Composite model of seismic monitoring data analysis during mining
operations on the example of the Kukisvumchorrskoye deposit of JSC Apatit
- URL: http://arxiv.org/abs/2301.05701v1
- Date: Fri, 13 Jan 2023 18:49:03 GMT
- Title: Composite model of seismic monitoring data analysis during mining
operations on the example of the Kukisvumchorrskoye deposit of JSC Apatit
- Authors: Ilia Revin
- Abstract summary: It is almost impossible to single out a methodology and approaches for data collection and analysis in developing seismic monitoring systems.
In the process of mining in rock massif, changes in the state of structural inhomogeneities are most clearly manifested.
The developed method of evaluating the results of monitoring geomechanical processes in the rock massif allowed us to forecast of zones of possible rock bursts.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Geomechanical monitoring of a rock massif is an actively developing branch of
geomechanics. It is almost impossible to single out a methodology and
approaches for data collection and analysis in developing seismic monitoring
systems. In the process of mining in rock massif, changes in the state of
structural inhomogeneities are most clearly manifested. Existing natural
structural inhomogeneities are revealed, there are movements in discontinuous
disturbances, and new technogenic disturbances are formed, which are
accompanied by a change in the natural stress state of various blocks of the
massif. An important task is to develop a mining forecasting model that can
take into account the structural heterogeneity of the rock massif and select
the necessary forecast horizon depending on monitoring data The developed
method of evaluating the results of monitoring geomechanical processes in the
rock massif allowed us to forecast of zones of possible rock bursts.
Related papers
- Observation-Guided Meteorological Field Downscaling at Station Scale: A
Benchmark and a New Method [66.80344502790231]
We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset.
Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors.
Our proposed method outperforms other specially designed baseline models on multiple surface variables.
arXiv Detail & Related papers (2024-01-22T14:02:56Z) - iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive
Noise Models [48.33685559041322]
This paper focuses on identifying the causal mechanism shifts in two or more related datasets over the same set of variables.
Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/iSCAN.
arXiv Detail & Related papers (2023-06-30T01:48:11Z) - Mitigation of Spatial Nonstationarity with Vision Transformers [1.690637178959708]
We show the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance.
We propose the mitigation of such impacts using self-attention (vision transformer) models.
arXiv Detail & Related papers (2022-12-09T02:16:05Z) - Direct Estimation of Porosity from Seismic Data using Rock and Wave
Physics Informed Neural Networks (RW-PINN) [2.741266294612776]
We present a rock and wave physics informed neural network (RW-PINN) model that can estimate porosity directly from seismic image traces with no or limited number of wells.
As an example, we use the uncemented sand rock physics model and normal-incidence wave physics to guide the learning of RW-PINN.
We demonstrate the complete workflow for executing petrophysical inversion of seismic data using self-supervised or weakly supervised neural networks.
arXiv Detail & Related papers (2022-09-30T18:53:15Z) - Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data.
We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism.
We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
arXiv Detail & Related papers (2022-06-30T06:00:13Z) - Weakly Supervised Representation Learning with Sparse Perturbations [82.39171485023276]
We show that if one has weak supervision from observations generated by sparse perturbations of the latent variables, identification is achievable under unknown continuous latent distributions.
We propose a natural estimation procedure based on this theory and illustrate it on low-dimensional synthetic and image-based experiments.
arXiv Detail & Related papers (2022-06-02T15:30:07Z) - Crack detection using tap-testing and machine learning techniques to
prevent potential rockfall incidents [68.8204255655161]
This paper proposes a system towards an automated inspection for potential rockfalls.
A robot is used to repeatedly strike or tap on the rock surface.
The sound from the tapping is collected by the robot and classified with the intent of identifying rocks that are broken and prone to fall.
arXiv Detail & Related papers (2021-10-10T22:53:36Z) - Analysis for full face mechanical behaviors through spatial deduction
model with real-time monitoring data [21.131001656350712]
The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix.
The external load applied on structure in the field was considered to study the mechanical behaviors of tunnel.
A double-driven model was developed to obtain the full-faced information.
arXiv Detail & Related papers (2021-09-27T16:28:21Z) - Prediction of geophysical properties of rocks on rare well data and
attributes of seismic waves by machine learning methods on the example of the
Achimov formation [0.0]
The object of the study is the productive intervals of Achimov sedimentary complex in the part of oil field located in Western Siberia.
The research shows a technological stack of machine learning algorithms, methods for enriching the source data with synthetic ones and algorithms for creating new features.
arXiv Detail & Related papers (2021-06-24T18:54:47Z) - Towards advancing the earthquake forecasting by machine learning of
satellite data [22.87513332935679]
We develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1,371 earthquakes of magnitude six or above.
Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.
arXiv Detail & Related papers (2021-01-31T02:29:48Z) - Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences [77.68028443709338]
We propose a fully Bayesian formulation of the Epidemic Type Aftershock Sequence (ETAS) model.
The occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process.
arXiv Detail & Related papers (2020-02-05T10:11:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.