Selected Publications

In the presence of background noise and interference, arrival times picked from a surface microseismic data set usually include a number of false picks which lead to uncertainty in location estimation. To eliminate false picks and improve the accuracy of location estimates, we develop a classification algorithm (RATEC) that clusters picked arrival times into event groups based on random sampling and fitting moveout curves that approximate hyperbolas. Arrival times far from the fitted hyperbolas are classified as false picks and removed from the data set prior to location estimation. Simulations of synthetic data for a 1-D linear array show that RATEC is robust under different noise conditions and generally applicable to various types of media. By generalizing the underlying moveout model, RATEC is extended to the case of a 2-D surface monitoring array. The effectiveness of event location for the 2-D case is demonstrated using a data set collected by a 5200-element dense 2-D array deployed for microearthquake monitoring.
Submitted to Geophysical Prospecting, 2017.

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace’s autocorrelation is centered at zero in the lag domain. The effect of white noise is concentrated near zero lag, so the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with colored noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.
Accepted in Geophysical Perspecting, 2016.

Recent Publications

. Weighted Random Sampling in Seismic Event Detection/Location (WRASED): Applications to Local, Regional, and Global Seismic Networks. Seismological Research Letters, 2017.


. Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering. Accepted in Geophysical Perspecting, 2016.


. Microseismic events enhancement in sensor arrays using autocorrelation based filtering. 78th EAGE Conference and Exhibition 2016, 2016.


Recent & Upcoming Talks

Versions. Under Control

Jun 13, 2017, Meeting with seismology group at GT.

An automatic arrival time picking method based on RANSAC curve fitting

Jun 2, 2016, 78th EAGE Conference & Exhibition 2016

Full waveform microseismic inversion using differential evolution algorithm

Dec 15, 2015, The IEEE Global Conference on Signal and Information Processing (GlobalSIP)

Recent Posts

More Posts

Motivation With the recent enormous success of deep learning in speech, image, and natural language processing, people start to dream about an intelligent brain with the assist of a massive amount of computation power on large datasets. Nvidia, the hardware company behind all the rapid development of deep learning, put together an ad page demonstrate the successful stories of AI. More and more researchers/companies start to realize the value and deep learning and begin to explore its inspirational applications.


To continue the discussion in previous post, we want a folder strucutre standard instead of HDF5 to store dataset temporarily for processing or permantantly for sharing. To enable the flexibility of such folder structure apporach, we only impose minimum requirements on such folder and leave the rest fine-definition to the meta-data file. So what is the best format for such meta data? Basically, we want a hash talbe that establishes relationship between keyword and values that are meaningful to the user/audience.


Recently, the increasing volume of data and application of neural networks have both forced to look at data format again. Previously, I thought the HDF5 format is the best for most of my application. The nice APIs to HDF5, e.g. H5py and DeepDish gives me both flexibility and easiness of using HDF5 to store and share my dataset. However, as my datasets start to grow substantially, loading them into the memory puts a significant burden on my I/O bus, especially I only need part of that dataset every time.


Recently, I have to write some paper in MS Word which I haven’t used for some years and never used for scientific paper writing. Coming from the LaTex community, the two major challenge I have are Equation writing/numbering Citation/reference generation Here is the results of my recent experience. Equation Editing Although you can never compare equation editing in Word with the wonderful experience in LaTex, Word started to offer some basic tools to help you out.


Motivation Being a PhD student, we are trying to build our scholoarship in five to seven years through literature review, conducting independant research, and peer-reviewed publication. However, the real haunting question that I asked myself every once a while is this: How to make an IMPACT on your community and the general public? It is particularly true when I am approach the end of my PhD studies. To advertise work and promoting myself become more than a concept in the PhD orientation book.



Seismic Denoise

Noise is a never-ending problem for seismic processing due to the complex acquisition environment and loopn processing pipeline. This project tries to alleviate the noise challenge by looking at the intrinsic characteristic of siemsic signal. Signal correaltion and machine-learned dictionaries are studied to find the best space for signal-noise separation of targed seismic sections or images.

Seismic Field Trips

Over years, I like to join in field trips for seismic station deployment. These trips give me great opportutnities to make friends from different departments as well as get close to the nature. Along the way, I developed good skills for station deployment and seismic acquisition.

Microseismic Monitoring

Surface microseismic offers a cheaper alternative to monitor hydraulic fracturing procees but is suffering the problem of severe background noise issue. This project tries to tackle the noise change through curve-fitting techniques and results in good performance for various applications.


  • ECE2031: Lab TA for the Digital Design Lab (Spring 2011 - Summer 2012)