris flow and a method for early warning of disasters relate to the technical field of debris flow protection. The device includes a computation device, sensors, an amplifier and an analog-to-digital converter. The sensors convert an acquired impact force signal into a digital signal by the amplifier and the analog-to-digital converter, and transmits the digital signal to the computation device. The computation device utilizes the digital signal to compute an energy coefficient of a liquid impact signal and a solid-liquid impact energy ratio, and a debris flow mode is monitored and identified in combination with a threshold range of the energy coefficient and a threshold range of the solid-liquid impact energy ratio. The device identifies the nature of the mountain torrent and debris flow through time-frequency analysis of an impact force signal generated by the debris flow to sensors.

Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.

A surface wave prospecting method for jointly extracting Rayleigh wave frequency dispersion characteristics in a seismoelectric field. A surface wave prospecting method includes following steps of: acquiring jointly acquired data, where the jointly acquired data includes seismic wave data and electric field data; carrying out jointly imaging processing on jointly acquired data to obtain a superposed frequency dispersion spectrum; carrying out extraction processing on superposed frequency dispersion spectrum to obtain a frequency dispersion curve, outperforming inversion processing on frequency dispersion curve to obtain a stratum structure profile. As seismic wave data and electric field data are adopted to carry out combined imaging processing to obtain superposed frequency dispersion spectrum, multi-mode frequency dispersion curve is extracted, multiplicity of solutions of inversion is greatly reduced during inversion, precision and stability of surface wave prospecting are greatly improved.

Some implementations of the present disclosure provide a method that include: accessing a set of seismic traces from a grid of locations inside an geo-exploration area, each seismic trace records seismic reflections from underneath the geo-exploration area at a location of the grid; accessing an input indicating a surface in the set of seismic traces; extracting a plurality of wavelets from the set of seismic traces, each wavelet covering an adjustable length around the surface; determining a reference wavelet for each wavelet of a corresponding adjustable length; and quantifying a quality of the surface based on correlating the plurality of wavelets with each reference wavelet of the corresponding adjustable length.