CN-121994091-A - Explosive partitioning method based on geophysical exploration technology

CN121994091ACN 121994091 ACN121994091 ACN 121994091ACN-121994091-A

Abstract

The invention relates to the technical field of geophysical exploration, in particular to a blasting partitioning method based on a geophysical exploration technology. The method comprises the steps of synchronously collecting multi-source data such as seismic waves, resistivity and excitation electricity, generating a high-precision static rock physical parameter three-dimensional model through cross gradient joint inversion, obtaining dynamic damage factor three-dimensional distribution through calibration blasting and full waveform inversion, fusing static and dynamic parameters, constructing a three-dimensional geomechanical model containing mechanical parameters by means of a machine learning model, realizing explosive intelligent partition based on multi-index fusion rules, automatically generating a customized blasting design scheme, and finally driving the model to self-evolve through blasting effect feedback. The method solves the defects of single data, dynamic and static disjointing, experience dependence and the like of the traditional method, and realizes the accurate, intelligent and self-adaptive optimization of blasting partition.

Inventors

XU ZHENYANG
Ma Sishun
WANG XUESONG
LIU XIN
JIAO DENGMING
Xue Boru

Assignees

辽宁科技大学

Dates

Publication Date: 20260508
Application Date: 20260211

Claims (9)

1. A method of explosive zoning based on geophysical prospecting technology, comprising: S1, multi-source data acquisition and joint inversion, namely synchronously acquiring multi-source data of a target area, and performing collaborative inversion on the multi-source data by adopting a cross gradient joint inversion algorithm to generate a static rock physical parameter three-dimensional model; S2, acquiring dynamic response parameters, namely performing small equivalent calibration blasting in a typical partition of a target area, distributing a vibration monitoring network to acquire blasting vibration signals, and calculating three-dimensional space distribution of dynamic damage factors representing dynamic crushing behaviors of a rock mass through a full-waveform inversion technology based on attenuation characteristics and full-waveform information of the vibration signals; S3, intelligent geomechanical modeling, namely fusing the static rock physical parameter three-dimensional model with the dynamic damage factor, inputting the fused multidimensional characteristic parameters into a pre-trained machine learning mapping model, and outputting a high-resolution three-dimensional geomechanical model which is a target area, wherein the model directly comprises key mechanical parameters of compressive strength and elastic modulus; S4, intelligent partitioning and design, namely based on the three-dimensional geomechanical model, combining a preset multi-index fusion partitioning rule based on mechanical parameters and dynamic response parameters, dividing blastability grades, and automatically generating corresponding blasthole network parameters and drug loading suggestions aiming at each grade; And S5, feeding back an optimized closed loop, namely collecting blasting effect evaluation data after blasting, comparing the blasting effect evaluation data with the partition prediction effect to generate a feedback signal, and performing incremental learning and parameter fine adjustment on the machine learning mapping model based on the feedback signal to realize self-evolution of the partition model.
2. The method of claim 1, wherein the multi-source data of the target area includes at least seismic exploration data, direct current resistivity method data and induced polarization method data.
3. The method for explosive partitioning based on geophysical prospecting technology according to claim 2, wherein the method for generating the three-dimensional model of the physical parameters of the static rock mass is specifically implemented as follows: Based on the seismic wave exploration data, the direct current resistivity method and the induced polarization method data, a cross gradient joint inversion algorithm is adopted for processing, a target area is discretized into a three-dimensional grid model, a forward modeling system of a seismic wave field and a stable current field is respectively constructed, a unified objective function is established, the objective function comprises an L2 norm fitting item of a seismic wave travel time residual error, an L2 norm fitting item of resistivity observation data and a cross gradient structure constraint item, a wave velocity value and a resistivity value of each three-dimensional grid node are iteratively updated based on a least square optimization algorithm, iteration is stopped when the objective function converges to a preset threshold value, and a static rock physical parameter three-dimensional model is generated.
4. The method for explosive partitioning based on geophysical prospecting according to claim 3, wherein the three-dimensional spatial distribution of the dynamic injury factor is implemented by: And taking wave velocity distribution in the static rock physical parameter three-dimensional model as an initial model, taking a vibration time course curve of each measuring point recorded by a vibration monitoring network in a blasting test as observation data, adopting a full waveform inversion technology, continuously adjusting the wave velocity parameter of the initial model through an iterative optimization algorithm to minimize residual errors between a synthetic seismic record obtained based on forward modeling of a current model and an on-site actual measurement vibration record, calculating a dynamic damage factor according to the update quantity of the wave velocity model in each iteration, and outputting the distribution model of the dynamic damage factor in a three-dimensional space of a target area when full waveform inversion iteration converges.
5. The method for explosive partitioning based on geophysical prospecting according to claim 4, wherein the pre-trained machine learning mapping model is implemented by: And (3) systematically arranging rock core sampling points in a target area, carrying out indoor rock mechanical test on the extracted rock core to obtain uniaxial compressive strength, elastic modulus and cohesive force mechanical parameter true values, simultaneously extracting multisource fusion characteristic parameters corresponding to space positions of the sampling points, wherein the multisource fusion characteristic parameters at least comprise P wave velocity, S wave velocity and resistivity parameters obtained from a static rock mass physical parameter three-dimensional model, dynamic damage factor values obtained from dynamic damage factor three-dimensional space distribution, taking the multisource fusion characteristic parameters as input characteristic vectors, taking the mechanical parameter true values as supervision learning targets, carrying out model training by adopting a gradient lifting decision tree algorithm, and determining optimal superparameter combination including maximum depth, learning rate and subsampling proportion of a tree to obtain a training model capable of establishing nonlinear mapping relation from geophysical characteristics to rock mechanical characteristics.
6. The method for partitioning a blasting capacity based on geophysical prospecting technology according to claim 5, wherein said partitioning the blasting capacity is implemented by: Extracting a plurality of discrimination indexes of each three-dimensional grid node from the three-dimensional geomechanical model, wherein the discrimination indexes comprise static mechanical indexes, dynamic response indexes and original structure indexes, and further carrying out logic judgment and classification based on a preset multi-index fusion partition rule, wherein the classification at least comprises a difficult-explosion region, an explosive region and a construction influence region, and finally generating a three-dimensional partition model which corresponds to the three-dimensional geomechanical model space and comprises different explosive class labels.
7. The method for explosive partitioning based on geophysical prospecting according to claim 6, wherein the method for automatically generating the corresponding blast hole network parameter and loading recommendation comprises the following specific implementation steps: And coupling the three-dimensional partition model containing the labels with different blasting performance grades with a built-in expert knowledge base, wherein the knowledge base prestores quantitative mapping relations between different blasting performance grades and blasting design parameters, and based on the mapping relations, the system automatically generates a customized blasting design scheme for each partition unit and outputs a digital blasting design diagram containing hole position coordinates, drilling depth, hole network density, explosive types and explosive loading distribution.
8. The method of claim 7, wherein the blast effect evaluation data comprises at least blast block distribution data, blast morphology and integrity data, blast deleterious effect monitoring data, and comprehensive economic and technical index data.
9. The method for explosive partitioning based on geophysical prospecting according to claim 8, wherein the generating the feedback signal is implemented by: Comparing actual observed values in blasting effect evaluation data, including average block size and large block rate in blasting block distribution data, root rate and overbreak amount in blasting form data, with expected effect values predicted based on the three-dimensional partition model and generated blasting design parameters, calculating differences between each observed value and each predicted value through a preset loss function, wherein the loss function is a weighted square sum of difference items, and jointly forming the calculated total loss function value and space position information of each partition in the corresponding three-dimensional partition model into the feedback signal.

Description

Explosive partitioning method based on geophysical exploration technology Technical Field The invention relates to the technical field of geophysical exploration, in particular to a blasting partitioning method based on a geophysical exploration technology. Background The blasting operation is used as a common construction means in engineering construction and is widely applied to engineering scenes such as mining, road construction, site leveling and the like. However, different geological conditions have significant influence on the blasting effect, and unreasonable blasting design may lead to poor blasting effect, which not only increases the difficulty and cost of subsequent construction, but also may cause potential safety hazards such as flying stones, seismic wave hazards and the like. Common geophysical prospecting methods include seismic prospecting, electrical prospecting, gravity prospecting, and the like. Seismic exploration utilizes propagation characteristics of manually excited seismic waves in underground media, such as reflection, refraction and the like to infer underground geological structures and rock characteristics, electrical exploration is used for detecting the geological structures by researching electrical property differences of the underground media, such as resistivity, dielectric constant and the like, and gravitational exploration is used for analyzing density distribution of underground substances according to the change of the earth gravitational field so as to infer the geological structures. The prior art has the following technical defects, which are specifically shown in the following: 1. Data singleness and model distortion the traditional method relies on independent inversion of single geophysical data, and lacks collaborative constraint and verification between different types of data. The inversion process has serious multi-resolution, the generated static rock mass model has low spatial resolution and fuzzy structural boundary, and complex geological structures cannot be truly depicted, such as broken zones and aquifers are difficult to accurately distinguish. 2. Dynamic and static disjointing and prediction misalignment, namely completely neglecting nonlinear response of the rock mass under the blasting dynamic load in the prior art, and carrying out partition prediction only by static physical parameters, so that mechanical judgment is severely disjointed from actual conditions. Due to the lack of quantitative characterization of dynamic crushing behavior of the rock mass, the distribution of explosion energy is not matched with the antiknock capability of the rock mass, and the problems of excessive crushing or insufficient explosion and the like are very easy to occur. 3. Experience dependence and solidification stiffness, namely from key mechanical parameter estimation to final blasting scheme design, the whole process is highly dependent on personal experience of engineers, and standardized and quantitative intelligent decision support is lacked. In addition, the established model is solidified, self-verification and iterative optimization cannot be carried out by collecting blasting effect data, so that the technology reusability is poor, and the precision is unstable in different projects. Disclosure of Invention The invention aims to provide a blasting partitioning method based on geophysical exploration technology, so as to solve the problems in the background technology. The invention aims to provide a blasting partition method based on a geophysical exploration technology, which comprises the steps of S1, multi-source data acquisition and joint inversion, wherein multi-source data of a target area are synchronously acquired, and a cross gradient joint inversion algorithm is adopted to carry out collaborative inversion on the multi-source data so as to generate a static rock physical parameter three-dimensional model. S2, acquiring dynamic response parameters, namely performing small equivalent calibration blasting in a typical partition of a target area, distributing a vibration monitoring network to acquire blasting vibration signals, and calculating to obtain three-dimensional space distribution of dynamic damage factors representing dynamic crushing behaviors of the rock mass through a full-waveform inversion technology based on attenuation characteristics and full-waveform information of the vibration signals. S3, intelligent geomechanical modeling, namely fusing the static rock physical parameter three-dimensional model with the dynamic damage factor, inputting the fused multidimensional characteristic parameters into a pre-trained machine learning mapping model, and outputting a high-resolution three-dimensional geomechanical model of a target area, wherein the model directly comprises the key mechanical parameters of compressive strength and elastic modulus. And S4, intelligent partitioning and design, namely based on the three-dimensional geom