Microseismic Event Location via Maximum Likelihood Estimation
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Solution Overview
Problem
Current systems face challenges in accurately identifying and filtering out correlated noise when locating microseismic events, which hampers the precise determination of seismic moment tensor in noisy environments.
Innovation Solution
The method involves positioning sensors in a study volume, modeling seismic-mechanical properties, simulating microseismic responses, filtering quasi-harmonic interference using inverse filters, and determining the seismic moment tensor at each grid point using the maximum likelihood method to predict the type and location of microseismic events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise filtering methods are used to locate microseismic events, then the system is simpler to implement, but the accuracy of seismic moment tensor determination deteriorates in noisy environments
Solution Approach 1:
The patent applies preliminary action by performing waveform simulation and model calibration before actual microseismic event detection. The system pre-computes theoretical waveforms for various source types and calibrates models using known events, creating a ready-to-use framework that improves measurement precision without adding complexity during real-time detection.
Solution Approach 2:
The patent uses copying by creating simulated waveform copies of expected microseismic signals based on theoretical models. These simulated waveforms are compared against actual sensor data to identify and characterize events, enabling accurate moment tensor determination through pattern matching rather than direct measurement in noisy conditions.
2Loss of information
If all sensor data is used for event detection, then more information is available for analysis, but the impact of correlated noise increases
Solution Approach 1:
The patent applies parameter changes by transforming the analysis from raw amplitude data to waveform shape parameters and frequency content. By changing the parameters used for detection from simple amplitude thresholds to complex waveform characteristics, the system can distinguish signal from correlated noise while utilizing all sensor information.
Solution Approach 2:
The patent introduces an intermediary by using simulated theoretical waveforms as a mediator between raw sensor data and event characterization. These simulated waveforms serve as a reference framework that helps separate signal from noise, allowing full utilization of sensor data while filtering out correlated noise through comparison with expected patterns.
3Measurement precision
If maximum likelihood method is used to determine seismic moment tensor at each grid point, then event location accuracy improves, but computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the study volume into discrete grid points and processing each point independently through the maximum likelihood method. This segmentation allows parallel computation and reduces the computational burden compared to analyzing the entire volume simultaneously, while maintaining high location accuracy at each point.
Solution Approach 2:
The patent uses partial action by applying the computationally intensive maximum likelihood method only to grid points where events are detected, rather than processing every grid point in the study volume. This selective application reduces overall computational power requirements while maintaining accuracy where needed.
Data Source
AI summary
System and methods used to localize microseismic events and determine the seismic moment tensor of microseismic events created by geological processes such as rock fracturing, rock slippage, fluid migration within rock pore spaces and man-made events such as hydraulic fracturing and reservoir stimulation. The recorded response from a microseismic event is usually contaminated with strongly correlated noise and to determine the most probable location of the source a unique processing method the maximum likelihood estimation (MLE) is applied.